# document

id: d926fcded09b68953efd271797cf4d67
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35501
content:
"[...] occurring faster in some well-studied regions than during the first \r half of the 20th century, and medium confidence that thermokarst \r formation, to which about 20% of the northern permafrost region \r is vulnerable (Olefeldt et  al., 2016), can lead to faster large-scale \r permafrost degradation in response to climate change."


id: f0a38f1945b4f87b1726e80f799faae1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 39711
content:
"Since SROCC, dedicated modelling of the evolution of ice- and \r organic-rich permafrost in the north-east Siberian lowlands (Nitzbon \r et al., 2020) has shown that not representing thermokarst-inducing \r processes in ice-rich terrain leads to a  systematic underestimation \r of the rapidity and magnitude of permafrost thaw. Simplified \r inventory-based modelling (Turetsky et  al., 2020) points towards \r similar conclusions. Although these pulse disturbances still need to \r be represented in CMIP-type models, there have been many new \r developments to that type of model since CMIP5 and AR5. Soil \r freezing and its thermal and hydrological effects are now included in \r a large number of land-surface modules that are part of the CMIP6 \r ensemble (S. Chadburn et al., 2015; Hagemann et al., 2016; Cuntz \r and Haverd, 2018; Guimberteau et al., 2018; Yokohata et al., 2020), \r sometimes allowing for the effects of excess ice (Lee et al., 2014). \r [...]"


id: e16d227cc7cad6e4552721e65bd78246
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36928
content:
"[...] Improved representation of snow insulation in models has led \r to more realistic simulated permafrost extents (e.g.,  Paquin and \r Sushama, 2015). In a post-CMIP5 ensemble of land-surface models \r driven by observed meteorological conditions (McGuire et al., 2016), \r inter-model spread was substantially reduced when the ensemble \r was restricted to models that appropriately represented the effect of \r snow insulation on the underlying soil (W. Wang et al., 2016). More \r detailed descriptions of high-latitude vegetation characteristics, \r vegetation dynamics, and snow-vegetation interactions have been \r included in several models since AR5 (S.E. Chadburn et  al., 2015; \r Porada et al., 2016; Druel et al., 2017)."


id: a33720a5e80258883ad20c168f383b4
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25926
content:
"Based on multiple studies, there is medium confidence that \r widespread retreat of coastal permafrost is accelerating in the \r Arctic (Gunther et al., 2015; Cunliffe et al., 2019; Isaev et al., 2019). \r There is also consistent evidence of complete permafrost thaw in \r areas of discontinuous and sporadic permafrost since about 1980, \r but this evidence is geographically scattered (Camill, 2005; Kirpotin \r et al., 2011; James et al., 2013; B.M. Jones et al., 2016; Borge et al., \r 2017; Chasmer and Hopkinson, 2017; Gibson et al., 2018). In spite \r of increasing evidence of landscape changes from site studies and \r remote sensing, quantifying permafrost extent change remains \r challenging because it is a subsurface phenomenon that cannot be \r observed directly (Jorgenson and Grosse, 2016; Trofaier et al., 2017). \r A modelling study for the Qinghai-Tibet Plateau between the 1960s \r and the 2000s (Ran et al., 2018) suggests transition from permafrost \r [...]"


id: d8fea8cc0c8e0860873c09d0dac001bf
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35471
content:
"[...] to seasonally frozen ground over an area of more than 400,000 km2. \r In summary, there is medium confidence that complete permafrost \r thaw in recent decades is a common phenomenon in discontinuous \r and sporadic permafrost regions. In addition, paleoclimatic evidence \r presented in Section  2.3.2.5 confirms a  long-term sensitivity of \r permafrost extent to climatic variations, although an analysis \r of North American speleothem records over the last two glacial cycles \r indicates that this apparent high sensitivity could be a consequence \r of regional-scale variability (Batchelor et al., 2019)."


id: 3e166b242cb8968d2f528b57e9a488d6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 8089
content:
"In spite of the aforementioned structural improvements to many \r models, the simulated current permafrost extent from available \r CMIP6 models shows no substantial improvement with respect to \r CMIP5 (see Figure 9.22a). The extent of the region where permafrost \r is simulated within the top 15 m in the Northern Hemisphere for the \r 1979-1998 period is characterized by very large scatter in the coupled \r CMIP5 and CMIP6 historical simulations compared to estimates \r of the present permafrost extent based on multiple observational \r lines of evidence (Zhang et al., 1999) and models based on satellite \r observations and reanalyses (Gruber, 2012; Obu et al., 2019). Outliers \r with very low simulated permafrost extent are models that have only \r a very shallow soil column (leading to an underestimate of thermal \r inertia at depth) and do not take into account soil water phase \r changes. These inadequacies lead to an overestimate of seasonal \r [...]"


id: 621d758913e948c5850ba95e64ac0073
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
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vector_id: 14535
content:
"[...] thaw depth, exceeding the total thickness of the models' soil \r columns (Burke et al., 2020). Excessive simulated permafrost extent \r can in several cases be traced to insufficient thermal insulation by the \r winter snow cover (Burke et al., 2020)."


id: f6c95fee6271d761b27c534be7aa5cd3
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
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vector_id: 40784
content:
"A strong transient temperature sensitivity of the volume of \r perennially frozen soil in the top 3 m below the surface is consistently \r suggested by the available CMIP6 models (Figure  9.22b). Relative \r to the current volume, the transient sensitivity of the modelled \r permafrost volume in the top 3 m to GSAT changes (with respect \r to the 1995-2014 average and up to +3degC change, that is, about up \r to +4degC with respect to pre-industrial levels) is about 25 +- 5 % degC-1\r (Burke et  al., 2020), but there is only medium confidence in this \r value and 1 standard deviation uncertainty range because of the \r model deficiencies discussed in 9.5.2.2. It is important to note that \r permafrost loss will not be limited to the top 3 m, with delayed \r response of deeper permafrost. The simulated transient temperature \r sensitivity of permafrost volume is slightly stronger in the SSP1-2.6 \r scenario than in other SSPs because subsurface temperature lag \r [...]"


id: bdbca77601660325abd24495c44f705a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30643
content:
"[...] increases with higher atmospheric warming rates, particularly when \r ground ice melting induces additional delays."


id: b1034e08f493a4946c9ab07d17162e6d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28369
content:
"The AR5 (Vaughan et  al., 2013) reported that NH SCE in June \r very likely decreased by 11.7 [8.8 to 14.6] % per decade over the \r 1967-2012 period, exceeding the absolute and relative reductions \r observed in March and April. The AR5 further reported very high \r confidence that NH March and April SCE decreased over the 90 years \r after 1922. The SROCC only assessed snow cover changes for the \r Arctic and mountain areas. For the Arctic (north of 60degN), SROCC \r (Meredith et al., 2019) expressed high confidence in SCE decreases \r of -3.5 +- 1.9% per decade in May and -13.4 +- 5.4% per decade in \r June, based on a combination of multiple datasets (Mudryk et al., \r 2017). Concerning mountain snow cover, SROCC (Hock et al., 2019b) \r reported with high confidence that mountain snow cover (both in \r terms of SCE and maximum SWE) has generally declined since the \r middle of the 20th century at lower elevations. At higher elevations, \r [...]"


id: 3d318d4f8b3b4d08c10a13c4b73730d6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7947
content:
"[...] SROCC reported medium confidence in generally insignificant \r snow cover trends (where these were available). The large-scale \r assessment provided in Section 2.3.2.2 of this Report reports very \r high confidence in substantial reductions of NH SCE (particularly in \r spring) since 1978, and states that there is limited evidence that this \r decline extends back to the early 20th century."


id: 5b7d6590557093e9d357804c9899b38d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13339
content:
"Hemispheric reconstructions with simple snow models and in situ \r observations have extended a  pre-satellite record to precede the \r satellite record and extend back to 1922 (Brown and Robinson, \r 2011), putting the satellite era in historical context. This study, also \r assessed in AR5, suggests an increase in North American spring \r (March-April) SCE from 1915 to about 1950, followed by a decrease \r of the same total magnitude afterwards. In Eurasia, a negative trend \r in April is visible over the entire 1922-2010 period of record, while \r in March, a step decrease at about 1985 separates two periods with \r insignificant trends. Overall, combining March and April, consistency \r between the continental trends since 1950, and agreement in sign \r with the NOAA satellite record since 1967, provides high confidence\r in Northern Hemisphere spring snow cover decrease since about \r 1950. Analysis of paleoclimate records (Pederson et  al., 2011; \r [...]"


id: ef2a1f58916ddeb2e291941232cc123d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 39449
content:
"[...] Belmecheri et al., 2016) suggests that recent snowpack reductions \r in western North America are exceptional on a millennial time scale \r (medium confidence)."


id: 6c05a65115a5ff6c3707636828cd2871
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16305
content:
"Since AR5, the number of global or hemispheric-scale gridded \r SWE products has substantially increased. A  validation and \r intercomparison (Mortimer et al., 2020) of datasets - derived from: \r (i) reanalysis-based products; (ii) a combined surface observation - \r passive  microwave remote sensing product; and (iii) stand-alone \r passive microwave products - has led to better understanding of the \r strengths and limitations of each. These gridded products consistently \r identify negative trends in maximum pre-melt SWE across the \r 1981-2016 period over Eurasia and North America (Figure 9.23c,d; \r Mudryk et al., 2020). To further constrain SWE uncertainty, Pulliainen \r et  al. (2020) implemented a  bias correction based on snow course \r observations which yielded a current best estimate for the average \r 1980-2018 March SWE over NH non-alpine land north of 40degN of 2938 \r [likely range 2846-3062] Gt. Using this method, the bias-corrected \r [...]"


id: 864b86cc83c0fa3d7b9fb8cf93fd6b8f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20782
content:
"[...] GlobSnow v3.0 dataset suggests a 4.6 Gt yr-1 decrease of March SWE \r over this 39-year period across North America, and a negligible trend \r across Eurasia. These SWE trends are consistent with the continental \r SCE trends over this period, as assessed above, but strong regional \r and temporal variability only allows medium confidence in the signs \r and magnitudes of these trends. However, there is high confidence in \r a general decline of NH spring SWE since 1981 (Section 2.3.2.2). In the \r longer term (see also Section  2.3.2.2), annual maximum SD trends "


id: 83c53f60419e9c926835e3d0baa7ace4
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20329
content:
"Since AR5, progress in the observation, description and understanding \r of snow microstructure (Kinar and Pomeroy, 2015; Calonne et  al., \r 2017) and its links to physical (thermal and radiative) properties (Lowe \r et al., 2013; Calonne et al., 2014) has prompted efforts to represent \r physical properties as a function of the evolving snow microstructure in \r models (Carmagnola et al., 2014; Calonne et al., 2015). However, even \r state-of-the-art snow models intended for meteorological and climate \r applications still struggle to correctly represent the time evolution of \r the snow thermal properties, particularly of cold and dry tundra snow \r (Domine et al., 2016). Moreover, most, if not all, CMIP6 climate models \r do not explicitly represent the darkening of snow by deposition of black \r carbon and other light-absorbing aerosol species known to influence \r snow melt rates (Section 7.3.4.3). Regardless of these shortcomings, \r [...]"


id: 41b47aa72654cbafc1c370cc9bba4c16
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
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vector_id: 8752
content:
"[...] snow modules of climate models continue to be improved. Recent \r progress includes the incorporation of multiple energy balances \r within the canopy and between sub-grid tiles with different snow \r heights (Aas et al., 2017; Boone et al., 2017) and inclusion of advanced \r specific snow models in coupled climate models (Niwano et al., 2018; \r Voldoire et  al., 2019), opening the prospect of future progress in \r quantifying snow-related feedbacks in a  changing climate. Recently \r developed multi-physics snow models (Essery, 2015; Lafaysse et al., \r 2017), which are able to emulate the behaviour of a large number of \r models in a broad range of climates, allow model shortcomings and \r key parameter uncertainties, for example, concerning snow masking by \r vegetation or snow thermal conductivity, to be identified. Guidance for \r future model improvement can be provided by improved diagnostics, \r such as a concise metric of snow insulation (A.G. Slater et al., 2017), \r [...]"


id: 9b4f6f1715485583ef7073f72f59676c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 24573
content:
"[...] which builds on an observed relation between effective seasonal mean \r SD and the dampening of winter season temperature decrease within \r the soil, and allows an efficient quantification of inaccuracies in the \r simulated snow insulation effect."


id: e4db4ea869c1692cdc462376b081cfa6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 37547
content:
"There is high confidence that large inter-model variations in the \r snow-cover sensitivity to temperature can largely be explained by \r inaccuracies in the simulated snow-albedo feedback (Qu and Hall, \r 2014); a multi-model sub-ensemble of CMIP5 models that simulate \r a  correct magnitude of this feedback presents a  40%  reduced \r spread in the projected 21st  century Northern Hemisphere land \r warming trend (Thackeray and Fletcher, 2016). Errors of the \r simulated feedback strength were linked to: (i) systematic positive \r albedo biases over the boreal forest belt, mostly due to unrealistic \r treatment of vegetation masking (Thackeray and Fletcher, 2016); \r (ii) inaccurate prescribed tree cover fraction and inappropriate \r parametrization of leaf area index in some models (Loranty et al., \r 2014; L. Wang et al., 2016); and (iii) low spatial resolution leading \r to inaccuracies in the strength of the simulated snow albedo \r feedback in mountainous regions (Letcher and Minder, 2015). \r [...]"


id: d52feab1b117ef8e54c9de2bbfb15f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 34830
content:
"[...] Although the representation of snow-albedo feedback improved \r in many CMIP5 models over CMIP3, some models deteriorated \r (Thackeray et al., 2018)."


id: 84633f9e6732c4f9b34d1c188b7a98eb
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20440
content:
"The SROCC (Oppenheimer et al., 2019) discussed the development \r and application of new statistical methodologies for reconstructing \r global mean sea level (GMSL) from tide gauge data over the \r 20th  century (Box  9.1). Based on an ensemble of tide gauge \r reconstructions, SROCC assessed an average rate of GMSL rise \r of 1.38  [0.81 to 1.95, very likely range]  mm yr-1 for the period \r 1901-1990. Since SROCC, two new GMSL reconstructions have \r been published (Dangendorf et  al., 2019; Frederikse et  al., 2020b) \r and are included in an updated ensemble estimate of GMSL change \r (Section 2.3.3.3; Palmer et al., 2021). Based on these updated data and \r methods, the GMSL change over the (pre-satellite) period 1901-1990 \r is assessed to be 0.12 [0.07 to 0.17, very likely range] m with an \r average rate of 1.35 [0.78 to 1.92, very likely range] mm yr -1 (high \r confidence) (Table  9.5; Section  2.3.3.3) in agreement with SROCC \r [...]"


id: e350c26ed4b9a6e74732c227bea7199f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 37291
content:
"[...] assessment. Both this assessment and SROCC have substantially \r larger uncertainties than the AR5 assessment, which was based on \r a single tide gauge reconstruction and did not account for structural \r uncertainty (see Palmer et al., 2021 for a discussion)."


id: a75406fb793605cd8ca1e1775458ec24
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26661
content:
"The SROCC (Oppenheimer et  al., 2019) concluded that GMSL \r increased at a rate of 3.16 [2.79 to 3.53, very likely range] mm yr -1\r in the period 1993-2015 (the satellite altimetry era), and a  rate \r of 3.58  [3.10 to 4.06, very likely range]  mm yr -1 in the period \r 2006-2015  - the Gravity Recovery and Climate Experiment \r (GRACE)/Argo data era (high confidence). An updated assessment \r for the periods 1993-2018 and 2006-2018 yields values of \r 3.25  [2.88 to 3.61] and 3.69 [3.21 to 4.17]  mm yr -1 (high \r confidence) (Table  9.5), with the slightly larger central estimates \r consistent with the observed acceleration in GMSL rise since the \r late 1960s (Dangendorf et al., 2019), given the longer assessment \r periods. Based on the GMSL assessed time series presented in \r Section 2.3.3.3, GMSL acceleration is estimated as 0.075 [0.066 to \r 0.080] mm yr -2 for 1971-2018 and 0.094 [0.082-0.115] mm yr -2\r for 1993-2018 (high confidence). For the common period of \r [...]"


id: 94f7544fd713b3e4caeb2f0d65cfe8f4
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23413
content:
"[...] 1993-2010, the assessed rate of GMSL rise based on tide gauge \r reconstructions (3.19 [1.18 to 5.20]  mm  yr -1) is consistent \r with the assessment based on satellite altimetry (2.77  [2.26 to \r 3.28] mm yr -1), within the estimated uncertainties."


id: bc8dcd260cf6c719baf9392a157759c2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30419
content:
"The sea level budget in SROCC included the anthropogenic \r contribution of land-water storage (LWS; Box  9.1) change from \r a  single estimate (Wada, 2016). Since SROCC, two studies have \r combined estimates of natural LWS change with anthropogenic LWS \r changes from reservoir impoundment and groundwater depletion \r (Caceres et  al., 2020; Frederikse et  al., 2020b). For Caceres et  al. \r (2020), zero change is assumed for the period 1901-1948, since \r their LWS change estimates are not available before 1948. Given the \r large year-to-year changes associated with hydrological variability, \r the assessed changes in LWS (Table 9.5) are based on linear trends \r for each period, following Palmer et al. (2021). Structural uncertainty \r is estimated from the standard deviation of the trends across the \r two studies, and parametric uncertainty is estimated based on the \r Monte Carlo simulations of Frederikse et  al. (2020b). These two \r [...]"


id: 29e6530ab1931c8cc380917e75975c73
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 4598
content:
"[...] sources of uncertainty are combined in quadrature, and the assessed \r central estimate is taken as the average of the ensemble mean \r trends. Compared to SROCC-assessed LWS trend of -0.12  mm yr-1\r for the period 1901-1990, the updated assessment leads to a more \r negative trend of -0.16 [-0.35 to 0.04] mm yr-1, although the two \r are consistent within the estimated uncertainties. Previous studies \r and SROCC have highlighted the large uncertainty in estimates \r of LWS change over the 20th  century (Gregory et  al., 2013), and \r therefore SROCC assessment of low confidence in the estimated LWS \r contribution to GMSL change is maintained."


id: d7f0a9c656bea5160dfd584416a8f9b0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35298
content:
"For both periods in the satellite era  - that is, 1993-2018 and \r 2006-2018 - the sum of contributions is consistent with the total \r observed GMSL change (high confidence) (Table 9.5). However, the \r latter period, which is characterized by improved data quality and \r coverage associated with satellite and Argo observations, shows \r much closer agreement in the central estimates. The marginal \r sea level budget closure for the period 1993-2018 may indicate \r underestimated uncertainty, which may be structural as well as \r parametric. The sea level budget assessments across the various \r periods in Table 9.5 demonstrate that the acceleration in GMSL rise \r (Section 2.3.3.3) since the late 1960s is mostly the result of increased \r ice-sheet mass loss. However, all contributions to GMSL rise show \r their largest rate during 2006-2018, with the ice sheets accounting \r for 27% of the total change during this period. Because of the \r [...]"


id: 967ee7b81cbd34ca55cd68770c500a6a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23680
content:
"[...] increased ice-sheet mass loss, the total loss of land ice (glaciers and \r ice sheets) was the largest contributor to GMSL rise over the period \r 2006-2018 (high confidence)."


id: 8f94aec326268624e06dbf56a72b2c81
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 22472
content:
"Since SROCC, a  new ocean heat content reconstruction \r (Section 2.3.3.1; Zanna et al., 2019) has allowed global thermosteric \r sea level change to be estimated over the 20th century. As a result, \r the sea level budget for the 20th century can now be assessed for the \r first time. For the periods 1901-1990 and 1901-2018, the assessed \r very likely range for the sum of components is found to be consistent \r with the assessed very likely range of observed GMSL change \r (medium confidence), in agreement with Frederikse et  al. (2020b; \r Table 9.5). This represents a major step forward in the understanding \r of observed GMSL change over the 20th century, which is dominated \r by glacier (52%) and Greenland Ice Sheet mass loss (29%) and \r the effect of ocean thermal expansion (32%), with a  negative \r contribution from the LWS change (-14%). While the combined mass \r loss for Greenland and glaciers is consistent with SROCC, updates \r [...]"


id: 70f42a7255be69448bf84e19fdeb4042
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17135
content:
"Hamlington et al., 2020b). There was intensified sea level rise during \r the 1990s and 2000s, with 10-year trends exceeding 20 mm yr-1 in the \r western tropical Pacific Ocean, while sea level trends were negative \r on the North American west coast. During the 2010s, the situation \r reversed, with western Pacific sea level falling at more than 10 mm yr-1\r (Hamlington et al., 2020b). For the Atlantic Ocean, SROCC described \r regional sea level variability as being driven primarily by wind and heat \r flux variations associated with the North Atlantic Oscillation (NAO) and \r heat transport changes associated with Atlantic Meridional Overturning \r Circulation (AMOC) variability. During periods of subpolar North \r Atlantic warming, winds along the European coast are predominantly \r from the south and may communicate steric anomalies onto the \r continental shelf, driving regional sea level rise, with the reverse during \r [...]"


id: 55cda9b21b596dc762a230e156b38242
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12367
content:
"[...] periods of cooling (Chafik et al., 2019). High rates of sea level rise in the \r North Indian Ocean are accompanied by a weakening summer South \r Asian monsoon circulation (Swapna et al., 2017)."


id: 6d2efbe2c266a74af7228cde6b70c376
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updated_at: 2023-04-19 13:22:40
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vector_id: 16486
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"The simulated ocean dynamic and thermosteric response to external \r forcings during 1861-2005 is only larger than simulated internal \r variability in the Southern Ocean and North Pacific on a  1deg grid \r (Slangen et al., 2015). However, on spatial scales exceeding 2000 km, \r a  detectable signal is revealed in the last 45 years in 63% of the \r global ocean area (Richter et al., 2017). The thermosteric change in \r the upper 700 m in the period 1970-2005 shows similar observed \r and simulated forced geographical patterns, and anthropogenic \r forcing accounts for part (North Atlantic, 65%) or all (tropical Pacific, \r Southern Ocean) of the observed regional mean (Marcos and Amores, \r 2014). The influences of greenhouse gases and anthropogenic \r aerosols can be partially distinguished by considering geographical \r or vertical ocean temperature variations (Slangen et al., 2015; Bilbao \r et al., 2019; Fasullo et al., 2020). Zonal-mean forced ocean dynamic \r [...]"


id: 27f0630343713e679d9ec7e90ca7be05
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 4221
content:
"[...] sea level change alone is not detectable but, using spatial correlation, \r the global geographical pattern during the altimeter period is \r detectable in sea level trends (Fasullo and Nerem, 2018). This pattern \r may already or will soon be detectable in individual years, based \r on an analysis of CMIP5 climate model simulations (Bilbao et  al., \r 2015). Anthropogenic forcing, dominated by greenhouse gases, \r has strengthened the meridional sea level gradient in the Southern \r Ocean since the 1960s (Slangen et  al., 2015; Bilbao et  al., 2019; \r Fasullo et al., 2020). New evidence finds that observed zonal-mean \r total sea level trends during 1993-2018 in all basins are inconsistent \r with unforced variability alone, but are consistent with the modelled \r response to external forcing (Richter et al., 2020)."


id: a05ccf69866000238769ab7a5ba9f295
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25423
content:
"The SROCC estimated regional sea level changes from combinations \r of the various contributions to sea level change from CMIP5 climate \r model outputs, allowing comparison with satellite altimeter and \r tide gauge observations. Closure of the regional sea level budget is \r complicated by the fact that regional sea level variability is larger \r than GMSL variability. Also, there are more processes that need to \r be considered, such as vertical land movement and ocean dynamical \r changes (Box  9.1). A  number of observation-based studies have \r focused on specific areas, such as the Mediterranean (Garcia et al., \r 2006), the South China Sea (Feng et al., 2012), the east coast of the \r USA (Frederikse et al., 2017; Piecuch et al., 2018), the North Atlantic \r basin (Kleinherenbrink et al., 2016) and the north-western European \r continental shelf seas (Frederikse et  al., 2016). Studies using tide \r gauge data and observation-based estimates of the contributions \r [...]"


id: b12d7891c554cabddb0c35f9bca2a3b8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28398
content:
"[...] find that, while local agreement is not yet possible, the observational \r sea level budget can be closed on a  basin scale (Slangen et  al., \r 2014b; Frederikse et al., 2016, 2018, 2020b). A budget analysis for \r the GRACE era found that the budget closes in some, but not all, \r coastal regions: substantial parts of the sea level change signal in \r the North Atlantic could not be explained by steric or barystatic \r changes (Rietbroek et  al., 2016). This is in agreement with other \r work comparing climate model estimates to 20th-century tide \r gauge observations (Meyssignac et  al., 2017), where the majority \r of local spatial variability is determined by the ocean dynamic \r component. Vertical land movement is another major cause of local \r spatial variability in sea level change and, for instance, relevant for \r oceanic islands (Forbes et al., 2013; Martinez-Asensio et al., 2019). \r In summary, the regional sea level budget, using either observations \r [...]"


id: 84315eb11832dc7fa7e8843930267a1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 20413
content:
"[...] or models, can currently only be closed on basin scales (medium \r confidence), with large uncertainties remaining on smaller scales."


id: fe760408f8972cfd405ed69405d336fc
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 42142
content:
"concur that MIS 11 was an extremely long interglacial that exhibited \r positive annual at 0.5degC +- 1.6 degC (Irvali et al., 2020) and summer \r at 2.1degC-3.4  degC (Robinson et  al., 2017) temperature anomalies \r (de Wet et al., 2016). The GMSL was 6-13 m above present (medium \r confidence) (Section 2.3.3.3). The Greenland Ice Sheet lost 4.5-6 m \r (Reyes et al., 2014) or about 6.1 m (3.9-7 m, 95% confidence) sea \r level equivalent (SLE) by about 7  kyr after peak summer warmth \r (Robinson et al., 2017), with marine-based ice from AIS (Blackburn \r et al., 2020) contributing 6.4-8.8 m SLE at this time (Mas e Braga \r et al., 2021). Agreement between GMSL and ice-sheet reconstructions \r gives high confidence in identifying a  high sensitivity of both ice \r sheets to the protracted duration of thermal forcing, even at low \r warming levels (Reyes et al., 2014; Robinson et al., 2017; Irvali et al., \r 2020; Mas e Braga et al., 2021). Modelled mean mass loss rates for \r [...]"


id: b11fb98b4b84766d51de723be6efbfbc
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28389
content:
"[...] the Greenland Ice Sheet of 0.4 m kyr -1 during MIS  11 (Robinson \r et  al., 2017) are indistinguishable from recent mass loss rates \r averaged over 1992-2018 (Section 9.4.1.1). In summary, geological \r reconstructions and numerical simulations consistently show that \r past warming levels of <2degC (GMST) are sufficient to trigger multi\u0002metre mass loss from both the Greenland and Antarctic ice sheets \r if maintained for millennia (high confidence), in agreement with \r SROCC findings for comparable warming levels during MIS 5e, the \r Last Interglacial."


id: a4575467c735c68ddb9d2916dd604876
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26118
content:
"The MPWP climate had substantial polar amplification, up to 8degC \r above pre-industrial levels in Arctic Russia (Section 7.4.4.1; Fischer \r et  al., 2018). Ice-sheet model simulations indicate that Northern \r Hemisphere glaciation was limited to high-elevation regions in \r eastern and southern Greenland (medium confidence) (Figure 9.17; \r De Schepper et  al., 2014; Yan et  al., 2014; Koenig et  al., 2015; \r Dowsett et al., 2016; Berends et al., 2019) with Northern Hemisphere \r glaciation only becoming more widespread from the (cooler) late \r Pliocene (Bachem et al., 2017; Blake-Mizen et al., 2019; Knutz et al., \r 2019; Sanchez-Montes et al., 2020). Southern Hemisphere glaciation \r was characterized by an Antarctic Ice Sheet (AIS) reduced in volume \r from the present (medium confidence) (Figure 9.18; Dowsett et al., \r 2016; Berends et al., 2019; Grant et al., 2019; Miller et al., 2020) with \r mountain ice fields in the Andes of South America (De Schepper et al., \r [...]"


id: 27f134c83933dc231057b33314ab48cf
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 4222
content:
"[...] 2014). Ice-sheet models are inconsistent in the magnitude of the sea \r level contribution from Antarctica (DeConto and Pollard, 2016; Yan \r et al., 2016; Golledge et al., 2017b; Berends et al., 2019; DeConto \r et al., 2021) but near-field sedimentological reconstructions support \r precessionally modulated and eccentricity-paced multi-metre sea \r level contributions from the Wilkes Subglacial Basin over 3-5 kyr \r (Patterson et al., 2014; Bertram et al., 2018). In summary, under a past \r warming level of around 2.5degC-4degC, ice sheets in both hemispheres \r were reduced in extent compared to present (high confidence). \r Proxy-based evidence (Section  2.3.3.3) combined with numerical \r modelling indicates that, on millennial time scales, the GMSL \r contribution arising from ice sheets was >5 m (high confidence) \r or >10 m (medium confidence) (Figures 9.17 and 9.18; Moucha and \r Ruetenik, 2017; Berends et al., 2019; Dumitru et al., 2019)."


id: 34fae225273c61ff481c3eb4c87c3152
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 6564
content:
"Since AR5, information has improved about the LIG, when GMST \r was about 0.5degC-1.5degC above 1850-1900 (medium confidence) \r (Section 2.3.1.1). The LIG had higher summer insolation than present \r and polar amplified sea surface and surface air temperatures that \r reached >1degC-4degC and >3degC-11 degC in the Arctic respectively (Landais \r et al., 2016; Capron et al., 2017; Fischer et al., 2018). Mean annual and \r maximum summer ocean temperatures peaked early (129-125 ka) \r in the interglacial period, reaching 1.1 +- 0.3 degC above the modern \r global mean (Shackleton et  al., 2020) with summer anomalies of \r 2.5degC-3.5 degC in the Southern Ocean (Bianchi and Gersonde, 2002) \r and spatially variable timing (Chadwick et al., 2020). It is virtually \r certain that GMSL was higher than today, likely by 5-10 m (medium \r confidence) (Section  2.3.3.3). Global mean thermal expansion \r peaked at about 0.9 +- 0.3 m early in the LIG (about 129 ka), declining \r [...]"


id: 897e05ed25a1d74f985c32162bf2696d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 21366
content:
"[...] to modern levels by about 127 ka (Shackleton et al., 2020). With no \r more than 0.3 +- 0.1 m of GMSL rise from glaciers (Section 9.5.1), at \r most 1.0 +- 0.3 m of the GMSL rise originated from sources other than \r the polar ice sheets."


id: 9d04ea7ceb3f171f8a351c3b794012f3
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 24865
content:
"During Meltwater pulse 1A (MWP-1A), GMSL very likely (medium \r confidence) rose by 8-15 m (Liu et al., 2016). Consistent with AR5, the \r drivers of this rapid rise remain ambiguous. The spatial patterns of RSL \r change over this interval are inadequately observed to constrain the \r relative contributions of the North American and Antarctic ice sheets (Liu \r et al., 2016). Modelling studies of the North American Ice Sheet permit \r a 3-6 m (Gregoire et al., 2016) or 6-9 m contribution over the duration \r of MWP-1A (Tarasov et al., 2012). Sedimentological evidence (Weber \r et al., 2014; Bart et al., 2018) provides near-field evidence for an Antarctic \r contribution, consistent with modelling studies (Golledge et al., 2014; \r Stuhne and Peltier, 2015), but does not constrain the magnitude of the \r contribution. A recent statistical analysis of Norwegian Sea and Arctic \r Ocean sediments suggests a 3-7 m contribution from the Eurasian Ice \r [...]"


id: 18af993a68ef9aeb2d5cab80a6ca0aad
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 1558
content:
"[...] Sheet (Brendryen et al., 2020), a possibility not considered in AR5 or \r the meta-analysis of Liu et al. (2016). In summary, MWP-1A appears \r to have been driven by a combination of melt in North America (high \r confidence), Eurasia (low confidence), and Antarctica (low confidence), \r but the budget is not closed."


id: bce286554814e7501896a2a7b26f4398
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 30472
content:
"Far-field GMSL records suggest that the AIS contributed to LIG sea \r level from 129.5-125 ka (Figure 9.18) but direct evidence is sparse. \r Thinning of part of the WAIS is interpreted from a 130-80 ka hiatus in \r the Patriot Hills horizontal ice core record (Turney et al., 2020). Marine \r sediment records suggest a dynamic response of the Wilkes Subglacial \r Basin (WSB) of the EAIS during this period, indicating a response time \r scale of 1000-2500 yr (Wilson et al., 2018), consistent with modelling \r studies (Mengel and Levermann, 2014; Golledge et  al., 2017b; \r Sutter et al., 2020). Isotopic changes in the Talos Dome ice core are \r inconsistent with local surface lowering, limiting retreat to 0.4-0.8 m \r SLE from this sector (Sutter et al., 2020). Ice-sheet models forced with \r unmodified atmosphere-ocean models (Goelzer et  al., 2016; Clark \r et al., 2020) simulate 3-4.4 m SLE mass loss, primarily from the WAIS, \r [...]"


id: e3d34e11d91596ed56dd793edd9e5f94
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 37369
content:
"[...] with no retreat in WSB (e.g., Figure 9.18). Models forced with proxy\u0002based or ad hoc LIG ocean temperature anomalies (DeConto and \r Pollard, 2016; Sutter et al., 2016) indicate collapse of West Antarctica \r under 2degC-3degC ocean forcing yielding 3-7.5 m sea level contribution, \r but modest or no retreat in the WSB. Based on limited evidence and \r limited agreement between models, there is low confidence in both \r the magnitude and timing of LIG mass loss from the AIS."


id: cefc53e9ca1c1ef567c930f333adad65
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 33752
content:
"Around half (50-60 m) of the GMSL rise since the LGM occurred \r during the early Holocene at a sustained rate of about 15 m kyr-1 from \r around 11.4-8.2 ka (Lambeck et  al., 2014), possibly punctuated by \r abrupt meltwater pulses (Smith et al., 2011; Carlson and Clark, 2012; \r Tornqvist and Hijma, 2012; Harrison et  al., 2019). An abrupt (about \r 1.1 m) sea level rise around 8.2 ka was associated with drainage of the \r pro-glacial Agassiz and Ojibway lakes, attributed to accelerated melt \r from collapsing Laurentide Ice Sheet ice saddles (Matero et al., 2017). \r The Laurentide Ice Sheet provided the greatest contribution (27 m) to \r early Holocene GMSL (Peltier et al., 2015; Roy and Peltier, 2017), the \r Scandinavian Ice Sheet contributed about 2 m from the beginning of \r the Holocene until its demise by around 10.5 ka, (Cuzzone et al., 2016), \r while the Barents Sea Ice Sheet contributed a  small but unknown \r amount (Patton et al., 2015, 2017; Auriac et al., 2016). The Greenland \r [...]"


id: 5f4dcfa07a9cb3e0671dcbf2d6ff2ff
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14075
content:
"[...] Ice Sheet contributed about 4 m, consistent with ice thinning rates \r inferred from the Camp Century ice core (Lecavalier et  al., 2017; \r McFarlin et  al., 2018). Recent estimates of Antarctic contributions \r during the early Holocene vary considerably from about 1.2 m to \r 8.5 m (Whitehouse et al., 2012; Ivins et al., 2013; Argus et al., 2014; \r Briggs et al., 2014; Golledge et al., 2014; Pollard et al., 2016; Roy and \r Peltier, 2017; Albrecht et al., 2020). In summary, the early Holocene was \r characterized by steadily rising GMSL as global ice sheets continued \r to retreat from their LGM extents. This steady rise was punctuated by \r abrupt pulses during episodes of rapid meltwater discharge."


id: 944a98f2625ee64853786560f9dd319
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 23296
content:
"and extent, there is medium confidence that minima were reached \r at different times in different areas during the period 8-3  ka  BP \r (Larsen et  al., 2015; Young and Briner, 2015; Briner et  al., 2016). \r Geochronological and numerical modelling studies indicate that it \r is likely (medium confidence) that the period of smaller-than-present \r ice extent in all sectors of Greenland persisted for at least 2000 \r to 3000 years (Larsen et  al., 2015; Young and Briner, 2015; Briner \r et al., 2016; Nielsen et al., 2018). Based on ice-sheet modelling and \r carbon-14 (14C) dating (Kingslake et al., 2018) suggested that West \r Antarctic grounding lines retreated prior to around 10 ka BP, followed \r by a  readvance. Other studies from the same region conclude \r that retreat was fastest from 9-8 ka BP (Spector et  al., 2017), or \r from 7.5-4.8  ka  BP (Venturelli et  al., 2020). Marine geological \r evidence indicates open marine conditions east of Ross Island by \r [...]"


id: bebf376029184f9f9515d695b0e2e8d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 30825
content:
"[...] 8.6 +- 0.2 ka BP (McKay et al., 2016). In the western Weddell Sea, \r Johnson et al. (2019) reported rapid glacier thinning from 7.5-6 ka BP. \r Hein et al. (2016) concluded that the fastest thinning further south \r took place from 6.5-3.5 ka BP, potentially contributing 1.4-2 m to \r GMSL. Geophysical data indicate stabilization or readvance in this \r area around 6 +- 2 ka BP (Wearing and Kingslake, 2019). In coastal \r Dronning Maud Land (East Antarctica) rapid thinning occurred \r 9-5 ka BP (Kawamata et al., 2020), whereas glaciers in the Northern \r Antarctic Peninsula receded during the period 11-8 ka BP and \r readvanced to their maximal extents by 7-4 ka BP (Kaplan et  al., \r 2020). In summary, higher-than-pre-industrial GMST during the \r mid-Holocene coincided with recession of the Greenland Ice Sheet \r to a  smaller-than-present extent (high confidence). Multiple lines \r of evidence give high confidence that thinning or retreat in parts \r [...]"


id: e4c717f7c0aabe6bebe65d8eeb48f082
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 37532
content:
"[...] of Antarctica during  the Holocene  took place at different times in \r different places. However, limited data means there is only low \r confidence in whether or not the ice sheet as a whole was smaller \r than present during the mid-Holocene."


id: 7df56aa5809c3fe401ce29c6694c3c77
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 19342
content:
"Sections 9.6.3.3 and 9.6.3.4 present likely ranges of the new global \r mean sea levels, incorporating only processes in whose projections \r there is at least medium confidence, consistent with headline \r projections in AR5 and SROCC. As emphasized by SROCC, there is \r a  substantial likelihood that sea level rise will be outside the likely\r range. As described in Box 1.1, since the definition of 'likely' refers to \r at least 66% probability, there may be as much as a 34% probability \r that the processes in which there is at least medium confidence will \r generate outcomes outside the likely range. Furthermore, additional \r processes in which there is low confidence (Section 9.4.2.4; Box 9.4) \r may also contribute to sea level change. The presentation of likely sea \r level change (Tables 9.8-9.9 and in Figures 9.27, 9.29) is therefore \r accompanied by a low confidence range intended to reflect potential \r contributions from additional processes under high-emissions \r [...]"


id: 64ba7339cc45ab0d1e570149929c6de9
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vector_id: 15001
content:
"[...] scenarios. The low confidence range incorporates ice-sheet projections \r based on Structured Expert Judgement (SEJ)  - that is, a  formal, \r calibrated method of combining quantified expert assessments that \r incorporates all potential processes  - and projections from an AIS \r model that includes the marine ice cliff instability (a specific uncertain \r process not generally included in ice-sheet models; Section 9.4.2.4)."


id: f5327800d896574cdeb9a935dfb21149
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 40505
content:
"The AR5 (Church et al., 2013b) generated GMSL projections for the RCPs \r by combining information from CMIP5 climate models with glacier \r and ice-sheet surface mass balance (SMB) models and assessments \r of projected ice-sheet dynamic and land-water storage contributions \r (Section 9.6.3.2). The SROCC (Oppenheimer et al., 2019) updated AR5 \r projections based on a revised assessment of the AIS contribution to \r GMSL rise. The AR5 and SROCC employ a  baseline period of 1986 \r to 2005, which is updated in this Report to a baseline period of 1995 \r to 2014 (Section  1.4.1). Between these two periods, GMSL rose by \r 3 cm, and this correction is applied to projections from previous reports \r to allow comparison (Table  9.8). Accounting for this shift, SROCC \r concludes that, with medium confidence, GMSL will rise between \r 0.40 (0.26-0.56, likely range) m (RCP2.6) and 0.81 (0.58-1.07 m, likely\r range) m (RCP8.5) by 2100 relative to 1995-2014. The AR5 and SROCC \r [...]"


id: 748f058521de91d8a130ed4a607ade1f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 17741
content:
"[...] GMSL projections for the 2007-2018 period have been shown to be \r consistent with observed trends in GMSL and regional weighted mean \r tide gauges (J. Wang et al., 2021). "


id: 68e2a3c50e4736d0a5b6f2b16091cbfd
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 15722
content:
"The combination of tide gauge observations and geological \r reconstructions indicates that a sustained increase of GMSL began \r between 1820-1860 and led to a 20th-century GMSL rise that was \r very likely (high confidence) faster than in any preceding century in \r the last 3000 years (Section 2.3.3.3). At a regional level, tide gauge \r and geological data from the North Atlantic and Australasia show \r inflections in RSL trends between 1895-1935, with an increase of \r 0.8 to 2.5  mm yr -1 across the inflection (Gehrels and Woodworth, \r 2013). A statistical meta-analysis of globally distributed geological \r and tide gauge data (Kopp et al., 2016) found that, in all 20 examined \r regions with geological records stretching back at least 2000 years, \r the rate of RSL rise in the 20th century was greater than the local \r average over 0-1700 CE. In four of the 20 regions, all in the North \r Atlantic (Connecticut, New Jersey, North Carolina, and Iceland), the \r [...]"


id: ccfe97c4c6e9cd18a4d3e573487a6018
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 33409
content:
"[...] 19th  century rate was also greater than the 0-1700 CE average \r (90% confidence interval). In summary, rates of RSL rise exceeding \r the pre-industrial background rate of rise are apparent in parts of the \r North Atlantic in the 19th century (medium confidence), and in most \r of the world in the 20th century (high confidence)."


id: a91034c9752849f3e730fe00c7fc2647
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 26956
content:
"the updated projections to AR5 (Church et al., 2013b) and SROCC \r (Oppenheimer et  al., 2019) (Tables 9.7 and 9.8). Since there is no \r single model that can directly compute all of the contributions \r to sea level change (Box  9.1), the contributions to sea level are \r computed separately and then combined (Tables 9.8 and 9.9). For \r consistency with global surface air temperature (GSAT) projections \r (Section 4.3.1.1), and assessment of equilibrium climate sensitivity \r (ECS) and transient climate response (TCR; Section 7.5), temperature\u0002dependent projections (thermal expansion, ice sheets, glaciers) are \r forced by GSAT projections from a two-layer energy budget emulator \r (Smith et  al., 2018) that is calibrated to be consistent with the \r assessment of ECS and TCR (Box 7.1, Supplementary Material 7.SM.2). \r Throughout, likely ranges are assessed based on the combination \r of uncertainty in the GSAT distribution and uncertainty in the \r [...]"


id: 3745b532a76bc3f48eec9bbbdd429b9f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 6939
content:
"[...] relationships between GSAT and changes to individual components. \r In general, 17th-83rd percentile results, incorporating both GSAT \r and sea level process uncertainty, are interpreted as likely ranges. \r This is distinct from the approach used by AR5, which interpreted \r the 5th-95th percentile range of CMIP5 projections, and therefore \r of GMSL projections driven by them, as likely ranges. The shift in \r interpretation is consistent with the use of the emulator for GSAT \r (Box 4.1, Cross-Chapter Box 7.1). Very likely ranges are not assessed \r because of the potential for processes in whose projections there \r is currently low confidence to substantially augment total projected \r GMSL change."


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"For processes whose projections we have at least medium \r confidence in, the updated projections use emulated Ice Sheet \r Model Intercomparison Project for CMIP6 (ISMIP6) projections of the \r Greenland Ice Sheet (Section 9.4.1.3; Figure 9.17; Tables 9.2 and 9.7; \r Box 9.3). Since the ISMIP6 emulator does not account for temporal \r correlation, a parametric fit to the ISMIP6 results is used to calculate \r rates of change (Supplementary Material 9.SM.4.4). For projections \r beyond 2100 (when the ISMIP6 simulations end), the polynomial fit \r is extrapolated based on two alternate approaches: (i) an assumption \r of constant rates of mass change after 2100; and (ii) for SSP1-2.6 \r and SSP5-8.5, a quadratic function of time extending to 2300 based \r on the multi-model assessment of contributions under RCP2.6 and \r RCP8.5 at 2300 (Section  9.4.1.4). Differences between the two \r approaches are small up to 2150, and since the latter approach is not \r [...]"


id: e8c975376fb7ce7b6611364633e59ee9
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 38306
content:
"[...] available for all scenarios, only the former (constant rates) is used \r for time series projections up to 2150. Both approaches are used for \r examining uncertainty in the timing of different levels of GMSL rise \r and to inform projections for the year 2300 (Section  9.4.1.4). For \r 2100, the ISMIP6 emulator yields the likely contribution from the \r Greenland Ice Sheet shown in Table 9.2 and Figure 9.17, representing \r a slight narrowing from AR5 projections."


id: 625592c21ce7a7610723199aefc33992
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14577
content:
"For the Antarctic Ice Sheet (AIS), AR5 applied a temperature-based \r scaling approach for SMB and a quadratic function of time, calibrated \r to a multi-model assessment, for dynamic contributions. The SROCC \r used a new assessment based on the results of five process-based \r studies (Section  9.4.2.5). For processes in whose projections we \r have at least medium confidence, the likely range projections \r for the AIS are based on: (i) the emulated ISMIP6 ensemble; and \r (ii) the  LARMIP-2 ensemble, augmented with AR5 parametric \r Antarctic SMB model. The GMSL projections are produced with both \r distributions and combined in a  'p-box' (Kriegler and Held, 2005; \r Le Cozannet et  al., 2017), which represents the upper and lower \r bounds of the distribution (Section 9.4.2.5, Box 9.3 and Table 9.3). \r A  likely range is then identified, spanning the lower of the two \r 17th percentile projections and the higher of the two 83rd percentile \r [...]"


id: b516c9e039bed49794cf0f28a12543fc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 29097
content:
"[...] projections,5 with the median taken as the mean of the medians of \r the two projections. Since the ISMIP6 emulator does not account \r for temporal correlation, the AR5 parametric AIS model is substituted \r for the emulator in the p-box for rates of change. As AR5 projections \r are modestly lower than those from the ISMIP6 emulator, this \r substitution modestly broadens the likely range at the low end for \r projections of rate and changes beyond 2100. For projections beyond \r 2100 (when the ISMIP6 and LARMIP-2 simulations end), the AIS \r simulations are extrapolated using the same two approaches as the \r Greenland Ice Sheet (GrIS) projections (Section  9.4.1.4). The likely\r ranges to 2100 are consistent with SROCC (Table 9.8)."


id: 6938a5881aaaa94b08eac47333026606
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
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content:
"In AR5 and SROCC, global mean thermosteric sea level rise \r was derived from the 21 members of the CMIP5 ensemble that \r provided the required variables (Section  9.2.4.1). The AR5 and \r SROCC removed drift estimated based on a  pointwise polynomial \r fit to pre-industrial control simulations. They extended projections \r to scenarios not provided by the models by calculating the heat \r content of the climate system from GMST and net radiative flux, \r and converting this to global mean thermosteric sea level rise \r using each model's diagnosed expansion efficiency coefficient. The \r AR5 and SROCC derived the associated uncertainties by assuming \r a normal distribution, with the 5th-95th percentile CMIP5 ensemble \r interpreted as the likely range. In this Report, global mean thermosteric \r sea level rise is derived from a  two-layer energy budget emulator \r consistent with the assessment of ECS and TCR (Section  9.2.4.1; \r [...]"


id: 8939b294541a2a589cff5e3087799145
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 21311
content:
"[...] Supplementary Material 9.SM.4.2 and 9.SM.4.3). Despite the change \r in methodology, this leads to a  likely global mean thermosteric \r contribution (17th-83rd percentile) between 1995-2014 and 2100 \r that represents a minimal change from AR5 and SROCC (Table 9.8)."


id: 9010edc2ca6f6c3cbc36e2e3d7493d37
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 22552
content:
"Gravitational, rotational, and deformational (GRD) effects (Box 9.1) \r lead to distinct variations in the RSL change pattern, which are similar \r across a range of benchmarked GRD solvers (Martinec et al., 2018; \r Palmer et al., 2020). There is high confidence in the understanding \r of GRD processes. RSL rise associated with GRD is very likely to be \r largest in the Pacific, due to the combined effects of projected GrIS, \r AIS and glacier mass loss (high confidence) (e.g., Kopp et al., 2014; \r Slangen et  al., 2014b; Larour et  al., 2017; Mitrovica et  al., 2018). \r The GRD effect associated with mass loss from an ice sheet is sensitive \r to the spatial distribution of that mass loss. For example, the GRD \r contribution to RSL rise in Australia will be larger for Antarctic mass \r loss sourced fromthe Antarctic Peninsula than for Antarctic mass loss \r sourced fromThwaites Glacier. In parts of north-eastern North \r America and north-western Europe, GRD effects associated with \r [...]"


id: 6939a5fe3ef20b1084104e51804a3925
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15776
content:
"[...] mass loss from southern Greenland will lead to an RSL fall, whereas \r mass loss from northern Greenland will lead to an RSL rise (high \r confidence) (Figure 9.26; Larour et al., 2017; Mitrovica et al., 2018). \r The AR5 and SROCC computed RSL patterns using a gravitationally \r self-consistent GRD solver given the amounts, locations and timing \r of the projected barystatic sea level changes driven by glaciers, ice \r sheets and LWS (Church et al., 2013b). A similar GRD solver is used \r in the updated projections (following Slangen et  al., 2014b). The \r Earth model used is based on the Preliminary reference Earth model \r (PREM: Dziewonski and Anderson, 1981), and is elastic, compressible \r and radially stratified."


id: ba181c599c8459d192804248eb20fa1e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29964
content:
"In the updated projections, a statistical relationship is applied, linking \r historical and future SSP global population to dam impoundment and \r groundwater extraction (Rahmstorf et al., 2012; Kopp et al., 2014). \r The population/groundwater depletion relationship is calibrated \r based on the same studies used in AR5 (Konikow, 2011; Wada et al., \r 2012), reduced by about 20% to account for water retained on land \r (Wada et al., 2016). The population/dam impoundment relationship \r is calibrated based on Chao et al. (2008). However, while historically \r dam impoundment has been declining with population, recent \r literature shows that planned dam construction considerably \r exceeds the historical trend (Zarfl et al., 2015; Hawley et al., 2020). \r Over 2020-2040, the impoundment contribution to GMSL rise \r based on past trends would be about -0.1 mm yr -1, compared to \r about -0.5 mm yr -1 if all currently planned dams are built (Hawley \r [...]"


id: 16fd53245d1782168b65c8c489b4dd05
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 1265
content:
"[...] et al., 2020) and the statistical projection is therefore augmented \r by an additional -0.4 to 0.0 mm yr -1 over 2020-2040 to account \r for the possible effects of planned dam construction. As in AR5 and \r SROCC, climatically driven changes to land-water storage (LWS) \r have not been included in published sea level projections, as they \r are not well quantified (e.g., Jensen et al., 2019) or are considered \r negligible (e.g.,  permafrost, Section  9.5.2). This approach yields \r a likely global-mean land-water storage contribution (Figure 9.27, \r Table  9.8) that is slightly lower and narrower than the AR5 and \r SROCC likely ranges. Since the projections are explicitly population \r driven, these projections also exhibit a weak scenario dependence, \r with a contribution around 0.01 m higher under SSP3 than under \r other scenarios."


id: 9a49558c680e6894e15711084e41bc39
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 24388
content:
"Glacial Isostatic Adjustment (GIA) leads to vertical land motion \r (VLM; see Box 9.1) and changes in sea surface height, both of which \r contribute to RSL change. GIA uncertainty is caused by uncertainty \r in the rheological structure of the solid Earth, which drives the \r longer-term viscous Earth deformation, as well as uncertainty in \r the modelled global ice history (e.g., Whitehouse, 2018). In AR5 and \r SROCC, GIA contributions to RSL change were calculated using a sea \r level equation solver with an ice-sheet history taken as the mean \r of the ICE5G (Peltier et al., 2015) and ANU (Lambeck et al., 2014) \r ice-sheet models. Since AR5, new global models are emerging that \r more rigorously treat ice and Earth structure uncertainty (Caron \r et al., 2018). However, there is also a growing recognition that lateral \r variations in Earth structure limit the utility of global models that treat \r the solid Earth as though it were laterally uniform (Love et al., 2016; \r [...]"


id: 9765dcf5ee9b8cfe7d0001caca6196bf
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 23845
content:
"In AR5 and SROCC, the ocean dynamic sea level contribution to RSL \r projections was derived from the CMIP5 ensemble, after removing the \r drift estimate based on pre-industrial control simulations. This Report \r uses updated simulations from the CMIP6 ensemble (Section 9.2.4.2; \r Supplementary Material 9.SM.4.2) to project the ocean dynamic \r sea level contribution to RSL change (Section 9.2.4.2; Figure 9.26). \r To produce ocean dynamic sea level projections consistent with the \r global mean thermosteric projections from the two-layer energy \r budget emulator, we follow the approach of Kopp et  al. (2014), \r employing a  correlation between global-mean thermosteric sea \r level change and ocean dynamic sea level derived from the CMIP6 \r ensemble (Supplementary Material 9.SM.4.3). Since CMIP6 models \r are of fairly coarse resolution (typically about 100 km), and even \r the models participating in HighResMIP (near 10 km resolution) \r [...]"


id: 105a37afa3d4b37a49fa68f5491defda
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 66
content:
"[...] do not capture all the phenomena that contribute to coastal ocean \r dynamic sea level change, there is low confidence in the details of "


id: 8e6d8d078d11c981de8681da205ea63f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 22245
content:
"constant over the projected period. In areas where rapid subsidence \r occurs in a cluster of tide gauges (e.g., the western Gulf of Mexico), \r the associated rates are interpolated between the tide gauges. \r In areas where the available tide gauges exhibit large, tectonically \r driven VLM that changes considerably in rate over short distances \r (e.g., Alaska and the Bering Strait), a sizable uncertainty propagates \r into the RSL projections (Figure 9.26). Rates of RSL rise are likely to \r be underestimated due to subsidence in shallow strata that are not \r recorded by tide gauges (Keogh and Tornqvist, 2019) and in some \r locations may therefore be minimum values, especially if anomalously \r high subsidence rates associated with fluid extraction are also \r considered (e.g.,  Minderhoud et  al., 2017). Therefore, depending \r on location, there is low to medium confidence in the GIA and VLM \r projections employed in this Report. In many regions, higher-fidelity \r [...]"


id: 159a6f692d17232474ba4afd87ef5c63
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 1015
content:
"VLM rates comparable to or greater than rates of GMSL change. \r Complete global projections of these processes are not available \r because of the small spatial scales, the sensitivity of subsidence to \r local human activities, and the stochasticity of tectonics (Woppelmann \r and Marcos, 2016; Oppenheimer et al., 2019). Therefore, integrated \r RSL projections to date have either included only the component of \r VLM associated with GIA (as in AR5 and SROCC), or used a constant \r long-term background rate of change (including both GIA and other \r long-term drivers of VLM) estimated from historical tide gauge trends \r (e.g.,  Kopp et  al., 2014). The updated projections use the second \r approach and extrapolate the field of long-term background rates \r of RSL change, including long-term VLM derived from tide gauges, \r to global coverage using a  spatio-temporal statistical approach \r (Supplementary Material 9.SM.4.6; Kopp et al., 2014). The combined \r [...]"


id: 5f5aae515e953083676a6fc4bdd8ee62
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14088
content:
"Since AR5, a small number of modelling studies have examined the \r reversibility of the multimillennial sea level commitment under carbon \r dioxide (CO2) removal, solar radiation modification or local ice shelf \r engineering. The slow response of the deep ocean to forcing leads \r to global-mean thermosteric sea level fall occurring long afterward, \r even if CO2 levels are restored after a transient increase: global mean \r thermosteric sea level rise takes more than a millennium to reverse \r (Ehlert and Zickfeld, 2018). Rapid reversion to pre-industrial CO2\r concentrations has been found to be ineffective at fostering regrowth \r of the AIS (DeConto et al., 2021) but may reduce the multimillennial \r sea level commitment (DeConto and Pollard, 2016). Altering \r sub-ice-shelf bathymetry (Wolovick and Moore, 2018) or triggering \r ice shelf advance through massive snow deposition (Feldmann et al., \r 2019) might interrupt marine ice sheet instability (Section 9.4.2.4) \r [...]"


id: 27dc8640fea931b49629a281d8342b7b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 4207
content:
"[...] and thus reduce sea level commitment. A reversion to pre-industrial \r Greenland Ice Sheet temperatures with solar radiation modification \r is projected to stop mass loss in Greenland but leads to minimal \r regrowth (Applegate and Keller, 2015). Based on limited evidence, \r carbon dioxide removal, solar radiation modification, and local \r ice-shelf engineering may be effective at reducing the yet-to-be\u0002realized sea level commitment, but ineffective at reversing GMSL rise \r (low confidence)."


id: ed7468a8b3a6f50fea542f74c941404
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39142
content:
"In Antarctica, high warming might lead to floating ice shelves starting to break up earlier than expected due to processes not yet \r accounted for in ice-sheet models or in current climate models used to force ice-sheet projections. Such processes include hydrofracturing \r driven by surface meltwater, and increase in ocean thermal forcing driven by ocean circulation changes (Sections 9.2.2.3, 9.2.3.2 and \r 9.4.2.3; Hellmer et al., 2012, 2017; Silvano et al., 2018; Hazel and Stewart, 2020). In particular, the Thwaites and Pine Island Glacier \r ice shelves could potentially disintegrate this century, which might trigger MICI before 2100 (DeConto and Pollard, 2016; DeConto \r et al., 2021). MISI could potentially develop earlier and faster than simulated by the majority of models if fast flowing ice streams \r follow plastic, instead of currently assumed more viscous, sliding laws (Sun et al., 2020). Oceanic feedbacks could drive high-end sea \r [...]"


id: e53fa7d5dd31bcb0ad7b8bc31ebcb00f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37634
content:
"[...] level rise by changes in the meltwater-driven overturning circulation in ice cavities that cause additional melting (Jeong et al., 2020); \r by a warming of the ocean water in contact with the ice shelves due to increased stratification and thus reduced vertical mixing \r (Sections 9.2.2.3 and 9.2.3.2; Golledge et al., 2019; Moorman et al., 2020; Sadai et al., 2020); or by an increase in sea ice cover due \r to increased ocean stratification (Section 9.3.2.1), which could reduce the amount of warm, moist air that reaches the continent, \r and limit the mass gain from snowfall over the ice sheet (Sadai et al., 2020)."


id: 6aa5e9a090f96b3e3dc91436260857d3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 16044
content:
"Identifying the potential drivers of a high-end sea level rise allows identification of sites and observables that can provide early \r warnings of a much faster sea level rise than the likely range of this and previous reports. One potential site for such monitoring is \r Thwaites Glacier, which is melting faster in some places and slower in others than models simulate. At this glacier, the effect of tides \r and channelling of warm water flows on the melting is evident (Milillo et al., 2019), making the floating ice shelf potentially vulnerable \r to breakup from hydrofracturing, driven by surface meltwater, much earlier than expected. In addition, the glacier is retreating towards \r a zone with deeper bedrock, which at its present rate of retreat would be reached in 30 years (Yu et al., 2019). Thwaites Glacier is \r therefore a strong candidate to experience large-scale MISI and/or MICI (Golledge et al., 2019; DeConto et al., 2021), making it the \r [...]"


id: c8d540d70d3d822108e5a368dbef7dbd
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 32661
content:
"[...] ideal site for monitoring early warning signals of accelerated sea level rise from Antarctica. Such signals could possibly be observed \r within the next few decades (Scambos et al., 2017)."


id: c68d45212a83bc89c4b9ebb07c3da9e8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 32233
content:
"deployments of in situ measurements in the very dynamic surf \r zone means that long-term records of ETWL or ECWL are limited to \r a few sites; tidal gauges are typically located in sheltered locations \r (e.g.,  harbours) where wave contributions are absent (Lambert \r et al., 2020). Consequently, trends in wave contributions to ESL are \r typically derived from trends in wave conditions observed offshore. \r On the basis of satellite altimeter observations, SROCC reported \r increasing extreme wave heights in the Southern and North Atlantic \r oceans of around 1.0 and 0.8 cm yr -1, respectively, over the period \r 1985-2018 (medium confidence). The SROCC (Collins et al., 2019) \r also identified sea ice loss in the Arctic as leading to increased wave \r heights over the period 1992-2014 (medium confidence). Since \r SROCC, the satellite wave record has been shown to be sensitive \r to alternate processing techniques, leading to important differences \r [...]"


id: e2e592d5965aaa5a0a2e4961be208048
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37217
content:
"[...] in reported trends (Timmermans et  al., 2020). The most common \r observation platforms for surface waves over the past 30 years are \r in situ buoys. However, evolving biases associated with changing \r instrument type, configuration and sampling methodology introduce \r artificial trends (e.g.,  Gemmrich et  al., 2011; Timmermans et  al., \r 2020). Accurate metadata is required to address these issues, and, \r while available locally, are only beginning to be globally coordinated \r (Centurioni et  al., 2019). Wave reanalysis and hindcast products \r have also been used to investigate total water level at global scale \r (Melet et  al., 2018; Reguero et  al., 2019). Their applicability for \r trend analysis is limited by inhomogeneous data for assimilation \r (Stopa et al., 2019), but they inform relationships between seasonal, \r interannual to inter-decadal variability of climate indices and wind\u0002wave characteristics (A.G. Marshall et al., 2015, 2018; Kumar et al., \r [...]"


id: 92a38f5b354ae930e48dab008e4279ad
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 23019
content:
"[...] 2016; Stopa et al., 2016). To summarize, satellite era trends in wave \r heights of order 0.5 cm yr -1 have been reported, most pronounced in \r the Southern Ocean. However, sensitivity of processing techniques, \r inadequate spatial distribution of observations, and homogeneity \r issues in available records limit confidence in reported trends \r (medium confidence)."


id: 7cecb5ad43f186f1157096879d1637aa
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 19169
content:
"The SROCC (Oppenheimer et al., 2019) reported variations in storm \r surge not related to changes in RSL, and concluded with high \r confidence that consideration of localized storm surge processes was \r essential to monitor trends in ESL. SL events driven by storm surge \r are a response to tropical and extratropical cyclones. While historical \r trends in extra-tropical cyclones are less clear (Section  11.7.2.1), \r there is mounting evidence for an increasing proportion of stronger \r tropical cyclones globally, with an associated poleward migration \r (Section 11.7.1.2). These changes are captured in the ESL record, for \r example, via increasing intensity and poleward shift in the location of \r typhoon-driven storm surges reported across 64 years (1950-2013) \r in the western North Pacific (Oey and Chou, 2016). Along the \r east coast of the USA, there has been an increase in frequency of \r ESL events due to tropical cyclone changes since 1923 that can \r [...]"


id: e8916971b657a446d9d4ddcf605c3cb8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 38255
content:
"[...] be statistically linked to changes in global average temperature \r (Grinsted et al., 2013), and the signal is projected to emerge around \r 2030 (Lee et al., 2017). At century and longer time scales, geological \r proxies such as overwash deposits in coastal lagoons or sinkholes can \r be used to reconstruct past changes in storm activity (e.g., Brandon \r et  al., 2013; Lin et  al., 2014) and put recent events into historical \r perspective (e.g.,  Brandon et  al., 2015). However, there is low \r confidence in the current ability to quantitatively compare geological \r proxies with gauge data. Historical storm surge activity is being \r increasingly assessed with use of hydrodynamic model simulations \r and data-driven global reconstructions to supplement tide gauge \r observations to investigate historical changes at centennial to \r millennial time scales (e.g., Ji et al., 2020; Muis et al., 2020; Tadesse \r et al., 2020). Large regional variations and limited observational data \r [...]"


id: 6652df79a9389cd897d2347e205ce3eb
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15265
content:
"Only a  few studies have attempted to quantify the role of \r anthropogenic climate change in ESL events (e.g., Mori et al., 2014; \r Takayabu et al., 2015; Turki et al., 2019). Detection and attribution of \r the human influence on climatic changes in surges, and waves remains \r a  challenge (Ceres et  al., 2017), with limited evidence to suggest \r in some instances  - for example, poleward migration of tropical \r cyclones in the Western North Pacific (Section  11.7.1.2), changes \r in surges and waves can be attributed to anthropogenic climate \r change (low confidence). With RSL change being considered the \r primary driver of observed tidal changes, there is medium confidence\r that these changes can be attributed to human influence. The close \r relationship between local ESL and long-term RSL change, combined \r with the robust attribution of GMSL change (Section 9.6.1.4), implies \r that observed global changes in ESL can be attributed, at least in \r [...]"


id: 280ef43835e421e68bdba7a4b6247b19
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 4246
content:
"[...] part, to human-caused climate change (medium confidence), but \r reconciling regional variation in these changes is not yet possible \r (Section 9.6.1.4)."


id: b7f5014197688e5becd463e39e16439f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29580
content:
"There are two distinct methods used to project future ESL changes: \r (i) The static, or mean sea level offset, approach employs historical \r distributions of tidal, surge and wave components and adjusts future \r ESL distributions for mean RSL rise; (ii) The dynamic approach employs \r hydrodynamic and/or wave models forced with atmospheric fields \r derived from general circulation models (GCMs) to project changes \r in tidal, storm surge and wave distributions, which are then combined \r with RSL projections to project future ESLs; and (iii) The  dynamic \r approach is computationally expensive. Use of the dynamic approach \r on large spatial or global scales has only recently been successful \r to project 21st-century changes in ETWL (Vousdoukas et al., 2017, \r 2018) and ECWL (Melet et  al., 2020). Kirezci et  al. (2020) assume \r stationarity in global wave and storm surge simulations to assess \r projected 21st-century changes in episodic coastal ETWL-driven \r [...]"


id: cabf7603178f7f75d552211cefbde54c
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content:
"In the dynamic approach, the low resolution of the forcing fields \r arising from GCMs limits the ability to resolve historical and future \r changes in tropical and extra-tropical storm frequency and intensity, \r and resolution of local geography and morphology limit ability to \r represent ECWL (Box 9.1). Not all relevant processes - such as river \r discharge - are included in the dynamic models, and ESL events are \r typically a  combination of multiple contributing processes, which \r are often not independent (Jevrejeva et  al., 2019). In both static \r and dynamical approaches, global assessment of the performance \r of modelled storm surge and wave contributions to ESL is limited \r by poor coverage of observations (limited to tide gauges for \r ESWL, Muis et al., 2020), and unavailable for the wave dependent \r ETWL and ECWL estimates (Vitousek et al., 2017; Vousdoukas et al., \r 2018; Kirezci et al., 2020; Lambert et al., 2020; Melet et al., 2020). \r [...]"


id: 56e0c7dfa2dce5e36c79314012b3bae5
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] In studies to date, individual models are used to simulate different \r contributions to ESL, non-linear interactions are not well captured, \r and uncertainties associated with downscaling methodology are \r poorly resolved, leading to low confidence in available ESL projections \r that include these modelled wave and surge contributions."


id: eb7798957e4566e974a6000ab9730b96
created_at: 2023-04-19 13:22:41
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content_type: text
index: climate_gpt_v2
vector_id: 38783
content:
"The SROCC (Oppenheimer et al., 2019) presents projections of ESL \r derived using a  static approach. Such projections often quantify \r changes in ESL event frequency, expressed as 'frequency amplification \r factors' (Hunter, 2010, 2012). Like RSL projections, frequency \r amplification factors increase under higher-emissions scenarios, \r and differences between scenarios increase over time. The  SROCC \r concludes that even small to moderate changes in mean RSL can \r lead to hundred- to thousand-fold increases in the frequencies \r with which certain thresholds are exceeded  - for example, what \r is currently a  1-in-100-year ESL height (1% annual probability or \r 0.01 expected annual events) will be expected once or even multiple \r times per year in future at many locations (Figure 9.32). The SROCC \r showed that currently rare ESL events (e.g., with an average return \r period of 100 years) will occur annually or more frequently at most \r [...]"


id: b4c59f9d759018d1dc5442b3aea5d9b8
created_at: 2023-04-19 13:22:41
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content:
"[...] available locations for RCP4.5 by the end of the century (high \r confidence). Results from these assessments are sensitive to the type \r of ESL probability distribution assumed (Buchanan et al., 2016; Wahl \r et al., 2017), as well as the magnitude and uncertainty of projected \r RSL change (Slangen et al., 2017; Wahl et al., 2017; Frederikse et al., \r 2020a). Frequency amplification factors tend to be largest in tropical \r regions due in part to higher RSL rise projections, but primarily to \r the relative rarity of high ESLs in areas with little historical exposure \r to tropical or extratropical cyclones. Alternative representation of \r changes in ESL, such as presenting changes in exceedances per year \r (Sweet and Park, 2014), are subject to similar sensitivities, and lead \r to medium confidence in projected changes of event frequency using \r these methods."


id: fde6cf82611f0b5d28b53cb589df8052
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"Since SROCC, a quantitative assessment of a community ensemble \r of global wind-wave projections (Morim et al., 2019) found robust \r projected changes of around 5-10% (positive or negative, depending \r on region) in annual mean significant wave height, mean wave \r period, and/or mean wave directions along about 52% of the world's \r coastline that exceed internal climate variability under RCP8.5 by \r 2100. Continued retreat of sea ice cover in the Arctic will lead to \r more energetic wind-wave conditions (Casas-Prat and Wang, 2020). \r Wave climate modelling methods introduce up to around 50% of \r the ensemble variance in mean wave climate projections (Morim \r et  al., 2019). GCMs do not typically resolve the higher-resolution \r tropical and extratropical storm features required to accurately \r determine the contribution of extreme waves to ESLs and individual \r studies have sought to improve resolution to address these issues \r [...]"


id: 81ff290e2b3a21bcfa7f7ab37e438796
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 19993
content:
"[...] (e.g., Timmermans et al., 2017). To date, projections of wave height \r extremes have been constrained to single wave model configurations \r (e.g., Timmermans et  al., 2017; Meucci et  al., 2020). In summary, \r there is medium confidence in projections of changes in mean wave \r climate but low confidence in the projected changes in extreme \r wave conditions due to limited evidence."


id: 68ec8a0b51c810120ee87a4d391a3146
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15730
content:
"catchments are steep and cause high rainfall near the coast, such as \r in south-west UK (Svensson and Jones, 2004). The compound effect \r of storm surge and rainfall contributes greater projected flood risk \r than climate-induced amplification (Hsiao et al., 2021). However, at \r other locations, co-occurrence was unimportant because streamflow \r timing did not coincide with the coastal peak storm surge (Hudson \r River, Orton et al., 2012; Rhine delta, Klerk et al., 2015). The SROCC \r (Oppenheimer et  al., 2019) detailed the complexity of interactions \r in deltaic environments. Direct increases in flooding driven by \r increasing RSL and storm surge, rain, or correlations between these \r flood-drivers (e.g.,  Moftakhari et  al., 2017; Orton et  al., 2020) are \r expected to be further accompanied by increases in flooding due to \r subsidence (vertical land movement) and sedimentation (RSL-driven \r blockage of river flows). The probability of concurrent surge, wave \r [...]"


id: cd6543add6aee5ae000b3dd7d90e0aaf
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 33473
content:
"[...] and precipitation events has been projected to increase by more \r than 25% by 2100 compared to present, with high northern latitudes \r displaying compound flooding becoming more than 2.5 times as \r frequent, and weakening in the subtropics (Bevacqua et al., 2020). \r However, the number of studies on compound events is still limited \r and so there is low confidence in understanding the extent by which \r compound events of surge with rain will change in response to RSL \r rise and climate change."


id: f2883146c3a82d8983ea5b9917c23c11
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 40067
content:
"Correlations between changes in sea level-forced (mean sea level \r and tidal) and atmospherically-forced drivers (ocean surface waves \r and surges) of ESLs have only been considered in a  few studies, \r although high surge and high waves co-occur along a  majority of \r the world's coastlines (Marcos et  al., 2019). Along the east coast \r fo the USA, ocean dynamic sea level change and change in power \r dissipation index (a proxy for North Atlantic tropical cyclone activity) \r are correlated across CMIP5 GCMs, resulting in an increase in ESLs \r relative to analyses assuming independence of these changes \r (Little et al., 2015). In the Irish Sea, dynamically coupled wave-tide \r modelling results in high water wave heights up to 20% higher than \r in an uncoupled analysis (Lewis et al., 2019). In the German Bight, \r RSL rise relaxes the breaking criterion of nearshore waves (assuming \r no geomorphological response), allowing larger waves to propagate \r [...]"


id: fdaea7d3560c1c2f2400220aff89acff
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 41990
content:
"[...] closer to shore, leading to increased wave runup (Arns et al., 2017). \r In south-western Australia, the influence of projected SLR was found \r to exceed the influence of projected changes in forcing winds on wave \r characteristics at the coast (Wandres et al., 2017). Thus, projections of \r ESL that do not consider correlations between and among sea level \r forced and atmospherically forced drivers can differ strongly from \r coupled projections (medium confidence). "


id: aba5ae84112a9536f6ff74799211b0cc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 27438
content:
"Humans are changing the climate and there are mechanisms that amplify the warming in the polar regions \r (Arctic and Antarctic). The Arctic is already warming faster than anywhere else (see FAQ 4.3). This is significant \r because these colder high latitudes are home to our two remaining ice sheets: Antarctica and Greenland. Ice \r sheets are huge reservoirs of frozen freshwater, built up by tens of thousands of years of snowfall. If they were \r to completely melt, the water released would raise global sea level by about 65 m. Understanding how these ice \r sheets are affected by warming of nearby ocean and atmosphere is therefore critically important. The Greenland \r and Antarctic ice sheets are already slowly responding to recent changes in climate, but it takes a long time \r for these huge masses of ice to adjust to changes in global temperature. That means that the full effects of \r [...]"


id: bf501d1ad9889d35f7c893a1a8021d60
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 30928
content:
"[...] a warming climate may take hundreds or thousands of years to play out. An important question is whether these \r changes can eventually be reversed, once levels of greenhouse gases in the atmosphere are stabilized or reduced \r by humans and natural processes. Records from the past can help us answer this question."


id: e6775948067da6ad0a754ba0c7041d57
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37849
content:
"For at least the last 800,000 years, the Earth has followed cycles of gradual cooling followed by rapid warming \r caused by natural processes. During cooling phases, more and more ocean water is gradually deposited as \r snowfall, causing ice sheets to grow and sea level to slowly decrease. During warming phases, the ice sheets \r melt more quickly, resulting in more rapid rises in sea level (FAQ 9.1, Figure 1). Ice sheets build up very slowly \r because growth relies on the steady accumulation of falling snow that eventually compacts into ice. As the \r climate cools, areas that can accumulate snow expand, reflecting back more sunlight that otherwise would keep \r the Earth warmer. This means that, once started, glacial climates develop rapidly. However, as the climate cools, \r the amount of moisture that the air can hold tends to decrease. As a result, even though glaciations begin quite \r [...]"


id: aa63e12fd84416368d0ebb5c1416d5ff
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 27204
content:
"[...] quickly, it takes tens of thousands of years for ice sheets to grow to a point where they are in balance with the \r colder climate."


id: c315845b3d3981876e2c8191342d271
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 31597
content:
"Ice sheets retreat more quickly than they grow because of processes that, once triggered, drive self-reinforcing \r ice loss. For ice sheets that are mostly resting on bedrock above sea level - like the Greenland Ice Sheet - the main \r self-reinforcing loop that affects them is the 'elevation-mass balance feedback' (FAQ 9.1, Figure 1, right). In this \r situation, the altitude of the ice-sheet surface decreases as it melts, exposing the sheet to warmer air. The lowered \r surface then melts even more, lowering it faster still, until eventually the whole ice sheet disappears. In places \r where the ice sheet rests instead on bedrock that is below sea level, and which also deepens inland, including \r many parts of the Antarctic Ice Sheet, an important process called 'marine ice sheet instability' is thought to drive \r rapid retreat (FAQ 9.1, Figure 1, left). This happens when the part of the ice sheet that is surrounded by sea water \r [...]"


id: 820c20f5f04c3f010f775135e2c6e4c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 20000
content:
"[...] melts. That leads to additional thinning, which in turn accelerates the motion of the glaciers that feed into these \r areas. As the ice sheet flows more quickly into the ocean, more melting takes place, leading to more thinning \r and even faster flow that brings ever-more glacier ice into the ocean, ultimately driving rapid deglaciation of \r whole ice-sheet drainage basins."


id: 3e5d7c987c7d54ef1dd872689bbf522e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 8150
content:
"In many places, local sea level change will be larger or smaller than the global average. From year to year and \r place to place, changes in ocean circulation and wind can lead to local sea level change. In regions where large \r ice sheets, such as the Fennoscandian in Eurasia and the Laurentide and Cordilleran in North America, covered \r the land during the last ice age, the land is still slowly rising up now that the extra weight of the ice sheets is \r gone. This local recovery is compensating for global sea level rise in these regions and can even lead to local \r decrease in sea level. In regions just beyond where the former ice sheets reached and the Earth bulged upwards, \r the land is now falling and, as a result, local sea level rise is faster than the global rate. In many regions within \r low-lying delta regions (such as New Orleans and the Ganges-Brahmaputra delta), the land is rapidly subsiding \r [...]"


id: 88b0d1e297f1138d03c65f9c6d95db33
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content:
"[...] (sinking) because of human activities such as building dams or groundwater and fossil fuel extraction. Further, \r when an ice sheet melts, it has less gravitational pull on the ocean water nearby. This reduction in gravitational \r attraction causes sea level to fall close to the (now less-massive) ice sheet while causing sea level to rise farther \r away. Melt from a polar ice sheet therefore raises sea level most in the opposite hemisphere or in low latitudes - \r amounting to tens of centimetres difference in rise between regions by 2100."


id: 23c859808da2bd235ace1d910380c5ff
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 3488
content:
"Gudfina Adalgeirsdottir (Iceland), Bhupesh  Adhikary (Nepal), Muhammad Adnan (Pakistan), \r Bodo Ahrens (Germany), Muhammad  Amjad (Pakistan), Paola  A.  Arias (Colombia), Farooq \r Mohamed Azam (India), Segolene  Berthou (United Kingdom/France), Melissa  S.  Bukovsky \r (United States of America), Alex J. Cannon (Canada), Ana Casanueva (Spain), Annalisa Cherchi \r (Italy), Erika Coppola (Italy), Faye Abigail Cruz (Philippines), Joseph D. Daron (United Kingdom), \r Marie-Estelle Demory (Switzerland/France, Switzerland), Claudine Dereczynski (Brazil), Alejandro \r Di Luca (Australia, Canada/Argentina), Leandro  B.  Diaz (Argentina), Herve  Douville (France), \r Sergio Henrique Faria (Spain/Brazil), Baylor Fox-Kemper (United States of America), Shin Fukui \r (Japan), Laura Gallardo (Chile), Subimal  Ghosh (India), Nathan P. Gillett (Canada), Melissa \r I. Gomis (France/Switzerland), Hugues  Goosse (Belgium), Irina  V. Gorodetskaya (Portugal/\r [...]"


id: 2ba110ad67109d1599f56ca3c8d8ef68
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 4929
content:
"[...] Belgium, Russian Federation), Michael Grose (Australia), Jose Manual Gutierrez (Spain), Pandora \r Hope (Australia), Akm  Saiful  Islam (Bangladesh), Christopher D. Jack (South Africa), Richard \r G. Jones (United Kingdom), Martin W. Jury (Spain/Austria), Asif Khan (Pakistan), Akio  Kitoh \r (Japan), Svitlana Krakovska (Ukraine), Gerhard  Krinner (France/Germany, France), Hiroyuki \r Kusaka (Japan), Stefan Lange (Germany), Flavio Lehner (United States of America/Switzerland), \r Christopher Lennard (South Africa), Jian Li (China), Fei Liu (China), Martin Menegoz (France), \r Thanh  Ngo-Duc (Vietnam), Dirk  Notz (Germany), Friederike  Otto (United Kingdom/Germany), \r Wendy Parker (United States of America), Carlos  Perez  Garcia-Pando (Spain), Izidine  Pinto \r (South Africa/Mozambique), Jan  Polcher (France/Germany), Krishnan  Raghavan (India), \r Roshanka  Ranasinghe (The Netherlands/Sri  Lanka, Australia), Ingo  Richter (Japan/Germany), \r [...]"


id: eb48436bf81b4823c86c61c12f2e278d
created_at: 2023-04-19 13:22:41
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vector_id: 38750
content:
"[...] Alex C. Ruane (United States of America), Lucas Ruiz (Argentina), Sajjad Saeed (Belgium, Italy/\r Pakistan), Ramiro I. Saurral (Argentina), Reinhard K.H. Schiemann (United Kingdom/Germany), \r Sonia I. Seneviratne (Switzerland), Chris  Shaw (United  Kingdom), Theodore  G.  Shepherd "


id: 35e977a2943233f56408808c54537f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 6730
content:
"Although climate change is a global phenomenon, its manifestations \r and consequences are different in different regions, and therefore \r climate information on spatial scales ranging from sub-continental to \r local is used for impact and risk assessments. Chapter 10 assesses \r the foundations of how regional climate information is distilled \r from multiple, sometimes contrasting, lines of evidence. Starting \r from the assessment of global-scale observations in Chapter  2, \r Chapter 10 assesses the challenges and requirements associated with \r observations relevant at the regional scale. Chapter 10 also assesses \r the fitness of modelling tools available for attributing and projecting \r anthropogenic climate change in a regional context starting from the \r methodologies assessed in Chapters 3 and 4. Regional climate change \r is the result of the interplay between regional responses to both \r natural forcings and human influence (considered in Chapters 2, 5, 6 \r [...]"


id: 8c8d1f87fb10c34088aed9aeed0f5705
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 21911
content:
"[...] and 7), responses to large-scale climate phenomena characterizing \r internal variability (considered in Chapters 1-9), and processes and \r feedbacks of a regional nature."


id: 483e3b32f9860c2bec1943ac376c543a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 9923
content:
"Global and regional climate models are important sources \r of climate information at the regional scale. Global models by \r themselves provide a useful line of evidence for the construction of \r regional climate information through the attribution or projection \r of forced changes or the quantification of the role of the internal \r variability (high confidence). Dynamical downscaling using regional \r climate models adds value in representing many regional weather and \r climate phenomena, especially over regions of complex orography or \r with heterogeneous surface characteristics (very high confidence). \r Increasing climate model resolution improves some aspects of \r model performance (high confidence). Some local-scale phenomena \r such as land-sea breezes and mountain wind systems can only be \r realistically represented by simulations at a  resolution of the order \r of 10 km or finer  (high confidence). Simulations at kilometre-scale \r [...]"


id: c59bd02be8ec205c5a6dd0cbe364ae2b
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updated_at: 2023-04-19 13:22:41
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vector_id: 32069
content:
"[...] resolution add value in particular to the representation of convection, \r sub-daily precipitation extremes (high confidence) and soil-moisture-\r precipitation feedbacks (medium confidence). Sensitivity experiments \r aid the understanding of regional processes and can provide additional \r user-relevant information. {10.3.3, 10.4, 10.5, 10.6} "


id: d0d1e17984d37cd9e05cbd67de5fc6ad
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 34088
content:
"Different types of climate model ensembles allow for the \r assessment of regional climate projection uncertainties, \r although ensemble spread is not a  full measure of the \r uncertainty (very high confidence). Multi-model ensembles \r enable the assessment of regional climate response uncertainty \r (very  high confidence). Discarding models that fundamentally \r misrepresent processes relevant for a  given purpose improves the \r fitness of multi-model ensembles for generating regional climate \r information (high confidence). At the regional scale, multi-model \r mean and ensemble spread are not sufficient to characterize \r low-likelihood, high-impact changes or situations where different \r models simulate substantially different or even opposing changes \r (high confidence). In such cases, storylines aid the interpretation \r of projection uncertainties. Since AR5, the availability of multiple \r single-model initial-condition large ensembles (SMILEs) allows for \r [...]"


id: 1f9d26eaf09774aa33f3fb34007cf2bd
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updated_at: 2023-04-19 13:22:41
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vector_id: 2772
content:
"[...] a more robust separation of model uncertainty and internal variability \r in regional-scale projections and provides a  more comprehensive \r spectrum of possible changes associated with internal variability \r (high confidence). {10.3.4} "


id: 1db4e34a8ddf2731fdb5989d14e09b65
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 2412
content:
"The performance of global and regional climate models \r and their fitness for future projections depend on their \r representation of relevant processes, forcings and drivers \r and on the specific context. Improving global model performance \r for regional scales is fundamental for increasing their usefulness \r as regional information sources. It is also key for improving the \r boundary conditions for dynamical downscaling and the input for \r statistical approaches, in particular when regional climate change \r is strongly influenced by large-scale circulation changes. Increasing \r resolution per se does not solve all performance limitations. \r Including the relevant forcings (e.g.,  aerosols, land-use change \r and stratospheric ozone concentrations) and representing the \r relevant feedbacks (e.g.,  snow-albedo, soil-moisture-temperature, \r soil-moisture-precipitation) in global and regional models is \r a prerequisite for reproducing historical regional trends and ensuring \r [...]"


id: 4fde0712e6a0ddb0016aff62861ef83
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 11346
content:
"[...] fitness for future projections (high confidence). The sign of projected \r regional changes of variables such as precipitation and wind speed \r is in some cases only simulated in a trustworthy manner if relevant \r regional processes are represented (medium confidence). {10.3.3, \r 10.4.1, 10.4.2, 10.6.2, Cross-Chapter Box 10.2} "


id: 14dc895c613499e57b28e023c6ea959b
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updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 898
content:
"While human influence has contributed to multi-decadal mean \r precipitation changes in several regions, internal variability \r can delay emergence of the anthropogenic signal in long-term \r precipitation changes in many land regions (high confidence).\r Multiple attribution approaches, including optimal fingerprinting, \r grid-point detection, pattern recognition and dynamical adjustment \r methods, as well as multi-model, single-forcing large ensembles and \r multi-centennial paleoclimate records, support the contribution of \r human influence to several regional multi-decadal mean precipitation \r changes  (high confidence). At regional scale, internal variability \r is stronger and uncertainties in observations, models and human \r influence are all larger than at the global scale, precluding a robust \r assessment of the relative contributions of greenhouse gases, \r stratospheric ozone, different aerosol species and land-use/land-cover \r [...]"


id: 4daaaed8ca4c71d1c81deeade077692d
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updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 10941
content:
"[...] changes. Multiple lines of evidence, combining multi-model ensemble \r global projections with those coming from SMILEs, show that internal \r variability is largely contributing to the delayed or absent emergence \r of the anthropogenic signal in long-term regional mean precipitation \r changes (high confidence). {10.4.1, 10.4.2, 10.4.3, 10.6.3, 10.6.4}"


id: 6b76da856348060825e49851db47b468
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 16207
content:
"The performance of statistical downscaling, bias adjustment \r and weather generators in climate change applications \r depends on the specific model and on the dynamical climate \r model driving it. Knowledge is still limited about suitable predictors \r for statistical downscaling of regional climate change, particularly for \r precipitation. Bias adjustment cannot overcome all consequences \r of unresolved or strongly misrepresented physical processes, \r such as large-scale circulation biases or local feedbacks, and may \r instead introduce other biases and implausible climate change \r signals (medium confidence). Using bias adjustment as a  method \r for statistical downscaling, particularly for coarse-resolution global \r models, may lead to substantial misrepresentations of regional \r climate and climate change (medium confidence). Instead, dynamical \r downscaling may resolve relevant local processes prior to bias \r adjustment, thereby improving the representation of regional \r [...]"


id: 501713e88b49edd53ef67f993cdeb317
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updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 11378
content:
"[...] changes. The performance of statistical approaches and their fitness \r for future projections depends on predictors and change factors \r taken from the driving dynamical models (high confidence). {10.3.3, \r Cross-Chapter Box 10.2}"


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content:
"Regional climate change refers to a change in climate in a given region \r (Section 10.1.2.1) identified by changes in the mean or higher moments \r of the probability distribution of a climate variable and persisting for \r a  few decades or longer. It can also refer to a  change in temporal \r properties such as persistence and frequency of occurrence of weather \r and climate extreme events. Regional climate change may be caused by \r natural internal processes such as atmospheric internal variability and \r local climate response to low-frequency modes of climate variability \r (Technical Annex IV), as well as by changes in external forcings such as \r modulations of the solar cycle, orbital forcing, volcanic eruptions, and \r persistent anthropogenic changes in the composition of the atmosphere \r or in land use and land cover (Cross-Chapter Box 3.2; IPCC, 2018a), \r in addition to the interactions and feedbacks between them. Process \r [...]"


id: 33a1de49b807f4fc7b3eefe958e6f7ca
created_at: 2023-04-19 13:22:41
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"[...] interaction in space is pervasive, which means that small spatial scales \r often have an influence on the larger scales (Palmer, 2013; Sandu \r et al., 2016). Depending on the context, a region may refer to a large \r area such as a monsoon region, but may also be confined to smaller \r areas such as coastlines, mountain ranges or human settlements like \r cities. Users (understood as anyone incorporating climate information \r into their activity) often request climate information for these range of \r scales since their operating and adaptation decision scales range from \r the local to the sub-continental level."


id: 5961aff223649c7283b4a3c8ef3bd931
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"(Vaittinada Ayar et al., 2016; Zhang et al., 2017; L. Cai et al., 2018). \r For simulations that use CMIP3 boundary conditions other periods \r have been used. As a consequence, these regional simulations mix \r for the recent period historical simulations with projections. The \r mismatch needs to be considered when assessing results obtained \r from both global and regional models in the context of the climate \r information distillation process, or when linking the regional chapters \r to the assessments performed in previous chapters. The choice \r of baseline provides a  source of uncertainty for the assessment \r of climate impacts (e.g., for the response of bird species in Africa; \r Baker et al., 2016). Besides, a range of different baselines may need \r to be considered to satisfy a variety of users, since this choice affects \r the perceived result (Dobor and Hlasny, 2019). The influence of the \r different baseline periods can be explored using the Interactive Atlas \r [...]"


id: 16207438ebb8f7367b9148d7a75cc486
created_at: 2023-04-19 13:22:41
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"[...] where different baselines are available, for example, 1986-2005 \r (according to AR5), 1995-2014 (this Report), and both 1961-1990 \r and 1981-2010 (WMO)."


id: b3e22cf56f4005f27b03bc40d85f98c9
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"One way of overcoming the baseline uncertainty is to define the \r results for a given model based on specific global mean temperature \r changes from the pre-industrial period (e.g.,  Sylla et  al., 2018 for \r West Africa; Kjellstrom et al., 2018 for Europe; Taylor et al., 2018 for \r the Caribbean; Montroull et al., 2018 for South America). The specific \r global mean temperature is known as global warming level (GWL; \r Sections 1.6.2 and 10.6.4, and Cross-Chapter Box  11.1). The GWL \r is a useful dimension of integration because important changes in \r regional climate, including many types of extremes, scale quasi\u0002linearly with the GWLs, often independently of the underlying \r emissions scenarios (e.g., Hoegh-Guldberg et al., 2018; Beusch et al., \r 2020; Seneviratne and Hauser, 2020), always taking into account \r caveats described in Cross-Chapter Box 11.1. In addition, GWLs allow \r a  separated analysis of the global and regional climate responses \r [...]"


id: 941dd014ba58fc00af1e74ecb64b255f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] associated with a  warming level (Section  10.6.4; Seneviratne and \r Hauser, 2020). The choice of global temperature goal in the context \r of the 2015 Paris Agreement means that there is an increasing desire \r for the regional climate information to be expressed as a function \r of GWLs."


id: 39189c680a625d9c8082dbb9a0bd7e6c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 7252
content:
"Variability in regional climate arises from natural and anthropogenic \r forcings, internal variability including the local expression of large-scale \r remote drivers (also known as teleconnections), and the feedbacks \r between them. Due to the many possible drivers of variability and \r change (Figure  10.3), quantifying the interplay between internal \r modes of decadal variability and any externally forced component \r is crucial in attempts to attribute causes of regional climate changes \r (e.g., Hoell et al., 2017; Nath et al., 2018). A regional climate signal \r could arise purely due to some anthropogenic influence or conversely, \r entirely due to internal variability, but it is most likely the result of \r a combination of both (Section 10.4). This section briefly introduces \r these sources of regional variability and should be read along with \r corresponding sections in Chapters 3, 6 and 7. Section 10.3 assesses \r [...]"


id: 32d0ebeb585e934a762d2a3dc66358ae
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index: climate_gpt_v2
vector_id: 6207
content:
"[...] their representation in climate models, Section 10.4 discusses their \r relevance for the attribution of multi-decadal trends and Section 10.6 "


id: 63695cb145008fc367655ba9498fb1f2
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"Natural aerosols include mineral dust, volcanic aerosol and sea salt. \r The feedback processes between climate and mineral dust as well \r as sea salt are assessed in Section 6.4, while the volcanic aerosol \r is dealt with in Cross-Chapter Box  4.1. Mineral dust created by \r wind erosion of arid and semi-arid surfaces dominates the aerosol \r load over some areas. The major sources of contemporary dust are \r located in the arid topographic basins of northern Africa, Middle \r East, Central and south-west Asia, the Indian subcontinent, and East \r Asia (Prospero et al., 2002; Ginoux et al., 2012) and emissions are \r controlled by changes in surface winds, precipitation, and vegetation \r (Ridley et al., 2014; W. Wang et al., 2015; DeFlorio et al., 2016; Evan \r et al., 2016; Pu and Ginoux, 2018). Dust both scatters and absorbs \r radiation and serves as a nuclei of warm and cold clouds (Chapter 6). \r The surface direct radiative effect is likely negative over land and \r [...]"


id: f700505511c82af18cd70bdb872b9482
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] ocean, especially when the assumed solar absorption by dust is \r large (Miller et al., 2014; Strong et al., 2015). Surface temperature \r and precipitation adjust to the direct radiative effect with both sign \r and magnitude depending on the dust absorptive properties. Dust \r often cools the surface,  but in regions such as the Sahara surface \r air temperature increases as the shortwave absorption by dust is \r increased, leading to increases of surface temperature over the major \r reflective dust sources (Miller et al., 2014; Solmon et al., 2015; Strong \r et al., 2015; Jin et al., 2016; Sharma and Miller, 2017)."


id: 8472cad626bce980247f0c176195054c
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 20447
content:
"Variations in solar forcing (Section  2.2.1) could influence regional \r climate through its modulation of circulation patterns, although this \r research field is still hampered by large observational and modelling \r uncertainties. The 11-year solar cycle has been suggested to affect the \r leading atmospheric circulation modes of the North Atlantic region \r in model-based studies (Gray et al., 2013; Thieblemont et al., 2015; \r Sjolte et al., 2018). In particular the solar cycle has been suggested as \r an important source of near-term predictability of the North Atlantic \r Oscillation (NAO; Kushnir et  al., 2019), while other studies have \r not found evidence for links between the solar cycle and NAO in \r observational records (Ortega et al., 2015; Sjolte et al., 2018; Chiodo \r et al., 2019). On centennial time scales, solar fluctuations were found \r to be correlated with the Eastern Atlantic Pattern (Sjolte et al., 2018). \r [...]"


id: bec84a11e8a1dde381e7ee7e78a4c74
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vector_id: 30834
content:
"[...] Possible influences on winter circulation and temperature over \r Eurasia (Chen et al., 2015) and North America (Liu et al., 2014; Li and \r Xiao, 2018) have also been identified."


id: b3e5dbfac52ae784bd00bbaa7efa95ca
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vector_id: 28892
content:
"Volcanic eruptions load the atmosphere with large amounts of sulphur, \r which is transformed through chemical reactions and micro-physics \r processes into sulphate aerosols (Cross-Chapter Box 4.1; Stoffel et al., \r 2015; LeGrande et al., 2016). If the plume reaches the stratosphere, \r sulphate aerosols can remain there for months or years (about two to \r three for large eruptions) and can then be transported to other areas by \r the Brewer-Dobson circulation. If the eruption occurs in the tropics, its \r plume is dispersed across the Earth in a few years, while if the eruption \r occurs in the high latitudes, aerosols mainly remain in the same \r hemisphere (Pausata et  al., 2015). The global temperature response \r observed after the last five major eruptions of the last two centuries \r is estimated to be around -0.2degC (Swingedouw et  al., 2017), in \r association with a general decrease of precipitation (Iles and Hegerl, \r [...]"


id: 8319b0cafafe002f920537a22dec0027
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index: climate_gpt_v2
vector_id: 20190
content:
"[...] 2017). Nevertheless, the statistical significance of the regional response \r remains difficult to evaluate over the historical era (Bittner et al., 2016; \r Swingedouw et al., 2017) due to the small sampling of large volcanic \r eruptions over this period and the fact that the signal is superimposed \r upon relatively large internal variability (Gao and Gao, 2018; Dogar \r and Sato, 2019). Evidence from paleoclimate observations is therefore \r crucial to obtain a  sufficient signal-to-noise ratio (Sigl  et  al.,  2015). "


id: 8f06981b953abf9098b192c7817eb888
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 22360
content:
"Due to the large ocean heat capacity and their long temporal scales, \r multi-annual to multi-decadal modes of ocean variability such as \r the Pacific Decadal Variability (PDV; Newman et al., 2016) and the \r Atlantic Multi-decadal Variability (AMV; Buckley and Marshall, 2016) \r are key drivers of regional climate change. In the case of the AMV \r both natural (volcanic) and anthropogenic (aerosol) external forcings \r are thought to be involved in its timing and intensity (Section 3.7.7). \r These modes not only affect nearby regions but also remote parts \r of the globe through atmospheric teleconnections (Meehl et  al., \r 2013; Dong and Dai, 2015) and can act to modulate the influence of \r natural and anthropogenic forcings (Davini et al., 2015; Ghosh et al., \r 2017; Menegoz et  al., 2018b). The dynamics of the ocean modes \r is simultaneously affected by other modes of variability spanning \r the full range of spatial and temporal scales due to non-linear \r [...]"


id: 3decef7e617926206371990deea3eea8
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content:
"[...] interactions (Figure 10.3; Kucharski et al., 2010; Dong et al., 2018).\r This mutual interdependence can result in changing characteristics of \r the connection over time (Gallant et al., 2013; Brands, 2017; Dong and \r McPhaden, 2017), and of their regional climate impact (Martin-Gomez \r and Barreiro, 2016, 2017). As with atmospheric modes of variability, \r the regional influence of ocean modes of variability on regional \r climates can be seasonally dependent (Haarsma et al., 2015)."


id: 75a5214b3805e68fa29324eb5aa10858
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content:
"Regional climate information is synthesized from different lines of \r evidence from a  number of sources (Sections 10.2-10.4) taking \r into account the context of a  user vulnerable to climate variability \r and change at regional scales (Baztan et  al., 2017) and the values \r of all relevant actors (Corner et  al., 2014; Bessette et  al., 2017) in \r a process called distillation (Section 10.5). Distillation, understood as \r the process  of synthesizing information about climate change from \r different lines of evidence obtained from a  variety of sources and \r taking into account the user context and the values of all relevant \r actors, allows the connection of global climate change to the local \r and regional scales, where adaptation responses and policy decisions \r take place. Climate information is translated into the user context in \r a co-production process that introduces further user-relevant elements \r leading to user-relevant climate information (Figure  10.1; Pettenger, \r [...]"


id: cc2b40b3f32321ab91a711c5e0156dcf
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index: climate_gpt_v2
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content:
"[...] 2016; Verrax, 2017) for a specific demand like, for instance, guiding \r climate-resilient development (Kruk et al., 2017; Parker and Lusk, 2019)."


id: 3f758928339d598ff8522a308858377c
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"Chapter 11 assesses observed, attributed and projected changes in \r weather and climate extremes, provides a mechanistic understanding \r on how changes in extremes are related to human-induced climate \r change and provides regional, continental and global-scale \r assessments on changes in extremes, including compound events. \r Chapter  12 identifies elements of the climate system relevant for \r sectoral impacts referred to as climatic impact-drivers (CIDs), assesses \r past and future evolutions of sector-relevant CIDs for each AR6 \r region, synthesizes such evolutions for different time periods and by \r GWL, and assesses how CIDs are used in climate services. The Atlas \r assesses observed, attributed and projected changes in mean climate, \r performs a comparison of CMIP5, CMIP6 and CORDEX simulations, \r evaluates downscaling performance and assesses approaches to \r communicate climate information. The Interactive Atlas facilitates \r [...]"


id: 8615970540fed72c2bfd3b982ef7e392
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index: climate_gpt_v2
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content:
"[...] the exploration of datasets assessed in all chapters through a wide \r range of maps, graphs and tables generated in an interactive manner. \r This allows for the comparison of changes at warming levels and \r scenario/time-period combinations, display of  indices for extremes \r and CIDs, and serves all chapters in the report to facilitate synthesis \r information and support the Technical Summary and the Summary \r for Policymakers."


id: 21edf2c69995a834af286a03d63f29ee
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index: climate_gpt_v2
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content:
"Working Group II (WGII) Chapter 21 (Hewitson et al., 2014b) addressed the regional climate change context from the perspective of \r impacts, vulnerability and adaptation. This chapter emphasized that a good understanding of decision-making contexts is essential \r to define the type and scale of information required from physical climate. Further, the chapter identified that the regional climate \r information was limited by the paucity of comprehensive observations and their analysis along with the different levels of confidence \r in projections (high confidence). Notably, at the time of AR5, many studies still relied on global datasets, models, and assessment \r methods to inform regional decisions, which were not considered as effective as tailored regional approaches. The regional scale \r was not defined but instead it was emphasized that climate change responses play out on a range of scales, and the relevance and \r [...]"


id: 39b9bfe2fe0ea5b5d30d41e5d4905e7
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content:
"[...] limitations of information differ strongly from global to local scales, and from one region to another."


id: 5a16f7a2ac83789726c53dadd11808f4
created_at: 2023-04-19 13:22:41
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content_type: text
index: climate_gpt_v2
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content:
"In this box, the possible influences of the Arctic warming on the lower latitudes are assessed. This linkage was also the topic of Box 3.2 \r of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b). It is a topic that has been strongly \r debated (Ogawa et al., 2018; K. Wang et al., 2018). Separate hypotheses have emerged for winter and summer that describe possible \r mechanisms of how the Arctic can influence the weather and climate at lower latitudes. They involve changes in the polar vortex, \r storm tracks, jet stream, planetary waves, stratosphere-troposphere coupling, and eddy-mean flow interactions, thereby affecting the \r mid-latitude atmospheric circulation, and the frequency, intensity, duration, seasonality and spatial extent of extremes and climatic \r impact-drivers like cold spells, heatwaves, and floods (Cross-Chapter Box 10.1, Figure 1). However, we note that a decrease in the \r [...]"


id: 7a89a6658d3e1ca2c495fa86c4bac1fe
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content:
"[...] intensity of cold extremes has been observed in the Northern Hemisphere mid-latitudes in winter since 1950 (Section 11.3.2; van \r Oldenborgh et al., 2019). Since SROCC, new literature has appeared, and the mechanisms and their criticisms are assessed here as an \r update and extension to the SROCC box."


id: 9e28d22aad5ddcc9d1a0dc542829c169
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index: climate_gpt_v2
vector_id: 25059
content:
"It has been proposed that Arctic amplification, by reducing the equator-pole temperature contrast, could result in a weaker and \r more meandering jet with Rossby waves of larger amplitude (Francis et al., 2017; Zhang and Luo, 2020). This may cause weather \r systems to travel eastward more slowly and thus, all other things being equal, Arctic amplification could lead to more persistent \r weather patterns over the mid-latitudes (Francis and Vavrus, 2012). The persistent large meandering flow may increase the likelihood \r of connected patterns of temperature and precipitation climatic impact-drivers because they frequently occur when atmospheric \r circulation patterns are persistent, which tends to occur with a strong meridional wind component. Another possible consequence of \r Arctic warming is on the NAO/AO that shows a negative trend over the 1990s and early 2000s (Robson et al., 2016; Iles and Hegerl, \r [...]"


id: 21cf46d2473cb6fdeaf5720dad2d73fd
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vector_id: 3165
content:
"[...] 2017), and has been linked to the reduction of sea ice in the Barents and Kara seas, and the increase in Eurasian snow cover (Cohen \r et al., 2012; Nakamura et al., 2015; Yang et al., 2016). During negative NAO/AO the storm tracks shift equatorward and winters are \r predominantly more severe across northern Eurasia and the eastern United States, but relatively mild in the Arctic. This temperature \r pattern is sometimes referred to as the 'warm Arctic-cold continents (WACC)' pattern (Chen et al., 2018). However, L. Sun et al. (2016) \r noticed that the WACC is a manifestation of natural variability. Enhanced sea ice loss in the Barents-Kara Sea has also been related \r to a weakening of the stratospheric polar vortex (Kretschmer et al., 2020) and its increased variability (Kretschmer et al., 2016) that \r would induce a negative NAO/AO (Kim et al., 2014), the WACC pattern (Kim et al., 2014), and an increase in cold air outbreaks (CAO) \r [...]"


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content:
"[...] in mid-latitudes (Kretschmer et al., 2018). Arctic warming might also increase Eurasian snow cover in autumn caused by the moister \r air that is advected into Eurasia from the Arctic with reduced sea ice cover (Cohen et al., 2014; Jaiser et al., 2016), although Peings \r (2019) suggests a possible influence of Ural blockings on both the autumn snow cover and the early winter polar stratosphere. The \r circulation changes over the Ural-Siberian region are also suggested to provide a link between Barents-Kara sea ice and the NAO \r (Santolaria-Otin et al., 2021)."


id: 1ae481126887ca098e8734efa56778fe
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"As in winter, Arctic summer warming may result in a weakening of the westerly jet and mid-latitude storm tracks, as suggested for \r the recent period of Arctic warming (Coumou et al., 2015; Petrie et al., 2015; Chang et al., 2016). Additional proposed consequences \r are a southward shift of the jet (Butler et al., 2010) and a double jet structure associated with an increase of the land-ocean thermal \r gradient at the coastal boundary (Coumou et al., 2018). It is hypothesized that weaker jets, diminished meridional temperature \r contrast, and reduced baroclinicity might induce a larger amplitude in stationary wave response to stationary forcings (Zappa et al., \r 2011; Petoukhov et al., 2013; Hoskins and Woollings, 2015; Coumou et al., 2018; Mann et al., 2018; R. Zhang et al., 2020), and \r also that a double jet structure would favour wave resonance (Kornhuber et al., 2017; Mann et al., 2017). Some studies suggest \r [...]"


id: 3430f6c959fef569a65fea83ce4984f7
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content:
"[...] that this is corroborated by an observed increase of quasi-stationary waves (Di Capua and Coumou, 2016; Vavrus et al., 2017; \r Coumou et al., 2018)."


id: b9e39c4c48d7bdf06ee14ab006f42b2e
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index: climate_gpt_v2
vector_id: 29925
content:
"often criticized for the lack of statistical significance and the inability to disentangle cause and effect (Barnes, 2013; Barnes and \r Polvani, 2013; Screen and Simmonds, 2013; Barnes et al., 2014; Hassanzadeh et al., 2014; Barnes and Screen, 2015; Sorokina et al., \r 2016; Douville et al., 2017; Gastineau et al., 2017; Blackport and Screen, 2020a; Oudar et al., 2020; Riboldi et al., 2020). The role of the \r Barents-Kara sea ice loss is challenged by Blackport et al. (2019) who find a minimal influence of reduced sea ice on severe mid-latitude \r winters, and by Warner et al. (2020) who suggest that the apparent winter NAO response to the Barents-Kara sea ice variability is \r mainly an artefact of the Aleutian Low internal variability and of the co-variability between sea ice and the Aleutian Low originating \r from tropical-extratropical teleconnections. Also Gong et al. (2020) do not find a link between Rossby wave propagation into the \r [...]"


id: b6bcf6d458ba9a80dd0dc55be4896cce
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29351
content:
"[...] mid-latitudes and Arctic sea ice loss. Mori et al. (2019) argue that models underestimate the influence of the Barents-Kara Sea ice \r loss on the atmosphere, which is disputed by Screen and Blackport (2019). Other studies have stressed the importance of atmospheric \r variability as a driver of Arctic variability (Lee, 2014; Woods and Caballero, 2016; Praetorius et al., 2018; Olonscheck et al., 2019). \r Analysing observed key variables of mid-latitude climate for 1980-2020, Blackport and Screen (2020b) and Riboldi et al. (2020) argue \r that the Arctic influence on mid-latitudes is small compared to other aspects of climate variability, and that observed periods of strong \r correlation are an artefact of internal variability or intermittency (Kolstad and Screen, 2019; Siew et al., 2020; Warner et al., 2020)."


id: 14955881db51a7bc02d9d084fe299638
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 848
content:
"Summarizing, different hypotheses have been developed about the influence of recent Arctic warming on the mid-latitudes in both \r winter and summer. Although some of the proposed mechanisms seem to be supported by various studies, the underlying mechanisms \r and relative strength compared to internal climate variability have been questioned. A recent review (Cohen et al., 2020) states that \r divergent conclusions between model and observational studies, and also between different model studies, continue to obfuscate \r a clear understanding of how Arctic warming is influencing mid-latitude weather. In this context, Shepherd (2016b) stresses the need \r for collaboration between scientists with different viewpoints for further understanding that could be achieved by carefully designed, \r multi-investigator, coordinated, multi-model simulations, data analyses and diagnostics (Overland et al., 2016). In agreement with \r [...]"


id: 4a53703b5dc2b8636e750733d0c584a
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 10315
content:
"[...] Box 3.2 of SROCC, there is low to medium confidence in the exact role and quantitative effect of historical Arctic warming and sea ice \r loss on mid-latitude atmospheric variability."


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"Regarding future climate, it is important to note that mid-latitude variability is also affected by many drivers other than the Arctic \r changes and that those drivers as well as the linkages to mid-latitude variability might change in a warmer world. The AMV, PDV, \r ENSO (see Annex IV), upper tropospheric tropical heating, polar stratospheric vortex, and land surface processes associated with soil \r moisture (Miralles et al., 2014; Hauser et al., 2016) and snow cover (Nakamura et al., 2019; Sato and Nakamura, 2019) are a few \r examples. A considerable body of literature has shown that changes to the NAO/AO on seasonal and climate change time scales \r can be driven by variations in the wavelength and amplitude of Rossby waves, mainly of tropical origin (Fletcher and Kushner, 2011; \r Cattiaux and Cassou, 2013; Ding et al., 2014; Goss et al., 2016). The influence of future Arctic warming on mid-latitude circulation \r [...]"


id: 7436c97a5dddaaa5611b2172928fbfd0
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] is difficult to disentangle from the effect of such a plethora of drivers (Blackport and Kushner, 2017; F. Li et al., 2018). One of the \r consequences of climate change is a poleward shift of the jet induced by the tropical warming (Barnes and Polvani, 2013), which is \r less obvious in winter especially over the North Atlantic (Peings et al., 2018; Oudar et al., 2020), and the increase of the meridional \r temperature gradient in the upper troposphere, which increases storm track activity (Barnes and Screen, 2015; Parding et al., 2019). \r Although climate models indicate that future Arctic warming and the associated equator-pole temperature gradient decrease could \r affect mid-latitude climate and variability (Haarsma et al., 2013a; McCusker et al., 2017; Zappa et al., 2018), and even the tropics and \r subtropics (Deser et al., 2015; Cvijanovic et al., 2017; K. Wang et al., 2018; England et al., 2020; Kennel and Yulaeva, 2020), they do \r [...]"


id: 6d851b9037e69b90ef7dd147622ba56
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 16532
content:
"Surface or in situ observations can come from a variety of networks: \r climate reference networks, mesoscale weather and supersite \r observation networks, citizen science networks, among others, all \r with their strengths and weaknesses (McPherson, 2013; Thorne et al., \r 2018). Supersite observatories are surface and atmospheric boundary \r layer observing networks that measure a large number of atmospheric \r and soil variables at least hourly over a  decade or more, ideally \r located in rural areas (Ackerman and Stokes, 2003; Haeffelin et al., \r 2005; Xie et al., 2010; Chiriaco et al., 2018). Adequate calibration of \r instruments, quality control and homogenization are essential in these \r sites. They produce valuable data needed to diagnose processes and \r changes in regional and local climate. Many climate datasets have \r been developed from in situ station observations, at different spatial \r scales and temporal frequencies (Annex I: Observational Products). \r [...]"


id: 2bac3cd8d9e576806a9c64637a6ffc3a
created_at: 2023-04-19 13:22:41
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content:
"[...] These include sub-daily (Dumitrescu et al., 2016; Blenkinsop et al., \r 2017), daily (Chen et  al., 2008; Camera et  al., 2014; Journee \r et al., 2015; Funk et al., 2015; Aalto et al., 2016; Beck et al., 2017a, b; \r Schneider et al., 2017) or monthly time scales (Cuervo-Robayo et al., \r 2014; Aryee et al., 2018). Sub-daily data is useful for estimating storm \r surge (Mori et al., 2014) or river discharge (Shrestha et al., 2015), \r daily data for carbon-stock dynamics (Haga et al., 2020) or tourism \r (Watanabe et  al., 2018), and monthly data for beach morphology \r (Bennett et al., 2019)."


id: 122127b3a2b91108737a2df24d1d93b4
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 377
content:
"Availability of active sensors on LEO satellites enables measurement \r of microphysical properties of aerosol, cloud and precipitation, which \r can advance regional climate studies and process evaluation studies \r to improve regional climate models (high confidence). An example is \r the polar-orbiting 'afternoon-train' satellite constellation (known as \r the A-train), incorporating Aqua, CALIPSO, Cloudsat, PARASOL, Glory \r and Aura satellites. Vertical profiling observations from Cloudsat \r (with a W-band cloud radar) and CALIPSO (with a cloud lidar) led \r to considerable advances in measurements of cloud microphysics \r (Stephens et al., 2018). Precipitation and its extremes are essential \r concerns of regional climate studies. The GPM (65degN-65degS, \r 2014-present) and the preceding TRMM (36.5degN-36.5degS, \r 1997-2015) with Ku-/Ka-band precipitation radars have provided \r three-dimensional measurements of precipitation with about 5 km \r [...]"


id: 244fb02c4b3c08134ed10e36e66c7bf6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 3578
content:
"[...] resolution and sub-daily sampling (Skofronick-Jackson et al., 2017). \r Their non-sun-synchronous observation works to cross-calibrate the \r constellation satellites to produce global high-resolution mapped \r products of precipitation, such as Integrated Multi-satellitE Retrievals \r for GPM (IMERG; Huffman et  al., 2007) and the Global Satellite \r Mapping of Precipitation (GSMaP; Kubota et al., 2007), with hourly \r sampling at about 11 km resolution. The CPC MORPHing technique \r (CMORPH) has provided 30 min interval global precipitation with \r about 8 km coverage since 2002 (Joyce et al., 2004). Precipitation \r estimations from Remotely Sensed Information using Artificial Neural \r Networks (PERSIANN) is a  sub-daily to daily rainfall product that \r covers 50degS to 50degN globally with 25 km resolution from 2000 to \r the present (Nguyen et al., 2019), and is used for semi-global-scale \r precipitation coverage (Benestad, 2018). TRMM/GPM observations \r [...]"


id: fe2bb93223d8e33ea9ff712e5dd57d1e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 42082
content:
"[...] have enabled estimates to be obtained for global four-dimensional \r convective heating (Shige et al., 2009; Tao et al., 2016; Takayabu and \r Tao, 2020)."


id: 45b6bc3bacdcd1abebb0af8d1a459086
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 9480
content:
"The use of these data has enhanced our understanding of precipitation \r processes at regional scale (high confidence), such as diurnal cycles \r in a large river valley (H. Chen et al., 2012), and in coastal (Hassim \r et al., 2016; Yokoi et al., 2017) and mountainous regions (Hirose et al., \r 2017). Three-dimensional observations revealed the contrasts in \r regional characteristics of rainfall extremes in monsoon regions and \r continental dry regions (Sohn et al., 2013; Hamada and Takayabu, \r 2018). Satellite measurements are also used to evaluate climate \r model performance, as well as to develop new parametrizations. As \r a demonstration of the utility of these products in studying model bias, \r a subtropical cumulus congestus regime has been identified that may \r be implicated in the unrealistic double Inter-tropical Convergence \r Zone (ITCZ) found in some climate models (Takayabu et al., 2010; \r Hirota et al., 2011, 2014). Another example is a parametrization of \r [...]"


id: 83c35c174e74662ea5ccf98ae96469e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 20326
content:
"[...] a land surface model that was developed specifically for a certain soil \r type. By assimilating satellite brightness temperature observations \r with their LDAS-UT scheme, Yang et al. (2007) successfully optimized \r a land surface model for the Tibetan Plateau."


id: ad607d5129014a19407ebb835c4a412a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 27741
content:
"Reanalyses incorporate an increasing volume of observations from \r a growing number of sources over time, which sometimes presents \r a  difficulty for trend analysis. However, regional reanalyses are \r valuable for regional climate assessments, since they can employ \r high-resolution model simulations due to their limited spatial domain. \r Their accuracy is also better than global reanalyses since they are \r often developed over regions with a high density of observational \r data (sometimes not freely available for all regions) to be assimilated \r into the model (e.g., Yamada et al., 2012). Regional reanalyses can \r assimilate locally dense and high-frequency observations, such as \r from local observation networks (Mahmood et al., 2018; Su et al., \r 2019) and radar precipitation (Wahl et al., 2017) in addition to the \r observations assimilated by global reanalyses. In some regional \r reanalyses, satellite-derived high-resolution sea ice (Bromwich \r [...]"


id: dbad74a7ea8b6177290f3579bc9c7f85
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 35941
content:
"[...] et al., 2016, 2018) and sea surface temperature (Su et al., 2019) are \r also applied as lower boundary conditions. The periods of regional \r reanalyses are limited by the availability of the observations for \r assimilation and by the global reanalyses needed as lateral boundary \r conditions. Most regional reanalyses cover the past 10 to 30 years. \r There are also regional reanalysis activities that use conventional \r observations only, which produce consistent datasets over 60 years \r to capture precipitation trends, extremes and changes (Fukui et al., \r 2018). Existing regional reanalyses cover North America (Mesinger \r et  al., 2006), Europe (Dahlgren et  al., 2016; Jermey and Renshaw, \r 2016; Kaspar et al., 2020), the Arctic (Bromwich et al., 2016, 2018), \r South Asia (Mahmood et al., 2018), and Australia (Su et al., 2019). \r A project for regional reanalysis covering Japan has also started (Fukui \r et al., 2018), where grid spacing is between 5 and 32 km, although \r [...]"


id: bd69adbdab6e2248006d809e8d04e77d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 30577
content:
"[...] cumulus parametrizations are still needed to compute sub-grid \r scale cumulus convection. Recently, reanalyses using convection\u0002permitting regional models have been published (e.g., Wahl et  al., \r 2017, for central Europe)."


id: ff269d887e8e18387804720ca255ef65
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 42294
content:
"For application at a  regional scale, it is important to consider \r variations in the spatiotemporal resolution of the satellite products. \r A simple concatenation of data in time can show artificial jumps that \r are artefacts of changes in calibration and processing algorithms, or \r related to satellite orbital stability or changing performance of the \r instruments (Wielicki et al., 2013; Barrett et al., 2014). Recalibration and \r cross-calibration are then prerequisites for obtaining homogeneous \r time series of measurements across different or successive satellites \r that can then be used to produce long series that are valid as climate \r data records (Kanemaru et al., 2017; Merchant et al., 2017). Scale \r representativeness is also an issue in utilizing soil observations \r (Taylor et  al., 2012, 2013). Although a  variety of technologies to \r measure soil moisture at the point scale exist (Dobriyal et al., 2012), \r [...]"


id: fd40afad0939b282407966e8835f6b2c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 41909
content:
"[...] its spatial representativeness is less than 1 m2 (Ochsner et al., 2013; \r L. Liu et al., 2016). Therefore, to be able to use in situ soil moisture for \r validating coarser-scale data from satellites or models, networks of \r point-scale measurements are used (Crow et al., 2015; Polcher et al., \r 2016). Smaller networks are typically of the size of a single climate \r model gridcell or a  satellite pixel and are suitable for monitoring \r watersheds, while small numbers of those representing larger areas \r (>100 km2) are emerging (Ochsner et al., 2013)."


id: 58ada887d81a090d7744d1efdd25d434
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"The regional reanalyses represent the frequencies of extremes and \r the distributions of precipitation, surface air temperature, and surface \r wind better than global reanalyses (high confidence). This is due to the \r use of high-resolution regional climate models (RCMs), as indicated \r by different regional climate modelling studies (Mesinger et al., 2006; \r Bollmeyer et al., 2015; Bromwich et al., 2016, 2018; Dahlgren et al., \r 2016; Jermey and Renshaw, 2016; Fukui et al., 2018; Su et al., 2019). \r Regional reanalyses, however, retain uncertainties due to deficiencies \r in the physical parametrization used in RCMs and by the use of \r relatively simple data assimilation algorithms (Bromwich et al., 2016; \r Jermey and Renshaw, 2016; Su et  al., 2019). Regional reanalyses \r can provide estimates that are more consistent with observations \r than dynamical downscaling approaches, due to the assimilation \r of additional local observations (high confidence) (Bollmeyer et al., \r [...]"


id: 24e8f56e4ea683970efb3008a884846e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 3684
content:
"Homogenization aims to make data spatially and temporally \r 'homogeneous'. Changes in a  homogeneous time series are solely \r due to large-scale climatic changes (whether forced or due to internal \r variability). Station data are influenced by factors that act at regional \r scales, from the mesoscale and local scale down to the microscale \r (WMO, 2019). Station time series contain inhomogeneities such as \r artificial jumps or trends, which hamper assessments of regional long\u0002term trends. Typical reasons for this are the urbanization of a station's \r surroundings, which can lead to warming (Hamdi, 2010; Hansen et al., \r 2010; Adachi et al., 2012; Jones, 2016; Y. Sun et al., 2016), or relocations \r outside of the urban area, which could lead to cooling (Tuomenvirta, \r 2001; Yan et  al., 2010; Xu et  al., 2013; Dienst et  al., 2017, 2019). \r Another potential source of inhomogeneity is a change in measurement \r methods that affect most instruments of an observational network \r [...]"


id: 8aebe7d60c304eca46d1dca83d5501aa
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updated_at: 2023-04-19 13:22:41
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content:
"[...] over a limited time span, such as the transition to Stevenson screens \r (Parker, 1994; Bohm et al., 2010; Brunet et al., 2011; Auchmann and \r Bronnimann, 2012) or to automatic weather stations (WMO, 2017b)."


id: b593dffd54b0a520b216ed73f00f581c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 29175
content:
"The usefulness of any observational dataset is conditioned by \r the availability and outcome of a  quality control (QC) process. \r The objective of the QC is to verify that data are representative \r of the measured variable and to what degree the value could be \r contaminated by unrelated or conflicting factors (WMO, 2017a). \r Data quality assessment is key for ensuring that the data are credible \r and to establish trusted relationships between the data provider and \r the users (Nightingale et al., 2019). QC is performed for all relevant \r global climate datasets (e.g., Menne et al., 2018). For instance, QC \r informs users that old reanalysis datasets can be inconsistent in the \r long term because they assimilated inhomogeneous observations \r over the reanalyses period (Kobayashi et al., 2015). As a consequence, \r the evaluation against independent observations suggests that \r [...]"


id: 1542ea478cb188b6f64123d00dccf960
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] reanalyses should not be automatically regarded as climate\u0002quality products for monitoring long-term trends at the regional \r level (Manzanas et al., 2014; Torralba et al., 2017). QC needs to be \r systematically carried out by the institutions responsible for handling \r the data (e.g., Cao et al., 2016b)."


id: b06e152694cf5cf6be0ef2311098006d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 28277
content:
"Kidd et  al. (2017) made some assumptions about GPCC-available \r gauges and indicated that only 1.6% of Earth's surface lies within \r 10  km of a  rain gauge, and many areas of the world are beyond \r 100  km from the nearest rain gauge. Data scarcity is especially \r critical over Africa (Nikulin et  al., 2012; Dike et  al., 2018) but the \r apparent data scarcity could be due to reasons other than actual \r paucity of data, as stated earlier. For instance, over South Africa, \r the number of weather stations collecting daily temperature used \r in the fourth version of the Climatic Research Unit Temperature \r dataset (CRUTEM4, Osborn and Jones, 2014) has significantly \r declined since 1980 (Archer et  al., 2018). Although CRUTEM4 has \r now been replaced by CRUTEM5 (Osborn et  al., 2021) it has yet \r to take advantage of the significant international efforts to curate \r and make available improved global holdings (Rennie et al., 2014) \r [...]"


id: ca3e35707f669238dc2005747eec1a24
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vector_id: 32924
content:
"[...] which increased the global available station count for monthly mean \r temperatures. This includes additional stations from many African \r countries. The apparent decline in stations since the 1980s could also \r be due to countries not contributing their data to the SYNOP/CLIMAT \r networks for reasons other than having non-operational stations."


id: 3f6f9c7ccf29fabd25bf10239957f5b7
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 8345
content:
"Second, in addition to systematic errors, homogenized data also \r suffer from random errors, introduced by the homogenization process. \r These errors are largest at the station level but are also present in \r network-averaged signals (Lindau and Venema, 2018b). These errors \r are determined by the break time series, as well as the noise series \r and the performance of the homogenization method, are spatially \r correlated, and have an impact on activities such as interpolation and \r statistical post-processing of climate simulations (Section 10.2.3.1). \r Third, the above discussion pertains to the homogenization of \r monthly and annual means. Homogenization of daily variability \r around the mean is more difficult. For daily data, specific correction \r methods are used (Della-Marta and Wanner, 2006; Mestre et  al., \r 2011; Trewin, 2013; C. Zhou et al., 2021) that are able to improve the \r homogeneity of test cases, although recent independent validation \r [...]"


id: 802c25ef8671595956b5b463829e72af
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updated_at: 2023-04-19 13:22:41
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vector_id: 19679
content:
"[...] efforts were not able to show much improvement (Chimani et al., \r 2018). The difference with homogenization methods of monthly \r and annual means may stem from assumptions on the nature of \r inhomogeneities for daily data, which are not yet well understood \r (Chimani et al., 2018)."


id: 2d12f595bd65f242070f3432ebc59ea3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5207
content:
"Even in Europe, precipitation station density in the widely used E-OBS \r gridded dataset varies largely in space and time across regions (Prein \r and Gobiet, 2017). This variability is partly due to the reluctance of \r some data owners to share their data with an international effort. \r Regardless of the reason, low station density is a major source of \r uncertainty (Isotta et al., 2015). Kirchengast et al. (2014) and O and \r Foelsche (2019) found that at least 2 to 5 (12) stations are required for \r capturing the area-averaged precipitation amount of heavy summer \r precipitation events on a daily (hourly) basis with a normalized root\u0002mean-square error of less than 20%. Like the E-OBS dataset, gridded \r daily temperature and precipitation datasets are being developed \r for other regions of the world. Examples include south-east Asia \r (SA-OBS, Van den Besselaar et al., 2017), and Latin America and West \r Africa (ICA&D, Van den Besselaar et al., 2015). Despite the uneven \r [...]"


id: b478c937cbb8cf10351378d29ac57ca5
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updated_at: 2023-04-19 13:22:41
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vector_id: 28997
content:
"[...] distribution of stations in space and time, the value in these initiatives \r is illustrated by the large number of studies in which the data product \r is used. This is the case, for instance, in the work of Condom et al. \r (2020) over the Andes, a region with prominent data scarcity, and \r the African Monsoon Multidisciplinary Analyses project over West \r Africa (AMMA; e.g.,  Lebel and Ali, 2009). There have been efforts \r to reduce data scarcity through initiatives such as the International \r Surface Temperature Initiative (ISTI, Thorne et al., 2011), GHCND, and \r the Expanding Met Office Hadley Centre ISD with quality-controlled, \r sub-daily station data from 1931 (HadISD, Dunn et al., 2016)."


id: ab10db97ed7528cd4109a12e203efcc7
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 27323
content:
"being the dominant factor (Herrera et al., 2019). Uncertainty due to \r interpolation is typically small for temperature but substantial for \r precipitation and its derivatives, such as drought indices (Chubb et al., \r 2015; Hellwig et al., 2018). The largest uncertainties typically occur \r in sparsely sampled mountain areas (Section 10.2.2.5). Interpolation \r generally give rise to smoothing effects, such as low variability of \r the derived dataset with respect to the in situ observations (Chen \r et al., 2019). As a result, the effective resolution of gridded data is \r typically much lower than its nominal resolution. For instance, a 5 km \r gridded precipitation dataset for the European Alps has an effective \r resolution of about 10 to 25 km (Isotta et al., 2014). In an example \r for precipitation in Spain, the effective resolution converged to the \r nominal resolution only when at least 6 to 7 stations were inside \r [...]"


id: 1959f3a328a3a717dc63e4f36c022c6b
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 1675
content:
"[...] the gridcell (Herrera et al., 2019). To account for the smoothing errors, \r new stochastic ensemble observation datasets have been introduced \r (Von Clarmann, 2014)."


id: 3ecd26301ce3d97f6f95565baad9ff8
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 8237
content:
"Spatiotemporal variability of meteorological parameters observed over \r mountainous areas is often large, indicating strong control exerted by \r local topography on meteorological parameters (Gultepe et al., 2014). \r Difficult access, harsh climatic conditions as well as instrumental \r issues make meteorological measurements extremely challenging \r at higher elevations (Azam et  al., 2018; Beniston et  al., 2018). \r Measurements of wind speed, temperature, relative humidity and \r radiative fluxes are critical for climate model evaluation, but difficult \r to handle due to their point-scale representativeness and small-scale \r spatiotemporal variability over mountainous terrain, and often need \r adjustment (Gultepe, 2015). High-altitude (>3000 metres) permanent \r meteorological stations are limited and current knowledge is mainly \r based on valley-bottom or low-elevation meteorological stations (Qin \r et  al., 2009; Lawrimore et  al., 2011; Gultepe, 2015; Condom et  al., \r [...]"


id: 2734cc44f79f169bc79ea861adfb546c
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updated_at: 2023-04-19 13:22:41
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vector_id: 4060
content:
"[...] 2020), which, generally do not represent the higher elevation climate \r (Immerzeel et al., 2015; Shea et al., 2015)."


id: 67b024859b1485dd472fd10e3ae946e4
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 15509
content:
"Measuring precipitation amounts, especially of solid precipitation, in \r mountainous areas is particularly challenging due to the presence \r of orographic barriers, strong vertical and horizontal precipitation \r rate variability, and the difficulty in finding representative sites for \r precipitation measurements (Barry, 2012). However, the precipitation \r amounts can be indirectly estimated by the observed point mass \r balances at glacier accumulation areas representing net snow \r accumulation (Haimberger et  al., 2012; Immerzeel et  al., 2015; \r Sakai et al., 2015; Azam et al., 2018). There is very high confidence\r that precipitation measurements, especially solid precipitation, in \r mountainous areas are strongly affected by the gauge location and \r setup. Precipitation measurements are also affected by the type of \r measurement method, presence/absence of shielding, presence/\r absence of a heating system and operating meteorological conditions \r [...]"


id: 46b476ac835e3cc65aaa51c0bcd6c6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 9645
content:
"[...] (Nitu et al., 2018). Solid precipitation measurements may have errors \r ranging from 20% to 50%, largely due to under-catch in windy, icing \r and riming conditions (Rasmussen et al., 2012), and therefore require \r corrections by applying transfer functions developed mainly from \r collected wind speed and temperature data (Kochendorfer et  al., \r 2017). The latest Solid Precipitation Intercomparison Experiment \r (SPICE) report recommends measurements of wind speed, wind "


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"Recent advances in remote-sensing methods provide an alternative, \r but they also have limitations over mountainous areas. Different \r versions of the Tropical Rainfall Measuring Mission (TRMM) \r products were found to perform differently over mountainous \r areas (Zulkafli et  al., 2014). Orographic heavy rainfall associated \r with Typhoon Morakot in 2009 was severely underestimated in all \r microwave products including TRMM 3B42 (Shige et al., 2013). The \r underestimation has been mitigated in the Global Satellite Mapping \r of Precipitation (GSMaP) product by considering the orographic \r effects (Shige et al., 2013). Studies have suggested a high accuracy of \r passive optical satellite (e.g., MODIS, Landsat) snow products under \r clear skies when compared with the field observations. However, \r cloud masking and sub-pixel cloud heterogeneity in these snow-cover \r products considerably restrict their applications (Kahn et al., 2011; \r [...]"


id: 7624f46ef3afab43ae0388d541ced067
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index: climate_gpt_v2
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content:
"[...] Brun et al., 2015; Tang et al., 2017; Stillinger et al., 2019). Gridded \r datasets (e.g.,  CRU, GPCC Full Data Product, GPCC Monitoring \r Product, ERA-Interim, ERA5, ERA5-land, MERRA-2, MERRA-2 bias \r adjusted, PERSIANN-CDR) are of paramount importance, yet they \r often lack enough in situ observations to improve the temporal \r and spatial distribution of meteorological parameters over complex \r mountain terrain (Zandler et al., 2019)."


id: 906b6a4b727dca610ef65be0b905480f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 22610
content:
"Statistical downscaling, bias adjustment and weather generators are \r post-processing methods used to derive climate information from \r climate simulations. They all require observational data for calibration \r as well as evaluation (Section  10.3.3.1). Typically, the so-called \r perfect prognosis methods use quasi-observations for the predictors \r (i.e.,  reanalyses) and actual observations for the predictands (the \r surface variables of interest). By contrast, bias adjustment methods \r use observations only for the predictands. Weather generators typically \r require only observed predictands, although some are conditioned \r on observed predictors as well. Very often these methods are based \r on daily data, because of user needs, but also because of the limited \r availability of sub-daily observations and the limited ability of climate \r models to realistically simulate sub-daily weather (Iizumi et  al., \r 2012). Some methods are calibrated on the monthly scale, but some \r [...]"


id: 3d6d596678efd93dde19403c3c3bb446
created_at: 2023-04-19 13:22:41
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vector_id: 7980
content:
"[...] of the generated time series are then further disaggregated to the \r daily scale (e.g., Thober et al., 2014). A few methods, mainly weather \r generators, represent sub-daily weather (Mezghani and Hingray, \r 2009; Kaczmarska et al., 2014). Many methods simulate temperature \r and precipitation only, although some also represent wind, radiation \r and other variables. The limited availability of high quality and long \r observational records typically restricts these applications to a  few \r cases (Verfaillie et al., 2017; Pryor and Hahmann, 2019). Overall, there \r is high confidence that limited availability of station observations, \r including variables beyond temperature and precipitation as well as \r sub-daily data, limit the use of statistical modelling of regional climate."


id: 1c2e9dd5df8390bf23ad79d061b77596
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 2146
content:
"Beyond climate monitoring, the quality and availability of multiple \r observational reference datasets play a  central role in model \r evaluation. In fact, when using observations for model evaluation, \r there are multiple examples where inter-observational uncertainty is \r as large as the inter-model variability. This has been shown for various \r aspects of the Indian monsoon (Section 10.6.3; Collins et al., 2013a) \r and for precipitation uncertainties over Africa (Section 10.6.4; Nikulin \r et al., 2012; Sylla et al., 2013; Dosio et al., 2015; Bador et al., 2020) \r and Europe (Prein and Gobiet, 2017). Kotlarski et al. (2019) compared \r three high-resolution observational temperature and precipitation \r datasets (E-OBS, a compilation of national/regional high-resolution \r gridded datasets, and the EURO4M-MESAN 0.22deg reanalysis based on \r a high-resolution limited-area model) with five EURO-CORDEX RCMs \r driven by ERA-Interim. Generally, the differences between RCMs are \r [...]"


id: b82f0fb84c83cb190928c3b38528a849
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29618
content:
"[...] larger than those between observation datasets, but for individual \r regions and performance metrics, observational uncertainty can \r dominate. They also showed that the choice of reference dataset \r can have an influence on the RCM performance score. Over the high \r mountain Asia region and East Asia, differences among gridded \r precipitation datasets can generate significant uncertainties in \r deriving precipitation characteristics (J. Kim et al., 2015; Kim and Park, \r 2016; Guo et al., 2017). Over western North America, observational \r uncertainty induces differences in multi-decadal precipitation \r trends (Lehner et al., 2018). Taking a very different perspective, the \r agreement between model simulations may be used to estimate \r the uncertainty and quality of observations (Massonnet et al., 2016). \r There is high confidence that an ensemble of multiple observational \r references at a regional scale is fundamental for model performance \r [...]"


id: 46485e7c519e742764ee4f41dd562e9f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 9585
content:
"[...] assessment. The uncertainties vary according to region, season, and \r statistical properties (Cross-Chapter Box 10.2)."


id: f182ce1a771c829ea1494e2204ca05a4
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39876
content:
"Paleo-reanalyses are enabling a new range of applications and have \r already provided useful information on seasonal-to-multi-decadal \r climate variability over past millennia. They are useful tools to study \r the co-variance between variables at interannual-to-centennial time \r scales and at regional to global spatial scales. In particular, they have \r highlighted the processes that can be responsible for changes in \r continental hydrology at multi-decadal time scales (Franke et al., 2017; \r Klein and Goosse, 2018; Steiger et al., 2018). Paleo-reanalyses have \r confirmed a large contribution of internal variability in past changes \r at regional scale during the pre-industrial period, superimposed on \r a weak common signal due to forcing changes (Goosse et al., 2012) \r and the absence of a globally coherent warm period in the common \r era before the recent warming (Neukom et al., 2019). Reconstructions \r of the atmospheric state obtained in the reanalysis also provide robust \r [...]"


id: 1c32286d22e81a804b89f844553b92f6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 2148
content:
"[...] evidence of a local enhancement of warming or cooling conditions \r due to changes in atmospheric circulation, such as for the warm \r conditions in some European regions around 950-1250 CE, the cooling \r observed in 1809/1810, or the cold and rainy 1816 summer in Europe \r (Cross-Chapter Box 4.1; Goosse et al., 2012; Hakim et al., 2016; Franke \r et al., 2017; Schurer et al., 2019)."


id: d881c54452b9033f2be2807f8586544e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 35384
content:
"Gutowski  Jr.  et  al., 2016) provides ensembles of high-resolution \r historical (starting as early as 1950) and future climate projections for \r various regions. RCMs in CORDEX typically have a horizontal resolution \r between 10 and 50 km. But much finer spatial resolution is required \r to fully resolve deep convection, an important cause of precipitation \r in much of the world. Therefore, an emerging strand in dynamical \r downscaling employs simulations at convection permitting scales, \r at horizontal resolutions of a few kilometres, where deep-convection \r parametrizations can be switched off, approximately simulating deep \r convection (Prein et  al., 2015; Stratton et  al., 2018; Coppola et  al., \r 2020). A recent study indicates that switching off the deep-convection \r parametrization may be beneficial also in simulations performed at \r coarser resolutions (Vergara-Temprado et  al., 2020). Alternatively, \r some RCMs make use of scale-aware parametrizations that are able \r [...]"


id: 8a1b098fbacd3f5a8fe7bc5e7d18cea9
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vector_id: 21471
content:
"[...] to adapt to increasing resolution without switching off the convection \r scheme (Hamdi et  al., 2012; De Troch et  al., 2013; Plant and Yano, \r 2015; Giot et al., 2016; Termonia et al., 2018; Yano et al., 2018)."


id: 55d6419457768fb25a200b93794e0398
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 12371
content:
"RCMs have often consisted of atmospheric and land components \r that do not include all possible Earth system processes and therefore \r neglect important processes such as air-sea coupling (in standard \r RCMs sea surface temperatures, SSTs, are prescribed from global \r model simulations or reanalyses) or the chemistry of aerosol-cloud \r interaction (aerosols prescribed with a  climatology), which may \r influence regional climate projections. Therefore, some RCMs have \r been extended by coupling to additional components like interactive \r oceans, sometimes with sea ice (Kjellstrom et al., 2005; Somot et al., \r 2008; Van Pham et al., 2014; Sein et al., 2015; Ruti et al., 2016; Zou \r and Zhou, 2016a; Zou et  al., 2017; Samanta et  al., 2018), rivers \r (Sevault et al., 2014; Lee et al., 2015; Di Sante et al., 2019), glaciers \r (Kotlarski et al., 2010), and aerosols (Zakey et al., 2006; Zubler et al., \r 2011; Nabat et al., 2015). The coupling of these components allows \r [...]"


id: f205501e9e9fc50c8d7f93003ac6437f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 39972
content:
"[...] for the investigation of additional climate processes such as regional \r sea level change (Adloff et al., 2018), ocean-land interactions (Lima \r et al., 2019; Soares et al., 2019a), or the impact of high-frequency \r ocean-atmosphere coupling on the climatology of Mediterranean \r cyclones (Flaounas et al., 2018)."


id: ce6c4d4f0ea5e8e4cafe95bc1b249acb
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 33657
content:
"Regional climate models (RCMs) are dynamical models similar \r to global models that are applied over a  limited area, but with \r a  horizontal resolution higher than that of standard global \r models. They are the basis for dynamical downscaling to produce \r sub-continental climate information (e.g., Chapters 11, 12 and Atlas) \r but are also often used for process understanding. At lateral and, if \r applicable, lower boundaries, RCMs take their values from a driving \r dataset, which could be a global model or a reanalysis. RCMs are \r typically one-way nested: they do not feed back into the driving \r model, although two-way nested global model-RCM simulations \r have been performed that examine regional influence on large-scale \r climate, potentially improving it (Lorenz and Jacob, 2005; Harris \r and Lin, 2013; Junquas et al., 2016). Spectral nudging (Kida et al., \r 1991; Waldron et al., 1996; von Storch et al., 2000; Kanamaru and \r [...]"


id: 67c0395ecb4c58878c6c5e86c15fe6cc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 15525
content:
"[...] Kanamitsu, 2007) can increase consistency with the driving model, \r whereby selected variables, such as the wind field, are forced to \r closely follow a prescribed large-scale field over a specified range \r of spatial scales. RCMs can inherit biases from the driving global \r model in addition to producing biases themselves (Hall, 2014; \r Hong and Kanamitsu, 2014; Dosio et  al., 2015; Takayabu et  al., \r 2016). The consistency between the circulation features simulated \r by the RCM and those inherited through the boundary conditions \r depends on (i) the relative importance of the large-scale forcing \r compared to local-scale phenomena, and (ii) the size of the RCM \r domain (e.g.,  Diaconescu and Laprise, 2013). Large domains also \r allow the RCM to generate much of its own internally generated \r unforced variability (Nikiema et  al., 2017, and references therein; \r Sanchez-Gomez and Somot, 2018)."


id: 918c811e18222a5e3e2ebce9bb55b94f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 22828
content:
"An alternative or addition to dynamical downscaling is the use of \r statistical approaches to generate regional projections. In AR5 these \r methods were collectively referred to as statistical downscaling, \r but their performance assessment has received little attention. \r A major conclusion was that a wide range of different methods exist \r and a  general assessment of their performance is difficult (Flato \r et  al., 2014). Since AR5, several initiatives have been launched to \r improve the understanding of statistical approaches such as VALUE \r (Validating and Integrating Downscaling Methods for Climate \r Change Research, now merged into the EURO-CORDEX activities; \r Maraun et  al., 2015), STaRMIP (Statistical Regionalization Models \r Intercomparisons and Hydrological Impacts Project; Vaittinada Ayar \r et  al., 2016) and BADJAM (Bias ADJustment of climate scenarios \r for Agricultural Model applications; Galmarini et  al., 2019). The \r [...]"


id: d3a86665e7bf04466123a5c05a296720
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 34576
content:
"[...] performance of different implementations of these approaches will \r be assessed in Section 10.3.3.7."


id: 1fd4535814b1c2bbb9b72467a4822adb
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 2802
content:
"extends univariate methods, which adjust statistics of individual \r variables separately, to joint adjustment of multiple variables \r simultaneously. Implementations remove biases in (i) specific \r measures of multivariate dependence, like correlation structure, via \r linear transformations (Bardossy and Pegram, 2012; Cannon, 2016), \r or, more flexibly, (ii) the full multivariate distribution via non-linear \r transformations (Vrac and Friederichs, 2015; Dekens et  al., 2017; \r Cannon, 2018; Vrac, 2018; Robin et al., 2019). Other research strands \r focus on the explicit separation of bias adjustment and downscaling \r (Section  10.3.1.3.5), or the integration of process understanding \r (Maraun et  al., 2017), such as by conditioning the adjustment on \r the occurrence of relevant phenomena (Addor et al., 2016; Verfaillie \r et al., 2017; Manzanas and Gutierrez, 2019). Some authors suggest \r to mitigate the influence of large-scale temperature or circulation \r [...]"


id: b51cb2cf3f7417e3b2e20a2708b6514d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29098
content:
"[...] biases by performing a bias adjustment of the driving fields prior to \r dynamical downscaling (Colette et al., 2012; Hernandez-Diaz et al., \r 2013, 2019). Issues that may arise when using bias adjustment are \r discussed in Cross-Chapter Box 10.2."


id: de9edb8d5f06f6692a91ef3427eb7855
created_at: 2023-04-19 13:22:41
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content_type: text
index: climate_gpt_v2
vector_id: 36429
content:
"Equivalent simulations can be conducted for individual events, \r thereby allowing for very high resolution. With counterfactual past \r climate conditions, such simulations can be used for conditional event \r attribution (Trenberth et  al., 2015; Chapter  11), using hypothetical \r future conditions to generate physical climate storylines of how \r specific events may manifest in a  warmer climate. The approach \r has been employed to study extreme events that require very high \r resolution simulations such as tropical cyclones (Lackmann, 2015; \r Takayabu et al., 2015; Lau et al., 2016; Kanada et al., 2017a; Gutmann \r et  al., 2018; Patricola and Wehner, 2018; J. Chen et  al., 2020) or \r convective precipitation events (Pall et al., 2017; Hibino et al., 2018). \r The range of possible events is broader and has included Korean \r heatwaves (Kim et al., 2018) and monsoon onset in West Africa (Lawal \r et  al., 2016). However, if only individual events are simulated, no \r [...]"


id: db962ccbadb3ff4b807ffb4277f208f7
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 35921
content:
"[...] immediate conclusions can be derived for changes to the occurrence \r probability of these events (F.E.L. Otto et al., 2016; Shepherd, 2016a)."


id: 711803eeb01faad55d45319ea7916abc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 17155
content:
"In contrast, time-slice experiments are designed to represent only \r a  specific period of time (typically 30 years). They are often run \r using global and regional models in atmosphere-only mode, forced \r by SSTs derived either from observations, as AMIP experiments, or \r from historical simulations and future projections of coupled global \r models. Compared to transient simulations, they offer advantages in \r being computationally cheaper (due to the lack of coupled ocean and \r short duration), which allows for the number of ensemble members \r (T. Zhang et  al., 2016), and/or the resolution (Haarsma et  al., \r 2013b; Davini et  al., 2017) to be increased. Convection-permitting \r simulations, both covering the globe or particular regions, are \r currently conducted for short time slices only (Kendon et al., 2017; \r Hewitt and Lowe, 2018; Coppola et al., 2020; Pichelli et al., 2021). \r Another high-resolution time-slice data base is d4PDF (Mizuta et al., \r [...]"


id: e6ae9bed9b936ac3d17f9dc205f34cb2
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 37883
content:
"[...] 2017; Ishii and Mori, 2020). Experiments covering a limited integration \r period have been carried out for coupled ocean-atmosphere RCMs \r (Sein et al., 2015; Zou and Zhou, 2016b, 2017). However, long spin-up \r periods are required to reach a stable stationary state in the deep \r ocean that otherwise might lead to invalid projections (Planton et al., \r 2012; Soto-Navarro et al., 2020)."


id: 50712d63dea92b805bb1b3e36be25c13
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 11431
content:
"In recent years, the role of internal variability in the interpretation of \r climate projections has become clearer, particularly at the regional \r scale (Section 10.3.4.3). A considerable fraction of CMIP5 and CMIP6 \r resources has been invested in generating an ensemble of centennial \r or multi-centennial control simulations with constant external \r forcings (Pedro et  al., 2016; Rackow et  al., 2018). As part of the \r CMIP6 DECK (Eyring et al., 2016a) pre-industrial control (piControl) \r simulations have been conducted (Menary et  al., 2018). Similarly, \r control simulations with present-day conditions (pdControl) have \r been performed to represent internal variability under more recent \r forcing conditions (Pedro et al., 2016; Williams et al., 2018). Control \r simulations have been used to study the role of internal variability, \r teleconnections and many other fundamental aspects of climate \r models (Z. Wang et  al., 2015; Krishnamurthy and Krishnamurthy, \r [...]"


id: bfee0f2da893f90f166c0e7c651e5e8
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vector_id: 31055
content:
"[...] 2016). Control simulations are also used along with large ensembles \r of historical or scenario simulations to assess the characteristics of \r the regional internal climate variability (Olonscheck and Notz, 2017)."


id: 30ab474ab24ad0bfe0d5f760cecdde44
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5818
content:
"While the ability of global models to simulate large-scale indicators \r of climate change has improved since AR5 (Chapter 3), the simulation \r of regional climate and climate change poses an additional challenge. \r Users demand regional climate projections for decision-making and \r have high expectations regarding accuracy and resolution (Rossler \r et  al., 2019a), but some scientists consider such projections still \r a  matter of basic research (Hewitson et al., 2014a). For instance, \r large-scale circulation biases or the misrepresentation of regional \r topography as well as regional phenomena and feedbacks are very \r relevant (Hall, 2014; Maraun and Widmann, 2018b). New global \r model ensembles such as CMIP6 (Eyring et al., 2016a), HighResMIP \r (Haarsma et  al., 2016) or, at the regional scale, the convection \r permitting simulations from the CORDEX Flagship Pilot Study (FPS) \r on convective phenomena (Coppola et al., 2020) have the potential \r [...]"


id: 98803072376e604124c457ca9f89fb81
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 24041
content:
"[...] to substantially improve the basis for generating regional climate \r information, yet uncertainties and (often unresolved) contradictions \r between model projections at the regional scale can be substantial \r (Fernandez et al., 2019)."


id: c50b4c209407fe29f89ac363786923a7
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
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vector_id: 31978
content:
"Figure 10.6 (continued): (a) Top row: Mean summer (June to August) near-surface air temperature (in degC) over the Mediterranean area in Berkeley Earth and respective mean \r bias for five multi-model historical experiments with global models (CMIP5, CMIP6 and HighResMIP) and regional climate models (CORDEX EUR-44 and EUR-11) averaged \r between 1986-2005. Bottom row: Box-and-whisker plot shows spread of the 20 annual mean summer surface air temperature averaged over land areas in the western \r Mediterranean region (33degN-45degN, 10degW-10degE, black quadrilateral in the first panel of the top row) for a set of references and single model runs of the five multi-model \r experiments (one simulation per model) between 1986-2005. Additional observation and reanalysis data included in the bottom row are CRU TS, HadCRUT4, HadCRUT5, \r [...]"


id: 8e7821fcf3cc7c0ba2b508115ff6242d
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index: climate_gpt_v2
vector_id: 22255
content:
"[...] E-OBS, WFDE5, ERA5, ERA-Interim, CERA-20C, JRA-25, JRA-55, CFSR, MERRA2, MERRA. Berkeley Earth is shown in the first box to the left. (b) As (a) but for precipitation \r rate (mm day-1) and showing CRU TS in the first panel of the top row. Biases of the five multi-model experiments are shown with respect to CRU TS. Additional observation \r and reanalysis data included in the bottom row are GPCC, REGEN, E-OBS, GHCN, WFDE5, CFSR, ERA-Interim, ERA5, JRA-55, MERRA2, MERRA. CRU TS is shown in the first \r box to the left. All box-and-whisker plots show the median (line), and the interquartile range (IQR = Q3-Q1, box), with top whiskers extending to the last data less than \r Q3 + 1.5 x IQR and analogously for bottom whiskers. Data outside the whiskers range appear as flyers (circles). Further details on data sources and processing are available \r in the chapter data table (Table 10.SM.11)."


id: 900e9b451a204ea7258c6f3f8164982b
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updated_at: 2023-04-19 13:22:41
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vector_id: 22551
content:
"Recent studies highlight the importance of user-defined or user-relevant \r diagnostics for model evaluation (Maraun et al., 2015; Rhoades et al., \r 2018; Rossler et  al., 2019b; Nissan et  al., 2020). Diagnostics have \r been used to assess the performance of climate models to produce \r useful input data for impact models as in the comparison between \r RCMs and convection-permitting models to capture flood-generating \r precipitation events in the Alps (Reszler et al., 2018). Alternatively, \r the observed impact can be compared to that simulated by an impact \r model that uses input from both observations and climate models. \r This approach has been used to evaluate the influence of statistical \r downscaling and bias adjustment on hydrological (Rojas et al., 2011; \r H. Chen et  al., 2012; Gutierrez et  al., 2019; Rossler et  al., 2019b), \r agricultural (Ruiz-Ramos et al., 2016; Galmarini et al., 2019), forest \r and wildfire (Abatzoglou and Brown, 2012; Migliavacca et al., 2013) \r [...]"


id: be49bfec4297507e4ac00bdcad619888
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vector_id: 30745
content:
"[...] (Bedia et al., 2013), snow depth (Verfaillie et al., 2017), and regional \r ocean modelling (e.g., Macias et al., 2018)."


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vector_id: 25113
content:
"Since AR5, model evaluation has made use of a broad combination \r of diagnostics (Colette et  al., 2012; Kotlarski et  al., 2014; Eyring \r et al., 2016b; Gleckler et al., 2016; Ivanov et al., 2017, 2018; Vautard \r et al., 2021), ranging from long-term means to indices of extreme \r events (Zhang et al., 2011; Sillmann et al., 2013) or a combination \r of these (Dittus et al., 2016). This evaluation has shown that global \r models have pervasive biases in some aspects of their large-scale \r behaviour (Section 1.5.3.1, Chapter 3). More complex diagnostics are \r used to characterize specific meteorological phenomena (Sprenger \r et al., 2017), such as feedbacks in the El Nino-Southern Oscillation \r (ENSO; Bellenger et  al., 2014), Madden-Julian Oscillation (MJO) \r characteristics (Benedict et  al., 2014; Jiang et  al., 2015; D.  Kim \r et  al., 2015; Ahn et  al., 2017), extratropical modes of variability \r (Lee et al., 2019), cyclone tracking (Neu et al., 2013; Flaounas et al., \r [...]"


id: fb523ece9c42cdbb7c1eecfb4cab0e6f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 41592
content:
"[...] 2018), front detection (Hope et  al., 2014; Schemm et  al., 2015), \r thunderstorm environment parameters (Bukovsky et  al., 2017), \r African easterly waves (McCrary et al., 2014; Martin and Thorncroft, \r 2015), land-atmosphere coupling (Spennemann and Saulo, 2015; \r Santanello et  al., 2018), and sea-atmosphere coupling (Bellenger \r et al., 2014; Mayer et al., 2017)."


id: eb218954ecfcd0f9abab97e4332cf37
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 38722
content:
"Downscaling is expected to improve the representation of a region's \r climate compared to the driving global model (Di Luca et al., 2015). \r Arguably, there should be a clear physical reason for the improvement, \r which is applicable to the evaluation of added value in downscaled \r projections (Giorgi et  al., 2016). The added value depends on the \r region, season, and governing physical processes (Lenz et al., 2017; \r Schaaf and Feser, 2018). Thus, added value of downscaling global \r model simulations is most likely where regional- and local-scale \r processes play an important role in a region's climate, for example \r in complex or heterogeneous terrain such as mountains (Lee and \r Hong, 2014; Prein et al., 2016b), urban areas (Argueso et al., 2014), \r along coastlines (Feser et al., 2011; Herrmann et al., 2011; Bozkurt \r et  al., 2019), or where convective processes are important (Prein \r et al., 2015). Examples of model improvements and added value are \r [...]"


id: 1739c0105e403983d3f9d55a18d66bb5
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 1305
content:
"Major large-scale meteorological phenomena for mid- to high \r latitude mean and extreme climate include atmospheric blocking \r and extratropical cyclones (Section 2.3.1.4). Atmospheric blocking is \r characterized by a  quasi-stationary, long-lasting, high-pressure \r system that blocks and diverts the movement of synoptic cyclones \r (Woollings et al., 2018). In regions where blocking occurs, it is known \r to lead to cold conditions in winter and warmth and drought during \r summer, defining the seasonal regional climate in certain years \r (Sousa et al., 2017, 2018b). Extratropical cyclones are storm systems \r that propagate preferentially in confined storm-track regions, \r characterized by large eddy kinetic energy, heat and momentum \r transports that shape regional weather at mid- to high latitudes \r (Shaw et  al., 2016). Given their importance in shaping mean and \r extreme regional climate (Sections 3.3.3.3, 11.3 and 11.4), an \r [...]"


id: 6f11a2b7458ce08c1037d0b285d5643e
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 16808
content:
"[...] accurate representation of blocking and extratropical cyclones in \r global and regional climate models is needed to better understand \r regional climate variability and extremes as well as to project future \r changes (Section 11.7.2; Grotjahn et al., 2016; Mitchell et al., 2017; \r Rohrer et al., 2018; Huguenin et al., 2020). An overview of CMIP5 and \r CMIP6 model performance in simulating blocking and extratropical \r cyclones is given in Section 3.3.3.3. CMIP6 models still suffer from \r long-standing blocking biases identified in previous generations \r of models. However, blocking location has improved compared to \r CMIP5, while comparable performance is seen for blocking frequency \r and persistence (Figure 10.7). Increasing horizontal model resolution \r to about 20 km in the HighResMIP experiments improves the \r representation of blocking frequency and its spatial pattern in most \r models, but no clear effect could be shown for blocking persistence. \r [...]"


id: 19ec65e6d5c163cea865265902d8bc00
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 1761
content:
"[...] Biases associated with these two phenomena are highly region- and \r season-dependent and their amplitudes vary among CMIP models \r (Drouard and Woollings, 2018; Schaller et al., 2018; Woollings et al., \r 2018; Harvey et al., 2020; Schiemann et al., 2020)."


id: 2ffe64593bcd6864e1c89341e6dbdfbb
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5704
content:
"the connection between large-scale and regional biases is obvious, \r given the strong control of regional climate by large-scale phenomena, \r research on this connection is still limited. Munday and Washington \r (2018) relate CMIP5 model rainfall biases over South Africa to \r anomalous low-level moisture transport across high topography \r due to upstream wind biases and inaccurate representation of \r unresolved orographic drag effects. Addor et  al. (2016) show that \r the overestimated frequency of westerly synoptic situations was \r a significant contributor to the wet bias in several RCMs in winter over \r Switzerland. Pepler et al. (2014, 2016) suggest that better capturing \r westerly-driven synoptic systems such as cold fronts and cut-off lows \r in climate models could be key in simulating the observed pattern \r correlation between rainfall and zonal wind in southern south-east \r Australia. Cannon (2020) shows global improvement in performance \r [...]"


id: 9dd530a3151dc8250e5cf3a13637e257
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 25005
content:
"driving reanalysis near regions with complex orography and/or large \r water masses (Poan et al., 2018). However, this is not necessarily true \r if the domain is large enough because the RCM and its biases will then \r control the circulation leading to a biased performance with regard \r to storm characteristics (Pontoppidan et al., 2019). An ensemble of \r 12 RCMs with and without air-sea coupling reasonably reproduced \r the climatology of Mediterranean cyclones, and air-sea coupling had \r a rather weak impact (Flaounas et al., 2018). Over the Gulf Stream, \r however, air-sea coupling played an important role in representing \r cyclone development (Vries et al., 2019). Sanchez-Gomez and Somot \r (2018) showed that the effect of RCM internal variability on density \r of cyclone tracks is very significant and larger than for other variables \r such as precipitation. It is larger in summer than in winter, in \r particular over the Iberian Peninsula, northern Africa and the eastern \r [...]"


id: c68e187909d2ac4e93b31b89915d9365
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 32235
content:
"ENSO teleconnection patterns for model experiments with fewer \r observational constraints. They also show that ENSO warm phase \r (El Nino) teleconnections are better represented than those for \r the cold phase (La Nina). Individual CMIP5 and CMIP6 models \r show a  good ability to represent the observed teleconnections at \r aggregated spatial scales (Power and Delage, 2018; Section  3.7.3 \r and Figure  3.38). The evaluation of the atmospheric dynamical \r linkages is also an important part of the assessment. Hurwitz et al. \r (2014) showed that CMIP5 models broadly simulate the expected \r (as seen in the MERRA reanalysis) upper-tropospheric responses to \r central equatorial Pacific or eastern equatorial Pacific ENSO events in \r boreal autumn and winter. CMIP5 models also simulate the correct \r sign of the Arctic stratospheric response, consisting of polar vortex \r weakening during eastern and central Pacific Nino events and \r [...]"


id: 43fe0b9770c5f4574412e6b61a01d6c4
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 9198
content:
"[...] vortex strengthening during both types of La Nina events. In contrast, \r most CMIP5 models do not capture the observed weakening of the \r Southern Hemisphere polar vortex in response to central Pacific \r ENSO events (Brown et al., 2013)."


id: 92881d0d5250b46cb17491f06e598bd9
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 23003
content:
"Convection is the process of vertical mixing due to atmospheric \r instability. Deep moist convection is associated with thunderstorms \r and severe weather such as heavy precipitation and strong wind \r gusts. Convection may occur in single locations, in spatially extended \r severe events such as supercells, and organized into larger mesoscale \r convective systems such as squall lines or tropical cyclones, and \r embedded in fronts (see below). Shallow and deep convection are \r not explicitly simulated but parametrized in standard global and \r regional models. In consequence, these models suffer from several \r biases. AR5 has stated that many CMIP3 and CMIP5 models simulate \r the peak in the diurnal cycle of precipitation too early, but increasing \r resolution and better parametrizations help to mitigate this problem \r (Flato et al., 2014). Similar issues arise for RCMs with parametrized \r deep convection (Prein et al., 2015), which also tend to overestimate \r [...]"


id: c3d4b1608936853d5f44c68d4c11c15c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 31728
content:
"Non-hydrostatic RCMs at convection-permitting resolution (4 km \r and finer) improve features such as the initiation and diurnal cycle \r of convection (Zhu et al., 2012; Prein et al., 2013a, b; Fosser et al., \r 2015; Stratton et al., 2018; Sugimoto et al., 2018; Finney et al., 2019; \r Berthou et  al., 2020; Ban et  al., 2021; Pichelli et  al., 2021), the \r triggering of convection by orographic lifting (Langhans et al., 2013; \r Fosser et al., 2015), and maximum vertical wind speeds in convective \r cells (Meredith et  al., 2015a). Also spatial patterns of precipitation \r (Prein et al., 2013a, b; Stratton et al., 2018), precipitation intensities \r (Prein et al., 2015; Fumiere et al., 2020; Ban et al., 2021; Pichelli et al., \r 2021), the scaling of precipitation with temperature (Ban et al., 2014), \r cloud cover (Bohme et al., 2011; Langhans et al., 2013) and its resultant \r radiative effects (Stratton et al., 2018), as well as the annual cycle \r [...]"


id: de1912198b81ee496cd6a290ead04d4c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36337
content:
"[...] of tropical convection (Hart et al., 2018) are improved. Phenomena \r such as supercells, mesoscale convective systems, or the local weather \r associated with squall lines are not captured by global models and \r standard RCMs. Convection-permitting RCM simulations, however, \r have been shown to realistically simulate supercells (Trapp et  al., \r 2011), mesoscale convective systems, their life cycle and motion (Prein \r et al., 2017; Crook et al., 2019), and heavy precipitation associated \r with a squall line (Kendon et al., 2014). There is high confidence that \r simulations at convection-permitting resolution add value to the \r representation of deep convection and related phenomena."


id: a22502e0e81b78f8e1ba51b8405a5d27
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 25762
content:
"Synoptically-forced winds may be channelled and accelerated in long \r valleys. For instance, the Tramontana, Mistral and Bora are northerly \r winds blowing down-valley from central France and the Balkans into \r the Mediterranean (Flaounas et  al., 2013). In winter, these winds \r may cause severe cold air outbreaks along the coast. Flaounas \r et al. (2013) have shown that a GCM with a horizontal resolution of \r roughly 3.75deg longitude/1.875deg latitude (roughly 400 km x 200 km \r depending on latitude) is unable to reproduce these winds because \r of the coarse representation of orography. Fifty-kilometre RCM \r simulations did not realistically represent the Mistral (Obermann \r et al., 2018) and Bora winds (Belusic et al., 2018), but simulations \r at 12 km added substantial value. Similarly, Cholette et  al. (2015) \r found that a 30 km RCM resolution was not sufficient to adequately \r simulate the channelling of winds in the St Lawrence River Valley in \r [...]"


id: 2c2e927b1b56ae35b8fb8fa0a9f248f6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5030
content:
"Convection is the key ingredient of tropical cyclones. An \r intercomparison of high-resolution AGCM simulations (Shaevitz \r et al., 2014) showed that tropical cyclone intensities appeared to be \r better represented with increasing model resolution. Takayabu et al. \r (2015) have compared simulations of typhoon Haiyan at different \r resolutions ranging from 20  km to 1 km (Figure  10.8). While the \r eyewall structure in the precipitation pattern was strongly smoothed \r in the coarse resolution simulations, it was well-resolved at the highest \r resolution. Gentry and Lackmann (2010) found similar improvements \r in simulating hurricane Ivan for horizontal resolutions between 8 km \r and 1 km. High-resolution coupled ocean-atmosphere simulations \r improve the representation of the radial structure of core convection \r and thereby the rapid intensification of the cyclone (Kanada \r et  al., 2017b). There is high confidence that convection-permitting \r [...]"


id: ac932c65668791c4cd24c952049e9254
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 27605
content:
"[...] resolution is required to realistically simulate the three-dimensional \r structure of tropical cyclones."


id: bf77fda88307edd3c544cc584f678d14
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 30950
content:
"A particularly relevant coastal phenomenon is the sea breeze, which \r is caused by the differential heating of water and land during the \r diurnal cycle and typically reaches several tens of kilometres inland. \r Reanalyses and global models have too coarse a  resolution to \r realistically represent this phenomenon, such that they typically \r underestimate precipitation over islands and misrepresent its diurnal \r cycle (Lucas-Picher et al., 2017). RCMs improve the representation of \r sea breezes and thereby precipitation in coastal areas and islands. \r Over Cuba and Florida only a  12 km-resolution RCM is able to \r realistically simulate the inland propagation of precipitation during \r the course of the day (Lucas-Picher et al., 2017). RCM simulations \r at 20 km horizontal resolution realistically represented the sea \r breeze circulation in the Mediterranean Gulf of Lions including the \r intensity, direction and inward propagation (Drobinski et al., 2018). \r [...]"


id: 2b9f4ad463d5537d66c3b3f73b33a601
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 4928
content:
"[...] Even though a coupled ocean-atmosphere simulation improved the \r representation of diurnal SST variations, the sea breeze representation \r itself was not improved."


id: 5e11d139817db41e620b0be9873f1aac
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 13846
content:
"Weather fronts are two-dimensional surfaces separating air masses \r of different characteristics and are a  key element of mid-latitude \r cyclones. In particular cold fronts are regions of relatively strong \r uplift and hence often associated with severe weather (e.g., Schemm \r et al., 2016). Stationary or slowly moving fronts may cause extended \r heavy precipitation. The evaluation of how climate models represent \r fronts, however, remains limited. Catto et  al. (2014) found in both \r ERA-Interim and CMIP5 models that frontal frequency and strength \r were realistically simulated, albeit with some biases in the location. \r Follow-up investigations, for boreal and austral winter (Catto et al., \r 2015) found frontal precipitation frequency to be too high and the \r intensity too low, but these compensating biases resulted in only \r a small total precipitation bias. Blazquez and Solman (2018) found \r similar results for Southern Hemisphere (SH) winter, and also showed \r [...]"


id: 2f2cb5772c2187aa1ea4760ab053d309
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5570
content:
"[...] that CMIP5 models typically overestimate the fraction of frontal \r precipitation compared to total precipitation. As for the reference, the \r ERA-Interim reanalysis misrepresents conditional symmetric instability \r associated with fronts, and the corresponding precipitation (Glinton \r et  al., 2017). Only a  few studies evaluating fronts in RCMs have \r been conducted. Kawazoe and Gutowski (2013) diagnosed strong \r temperature gradients associated with extreme winter precipitation \r in the North American Regional Climate Change Assessment Program \r (NARCCAP) RCM ensemble (Mearns et  al., 2012) and found the \r models agreed well with gradients in a  reanalysis. De Jesus et  al. \r (2016) diagnosed the representations of cold fronts over southern \r Brazil in two RCMs, finding that they were only underestimated \r by about 5% across the year, but in one RCM, summer cold fronts \r were underestimated by 17%. An RCM-based reanalysis suggests \r [...]"


id: 507d562bccac7aa7998103e7b8b8208f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 11437
content:
"[...] that high-resolution RCM simulations improve the representation of \r orographic influences on fronts (Jenkner et al., 2009)."


id: f79bea337eb508af6387fd4770e4fe20
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 40934
content:
"feedbacks like tropical cyclones in the Indian Ocean (Samson \r et al., 2014), Indian summer monsoon (Samanta et al., 2018), East \r Asian summer monsoon (Zou et  al., 2016), near coastline intense \r precipitation in the Mediterranean (Berthou et  al., 2015, 2018), \r air-sea fluxes influencing heat and humidity advection over land \r (Sevault et al., 2014; Lebeaupin Brossier et al., 2015; Akhtar et al., \r 2018) or snow bands in the Baltic region (Pham et al., 2017). The \r positive impact of ocean-coupling on the simulation of strongly \r convective phenomena such as Medicanes, a  class of severe \r cyclones in the Mediterranean, can only be diagnosed when using \r relatively fine atmospheric resolution of about 10 km (Akhtar et al., \r 2014; Flaounas et al., 2018; Gaertner et al., 2018). A positive impact \r of ocean coupling has been quantified in marginal sea regions with \r reduced large-scale influence (e.g.,  in the Baltic Sea area during \r [...]"


id: 2ea5b6d6e7776f395f702ec72468e2fc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5487
content:
"[...] weak phases of the NAO and thus weak influence of Atlantic \r westerlies (Kjellstrom et al., 2005; Pham et al., 2018). There is some \r evidence that coupled ocean components also positively impact \r RCM simulations of inland climates such as precipitation extremes \r in central Europe (Ho-Hagemann et al., 2017; Akhtar et al., 2019). \r There is high confidence that coupled ocean-atmosphere RCMs \r improve the representation of ocean-atmosphere feedbacks and \r related phenomena."


id: 94ce72414e80d05fafef94e6a106ac1d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 23387
content:
"The snow-albedo feedback contributes to enhanced warming at \r high elevations (Section  8.5; Pepin et  al., 2015). Global models \r often do not simulate it realistically due to their misrepresentation \r of orography in complex terrain (Hall, 2014; Walton et  al., 2015). \r The elevation dependence of historical warming, which is partly \r caused by the snow-albedo effect, is realistically represented across \r Europe by the ENSEMBLES RCMs (Kotlarski et  al., 2015). Some \r EURO-CORDEX RCMs simulate a  spring snow-albedo feedback \r close to that observed, whereas others considerably overestimate it \r (Winter et al., 2017). In a multi-physics ensemble RCM experiment, \r the cold bias in north-eastern Europe is amplified by the albedo \r feedback (Garcia-Diez et al., 2015). For the Rocky Mountains, RCM \r simulations generally reproduce the observed spatial and seasonal \r variability in snow cover, but strongly overestimate the snow albedo \r [...]"


id: e1bc81b2c96d10b15198cb7aacd84705
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36990
content:
"[...] (Minder et al., 2016). There is high confidence (medium evidence and \r high agreement) that RCMs considerably improve the representation \r of the snow-albedo effect in complex terrain."


id: 1c15023b32aee5c64aa8572733c989a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 2122
content:
"Soil-moisture feedbacks influence changes in both temperature \r and precipitation. More than 30% of CMIP5 models overestimate \r the influence of preceding precipitation (a proxy for soil moisture) \r on temperature extremes in Europe and the USA (Donat et  al., \r 2018), and many CMIP5 models simulate an unrealistic influence \r of evaporation on temperature extremes for wet regions in Europe \r and the US (Ukkola et al., 2018). RCMs were found to realistically \r simulate the correlation between latent and sensible heat fluxes and \r temperature (coupling strength) over Africa (Knist et al., 2017; Careto \r et al., 2018) and in northern and southern Europe, but to overestimate \r it in central Europe (Knist et al., 2017). Land surface models driven \r by global reanalysis agreed relatively well with observations. \r However, the coupling strength varied strongly across models at the \r regional scale, and a realistic partitioning of the incoming radiation \r [...]"


id: e2ee3caafdda64790dc280455c182b98
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37221
content:
"[...] into latent and sensible heat fluxes did not necessarily result in \r a realistic soil moisture-temperature coupling (Gevaert et al., 2018; \r Boe et al., 2020a)."


id: d9895d7570d7b0c1fb591f6c86fc3c7d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 35572
content:
"Dust, with its regional character in both emissions and climatic \r influences, has traditionally been specified in climate simulations \r with a  climatological estimate. In CMIP5 models, the influence of \r vegetation changes on mineral dust is largely underestimated while \r the influence of surface wind and precipitation are overestimated, \r resulting in a low bias of dust load (Pu and Ginoux, 2018). Interactive \r dust emission modules that simulate the dust optical depth in most of \r the key emission regions have only been recently introduced (Pu and \r Ginoux, 2018). However, coarse dust is underestimated in global \r models (Adebiyi and Kok, 2020). Simulations of future changes in \r dust are hindered by the uncertainties in future regional wind and \r precipitation as the climate warms (Evan et al., 2016), in the effect \r of CO2 fertilization on source extent (Huang et al., 2017), in the dust \r feedbacks (Evans et al., 2019), and in the effect of human activities \r [...]"


id: b232f11b384261f0e3f4aadfb5c092c8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 28584
content:
"[...] that change land use and disturb the soil, including cropping and \r livestock grazing, recreation and urbanization, and water diversion \r for irrigation (Ginoux et al., 2012)."


id: e6fbdacc87dcf549e0e649a59c03f97c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37924
content:
"Evaluating the representation of soil-moisture-precipitation feedbacks \r in climate models is challenging as different processes may induce \r feedbacks including moisture recycling, boundary-layer dynamics \r and mesoscale circulation. Moreover, the effects of soil moisture \r on precipitation may be region and scale dependent and may even \r change sign depending on the strength of the background flow (Taylor \r et al., 2013; Froidevaux et al., 2014; Guillod et al., 2015; Larsen et al., \r 2016; Tuttle and Salvucci, 2016). On seasonal-to-interannual time \r scales, CMIP5 models showed a stronger soil-moisture-precipitation \r feedback than estimated by satellite data (Levine et al., 2016). Taylor \r et  al. (2013) found that convection-permitting RCMs perform well \r at simulating surface-induced mesoscale circulations in daytime \r convection and the observed negative soil moisture feedback, \r whereas an RCM with parametrized convection, even when run at \r [...]"


id: 64566ed3a448d0fa67c9dc432fdc0dd2
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14933
content:
"[...] the same resolution, simulated an unrealistic positive feedback. \r There is medium evidence and high agreement that simulations at \r convection-permitting resolution are required to realistically represent \r soil-moisture-precipitation feedbacks."


id: a87fb7b1dc0d3970ba53212dae8007d4
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 26852
content:
"eruptions (larger than the one from Mount Pinatubo in 1991) may \r lead to a positive phase of the winter NAO in the following few years \r (with an uncertainty on the exact years affected), but this influence \r is not well-reproduced in climate models and requires very large \r ensembles (Driscoll et  al., 2012; Toohey et  al., 2014; Swingedouw \r et  al., 2017; Menegoz et  al., 2018b). The ability to simulate the \r effect of volcanic aerosol in global models is evaluated in VolMIP \r (Zanchettin et  al., 2016). Given the relevance of volcanic aerosol, \r a  good knowledge of the initial conditions is important because \r the response has proven to be sensitive to them (Menegoz et  al., \r 2018a; Zanchettin et  al., 2019). A  few decadal prediction systems \r have illustrated that current systems can predict some aspects of \r regional climate a few years in advance (Swingedouw et al., 2017; \r Illing et al., 2018; Menegoz et al., 2018a; Hermanson et al., 2020). \r [...]"


id: 5f3c01645afe3e6600dd6dcce36010ac
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14062
content:
"[...] However, a better performance requires information about volcanic \r location (Haywood et al., 2013; Pausata et al., 2015; Stevenson et al., \r 2016; F. Liu et al., 2018a), strength (Emile-Geay et al., 2008; H.-G. Lim \r et al., 2016; F. Liu et al., 2018b), and seasonality (Stevenson et al., \r 2017; Sun et al., 2019a, b)."


id: 41eaadf74728d98d1f34ef396e4767bb
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 8791
content:
"Some recent regional climate changes can only be simulated by \r climate models if anthropogenic aerosols are correctly included \r (Sections 10.4.2.1, 10.6.3 and 10.6.4; Chapters 6 and 8). Examples \r of the importance of correctly representing anthropogenic aerosols \r are the recent enhanced warming over Europe (Nabat et al., 2014; \r Dong et al., 2017), the cooling over the East Asian monsoon region, \r leading to a weakening of the monsoon (Section 8.3.2.4; Song et al., \r 2014; Q. Wang et  al., 2017), as well as changes in the monsoons \r of West Africa (Sections 8.3.2.4 and 10.4.2.1) and South Asia \r (Sections 8.3.2.4 and 10.6.3; Undorf et al., 2018). The relevance of \r appropriately representing anthropogenic aerosols has been widely \r studied in regional models (Boe et al., 2020a; Gutierrez et al., 2020), \r with an advantage for models with interactive aerosol schemes \r (Druge et  al., 2019; Nabat et  al., 2020). Without a  fully coupled \r [...]"


id: 69a40416b336d9f46b069982279dc304
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15853
content:
"[...] chemistry module, radiative forcing can be simulated by including \r simple models of sulphate chemistry or specifying the optical \r properties from observations and prescribing the effect of aerosols \r on the cloud-droplet number (Fiedler et  al., 2017, 2019; Stevens \r et al., 2017). In all cases, the specification of the aerosol load limits \r the trustworthiness of the simulations at the regional scale when \r enough detail is not provided (Samset et al., 2019; Shonk et al., 2020; \r Z. Wang et al., 2021)."


id: f48ca2cea7fd653eeed904912f12eb12
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 40399
content:
"Bias adjustment methods, if driven by reanalysis predictors, in \r principle adjust well all the aspects that they intend to address \r (Maraun and Widmann, 2018b). For temperature, all univariate \r methods are good for adjusting means, variance, and high quantiles \r (Gutierrez et  al., 2019; Hertig et  al., 2019). For precipitation, \r means, intensities, wet-day frequencies, and wet-dry and dry-wet \r transitions are well-adjusted (Gutierrez et al., 2019; Maraun et al., \r 2019b). The representation of high quantiles depends on the \r chosen method, although flexible quantile mapping performs best \r (Hertig et  al., 2019). Empirical (non-parametric) methods perform \r better than parametric methods over the observed range, but it is \r unclear how this translates into extrapolation to unobserved values \r (IPCC, 2015; Hertig et al., 2019). Many quantile mapping methods \r overestimate interannual variability (Maraun et al., 2019b). Temporal \r [...]"


id: 8dda526a8e1c2df482c614170499a41f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 22148
content:
"[...] and spatial dependence are usually not adjusted and thus inherited \r from the driving model (Maraun et al., 2019b; Widmann et al., 2019). \r Spatial fields are thus typically too smooth in space, even after bias \r adjustment (Widmann et al., 2019)."


id: f222131c6388293bbd1df5f87fb55e99
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39993
content:
"Weather generators represent well most aspects that are explicitly \r calibrated. This typically includes mean, variance, high quantiles \r (for precipitation, if explicitly modelled), and short-term temporal \r variability for both temperature and precipitation, whereas \r interannual variability is strongly underestimated (Frost et al., 2011; \r Hu et al., 2013a; Keller et al., 2015; Dubrovsky et al., 2019; Gutierrez \r et  al., 2019; Hertig et  al., 2019; Maraun et  al., 2019b; Widmann \r et al., 2019). There is growing evidence that some spatial weather \r generators fairly realistically capture the spatial dependence of \r temperature and precipitation (Frost et al., 2011; Hu et al., 2013a; \r Keller et al., 2015; Evin et al., 2018; Dubrovsky et al., 2019). There is \r high confidence that weather generators can realistically simulate \r a  wide range of local weather characteristics at single locations, \r but there is limited evidence and low agreement of the ability of \r [...]"


id: e2a4cb7e68ce7ea380a031c7064aeba7
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37175
content:
"[...] weather generators to realistically simulate the spatial dependence \r of atmospheric variables across multiple sites."


id: 884e88e87ad267eb4e2eb5ac2cca0f13
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 21139
content:
"RCM experiments are often set up such that changes in forcing agents \r are included only via the boundary conditions, but not explicitly \r included inside the domain. Jerez et al. (2018) demonstrated that not \r including time-varying GHG concentrations within the RCM domain \r may misrepresent temperature trends by 1-2degC per century. Including \r the past trend in anthropogenic sulphate aerosols in reanalysis-driven \r RCM simulations substantially improved the representation of recent \r brightening and warming trends in Europe (Nabat et al., 2014; see \r Sections 10.3.3.6 and 10.6.4, and Atlas.8.4). Similarly, Bukovsky (2012) \r argued that RCMs may not capture observed summer temperature \r trends in the USA because changes in land cover are not taken \r into account. Barlage et al. (2015) have revealed that including the \r behaviour of groundwater in land schemes increases the performance \r of an RCM model to represent climate variability in the central USA. \r [...]"


id: 25c50a077d73fae437d0e39b3e98b8d3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 3828
content:
"[...] Hamdi et al. (2014) found that an RCM that did not incorporate the \r historical urbanization in the land-use, land-cover scheme is not able \r to reproduce the warming trend observed in urban stations, with \r a larger bias for the minimum temperature trend."


id: bbcf5156fff5e6c34c71b9ce7992f8d6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 30272
content:
"Increasing resolution (Haarsma et al., 2016) or performing downscaling \r may be particularly important when it modifies the climate change \r signal of a  lower resolution model in a  physically plausible way \r (Hall, 2014). Improvements may result from a better representation \r of regional processes, upscale effects, as well as the possibility of \r a  region-specific model tuning (Sorland et  al., 2018). For instance, \r Gula and Peltier (2012) showed that a higher resolution allows for \r a more realistic simulation of lake-induced precipitation, resulting in \r a more credible projection of changes in the snow belts of the North \r American Great Lakes. Similarly, Giorgi et al. (2016) demonstrated \r that an ensemble of RCMs better represents high-elevation surface \r heating and in turn increased convective instability. As a result, the \r summer convective precipitation response was opposite to that \r simulated by the driving global models (Figure  10.9). Similarly, \r [...]"


id: f9fe6b48ca46bdbaacb63bbd94ded8e7
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 41348
content:
"[...] Walton et  al. (2015) showed that a  kilometre-scale RCM enables \r a  more realistic representation of the snow-albedo feedback in \r mountainous terrain compared to standard resolution global models, \r leading to a  more plausible simulation of elevation-dependent \r warming. Bukovsky et al. (2017) argue that strong seasonal changes \r in warm-season precipitation in the Southern Great Plains of the USA, \r projected by RCMs, are more credible than the weaker global model \r changes because precipitation is better simulated in the RCMs."


id: bf998975ba6bab2eb549e93454ba5356
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 30973
content:
"AR5 stated that confidence in climate model projections is based \r on the physical understanding of the climate system and its \r representation in climate models. A  climate model's credibility for \r future projections may be increased if the model is able to simulate \r past variations in climate (Sections 10.3.3.8, 10.4.1 and 10.6; Flato \r et al., 2014). In particular, the credibility of downscaled information \r depends on the quality of both the downscaling method and of the \r global model providing the large-scale boundary conditions (Flato \r et al., 2014). Credibility is closely linked to the concept of adequacy \r or fitness-for-purpose (Section 1.5.4.1; Parker, 2009). From a regional \r perspective, one may ask about the fitness of a climate model for \r simulating future changes of specific aspects of a specific regional \r climate. The required level of model fitness may depend on the user \r context (Section  10.5). A  key challenge is to link performance at \r [...]"


id: b006dd59f66c9b908609c7ae507b2d7b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 28211
content:
"[...] representing present and past climate (Sections 10.3.3.3-10.3.3.8) \r to the confidence in future projections (Section 1.3.5; Baumberger \r et al., 2017) and it is addressed in this subsection."


id: a6e26bba1b543a7a0a7366e72442fcc5
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 26572
content:
"A general idea of model fitness for a  given application may be \r obtained by checking whether relevant large- (Section 10.3.3.4) and \r regional-scale (Sections 10.3.3.5 and 10.3.3.6) processes are explicitly \r resolved (Figure 10.3). The basis for confidence in climate projection \r is a solid process understanding (Flato et al., 2014; Baumberger et al., \r 2017). Thus, the key to assessing the fitness-for-purpose of a model is \r the evaluation of how relevant processes controlling regional climate \r are represented (Collins et al., 2018). A process-based evaluation may \r be more appropriate than an evaluation of the variables of interest \r (e.g.,  temperature, precipitation), because biases in the latter may \r in principle be reduced if the underlying processes are realistically \r simulated (Cross-Chapter Box 10.2), while individual variables may \r appear as well-represented because of compensating errors (Flato \r et al., 2014; Baumberger et al., 2017). Combining a process-based \r [...]"


id: 3158c2404c60cd88b3d37f60be2a3629
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5930
content:
"[...] evaluation with a  mechanistic explanation of projected changes \r further increases confidence in projections (Bukovsky et al., 2017). \r Fitness-for-purpose can also be assessed by comparing the simulated \r response of a  model with simulations of higher resolution models \r that better represent relevant processes (Baumberger et al., 2017). \r For instance, Giorgi et al. (2016) have corroborated their findings on \r precipitation changes comparing standard RCM simulations with \r convection-permitting simulations."


id: 7b5fe490bb5d249734316f87637baee9
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 18904
content:
"Global models reproduce many of the features of observed climate \r and its variability at regional scales. However, global models \r can show a  variety of biases in, for instance, precipitation and \r temperature at scales ranging from continental (Prasanna, 2016) to \r sub-continental scales (Lovino et al., 2018), both in the mean and \r in higher order moments of the climatological distribution of the \r variable (Figure  10.6; Ren et  al., 2019; Xin et  al., 2020). Regional \r biases could occur even if all the relevant large-scale processes are \r correctly represented, but not their interaction with regional features \r such as orography or land-sea contrasts (Section  10.3.3.4). These \r biases have been considered an important limiting factor in model \r usability, especially at the regional scale (Palmer, 2016). In  spite \r of this, global model simulations have been extensively used to \r create regional estimates of climate change (Chapters 11, 12 and \r [...]"


id: 6590ead7087fcdaeb51e48cb048e60ff
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15136
content:
"[...] Atlas), taking into account the result of a performance assessment \r (Chapter  11, Sections 10.3.3.3-10.3.3.8, and Atlas; Jiang et  al., \r 2020). However, their application is limited in part by the effective \r resolution of these models (Klaver et al., 2020)."


id: f66e1851a07b23511af20c0a6d535277
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 40726
content:
"Global model performance at the regional scale is assessed in \r terms of the time or spatial averages of key variables (see Atlas; \r Brunner et  al., 2019), the ability to reproduce their seasonal \r cycle (Hasson et  al.,  2013) or a  set of extreme climate indicators \r (Chapter  11; Luo et  al., 2020) and the representation of regional \r processes and phenomena, feedbacks, drivers and forcing impacts \r (Sections  10.3.3.4-10.3.3.6). In many cases, the performance \r estimates have been used to select models for either an application \r or a more in-depth study (Lovino et al., 2018), to select the models \r that provide boundary conditions to perform RCM simulations \r (McSweeney et al., 2015) or to weight the results of the global model \r simulations (Sanderson et al., 2015; Brunner et al., 2020). While some \r large-scale metrics are improved between the CMIP5 and CMIP6 \r experiments (Chapter 3; Cannon, 2020), there is not yet concluding \r [...]"


id: cdcd4b643eba3fd9178d4304aa3091fd
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 33537
content:
"Bias adjustment assumes that model biases are time invariant \r (or more precisely, independent of the climate state), such that the \r adjustment made to present climate simulations is still applicable \r to future climate simulations. Many findings challenge the validity \r of this assumption, as already assessed in AR5 (Flato et al., 2014). \r Further research has addressed this issue by means of perfect \r model experiments (Section  10.3.2.5) and process understanding. \r Perfect-model studies with GCMs found that circulation, energy, \r and water-cycle biases are roughly state-independent (Krinner \r and Flanner, 2018), whereas temperature biases depend linearly \r on temperature (Kerkhoff et  al., 2014). Others show that regional \r temperature biases may depend on soil moisture and albedo, \r and may thus be state-dependent (Maraun, 2012; Bellprat et  al., \r 2013; Maraun et al., 2017; see Cross-Chapter Box 10.2 for further \r [...]"


id: f18b63d3909b45b3644bfc855212cf9a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39879
content:
"[...] limitations of bias adjustment). The fitness of weather generators for \r future projections depends on whether they account for all relevant \r changes in their parameters, either by predictors or change factors \r (Maraun and Widmann, 2018b)."


id: 30576838db1d6db3cbd55a72998a7f73
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5757
content:
"There is very high confidence (robust evidence and high agreement) \r that RCMs add value to global simulations in representing many \r regional weather and climate phenomena, especially over regions \r of complex orography or with heterogeneous surface characteristics \r and for local-scale phenomena. Realistically representing local-scale \r phenomena such as land-sea breezes requires simulations at \r a  resolution of the order of 10 km (high confidence). Simulations \r at  kilometre-scale resolution add value in particular to the \r representation of convection, sub-daily summer precipitation extremes \r (high confidence) and soil-moisture-precipitation feedbacks (medium \r confidence). Resolving regional processes may be required to correctly \r represent the sign of regional climate change (medium confidence). \r However, the performance of RCMs and their fitness for future \r projections depend on their representation of relevant processes, \r [...]"


id: dd4664bc7ad21f419faf3c54608d244f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36192
content:
"van Haren et al., 2015; Feng et al., 2017; Fabiano et al., 2020), although \r the resolution increase is not a  guarantee of overall improvement \r (Section 8.5.1; Fabiano et al., 2020; Hertwig et al., 2021). For instance, \r increasing resolution in global models has been shown to improve \r Asian monsoon rainfall anchored to orography and the monsoon \r circulation (Johnson et al., 2016), but fails to solve the major dry bias. \r It is also difficult to disentangle the role of resolution increase and \r model tuning on the performance of the GCM (Anand et al., 2018). \r Some efforts have been undertaken to complement the performance \r improvements of resolution by using stochastic parametrizations \r (Palmer, 2019), which explicitly acknowledge the multi-scale nature \r of the climate system, in standard resolution global models with some \r success (Dawson and Palmer, 2015; MacLeod et al., 2016; Zanna et al., \r 2017, 2019). The expectation is to achieve a similar performance to \r [...]"


id: f03b09d8da40d8b0d10cca552c6c51d1
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39654
content:
"Statistical downscaling, bias adjustment and weather generators \r outperform uncorrected output of global and regional models \r for a  range of statistical aspects at single locations due to their \r calibration (Casanueva et  al., 2016), but RCMs are superior when \r spatial fields are relevant (Mehrotra et  al., 2014; Vaittinada Ayar \r et al., 2016; Maraun et al., 2019a). Similarly, there is some evidence \r that bias adjustment is comparable in performance when applied to \r global models and dynamically downscaled global models only for \r single locations, but dynamical downscaling prior to bias adjustment \r clearly adds value once spatial dependence is relevant (Maraun \r et al., 2019a). These results may explain why dynamical downscaling \r does not add value to global model simulations for (single-site) \r agricultural modelling, when both global and regional models are \r bias adjusted (Glotter et al., 2014), but dynamical downscaling adds \r [...]"


id: 97b279306708cc87c026e9d11add1efa
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 23908
content:
"[...] value compared to bias-adjusted global model output for spatially \r distributed hydrological models (Qiao et al., 2014)."


id: b78dce227ab2f995725bb54a8339fc58
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29505
content:
"Since AR5, physical climate storyline approaches (see also \r Chapter  1, Section  10.5.3, Box  10.2, and Atlas.2.5.2) have been \r developed to better characterize and communicate uncertainties \r in regional climate projections (Shepherd, 2019). A  special class \r of such storylines attempts to attribute regional uncertainties \r to uncertainties in remote drivers. For instance, the Dutch \r Meteorological Service has presented climate projections for the \r Netherlands for different plausible changes of the mid-latitude \r atmospheric circulation and different levels of European warming \r (van den Hurk et al., 2014). Manzini et al. (2014) have quantified \r the impact of uncertainties in tropical upper troposphere warming, \r polar amplification, and stratospheric wind change on Northern \r Hemisphere winter climate change. Based on these results, Zappa \r and Shepherd (2017) separated the multi-model ensemble into \r physically consistent sub-groups or storylines of qualitatively \r [...]"


id: 4e9cc29a45cfd04227c0d477919ac0fd
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 11101
content:
"[...] different projections in relevant remote drivers of the atmospheric \r circulation. In a similar vein, (Ose et al., 2020) trace uncertainties in \r projections of the East Asian summer monsoon and Mindlin et al. \r (2020) conditioned the response of Southern Hemisphere mid\u0002latitude circulation and precipitation to greenhouse gas forcing on \r large-scale climate indicators (Section 8.4.2.9.2)."


id: b73e8a6c1d0e15339d802613e96b2240
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29452
content:
"Modelling chains for generating regional climate information range \r from the definition of forcing scenarios to the global modelling, \r and potentially to dynamical or statistical downscaling and bias \r adjustment (Section  10.3.1). The propagation and potential \r accumulation of uncertainties along the chain has been termed the \r cascade of uncertainty (Wilby and Dessai, 2010). Even within one \r model, like a global model, uncertainty propagates across scales. From \r a process point of view, these uncertainties are related to forcings \r and global climate sensitivity, and errors in the representation of the \r large-scale circulation (Section  10.3.3.3; McNeall et  al., 2016) and \r regional processes (Section  10.3.3.4), feedbacks (Section  10.3.3.5) \r and drivers (Section 10.3.3.6). From a modelling point of view, these \r uncertainties are related to the choice of dynamical and statistical \r models (Section  10.3.1) and experimental design (Section  10.3.2). \r [...]"


id: 8f7f6e7febee790449c4c897b166e226
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 22457
content:
"[...] The overall uncertainty can be statistically decomposed into the \r individual sources (Evin et al., 2019; Christensen and Kjellstrom, 2020), \r although there might be non-linear dependencies between them."


id: 2efddffeb954df28a862988159eb104
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5543
content:
"Climate response uncertainties (Chapter  1) can be represented by \r multi-model ensembles, although the sampled uncertainty typically \r underestimates the full range of uncertainty (Collins et al., 2013b; \r Shepherd et al., 2018; Almazroui et al., 2021). Traditionally, climate \r response uncertainty has been characterized by the ensemble spread \r around the multi-model mean change. The change has then further \r been qualified in terms of the agreement across models and compared \r to estimates of internal climate variability (Collins et  al., 2013b). \r Since AR5, several limitations of this approach have been identified \r (Madsen et  al., 2017) such as the failure to address physically \r plausible, but low-likelihood, high-impact scenarios (Chapters 1, 4, 8 \r and 9; Sutton, 2018) or that qualitatively different or even opposite \r changes may be equally plausible at the regional scale (Shepherd, \r 2014). In a  multi-model mean these different responses would be \r [...]"


id: 71319553275fc154b4abbeb34d6abcbf
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 17172
content:
"[...] lumped together, strongly dampened, and qualified as non-robust, \r whereas in fact high impacts might occur. Further, the multi-model \r mean itself is often implausible because it is a statistical construct \r (Zappa and Shepherd, 2017). Overall, there is high confidence that "


id: fb95a8e674c14a48e53a5cd51cd6d796
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 41632
content:
"Analysis of multi-model archives such as CMIP or CORDEX simulation \r results cannot easily disentangle model uncertainty and uncertainty \r related to internal variability. Since AR5, the development of \r single-model (global model and/or RCM) initial-condition large \r ensembles (SMILEs) has emerged as a  promising way to robustly \r assess the regional-scale forced response to external forcings and the \r respective contribution of internal variability and model uncertainty \r to future regional climate changes (Section 4.2.5; Deser et al., 2014, \r 2020; Kay et al., 2015; Sigmond and Fyfe, 2016; Aalbers et al., 2018; \r Bengtsson and Hodges, 2019; Dai and Bloecker, 2019; Leduc et al., \r 2019; Maher et al., 2019; von Trentini et al., 2019; Lehner et al., 2020). \r The recent development of a multi-model archive of SMILE simulations \r facilitates the quantification and comparison of  the influence of \r internal variability on global model-based regional climate projections \r [...]"


id: e3ee1cdbec8c298c617cfae5b7210e06
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updated_at: 2023-04-19 13:22:41
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vector_id: 37388
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"[...] between different models (Deser et  al., 2020; Lehner et  al., 2020). \r Another related development is the more frequent use of observation\u0002based statistical models to assess the influence of internal variability \r on regional-scale global and regional model projections (Thompson \r et  al., 2015; Salazar et  al., 2016). However, these methods often \r implicitly assume that regional-scale internal variability does not \r change under anthropogenic forcing, which is a strong assumption \r that does not seem to hold at regional and local scales (LaJoie and \r DelSole, 2016; Pendergrass et  al., 2017; W. Cai et  al., 2018; Dai \r and Bloecker, 2019; Mankin et al., 2020; Milinski et al., 2020)."


id: ca0df8257d0a06b2e32f7fe668bcf102
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"likely range (Chapter 4), or some other relevant measure of sensitivity, \r such as the projected range of tropical SSTs (Suzuki-Parker et al., 2018). \r A further choice is to emphasize models that do not have the same \r origins or that do not use similar parametrizations and thus might be \r viewed as independent, a criterion that could be applied to both global \r models (Chapter 4) and RCMs (Evans et al., 2014). Global models and \r RCMs could also be discarded that unrealistically represent processes \r controlling the regional climate of interest (McSweeney et al., 2015; \r Maraun et  al., 2017; Bukovsky et  al., 2019; Eyring et  al., 2019). \r Box 4.1 offers a more detailed discussion of the issues surrounding \r these approaches. Finally, global models may be selected to represent \r different physically self-consistent changes in regional climate (Zappa \r and Shepherd, 2017). Statistical methods can provide estimates of \r outcomes from missing global model-RCM combinations in a  large \r [...]"


id: 806966c2c8fbdbef63d764b37f4880a6
created_at: 2023-04-19 13:22:41
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"However, even using a relatively small set of global models can still \r involve substantial computation that strains available resources, \r both for performing the simulations and for using all simulations \r in the ensemble for further impacts assessment. The NARCCAP \r programme (Mearns et al., 2012) used only a subset of its possible \r global model-RCM combinations that balanced comprehensiveness \r of sampling the matrix with economy of computation demand, while \r still allowing discrimination, via ANOVA methods, of global model \r and RCM influences on regional climate change (Mearns et  al., \r 2013). An advantage of the sparse, but balanced matrix for those \r using the downscaling output for further studies, is that they have \r a smaller, yet comprehensive set of global model-RCM combinations \r to work with. Alternatively, data-clustering methods can clump \r together downscaling simulations featuring similar climate-change \r [...]"


id: d40cb9cd27f73c03fce61f5ff563cb9c
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] characteristics, so that only one representative simulation from \r each cluster may be needed for further impacts analysis, again \r systematically reducing the necessary number of simulations to work \r with (Mendlik and Gobiet, 2016; Wilcke and Barring, 2016)."


id: 610105af197281a897ea0645b576d1fe
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"Ensembles of climate simulations play an important role in quantifying \r uncertainties in the simulation output (Sections 10.3.4.2 and \r 10.3.4.3). In addition to providing information on internal variability, \r ensembles of simulations can estimate scenario uncertainty and \r model (structural) uncertainty. Chapter  4, especially Box  4.1, \r discusses issues involved with evaluating ensembles of global \r model simulations and their uncertainties. In a downscaling context, \r further considerations are necessary, such as the selection of global \r model-RCM combinations when performing dynamical downscaling. \r This is a  relevant issue when resources are limited. The structural \r uncertainty of both the global model and the downscaling method \r can be important (e.g., Mearns et al., 2012; Dosio, 2017), as well as \r further potential uncertainty created by inconsistencies between the \r global model and the downscaling method (e.g., Dosio et al., 2019), \r [...]"


id: dd14a7fe41574f4cfbffcc100ae910f4
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content:
"[...] which could include, for example, differences in topography or the \r way to model precipitation processes (Mearns et al., 2013)."


id: f50b10e05313b9cdf8cd622b093ede15
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"Independently of the resources, participation of multiple models in \r a  simulation programme such as CORDEX for RCMs or CMIP for \r global models creates ensembles of opportunity, which are ensembles \r populated by models that participants chose to use without there \r necessarily being an overarching guiding principle for an optimum \r choice. As discussed in Chapter 4, these ensembles are likely suboptimal \r for assessing sources of uncertainty. An important contributor to the \r suboptimal character of such an ensemble is that the models are \r not independent. Some may also have larger biases than others. Yet \r often, the output from models in these ensembles has received equal \r weight when viewed collectively, as was the case in much of the AR5 \r assessment (e.g., Collins et al., 2013b; Knutti et al., 2013; Flato et al., \r 2014; Kirtman et al., 2014). A number of emerging methodologies aim \r at optimizing the ensembles available by weighting the simulation \r [...]"


id: fed507d3115f79dc7716ab8b82ab5d0e
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vector_id: 42228
content:
"[...] results according to a  number of criteria relevant at the regional \r scale that aim at obtaining more realistic estimates of the uncertainty \r (Sanderson et al., 2015; Brunner et al., 2020)."


id: 87dc19c6ab9ee4fd325b8bce1247c690
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"Bias adjustment methods like quantile mapping can modify simulated climate trends, with impacts on changes to climate indices, \r in particular, extremes (Haerter et al., 2011; Dosio et al., 2012; Ahmed et al., 2013; Hempel et al., 2013; Maurer and Pierce, 2014; \r Cannon et al., 2015; Dosio, 2016; Casanueva et al., 2020). Some argue that these trend modifications are implicit corrections of \r state-dependent biases (Boberg and Christensen, 2012; Gobiet et al., 2015). However, others argue that the modification is generally \r invalid because the modification is linked to the representation of day-to-day rather than long-term variability (Pierce et al., 2015; \r Maraun et al., 2017); a given temperature value does not necessarily belong to the same weather state in present and future climate \r (Maraun et al., 2017); the modification affects the models climate sensitivity (Hempel et al., 2013); and is affected by random internal \r [...]"


id: 8cea8177a22cc23cfc475ad4d2ca1d46
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"[...] climate variability (Switanek et al., 2017). Thus, trend preserving quantile mapping methods have been developed (Section 10.3.1.3.2), \r although some authors found no clear advantage of these methods (Maurer and Pierce, 2014). Further research is required to fully \r understand the validity of trend modifications by quantile-mapping."


id: 5f9319d879e83cade40404b1463caabf
created_at: 2023-04-19 13:22:41
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content:
"The large-scale circulation has a strong impact on regional climate, thus circulation errors will cause regional climate biases (Section 10.3.3.3). \r As bias adjustment in general does not account for circulation errors, it is therefore important to understand the impact of these errors on the \r outcome of the bias adjustment (Addor et al., 2016; Photiadou et al., 2016; Maraun et al., 2017). If the frequency of precipitation-relevant \r weather types is biased, a standard bias adjustment (not accounting for this frequency bias) would remove the overall climatological bias, \r but the precipitation falling in a given weather type could still be substantially biased (Addor et al., 2016). Adjusting the number of wet \r days can artificially deteriorate the spell-length distribution (Maraun et al., 2017). In the presence of location biases of circulation patterns, \r [...]"


id: de2bc00c6c66633057207ae620c5a487
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"[...] bias adjustment may introduce physically implausible solutions (Maraun et al., 2017). Bias adjusting the location of circulation features \r (Levy et al., 2013) may introduce inconsistencies with the model orography, land-sea contrasts, and SSTs (Maraun et al., 2017)."


id: cfdd5c1b4ba43678095b17f853ddc3b3
created_at: 2023-04-19 13:22:41
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content:
"adjustment for downscaling has been shown to artificially modify long-term trends, misrepresent the spatial characteristics of extreme \r events, and misrepresent local weather phenomena such as temperature inversions (Maraun, 2013a; Gutmann et al., 2014; Maraun \r et al., 2017). Crucially, sub-grid influences on the local climate change signal are not represented. For instance, if a mountain chain is \r not resolved in the driving model, the snow-albedo feedback is not represented by the bias adjustment such that local temperature \r trends in high altitudes are under-represented (Cross-Chapter Box 10.2, Figure 1; Maraun et al., 2017). It has therefore been suggested \r to account for local random variability by combining bias adjustment with stochastic downscaling (Volosciuk et al., 2017; Lange, \r 2019), although this approach still does not account for local modifications of the climate change signal. Two approaches have been \r [...]"


id: 9fc020447b6bf8446f49df752c7bc755
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content:
"[...] proposed to represent these local changes: dynamical downscaling with high-resolution RCMs (Maraun et al., 2017) or statistical \r emulators of such (Walton et al., 2015). Sections 10.3.3.4-10.3.3.6 and 10.3.3.9 discuss other examples where RCMs improve the \r representation of regional phenomena and regional climate change."


id: 95600288afd897c379c2b98a1ae5e336
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index: climate_gpt_v2
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content:
"Impact models, as well as indices of climatic impact-drivers, often require input of several meteorological variables (Chapter 12). In several \r situations, for example, if the dependence between the variables is not well-simulated, univariate bias adjustment of the individual \r variables may increase biases in the resulting indicator (Zscheischler et al., 2019). A simple alternative would be a bias adjustment of \r the indicator, but such a procedure may substantially alter the climate change signal, in particular for extreme events (Casanueva et al., \r 2018). In principle, multivariate bias adjustment methods are good to adjust all statistical aspects of the multivariate distribution that they \r intend to adjust. Depending on the method, this includes the correlation structure or even broader aspects of the dependence (Cannon, \r 2016, 2018; Vrac, 2018; Francois et al., 2020). If multivariate adjustment includes a spatial dimension, then spatial dependence is adjusted \r [...]"


id: 86a07083d4f532f6152ed0a8103fa073
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content:
"[...] well (Vrac, 2018), but care is needed when applied across large areas (Francois et al., 2020). Adjustment of multivariate dependence \r necessarily modifies the temporal sequencing of the driving model (Cannon, 2016; Maraun, 2016). The extent of the modification depends \r on the chosen method and the number of variables to adjust (Vrac and Friederichs, 2015; Cannon, 2016; Vrac, 2018; Francois et al., 2020)."


id: 29177f80242d8013cad562c7f41af0e5
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updated_at: 2023-04-19 13:22:41
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content:
"In the light of these issues, several authors dismiss the use of bias adjustment for climate change studies (Vannitsem, 2011; Ehret et al., 2012). \r Ehret et al. (2012) and IPCC (2015) propose to at least provide the raw model output alongside the adjusted data. Maraun et al. (2017) \r argue that the target resolution should be similar to the model resolution to avoid downscaling issues. IPCC (2015) and Maraun et al. \r (2017) highlighted the relevance of understanding model biases and the misrepresentations of the underlying physical processes prior to \r any adjustment. Together with Galmarini et al. (2019), they point out the need for collaboration between bias adjustment users, experts in \r climate modelling and experts in the considered regional climate. As new research avenues, development of process-oriented bias adjustment \r methods (Addor et al., 2016; Verfaillie et al., 2017; Manzanas and Gutierrez, 2019) or run-time bias adjustment integrated into the climate \r [...]"


id: 6ecdf79c9bcff3a8c1491769d9e956fe
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content:
"[...] simulation, for example, to reduce circulation errors (Guldberg et al., 2005; Kharin et al., 2012; Krinner et al., 2019, 2020) are proposed."


id: 72d74d46a7b689adc134b0b4e943132a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 17453
content:
"Detection of mean precipitation changes caused by human influence is \r much more difficult, due to a larger role of internal variability at regional \r to local scales, as well as substantial modelling and observational \r uncertainty (Wan et al., 2015; Sarojini et al., 2016; C. Li et al., 2017). \r However, multi-decadal precipitation changes due to anthropogenic \r forcing have been detected for several regions. Ma et al. (2017b) show \r that anthropogenic forcing has strongly contributed to the observed \r shift of China daily precipitation towards heavy precipitation. The \r observed weakening of the East Asia summer monsoon, also known \r as the southern flooding and northern drought pattern has been \r partially linked to anthropogenic forcing (Section  8.3.2.4.2; Song \r et  al., 2014; Zhou et  al., 2017; Tian et  al., 2018). Changes in GHGs \r lead to increasing precipitation over southern China, while changes \r in anthropogenic aerosols over East Asia are the dominant factors \r [...]"


id: f2b4f0dfe90c056c0e148f647556b083
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updated_at: 2023-04-19 13:22:41
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vector_id: 40101
content:
"[...] determining drought conditions over northern China (Song et al., 2014; \r Tian et al., 2018). Based on all-forcing and single-forcing simulation \r ensembles with a high-resolution model, Delworth and Zeng (2014) \r found that the observed long-term regional austral autumn and winter \r rainfall decline over southern and particularly south-west Australia is \r partially reproduced in response to anthropogenic changes in GHGs \r and ozone in the atmosphere, whereas anthropogenic aerosols do \r not contribute to the simulated precipitation decline. In contrast, the \r observed increase of north-west Australian summer rainfall since 1950 \r has been partially attributed to anthropogenic aerosol based on CMIP5 \r detection and attribution single-forcing simulations (Section 8.3.2.4.6; \r Dey et al., 2019a, b)."


id: b6dbf18b08e03deec2e1fddc17e83841
created_at: 2023-04-19 13:22:41
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content:
"Attribution at sub-continental and regional scales is usually more \r complicated than at the global scale due to various factors: a larger \r contribution from internal variability, an increased similarity among \r the responses to different external forcings leading to a more difficult \r discrimination of their effects, the importance at regional scale of \r some omitted forcings in global model simulations, and model \r biases related to the representation of small-scale phenomena \r (Zhai et  al., 2018). Since AR5 and in addition to standard optimal \r fingerprint regression-based approaches (Section  3.2.1 and Zhai \r et al. 2018), several emerging methodologies have been increasingly \r used for regional-scale climate change attribution. These include \r several statistical approaches that differ in their use or omission of \r spatiotemporal co-variance information. Dynamical adjustment and \r pattern recognition techniques fall into the category of spatiotemporal \r [...]"


id: 8ab9c3c765f7125a75c5f91f13c6cdde
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content:
"[...] methods while univariate detection and attribution methods rely on \r single grid-point analysis. Finally, the development, evaluation and use \r of all these methodologies rely upon the availability of multiple and \r high-quality observational datasets (Section 10.2) as well as multi\u0002model simulations of the historical period constrained by different \r external forcing combinations, including single-forcing experiments \r and single-model initial-condition large ensembles (SMILEs)."


id: b7d3d117f4025edd7721f5de9f05b2a8
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updated_at: 2023-04-19 13:22:41
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content:
"and precipitation trends over Europe. Similarly, O'Reilly et al. (2017) \r applied dynamical adjustment techniques to more carefully \r determine the influence of the Atlantic Multi-decadal Variability \r (AMV; Annex IV.2.7) on continental climates. Over Europe, summer \r temperature anomalies induced thermodynamically by the warm \r phase of the AMV are further reinforced by circulation anomalies; \r meanwhile, precipitation signals are largely controlled by dynamical \r responses to the AMV. Based on a  partial least-squares approach, \r Gong et al. (2019) showed that recent winter temperature 30-year \r trends over northern East Asia are strongly influenced by internal \r variability linked to decadal changes of the Arctic Oscillation. Using \r dynamical adjustment purely on precipitation observations, R. Guo \r et  al. (2019) showed that human influence has led to increased \r winter precipitation across north-eastern North America, as well as \r [...]"


id: bfc5a2b3ced5e941db02f0527764caa4
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content:
"[...] a small region of north-western North America, and to an increase in \r precipitation across much of north-western and north central Eurasia. \r The latter results confirm previous findings obtained by standard \r optimal fingerprinting methods (Wan et al., 2015)."


id: 9a249103102bf00ca6e72a0d92de3db1
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
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content:
"The typical ensemble size of CMIP historical climate simulations for \r a  given model traditionally range between one and ten members, \r with three often being the default choice. At the regional scale, \r a simple ensemble average with such sample sizes does not provide \r robust estimates of the response patterns to external forcing (Maher \r et al., 2019; Deser et al., 2020). Since AR5, pattern filtering methods \r such as signal-to-noise maximizing empirical orthogonal functions \r (Ting et  al., 2009) have been shown to improve the identification \r of forced response patterns when few model members are available \r (Wills et al., 2020). Using SMILEs as a test bed, it has been shown that \r pattern filtering strongly reduces the number of ensemble members \r needed to estimate the forced response pattern compared to simple \r ensemble averaging. Pattern filtering allows the identification of low \r signal-to-noise signals such as the El Nino-like response to volcanic \r [...]"


id: 66d3340819436d98a1caaee14cccdc59
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updated_at: 2023-04-19 13:22:41
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content:
"Time scale separation methods such as the low-frequency \r component analysis and ensemble empirical mode decomposition \r methods take advantage of the longer time scale associated with \r anthropogenic external forcing compared to that of most internal \r modes of variability. The low-frequency component analysis method \r tries to find low-frequency variability patterns by searching for linear \r combinations of a moderate number of empirical orthogonal functions \r that maximize the ratio of low-frequency to total variance. It has first \r been used to separate internal modes of interannual and decadal \r variability from slowly varying and externally-forced variability \r in the Pacific and Atlantic oceans (Wills et  al., 2018, 2019). The \r methodology has also been applied to patterns of observed surface \r air temperature to isolate the slow components of observed changes \r that are consistent with the expected response to anthropogenic \r [...]"


id: f1062a38f3ffd3d59771ff35d41b3c01
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updated_at: 2023-04-19 13:22:41
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content:
"Methods to extract the response to external forcing in an observed or \r simulated single realization include dynamical adjustment (Smoliak \r et al., 2015; Deser et al., 2016; Sippel et al., 2019) and time scale \r separation methods (DelSole et al., 2011; Wills et al., 2018, 2020). \r Dynamical adjustment seeks to isolate changes in surface air \r temperature or precipitation that are due purely to atmospheric \r circulation changes. The residual can then be analysed and attributed \r to internal changes in both land or ocean surface conditions and \r the thermodynamical response to external forcing. Smoliak et  al. \r (2015) performed their dynamical adjustment using partial least \r squares regression of temperature to remove variations arising from \r sea level pressure changes. Deser et  al. (2016) used constructed \r atmospheric circulation analogues and resampling to estimate the \r dynamical contribution to changes in temperature. Sippel et  al. \r [...]"


id: f8cc3e26df7d6e9c2f99f501e762b629
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content:
"[...] (2019) used machine learning techniques known as regularized linear \r regression to provide estimates of circulation-induced components \r of precipitation and temperature variability from global to local \r scales. It is noteworthy that the dynamical adjustment method by \r itself cannot account for the component of the forced response \r associated with circulation changes that project onto atmospheric \r internal variability. However, this component can be estimated within \r a model framework by averaging the dynamical contribution across \r multiple members of a SMILE (Deser et al., 2016)."


id: 847b6dcf610175fd944fb49ff33d7fd9
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 20456
content:
"and all-forcing are compared with distributions of trends purely due \r to internal variability and derived from long simulations with constant \r pre-industrial external forcing. Consistency between observed and \r simulated historical trends is also assessed with statistical tests that \r can be applied independently over a large number of grid points. The \r fraction of area over a given region where the change is classified \r as detectable, attributable, or consistent/inconsistent, is then finally \r estimated. The method can be viewed as a  simple consistency \r test for both amplitude and pattern of observed versus simulated \r trends. Its application to CMIP3 and CMIP5 models suggests that \r 80% of the Earth's surface has a detectable anthropogenic warming \r signal (Knutson et al., 2013). Regarding regional land precipitation \r changes over the 1901-2010 and 1951-2010 periods, application of \r the univariate detection method based on CMIP5 models suggests \r [...]"


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updated_at: 2023-04-19 13:22:41
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content:
"[...] attributable anthropogenic changes at several locations such as \r increases over regions of the north-central USA, southern Canada, \r Europe, and southern South America and decreases over parts of the \r Mediterranean region, northern tropical Africa and south-western \r Australia (Delworth and Zeng, 2014; Knutson and Zeng, 2018)."


id: 69996d34a649b41baa0cacf22471f966
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updated_at: 2023-04-19 13:22:41
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vector_id: 15846
content:
"Another regional attribution technique is based on the similarity of \r past changes between observations and one or several simulations \r of a large ensemble that share the same time evolution for a suggested \r driver of these changes. Huang et al. (2020b) used a perturbed physics \r ensemble to attribute the drying trend of the Indian monsoon over \r the latter half of the 20th century to decadal forcing from the Pacific \r Decadal Variability (PDV; Annex IV.2.6). The ensemble members \r predicted different trends in PDV behaviour across the 20th century \r and the negative precipitation trend was only replicated in those \r members with a  strong negative-to-positive PDV transition across \r the 1970s, consistent with the observed PDV behaviour (see also the \r detailed case study in Section 10.6.3). In a similar manner, Cvijanovic \r et al. (2017) addressed the possible influence of Arctic sea ice loss on \r the North Pacific pressure ridge and, consequently, on south-western \r [...]"


id: f6f316208e5535357704a96991386163
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updated_at: 2023-04-19 13:22:41
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vector_id: 40814
content:
"[...] USA precipitation. They sampled the uncertainties in selected sea \r ice physics parameters to achieve a 'low Arctic sea ice' state in their \r perturbed simulations. They then compared the latter with control \r simulations representative of sea ice conditions at the end of the \r 20th century to assess changes purely due to sea ice loss."


id: 777f17e960155de089d6c27a68852bb
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 18228
content:
"The Sahel, fed by the West African monsoon, has experienced severe \r decadal rainfall variations (Figure 10.11a). Abundant rainfall in the \r 1950s-1960s was followed by a large negative trend (Figure 10.11b) \r until at least the 1980s, over which annual rainfall fell by 20-30% \r (Hulme, 2001). The subsequent partial recovery (B. Wang et al., 2021) \r is more uncertain: rain-gauge studies suggest a return to long-term \r positive anomalies in the western Sahel in the early 2000s (Panthou \r et  al., 2018), while CHIRPS merged satellite/gauge data show \r a wetter western Sahel since 1981 (Bichet and Diedhiou, 2018a, b). \r The recovery has been more significant over the central rather than \r the western Sahel (Lebel and Ali, 2009; Maidment et  al., 2015; \r Sanogo et al., 2015) and a multiple-gauge record supports a greater \r recovery to the eastern side (Nicholson et al., 2018). In this attribution \r example, drivers of the long-term drought and subsequent partial \r [...]"


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"[...] recovery are discussed, including anthropogenic GHG and aerosol \r emissions, and sea surface temperature (SST) variations that, in part, \r relate to internal variability. The reader is also referred to assessment \r in Section 8.3.2.4. We define the Sahel within 10degN-20degN across to \r 30degE, consistent with the eastern boundary used in Chapter 8, and \r the rainy season as spanning June to September."


id: 53f25cafc18d67a3ceb3ff8876c425ce
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"Sahel drought, while North Atlantic warming promotes increased \r rainfall (Rodriguez-Fonseca et al., 2015). The SST influence has been \r formalized in an AMV framework (Giannini et al., 2013; Martin and \r Thorncroft, 2014; Martin et al., 2014; Park et al., 2015), suggesting \r that relative North Atlantic SST warming increases the Northern \r Hemisphere differential warming, enhancing Sahel rainfall. The AMV \r influence is supported by CMIP5 initialized decadal hindcasts (Gaetani \r and Mohino, 2013; Mohino et al., 2016; Sheen et al., 2017), which \r outperform empirical predictions based on persistence. Some caution \r is needed since the full magnitude of internal variability is not captured \r in most CMIP5 models, as poor resolution prevents reproduction \r of AMV teleconnection responses (Vellinga et  al., 2016), and the \r magnitude of AMV-related SST variation may be underestimated \r in CMIP5 (Section 3.7.7, which also assesses that the AMV may be \r [...]"


id: 5ede019917b5688d8aae4dd88c259b07
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] partially forced). The influence of PDV has been studied to a lesser \r extent, with the PDV positive phase having a  negative impact on \r Sahel rainfall in combined observational/CMIP5 analysis (Villamayor \r and Mohino, 2015). The closer match between the observed rainfall \r declining trend and those in an atmosphere-only SMILE, in which SSTs \r are matched to observations, compared to three coupled SMILEs in "


id: c267fa423ba4a7bc744812ff10e0119
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 31484
content:
"In terms of anthropogenic emissions, regional aerosol emissions \r from Europe, and to a  lesser extent from Asia, have been shown \r in a  global model to weaken Sahel precipitation either through \r a weakened Saharan heat low or via the Walker circulation (Dong \r et al., 2014). Greenhouse gases (GHGs) and anthropogenic aerosol \r can be considered together to control ITCZ position based on \r temperature asymmetry at the hemispheric scale. GHGs increase \r Sahel precipitation, while aerosol reduces it (in coupled slab-ocean \r model experiments by Ackerley et al. (2011) following Biasutti and \r Giannini (2006)). This effect is stronger when models account for \r aerosol-cloud interactions (Allen et al., 2015). Perturbed physics GCM \r ensembles suggests that aerosol emissions were the main driver of \r observed drying over 1950-1980 (Ackerley et al., 2011), supported \r by CMIP5 single-forcing experiments (Polson et al., 2014). A coherent \r [...]"


id: 131b6fc9283e6239c214f5387679fc84
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 575
content:
"[...] drying signal in CMIP5 over the extended 1901-2010 period has \r also been found, although smaller than the observed trend (Knutson \r and Zeng, 2018). By applying aerosol scaling factors to the historical "


id: aba6106d9daa423517837f69ff094b21
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 27439
content:
"For the partial recovery in West African monsoon and Sahel rainfall \r since the late 1980s, a  detection study using three reanalyses \r (Cook and Vizy, 2015) shows a  connection to increasing Saharan \r temperatures at a rate two to four times greater than the tropical \r mean, also confirmed by multiple observational and satellite-based \r data (Zhou and Wang, 2016; Vizy and Cook, 2017) and the review \r of Cook and Vizy (2019). Reanalyses are also noted to significantly \r underestimate the Saharan warming (Zhou and Wang, 2016). \r Saharan warming causes a stronger thermal low and more intense \r monsoon flow, providing more moisture to the central and eastern \r Sahel, supported by CMIP5 models (Lavaysse et al., 2016), although \r not all models capture the observed rainfall-heat-low relationship. \r Sahel rainfall is also incorrectly located in prototype versions of \r a  few CMIP6 models, related to tropospheric temperature biases \r [...]"


id: eea85cdc03a5ed73afcaf3068d17d636
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39366
content:
"[...] (Martin et  al., 2017). Amplified Saharan warming has increased \r the wind shear, leading to a tripling of extreme storms since 1982, \r which may partially explain the recovery (Taylor et al., 2017). Instead, \r observations, multiple models and SST-sensitivity experiments with \r AGCMs have suggested that stronger Mediterranean Sea evaporation \r enhances low-level moisture convergence to the Sahel, increasing \r rainfall (Park et  al., 2016). Meanwhile, an AGCM study suggested \r that GHGs alone (in the absence of SST warming) could cause \r Sahel rainfall recovery, with an additional role for anthropogenic \r aerosol (Dong and Sutton, 2015); recent changes in North Atlantic \r SSTs, although substantial, did not exert a  significant impact on \r the recovery. Large spread in the recovery in a five-member AGCM \r ensemble suggests that atmospheric internal variability cannot be \r discounted (Roehrig et al., 2013)."


id: d265d69a77023fe43e8cf33c154881f4
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 34370
content:
"A positive trend in summer (December to February) precipitation has \r been detected in multiple observational sources in south-eastern \r South America since the beginning of the 20th  century (Gonzalez \r et al., 2013; Vera and Diaz, 2015; Wu et al., 2016; H. Zhang et al., \r 2016; Diaz and Vera, 2017; Saurral et  al., 2017). Sedimentary \r records from the Mar Chiquita lake indicate that the last quarter \r of the 20th  century was wetter than any period during the last \r 200  years (Piovano et  al., 2004). In this attribution example the \r drivers contributing to the positive trend for the period 1951-2014 \r are discussed (Figure  10.12a). Precipitation anomalies of Climatic \r Research Unit Time Series (CRU TS) as well as for the two members \r of a SMILE with the most negative and positive trends for 1951-2014 \r are displayed in Figure 10.12b. The trend for 1951-2014 using CRU TS \r and GPCC is illustrated in Figure 10.12c, and for the region defined by \r [...]"


id: e4df441d5c179b20a3352f1e17b2f1fd
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37550
content:
"[...] the black quadrilateral, it amounts to 2.8 (CRU TS) - 3.5 (GPCC) mm \r per month and decade (see black crosses in Figure  10.12d) while \r the mean summer monthly precipitation for the same period is \r 104 (CRU TS) -109 (GPCC) mm. The trend is also detectable in daily \r and monthly extremes (Re and Barros, 2009; Marengo et al., 2010; \r Penalba and Robledo, 2010; Doyle et al., 2012; Donat et al., 2013; \r Lorenz et al., 2016)."


id: 4d46d4f25a4bccb411e0a9f7adf43405
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 10875
content:
"Consistent timing of the southward ITCZ shift during the decline \r period in CMIP3 and CMIP5 historical simulations supports the role \r of external forcing, chiefly anthropogenic aerosol (Hwang et  al., \r 2013). The evolution of the observed decline and recovery is largely \r followed by the CMIP5 multi-model mean, further supporting the \r role of external drivers (Giannini and Kaplan, 2019). Updated results \r from CMIP6 for historical simulations with all and single forcings \r are represented in Figure  10.11d,e showing smaller trends than \r those observed. Giannini and Kaplan (2019) attempted to unify the \r driving mechanisms for decline and recovery based on singular-value \r decomposition of observed and modelled SSTs. Since the 1950s, \r tropical warming arising from GHGs and North Atlantic cooling \r from aerosol led to regional stabilization, suppressing Sahel rainfall. \r The subsequent reduction in aerosol emissions then led to North \r [...]"


id: d2adde12fb3e8cd43f65a9830464779f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 34411
content:
"[...] Atlantic warming and recovery of Sahel rainfall. Such mechanisms \r continue into the near-term future in idealized and modified RCP \r experiments, with scenarios featuring more aggressive reductions in \r aerosol emissions, or including aerosol-cloud interactions, favouring \r a greater northward shift of rainfall (Allen, 2015; Westervelt et al., \r 2017, 2018; Scannell et al., 2019). There is paleoclimate evidence of \r changes to Sahel rainfall in the past, in particular with enhancement \r of the West African monsoon during the mid-Holocene. However, the "


id: 40a5b4f2266fbb5646d06911c369fc70
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 8559
content:
"than those observed (e.g.,  CMIP6 trends in red open circles in \r Figure  10.12d). Diaz  et  al. (2021) showed that to capture the \r observed trend a multi-model ensemble of SMILEs is needed. Out of \r the 12 large ensembles examined (with ensemble size varying in the \r 16-100  range), only seven simulated the observed trend within \r their range. This could partly be explained by model biases in mean \r precipitation and its interannual variability. In the sub-ensemble of six \r models that reproduce reasonably well the observed spatial patterns \r of mean precipitation and interannual variability, the ensemble mean \r spread is lower, and the forced response, taken as the multi-model \r ensemble mean, is slightly more positive than that of the six poorly \r performing models. The signal-to-noise ratio, estimated as the ratio \r of the forced response to the spread due to internal variability, is \r also slightly higher for the best-performing models, suggesting that \r [...]"


id: b09abbac0affb358b55b22962fcb8dc8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 28303
content:
"[...] selecting the best-performing models may have an influence on \r both attribution of the observed trend and emergence of the forced \r response in future (Section 10.4.3)."


id: 286a13f94d181ee05ef9157585878961
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 4315
content:
"The positive trend of precipitation has also been attributed to \r anthropogenic GHG emissions and stratospheric ozone depletion. \r CMIP5 models only show a positive trend when including anthropogenic \r forcings (Vera and Diaz, 2015). These results were supported by \r Knutson and Zeng (2018) based on univariate detection/attribution \r analysis of annual mean trends for the 1901-2010 and 1951-2010 \r periods. However, the main features of summer mean precipitation \r and variability of South America are still not well-represented in all \r CMIP5 and CMIP6 models (Gulizia and Camilloni, 2015; Diaz and Vera, \r 2017; Diaz et al., 2021). This motivates the construction of ensembles \r that exclude the worst performing models (Section  10.3.3.4). The \r construction of ensembles of CMIP5 historical simulations with \r realistic representation of precipitation anomalies with opposite sign \r over south-eastern South America and eastern Brazil showed that the \r [...]"


id: 7f01ad95e607ddd3ad4ebb1abcfebdd8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 19498
content:
"[...] trend since the 1950s could be related to changes in precipitation \r characteristics only when simulations included anthropogenic \r forcings (Diaz and Vera, 2017). GHG emissions have been related \r to increased precipitation in south-eastern South America through \r three different mechanisms (Figure  10.12a). First, GHG warming \r induces a non-zonally uniform pattern of SST warming that includes \r a  warming pattern over the Indian and Pacific oceans that excites \r wave responses over South America (Junquas et al., 2013). Zonally \r uniform SST patterns of warming alone lead to precipitation signals \r opposite to those observed in an AGCM (Junquas et al., 2013). Second, \r GHG radiative forcing drives an expansion of the Hadley cell so that \r its descending branch moves poleward from the region, generating \r anomalous ascending motion and precipitation (H. Zhang et al., 2016; \r Saurral et al., 2019). The third mechanism by which increased GHG \r [...]"


id: e08af0a72006beda92517aa97339eb7b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36757
content:
"[...] can contribute to increased precipitation in the region is through \r a  delay of the stratospheric polar vortex breakdown. As depicted \r in Figure 10.12a, both stratospheric ozone depletion and increased \r GHGs have contributed to the later breakdown of the polar vortex \r in recent decades (McLandress et  al., 2010; Wu and Polvani, 2017; \r Ceppi and Shepherd, 2019). Mindlin et  al. (2020) developed future \r atmospheric circulation storylines (Section  10.3.4.2, Box  10.2) for \r Southern Hemisphere mid-latitudes with the CMIP5 models and found \r that for south-eastern South America summer precipitation, increases \r are related to the late-spring breakdown of the stratospheric polar \r vortex. The connecting mechanism is through a  lagged southward \r shift of the jet stream (Saggioro and Shepherd, 2019), which enhances \r cyclonic activity over the region (Wu and Polvani, 2017).\r A common feature among the above discussed studies is that even \r [...]"


id: e687d8d7465beee615e11b8162eb0ad3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 37857
content:
"[...] if global models simulate positive trends when forced with GHG \r and/or stratospheric ozone, these trends are in general smaller "


id: db7f2c7ce2c3db45d20124b89cfdca52
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 35894
content:
"Persistent hydroclimatic drought in south-western North America \r remains a much-studied event. Drought is a regular feature of the \r south-western North America's climate regime, as can be seen in \r both the modern record, and through paleoclimate reconstructions \r (Cook et al., 2010; Woodhouse et al., 2010; Williams et al., 2020), as \r well as in future climate model projections (Cook et al., 2015a). Since \r the early 1980s, which were relatively wet in terms of precipitation \r and streamflow, the region has experienced major multi-year \r droughts such as the turn-of-the-century drought that lasted \r from 1999 to 2005, and the most recent and extreme 2012-2014 \r drought that in certain locations is perhaps unprecedented in the \r last millennium (Section  8.3.1.6; Griffin and Anchukaitis, 2014; \r Robeson, 2015). Shorter dry spells also happened between these \r multi-year droughts making 1980 to present a  period with an \r [...]"


id: 19eba97c482d00182448e0ba22ed0b1a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 1760
content:
"[...] exceptionally steep trend from wet to dry (Figure 10.13a), leading \r to strong declines in Rio Grande and Colorado river flows (Lehner \r et al., 2017b; Udall and Overpeck, 2017). While robust attribution \r of this trend is complicated by the large natural variability in this \r region, the 20th century warming has been suggested to increase \r the chances for hydrological drought periods by lowering runoff \r efficiency (Woodhouse et al., 2016; Lehner et al., 2017b; Woodhouse \r and Pederson, 2018) and affecting evapotranspiration (Williams \r et al., 2020). There is some evidence suggesting that the Last Glacial \r Maximum, a period of low atmospheric CO2, about 21 ka ago, has \r a  thermodynamically-driven zonal mean precipitation response \r similar to that of the current state with relatively high CO2 levels "


id: d90a3f4672866db145262a3d4f56ac71
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 35484
content:
"when compared with the pre-industrial period. Pluvial conditions \r at that time and a reduction in precipitation from the Last Glacial \r Maximum to the pre-industrial period are consistent with drying \r trends for the region in models with GHG concentrations exceeding \r pre-industrial levels. However, the dominant large-scale drivers \r responsible for the precipitation changes observed during these \r two transitions are markedly different: mainly ice-sheet retreat and \r increasing insolation on one hand, increasing GHGs on the other \r hand. This suggests that the Last Glacial Maximum correspondence \r is fortuitous which strongly limits its use to capture future \r hydrological cycle changes (Section 8.3.2.4.4; Morrill et al., 2018; \r Lowry and Morrill, 2019). Furthermore, the conclusion of the Last \r Glacial Maximum drying versus wetting seems to strongly depend \r on the physical property of interest, hydrologic or vegetation \r [...]"


id: 34456ba6289c21e79527ebc34bd3fb11
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 6436
content:
"[...] indicators (Scheff et al., 2017). Droughts are characterized by deficits \r in total soil moisture content that can be caused by a combination \r of decreasing precipitation and warming temperature, which \r promotes greater evapotranspiration. Regional-scale attribution \r of the prevalence of south-western North America drought since \r 1980 then mostly focuses on the attribution of change in these \r two variables."


id: 642c6051c3dc8e719c702acf2d04ec54
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14909
content:
"It has also been suggested that the ocean-controlled influence \r is limited and internal atmospheric variability has to be invoked \r to fully explain the observed history of drought on decadal time \r scales (Seager and Hoerling, 2014; Seager and Ting, 2017). From \r roughly 1980 to the present, the regional climate signals show an \r interesting mix between forced and internal variability. Lehner et al. \r (2018) used a dynamical adjustment method and large ensembles \r of coupled and SST-forced atmospheric experiments to suggest \r that the observed south-western North America rainfall decline \r mainly results from the effects of atmospheric internal variability, \r which is in part driven by a PDV-related phase shift in Pacific SST \r around 2000 (Figure 10.13b,c). Based upon four SMILEs (three using \r a  GCM and another one an AGCM constrained by observed SSTs) \r and a  CMIP6 multi-model suite constrained by observed external \r [...]"


id: df28be74e17ebe830ec48de1620e8388
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36516
content:
"[...] forcings, Figure 10.13 shows, in agreement with Lehner et al. (2018), \r that observed SSTs with their associated atmospheric response are \r the main drivers of the south-western North America precipitation \r decrease during the 1983-2014 period. Once aspects of the internal \r variability are removed by dynamical adjustment, the observed \r precipitation change signal and simulated anthropogenically-forced \r components look more similar (Lehner et al., 2018)."


id: 51bfe0d432c32f06c9fd4519a4fef1a2
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 11668
content:
"The observed south-western North America drying fits the narrative \r of what might happen in response to increasing GHG concentrations \r due to a  poleward expansion of the subtropics, that is conducive \r to drying trends over subtropical to mid-latitude regions (Hu et al., \r 2013b; Birner et  al., 2014; Lucas et  al., 2014). However, several \r studies based on modern reanalyses and CMIP5 models have \r recently shown that the current contribution of GHGs to Northern \r Hemisphere tropical expansion is much smaller than in the Southern \r Hemisphere and will remain difficult to detect due to large internal \r variability, even by the end of the 21st  century (Section  3.3.3.1; \r Garfinkel et al., 2015; Allen and Kovilakam, 2017; Grise et al., 2018, \r 2019). In addition, the widening of the Northern Hemisphere tropical \r belt exhibits strong seasonality and zonal asymmetry, particularly \r in autumn and the North Atlantic (Amaya et al., 2018; Grise et al., \r [...]"


id: 938fbc67a9e2ab42b93f191026f5d879
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 23165
content:
"[...] 2018). Therefore, it seems that the recent Northern Hemisphere \r tropical expansion results from the interplay of internal and forced \r modes of tropical width variations and that the forced response \r has not robustly emerged from internal variability (Sections 3.3.3.1 \r and 10.4.3)."


id: 3507f5dfd8322efe53f1d07f1c03438d
created_at: 2023-04-19 13:22:41
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vector_id: 6576
content:
"variables and space-time co-variance are fully preserved in the \r time-shift approach, which is not the case for pattern-scaling (Herger \r et  al., 2015). Importantly, pattern scaling cannot account for the \r non-linearity arising from either interacting quasi-linear processes \r (Chadwick and Good, 2013) and purely non-linear mechanisms, \r which have been shown to be present in CMIP5 models for high \r GWL (4degC) and affect precipitation more than temperature at the \r regional-scale (Section 8.5.3.1; Good et al., 2015, 2016). The time-shift \r approach can also be used to test whether regional climate change \r patterns depend on the rate of global mean warming and external \r forcing pathways, in addition to global warming magnitude. A global \r evaluation of both approaches in projecting the forced temperature \r and precipitation response for a highly mitigated scenario based on \r a moderately mitigated one has been performed using a perfect-model \r [...]"


id: 2f10da053a91800159f62f40c666191d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5552
content:
"[...] framework (Tebaldi and Knutti, 2018). The amplitude of errors for \r both approaches appears to be substantially smaller than model \r uncertainty approximated by the CMIP5 multi-model spread."


id: de7eafc466db2c6b94f018710df30bba
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36411
content:
"Based on large and coordinated modelling exercises such as CMIP5 \r and CORDEX, the time-shift approach has been largely used to \r assess differences in regional climate impacts for different GWLs, \r with a  strong focus on 1.5degC versus 2degC (Karmalkar and Bradley, \r 2017; Dosio and Fischer, 2018; Karnauskas et al., 2018; W. Liu et al., \r 2018; Taylor et  al., 2018; Weber et  al., 2018; Chapter  3, SR1.5, \r Hoegh-Guldberg et al., 2018). Comparisons between pattern-scaling \r and time-shift approaches allow assessment of the scalability \r of the regional climate change signal and the extent to which \r pattern-scaling assumptions still hold at regional scale for a  wide \r range of GWL. This was the approach followed by Matte et al. (2019) \r in their assessment of the scalability of European regional climate \r projections. Based on EURO-CORDEX projections, they performed \r a  detailed comparison between the pattern scaling and the GWL \r [...]"


id: 4f7366a1a71a360586f0ea2a5724903c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 11262
content:
"[...] spatial patterns (GWL range: 1degC, 2degC and 3degC) for different seasons, \r regional model resolutions, and both temperature and precipitation. \r High pattern correlation values (greater than 0.9) are found between \r the scaled pattern and all GWL patterns for temperature. In the case \r of precipitation, the correspondence is slightly lower, especially in \r summer, for high GWLs (2degC and 3degC) and much lower for 1degC."


id: 6fef8edee6500615486d918c8e7ce0ad
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 16948
content:
"Robust assessments and comparisons of past emergence between \r observations and models are strengthened by the use of consistent \r definitions of signal and noise (Lehner et al., 2017a; Hawkins et al., \r 2020). In the case of future emergence under increasing greenhouse \r gas emissions, two main approaches have been followed to assess \r emergence. The first is based on estimating the signal and noise (and \r sometimes the signal-to-noise ratio as well) in individual models \r before using the resulting distribution median or mean to construct \r the final emergence metric (Hawkins and Sutton, 2012; Maraun, \r 2013b; Sui et  al., 2014; Barrow and Sauchyn, 2019). The second \r method first estimates the signal as a  multi-model mean change \r and the noise variance as a combination of internal variability and \r model structural differences (Giorgi and Bi, 2009; Mariotti et  al., \r 2015; Nguyen et al., 2018). The first approach allows the definition \r [...]"


id: 4cb861e9354f263ca82ca45d8595cb81
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 10762
content:
"[...] of emergence of the signal relative to internal variability only and \r treats model error as source of uncertainty (Maraun, 2013b; Lehner \r et al., 2017a). The second assumes that the multi-model mean is the \r optimal estimate of the signal and confounds internal variability and \r model structural differences in the noise estimate. It is noteworthy \r that most emergence studies implicitly assume model independence \r (Annan and Hargreaves, 2017; Boe, 2018; Box  4.1) and therefore \r sensitivity of emergence results to model selection or weighting is \r rarely performed (Akhter et al., 2018)."


id: 60f90a91da5adad45ab6436c8a73379b
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"Precise definitions of signal and noise as well as a  metric to \r measure the relative importance of the signal are key ingredients \r of the emergence framework and depend on the framing question. \r In particular, emergence study results can depend on the specific \r definitions of signal and noise such as the level of spatial and \r temporal aggregation (McSweeney and Jones, 2013). For instance, \r grid-point scale emergence will likely be delayed compared with \r region-average emergence (Section  11.2.4 and Cross-Chapter \r Box Atlas.1, Figure 2; Fischer et al., 2013; Maraun, 2013b; Lehner et al., \r 2017a). The signal is often estimated by a running mean multi-decadal \r average or probability distribution function of the physical variable \r under scrutiny in order to avoid false emergence due to manifestation \r of multi-decadal internal variability (King et al., 2015). In the case of \r extremes such as climate records, a notion of multi-year persistence \r [...]"


id: 9a23fb1beda752e9c60c414a14d32381
created_at: 2023-04-19 13:22:41
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"[...] or recurrence can also be used to fully characterize the anthropogenic \r signal and its emergence (Christiansen, 2013; Bador et al., 2016)."


id: 57c18c41fbe5966893eb62fddc772a46
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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"This section assesses approaches and challenges for producing \r climate information for climate risk assessments as well as for \r adaptation and policy decisions at regional scales (Section 10.1.2.1). \r An overview of the different sources used for developing regional \r climate information is given in Section 10.5.1. The role of the user \r context in the construction of climate information is assessed in \r Section 10.5.2. The distillation to combine multiple lines of evidence \r is assessed in Section 10.5.3. Finally, climate services in the context \r of regional climate information are assessed in Section 10.5.4. The \r role of storylines in constructing climate information is assessed in \r Box  10.2. The assessment of how regional climate information is \r distilled in the report is treated in Cross-Chapter Box 10.3, whereas \r the assessment of information on regional, physical climate processes \r that impact society or ecosystems, termed climatic impact-drivers \r [...]"


id: 3356f81d8f0a45d017293e18177c82b4
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"[...] (Section 10.1), appears in Chapter 12, as well as more information \r on climate services in Cross-Chapter Box 12.2."


id: ea5c4679b585998640abc89204ac9394
created_at: 2023-04-19 13:22:41
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"We now illustrate the potential influence of internal variability on \r late or lack of emergence for a few AR6 land regions (Figure 10.15b). \r For each of these AR6 regions, the time evolution of the percentage \r area with robust annual mean precipitation change is estimated \r for both the CMIP6 multi-model ensemble and the three coupled \r SMILEs used throughout Chapter 10. Similarity in percentage area \r time evolution between CMIP6 and the three coupled SMILEs \r suggests that internal variability can substantially influence the \r timing of emergence. For example, internal variability could explain \r the mid-21st  century emergence (percentage area greater than \r 50%) of the drying and wetting signal over the Mediterranean and \r South Asia (see also Section  10.6.3) regions, respectively. Internal \r variability can also contribute to the late and moderate emergence \r over South-Eastern South America (see also Section 10.4.2) and West \r [...]"


id: a9e22b2a3591e1756facb20f7bd7447e
created_at: 2023-04-19 13:22:41
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content:
"[...] South Africa (see also Section 10.6.2). In contrast, it cannot explain \r the lack of robust changes (percentage area less than 30%) over \r Western Africa at the end of the 21st century, suggesting that model \r differences are also contributing to the lack of emergence (Monerie \r et al., 2017a, b). In addition to different forced signals, the differences \r of time evolution between the three SMILEs, in particular for African \r regions, point to the issue of global model performance in accurately \r representing internal variability and its future changes. While \r overestimation and underestimation of internal variability in current \r models have been reported (Eade et al., 2014; Laepple and Huybers, \r 2014), methodological challenges to assess the magnitude and \r spatial pattern of model biases in simulating internal variability, still \r remain (Section 10.3.4.3). Therefore, the existence of model biases \r and the limited knowledge of their characteristics lead to limitations \r [...]"


id: dc987092880df3107aef319002255234
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 36077
content:
"[...] about a  precise quantification of internal variability influence on \r delayed regional-scale emergence."


id: ee300b550fcaf0b6d257e8be65db74a6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39275
content:
"The rise in demand for relevant regional climate information \r (Hewitt et  al., 2012, 2020; Lourenco et  al., 2016) has resulted in \r diverse approaches to produce it. Historically, the construction of \r climate information has been embedded in a  linear supply chain: \r extracting the source data, processing into maps or derived data \r products, preparing the material for communication, and delivering \r to users (Section  10.1.4). Typical products are open-access, \r web-portal delivery services of data (Hewitson et al., 2017), which \r may also be implemented as commercialized climate services \r (Webber and  Donner, 2017). Such a  chain, although it is intended \r to meet a demand for regional climate information, contains many \r assumptions that are not obvious to the recipients and that may \r introduce possible misunderstandings in the handover from one \r community to the next (Meinke et al., 2006; Lemos et al., 2012). In \r [...]"


id: b037010739cd89fd7d470705ad810baf
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] recognition that data is not necessarily relevant information, a new \r pathway towards a  tailored distillation of climate information has \r emerged. The construction of information assessed in this section \r draws on multiple sources (Figure  10.16), whereby the context \r framing for an application is addressed through co-design with \r users. The constructed information is then translated into the context \r of the user taking into account the values of all actors involved \r (Sections 10.5.2 and 10.5.3, and Figure 10.1)."


id: b4a9676b67d330d88516f29af562e6d5
created_at: 2023-04-19 13:22:41
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content:
"Figure  10.16 | Illustration of how using different sources can result in different and potentially conflicting information. Change in daily precipitation \r (2071-2100 RCP8.5 relative to 1981-2010) over Western Africa as simulated by an ensemble of regional climate models (RCMs) driven by global climate models (GCMs). \r (a) Change in daily precipitation (mm) for April to September, as mean of 17 CORDEX models (Dosio et al., 2020) (b-e) Time-latitude diagram of daily precipitation change for \r four selected RCM-GCM combinations. For each month and latitude, model results are zonally averaged between 10degW-10degE (blue box in a). Different GCM-RCM combinations \r can produce substantially different and contrasting results, when the same RCM is used to downscale different GCMs (b, d), or the same GCM is downscaled by different \r RCMs (d, e). GCM1=IPSL-IPSL-CM5A, GCM2=ICHEC-EC-EARTH, RCM1=RCA4, RCM2=REMO2009. Adapted from Dosio et al. (2020), CCBY4.0 https://creativecommons.org/\r [...]"


id: ed9dbd016fd2926b837e51e28d8da378
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updated_at: 2023-04-19 13:22:41
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content:
"[...] licenses/by/4.0/.  Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: 7c2ab58a1f74edeb4fc13bc21f81fee4
created_at: 2023-04-19 13:22:41
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content:
"Different sources of information may be more appropriate for \r some purposes than others, as they may provide information better \r aligned to the spatial and temporal scales of interest, in different \r formats, and tailored to different types of application. In some cases, \r a  purpose may be best served using several types of information \r together. For example, when model data is the primary source, it can \r be advantageous to employ data from multiple models or even from \r a range of different experiment types (Section 10.3.2) supported by \r assessing how the models reflect changes in driving processes. In this \r manner a purpose may be best served by seeking the congruence of \r several types of information together, though one needs to recognize \r how well the attributes of each source align with the specific need \r for information. Depending on resources, one may even design model \r experiments specifically for a given use, such as constructing physical \r [...]"


id: 21167c25c0d095aa1baf2d8e6fab2584
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 3042
content:
"[...] climate storylines of individual events (Section 10.3.2 and Box 10.2). \r Such analyses may be complemented by event attribution studies \r (Section 11.1.4)."


id: a533b13cb4e50618a5406552d9007b9
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"The importance of a  co-production process does not preclude the \r climate-research community from taking steps to develop and convey \r relevant information on its own. Indeed, communicating expert \r consensus about contested scientific issues is beneficial (Goldberg \r et  al., 2019). Climate services (Section  10.5.4), in particular, can \r become an effective means for using sources from the climate \r community and crafting these to be consistent with the needs, \r interests and values of stakeholder communities. However, simply \r presenting more information without recognizing user values and \r the contextual elements listed in Section 10.5.2.1 may be ineffective \r (Kahan, 2013). An aversion to climate information discordant with \r one's pre-existing beliefs can actually become stronger for people \r who are more scientifically literate: they feel more confident sifting \r through all sources of information to find support for their positions \r [...]"


id: 46f4b0a0483a8f442fbf2da186357d53
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
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content:
"[...] (Kahan, 2012). A  challenge is that if climate information is not \r framed carefully, recognizing context and user values, it may make \r the sceptical person less receptive to further information about \r climate change (Corner et al., 2012; Hart and Nisbet, 2012; Shalev, \r 2015). A  further complication is that audiences may view climate \r change as a problem distant in time and space (Spence et al., 2012), \r too threatening to acknowledge (Brugger et  al., 2015; McDonald \r et  al.,  2015), or too economically challenging to accept (Bessette \r et al., 2017). Identifying positive outcomes that align with user values, \r instead of adaptation and mitigation efforts, appears to promote the \r interest in and the success of climate information (Bain et al., 2012)."


id: f7b2fff2d0a180329d86ae18272f2545
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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"relevant for decisions at the regional scale (Section  10.5.2; Kruk \r et  al., 2017; Vizy and Cook, 2017; Djenontin and Meadow, 2018; \r Parker and Lusk, 2019; Norstrom et al., 2020; Turnhout et al., 2020). \r As a response, regional climate change information is increasingly \r being developed through participatory and context-specific dialogues \r that bring together producers and users across disciplines and define \r climate impacts as one of the many stressors shaping user decisions \r (Brown and Wilby, 2012; Lemos et  al., 2012). Although there are \r multiple practical issues involving communication (Rossler et  al., \r 2019a), such as providing data in a format that users can interpret, \r being mindful of the contextual issues raised in Section  10.5.2 \r allows non-scientists to be involved in decisions about approaches \r and assumptions for the distillation and thus to take ownership of \r the resultant information and to make informed decisions based on \r [...]"


id: 8fa64b7a9b5f2b3aa0e62aff1b6a73a0
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] the distilled information (Pettenger, 2016; Verrax, 2017). Importantly, \r the application of transdisciplinary engagement processes that \r emphasize the role of non-scientists in the learning and knowledge \r production process builds relationships and trust between information \r users and producers, which is arguably as important for the uptake \r of climate science into decision-making as the nature of the climate \r information itself (Section 10.5.2)."


id: 2f288121948801d4c323e572afc33953
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 5567
content:
"Nevertheless, the scale of regional climate information does not \r have to be the same as the decision scale. Physical-climate storylines \r (Box 10.2) valid at large scales can be used to develop understanding \r that is relevant to local decisions. For example, global climate change \r affecting Antarctic ice-mass loss is relevant to formulating Dutch \r responses to sea level rise (Haasnoot et  al., 2020). On the other \r hand, extreme precipitation processes can occur on scales of tens \r of kilometres and smaller and thus require high resolution climate \r information when projecting future changes (e.g., Xie et al., 2015). \r An important factor for developing effective climate information \r using the distillation process is aligning the vulnerabilities of the \r social and economic systems under consideration ranging from, for \r example, those important to a farmer to those important to a national \r agricultural ministry (Andreassen et al., 2018; O'Higgins et al., 2019). \r [...]"


id: c81079cb7bf6a6aac19fd29507d7fd6f
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content:
"[...] Thus, more sophisticated matching of spatial and temporal resolution \r of climate information with decision scales requires engagement \r across a hierarchy of governance structures at national, regional and \r local level (e.g., Lagabrielle et al., 2018)."


id: b730b122e8ff698eaceaae4abdb8cdd3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 29440
content:
"The preceding sections laid out the diversity of sources of climate \r information (Section  10.5.1) and important elements for its use \r in a  decision context (Section  10.5.2). Here, it is assessed how \r context-relevant climate information can be distilled from these \r sources of information. Although the term distillation lacks a clear \r definition in the literature, it has, in principle, two aspects: the \r construction of (potentially user-targeted) information that is \r defensible and evidence-based (Giorgi, 2020), and the translation \r of this information into a  specific context, targeting a  specific \r purpose and set of values. The former typically involves data from \r multiple sources, including expert knowledge, and comprehensively \r considers relevant uncertainties to give physically plausible climate \r information. The latter translates the information explicitly into the \r user context, such as by linking it to experience, by formulating \r [...]"


id: 393f42761a590f3df727dd096465d562
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 7283
content:
"[...] a  narrative, by highlighting the relevance for the user context, or \r by putting the climate information into the context of the relevant \r non-climatic stressors."


id: 7b809616e4c6e5f32fa2ab253405b00d
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 18933
content:
"The transdisciplinary interaction with stakeholders has been \r categorized into top-down, bottom-up and interactive approaches \r (Berkhout et al., 2013). Traditional top-down approaches frame the \r research from the perspective of global climate change as a driver \r of regional climate risk. Bottom-up approaches, also referred to as \r scenario-neutral impact studies (Prudhomme et al., 2010; A. Brown \r et al., 2012; C. Brown et al., 2012; Culley et al., 2016) begin with \r the user's articulation of vulnerability in the context of climatic \r and non-climatic stressors, follow with the definition of key system \r thresholds of climatic variables, and only incorporate climate data \r to assess the likelihood of threshold exceedances. Bottom-up \r approaches are special cases of robust decision-making (Lempert \r et al., 2006; Lempert and Collins, 2007; Walker et al., 2013; Weaver \r et  al., 2013), which are designed to account for uncertainties not \r [...]"


id: e7bf3a2b85a290e69bba9e567898473b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] represented by climate models as well as non-climatic stressors. \r Interactive approaches combine aspects of top-down and bottom-up \r approaches. The choice of approach depends on the context. While \r bottom-up approaches might be optimal in a local context, where \r case-specific risks are addressed, top-down approaches provide \r generic information that may serve a  range of different purposes, \r for example, at the national scale (Berkhout et al., 2013). All these \r approaches benefit from the integration of fully distilled climate \r information (Berkhout et al., 2013; Maraun and Widmann, 2018b)."


id: cdcd9e152dd61bf000a2f6d1910970e1
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index: climate_gpt_v2
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content:
"Awareness and understanding of the users' decision-making context \r is a central and key aspect of developing tailored, context-appropriate \r information (Briley et al., 2015), as clearly evidenced by the climate \r services' experiences (e.g.,  Vincent et  al., 2018). Understanding \r the context, however, is not trivial and requires understanding of \r both the user and provider (Guido et  al., 2020) if the information \r is to be robust, reliable and relevant (Giorgi, 2020). Translating the \r information into context requires consideration of terminology and \r expectations (Briley et al., 2015), issues of user interpretation (Daron \r et al., 2015), and hence necessitating engagement in co-production \r with all attendant challenges (Vincent et  al., 2021). The actual \r provision of climate information may be conducted at different levels \r of sophistication, ranging from generic data provision via web portals \r (Hewitson et al., 2017), potentially including impact-relevant climate \r [...]"


id: 342ede7010cdda17d844ed3bace34d58
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index: climate_gpt_v2
vector_id: 6418
content:
"[...] indicators, region-specific factsheets and stakeholder reports, social \r media (Pearce et  al., 2019), to a  close engagement with specific \r stakeholders in co-exploring the research (Steynor et al., 2016)."


id: 18874c65d84319f453de498956223585
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 1525
content:
"Climate services have been defined as the provision of climate \r information to assist decision-making (Sections 1.2.3, and 12.6, \r and Cross-Chapter Box 12.2). Services are expected to be based on \r scientifically credible information and expertise, have appropriate \r engagement from users and providers, have an effective access \r mechanism and aim at meeting the users' needs (Hewitt et  al., \r 2020). To achieve this, climate services synthesize context-relevant \r climate information addressing questions for a wide range of climate \r time scales. From this point of view, climate services are instruments \r for the production, translation and transfer of climate information \r and knowledge for their use in climate-informed decision-making \r and climate-smart policy and planning (Hewitt et  al., 2012). The \r appropriate provision of climate services considers the diagnosis of \r climate information needs, the service itself and a number of good \r [...]"


id: 7fa6b4d8cf21e820d7cef5313650eb6a
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updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"There is high confidence that distilling climate information for \r a  specific purpose benefits from a  co-production process that \r involves users of the information, considers the specific user context \r and the values of relevant actors such as users and scientists, and \r translates the resultant information into the broader user context. \r This process allows users to take ownership of the information, builds \r relationships and trust between information users and producers \r and helps to overcome barriers in the information construction. This \r process enhances trust in the information as well its usefulness, \r relevance, and uptake, especially when the communication involves \r complex, contextual details (high confidence). The optimal approach \r for the transdisciplinary collaboration with users depends on the \r specific context conditioned by the sources available and the actors \r involved, which together are dependent on the regions considered \r [...]"


id: ebfebf75ee0f722f0a4728f015da93bc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 38887
content:
"Physical climate storylines are self-consistent and plausible unfolding of a physical trajectory of the climate system, or a weather or \r climate event, on time scales from hours to multiple decades (Section 1.4.4.2). Storylines that condition climatic features and processes \r on a set of plausible but distinct large-scale climatic changes enables the exploration of uncertainties in regional climate projections \r (Box 10.2, Figure 1 and Section 10.3.4.2). For instance, Zappa and Shepherd (2017) condition projected changes in European surface \r wind speeds on different plausible projections of tropical upper tropospheric warming and the polar vortex strength in the CMIP5 \r multi-model ensemble. Storylines of specific events are generated to explore the unfolding and impacts of comparable events in \r counterfactual climates (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Hegdahl et al., 2020; Sillmann et al., 2021). Those \r [...]"


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updated_at: 2023-04-19 13:22:41
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vector_id: 28839
content:
"[...] event storylines can be based on pseudo-global warming studies (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; see \r Section 10.3.2.2), selected and possibly downscaled events from long-term climate projections (Hegdahl et al., 2020; Huang et al., 2020a),"


id: fdad022ed99aa794583b5f4d8a7cc2c9
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index: climate_gpt_v2
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content:
"Storylines can also be used to communicate climate information by narrative elements describing the main climatological features and \r the relevant consequences in the user context (Flottum and Gjerstad, 2017; Moezzi et al., 2017; Dessai et al., 2018; Scott et al., 2018; \r Jack et al., 2020). Co-produced narratives have been demonstrated to enhance knowledge integration in decision-making contexts \r (e.g., de Bruijn et al., 2016). Narrative elements have also been employed to convey information from climate models (Corballis, 2019). \r Jack et al. (2020) introduced the concept of climate risk narratives and developed a set of principles, such as using present tense in \r their presentation to avoid the effects of future discounting and writing individual narratives without uncertainty language to assume \r an imagined observer perspective. From this point of view, event storylines are particularly useful for communication purposes as they \r [...]"


id: ece60c255b76668c1c22493c208234ad
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 39049
content:
"[...] link to the experience and episodic memory of stakeholders (Schacter et al., 2007; Steynor et al., 2016; Shepherd et al., 2018)."


id: 973820edea9ebe03dfdbaf92db2e1e49
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 23798
content:
"Box 10.2, Figure 1 | Schematic of two types of physical climate storylines with a particular climate impact of concern (red). The storylines are \r defined by specified elements (dark blue). Variable elements (light blue) are simulated conditional on the specified elements. The white elements are 'blocked' since \r their state does not need to be known to determine the light blue elements. Other types of storylines could be defined by specifying other elements (e.g., storylines \r of different climate sensitivities or different representative concentration pathways). (a) Event storyline, where the particular dynamical conditions during the event \r as well as the regional warming are specified and control the hazard arising from the event. (b) Dynamical storyline, where the global warming level and remote \r drivers are specified and control the long-term changes in atmospheric dynamics and regional warming. In both storylines, the impact is also conditioned on specified \r [...]"


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created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 13326
content:
"Multiple, sometimes contrasting, lines of evidence are derived from the various data sources, methodologies and approaches \r that can be used to construct climate information (Section 10.5 and Figure 10.1). Such data sources and methodologies include \r theoretical understanding of relevant processes, drivers and feedbacks of climate at regional scale, observed data from multiple \r datasets (e.g., ground station networks, satellite products, reanalysis, etc.), simulations from different model types (including general \r circulation models (GCMs), regional climate models (RCMs), statistical downscaling methods, etc.) and experiments (e.g., Coupled \r Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6), Coordinated Regional Climate Downscaling Experiment (CORDEX), and \r single-model initial-condition large ensembles), methodologies to attribute observed changes or events to large- and regional-scale \r [...]"


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content:
"[...] anthropogenic and natural drivers and forcings as well as other relevant local knowledge (e.g., indigenous knowledge)."


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


id: d1e3d98cc4778360adeb85a121059cf1
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] assessments are usually based on models and observations for which the fitness-for-purpose is assessed with similar criteria as \r those described above (Cross-Working Group Box: Attribution in Chapter 1). The assessment is made either directly or indirectly by \r scrutinizing the data and methods of the relevant literature against the criteria listed above."


id: 531e587afe2c2be78125b6e7708090d2
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
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vector_id: 11892
content:
"This section presents three comprehensive examples of steps for \r distilling regional climate information from the multiple sources \r of regional climate information presented in this chapter. These \r examples build on the general framework presented in Section 10.5, \r examining in particular the strengths and challenges in linking the \r different sources, while also exposing the assumptions behind and \r consequences of decisions made in the process. The examples are \r framed taking into account societal perspectives that provide context \r for their regional climate statements. Although the nature of an \r IPCC Working Group I assessment precludes engaging with users of \r climate information (Section 10.5), we do cite relevant national and \r regional reports that give user perspectives to set a foundation from \r which one could distil climate information for users. We have chosen \r the recent Cape Town drought, Indian summer-monsoon trends \r [...]"


id: f7cbf1bb1205d82058ece47a5b504f48
created_at: 2023-04-19 13:22:41
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vector_id: 40970
content:
"[...] and the Mediterranean summer warming because they provide \r a geographically diverse set of locations and relevant processes and "


id: 5961b22fa19f79ad7c6f39a4dbcf4c55
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"Cape Town's crisis resulted from a  combination of a  strong, rare \r multi-year meteorological drought (Figure  10.18), estimated at 1 in \r 300 years (Wolski, 2018), and factors related to the nature of the water \r supply system, operational water management and water resource \r policies. Cape Town was very successful in implementing water-saving \r actions after the previous drought of 2000-2003, reducing water losses \r from over 22% to 15% (Frame and Killick, 2007; DWA, 2013), breaking \r the previous coupling of growth in water demand with growth in \r population. As a  consequence, Cape Town won a Water Smart City \r award from the C40 Cities program only three years prior to the crisis. \r However, the water-saving actions, together with changing priorities \r in water resource provision from infrastructure-oriented towards \r resource and demand management, may well have led to delays \r in implementation of the expansion of water supply infrastructure \r [...]"


id: 2e266d61be7921c136afe02df541705b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 5394
content:
"[...] (Muller, 2018). The expansion plan, formulated a decade prior to the \r crisis, included an expectation of long-term climate-change drying in \r the region (DWAF, 2007). The crisis also exposed structural deficiencies \r of water management and inadequacy of a policy process in which \r decisions about local water resources are taken at a  national level, \r particularly in a situation of political tension (Visser, 2018). The crisis \r was widely seen as a harbinger of future problems to be faced by the \r city, and a highlight of vulnerability of many cities in the world resulting \r from the interplay of three factors: (i) the fast urban-population growth, \r (ii) the economic, policy, infrastructural and water resource paradigms \r and constraints, and (iii) anthropogenic climate change."


id: c99157fb3ff52a6d6311450f56dd6a47
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"Cape Town's 'Day Zero' water crisis in 2018 threatened a shut-down \r of water supply to 3.4 million inhabitants of the city and resulted in \r domestic water use restriction of 50 litres per person per day lasting for \r nine months (pre-drought unconstrained water use was about 170 litres \r per person per day, DWA, 2013), punitive water tariffs, and temporary \r closure of irrigation systems. Problems with water supply in many \r large cities in developing countries are endemic and rarely reported \r internationally. The water crisis in Cape Town attracted considerable \r international attention to a city with functional government structures, \r well-developed services (compared to other urban centres in Africa), \r a centre of international tourism, and an economic hub with GDP of \r 22 billion USD (about 7,500 USD per capita, Gallie et al., 2018). Economic \r and social impacts of the crisis were significant. Loss of revenue for \r [...]"


id: 54bf997be4fd689ce859963a0d956dec
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 12180
content:
"[...] companies of all sizes resulted not only from the scaling down of water\u0002dependent activities, but also from the need to invest in water-efficient \r technologies and processes. Tourism was affected through reduced \r arrivals and  bookings, although only temporarily (CTT, 2018). In the \r agricultural sector, 30,000 people were laid-off and production dropped \r by 20% (Piennaar and Boonzaaier, 2018). The crisis initially polarized \r society, with conflict emerging between various water users and erosion \r of trust in the government, but eventually social cohesion and an acute \r awareness of limited water resources emerged (Robins, 2019)."


id: 8624d19b1329aa5ee3835372d2d6c2f3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"Figure 10.18 | Historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region. (a) Yearly accumulation of rainfall (in mm) \r obtained by summing monthly totals between January and December, with the drought years 2015 (orange), 2016 (red), and 2017 (purple) highlighted in colour. (b) Monthly \r rainfall for the drought years (in colour) compared with the 1981-2014 climatology (grey line). Rainfall in (a) and (b) is the average of 20 quality controlled and gap-filled \r series from stations within the Cape Town region (31degS-35degS, 18degW-20.5degW). (c) Time series of the SAM index and of historical and projected rainfall anomalies (%, baseline \r 1980-2010) over the Cape Town region. Observed data presented as 30-year running means of relative total annual rainfall over the Cape Town region for station-based \r [...]"


id: 92251d91a4e9a9c75b15da41a12ad0d8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] data (black line, average of 20 stations as in (a) and (b), and gridded data (average of all gridcells falling within 31degS-35degS, 18degW-20.5degW), GPCC (green line) and CRU TS \r (olive line). Model ensemble results presented as the 90th-percentile range of relative 30-year running means of rainfall and the SAM index from 35 CMIP5 (blue shading) and \r 35 CMIP6 (red shading) simulations, 6 CORDEX simulations driven by 1 to 10 GCMs (cyan shading), 6 CCAM (purple shading) simulations from individual ensemble members, \r and 50 members from the MIROC6 SMILE simulations (orange shading). The light blue, dark red and yellow lines correspond to NCEP/NCAR, ERA20C and 20CR, respectively. \r The SAM index is calculated from sea level pressure reanalysis and GCM data as per Gong and Wang (1999) and averaged over the aforementioned bounding box. CMIP5, \r [...]"


id: 9f72882e7285b95c1974c2fc3c8ba74f
created_at: 2023-04-19 13:22:41
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vector_id: 25269
content:
"[...] CORDEX and CCAM projections use RCP8.5, and CMIP6 and MIROC6 SMILE projections use SSP5-8.5. (d) Historical and projected trends in rainfall over the Cape Town region \r and in the SAM index. Observations and gridded data processed as in (c). Trends calculated as Theil-Sen trend with block-bootstrap confidence interval estimate. Markers show \r median trend, bars 95% confidence interval. Global models in each CMIP group were ordered according to the magnitude of trend in rainfall, and the same order is maintained \r in panels showing trends in the SAM. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: 3a9dcfdf0e59bbd4863fa22e7172595f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 7516
content:
"The Cape Town regional rainfall is also potentially linked to other \r hemispheric phenomena, such as the expansion of the tropics and, \r specifically, the South Atlantic high-pressure system and the position \r of the subtropical jet, which share some variability with the SAM. \r The relationships between these phenomena and Cape Town rainfall \r have not been thoroughly investigated outside of the context of \r the 2015-2017 drought, but the drought itself was associated \r with poleward expansion of the subtropical anticyclones in the \r South Atlantic and South Indian oceans and (a resulting) poleward \r displacement of the moisture corridor across the South Atlantic \r (Sousa et al., 2018a), as well as a weaker subtropical jet (Mahlalela \r et al., 2019). Burls et al. (2019) also link the decline in the number of \r rainy days to the increase in sea level pressure along the poleward \r flank of the South Atlantic high-pressure system and the intensity \r [...]"


id: 907877663c6f5d52f6e43d1f480157f
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 22621
content:
"[...] of the post-frontal ridging high. Additionally, there is a  possible \r linkage between Cape Town rainfall and near-shore cold sea surface \r temperature (SST) anomalies arising from Ekman upwelling due to \r reduced westerly and increased south-easterly winds. These might \r lead to suppression of convection and reduction of rainfall over \r land (Rouault et  al., 2010). All these phenomena are conceptually \r consistent with the poleward migration of the westerlies and \r expansion of the tropics."


id: 7f0fd36a4fe45f08fd2865a3e5747887
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 19504
content:
"Because the primary rainfall mechanism is frontal rain, the most \r relevant large-scale drivers are those that affect cyclogenesis, \r frontogenesis and the mid-latitude westerlies' latitudinal position \r and moisture supply. These drivers and, thus, the region's rainfall \r are linked to the Antarctic Oscillation (AAO; Reason and Rouault, \r 2005) or Southern Annular Mode (SAM), the dominant monthly and \r interannual mode of Southern Hemisphere atmospheric variability, \r and a  measure of the pressure gradient between mid- and high \r latitudes. (See Sections 3.3, 3.7, 4.3 and Annex IV.2.2 for more general \r discussion of the SAM.) While in the post-1930 period, the SAM \r displays a long-term positive trend, the Cape Town region's rainfall \r does not, and only the post-1979 trends of rainfall and SAM  are \r conceptually consistent. For example, a  positive trend in the SAM \r is associated with a negative trend in rainfall (Section 10.6.2.5 and \r [...]"


id: 9c3318dc09d967467721503d1c484ddc
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 24737
content:
"[...] Figure 10.18). There is also good agreement between the seasonality \r of the SAM and rainfall trends in the post-1979 period: a drying trend \r appears strongly in December to February and March to May, but not \r in June to August and September to November (Wolski et al., 2021), \r and trends in the SAM have similar seasonal dependence (E.-P. Lim \r et al., 2016; Section 3.7.2). Additionally, there is a similar seasonal \r pattern in the post-1979 trends in indices capturing the southern \r edge of the Hadley circulation (Grise et al., 2018)."


id: d4e2bd06928b4b325be5e85d537354e8
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 34768
content:
"simulations (Figure  10.18; Almazroui et  al., 2020c). The consistency \r across the models is a robust signal compared to the rest of southern \r Africa, where the climate change signal varies spatially: stronger drying \r in the west and moderate drying or weak wetting in the east (DEA, \r 2013, 2018; see Atlas.4.4 for further discussion of southern Africa \r precipitation projections). Rainfall changes projected for the Cape \r Town region are consistent with projected changes in hemispheric\u0002scale processes and regional-scale dynamics that point toward reduced \r frequency of frontal systems affecting that  region. These changes \r include robust signals in CMIP5 models for the Southern Hemisphere \r for a poleward expansion of the tropics (Hu et al., 2013b), poleward \r displacement of mid-latitude storm tracks (Chang et al., 2012), increased \r strength and poleward shift of the westerly winds (Bracegirdle et al., \r 2018) and subtropical jet-streams (Chenoli et  al., 2017), and a  shift \r [...]"


id: afddba90c955742986865cec82d4c897
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 28180
content:
"[...] toward a  more positive phase of the SAM (E.-P. Lim et  al., 2016). \r However, despite the consistency in circulation changes, the emergence \r of anthropogenic rainfall change above unforced variability in West \r Southern Africa remains uncertain for annual rainfall throughout most \r of the 21st century, even under SSP5-8.5 (Figure 10.15)."


id: 6e68d4dc2c4ca3b1e2e67c829656c62a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 16689
content:
"Paleoclimate studies reveal that long-term variability in the winter \r rainfall region of South Africa (including Cape Town) is consistent with \r a general framework of warming/cooling-induced latitudinal migration \r of the westerlies and transformation of the subtropical high-pressure \r belt and associated hemispherical processes (see  section  10.2.3.2 \r for assessment of paleoclimate analysis). The synchronicity of winter \r rainfall with Antarctic ice-core-derived polar temperature anomalies is \r consistently revealed in studies using different paleoclimate proxies and \r time scales of 1400 years (Stager et al., 2012), about 3000 years (Hahn \r et al., 2016) and 12,000 years (Weldeab et al., 2013). Changes in rainfall \r regimes at shorter (decadal) time scales appear to reflect influence \r of local processes such as the Agulhas current's interaction with the \r Atlantic, resulting in changes in SST and coastal upwelling, as well as \r [...]"


id: 128bba822edf38f9daf7fd1504514bf6
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 464
content:
"Due to the small scale of the Cape Town region, robust comparison \r of CMIP simulations to observations is difficult. However, in general, \r CMIP5 models capture the seasonality well, such as the dominance \r of austral winter rains, although they overestimate the peak and \r underestimate the shoulder season rainfall (Mahlalela et  al., \r 2019). Trends in rainfall are particularly difficult to assess as they \r are generally weak and depend strongly on the time period and \r dataset adopted for the analyses (Section 10.6.2.3). A multi-method \r attribution study (Otto et al., 2018) estimates the probability of the \r 2015-2017 drought to have increased by a  factor of three since \r pre-industrial times (with a wide 95% confidence interval of 1.5 to 6). \r However, throughout the 20th century, a substantial portion of the \r global models (about 36% of CMIP5 and 44% of CMIP6 models, as \r well as many of the MIROC SMILE members) simulate a statistically \r [...]"


id: 65796c4294dc1786d5eee422eaf1682c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15119
content:
"[...] significant (95% level) decline in total annual rainfall, while there is \r no robust long-term trend in observations (Figure 10.18). Section 10.4 \r offers a more detailed assessment of attribution challenges."


id: 9f45da235e0e31617e28cd81ee3e1905
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 25246
content:
"Dynamical downscaling studies implemented with a stretched-grid \r model (Engelbrecht et al., 2009) revealed a signal compatible with \r the driving CMIP5 ensemble, that is, consistent drying throughout \r the region, amplifying in time, irrespective of the considered \r emissions scenario and the generation of global models (DEA, 2013, \r 2018). A  multi-model CORDEX ensemble indicates a  robust signal \r of reduction of total annual rainfall in the future, although there is \r less agreement on how changes in rainfall occurrence may evolve \r in the region, such as through fewer consecutive rain days or longer \r dry spells (Abiodun et al., 2017; Maure et al., 2018). For the end of \r the century under RCP8.5, Dosio et  al. (2019) also found drying. \r Moreover, in their analysis, the drying is associated with an increase \r in the number of consecutive dry days and a reduction in number of \r rainy days. Their results are consistent with the driving global models \r [...]"


id: de2e38cbb9b454b3ef195329aa5fb018
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 36353
content:
"[...] for all the precipitation indices, and they are robust independent of \r the choice of the regional climate model (RCM) or global model. \r However, collectively, these analyses indicate that uncertainty \r remains in the characteristics of the precipitation decrease."


id: 8c6d869ac873398fd90c2d1ac3932db3
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 21892
content:
"Evidence from paleoclimate records (Sections 8.3.2.4.1) shows high \r confidence in a weakened Indian monsoon during cold epochs of the past \r such as the Younger Dryas (12,800-11,600 years ago) as measured by \r speleothem oxygen isotopes (Kathayat et al., 2016). There is a pressing \r need to understand if the monsoon will change in the future under \r anthropogenic forcing and to quantify such changes. Multiple datasets \r have shown robust negative trends since the 1950s until the turn of the \r century (Bollasina et al., 2011) followed by a recovery (Jin and Wang, \r 2017), yet repeated assessments project the monsoon to increase in \r strength under enhanced GHG forcing (Christensen et al., 2007, 2013; \r Sections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between \r future projections and observed historical trends makes the region an \r ideal choice for an in-depth assessment. The reader is also referred to \r the South Asia (SAS) regional assessment of precipitation extremes \r [...]"


id: e0f94ada9001ba199f294dc39535c2e7
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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vector_id: 36846
content:
"Stronger Northern Hemisphere aerosol emissions cool it relative to \r the Southern Hemisphere, increasing northward energy transport \r at the expense of moisture transport towards India (Bollasina et al., \r 2011). The attribution to anthropogenic aerosols is supported in CMIP5 \r single-forcing experiments, including some testing the sensitivity to \r local and remote emissions (Guo et al., 2015, 2016; Shawki et al., 2018), \r comparing CMIP5 GCMs forced by both aerosol and GHG to GHG \r only (Salzmann et al., 2014) and reducing emissions to pre-industrial \r levels (Takahashi et al., 2018). The large spread between individual \r model realisations of comparable magnitude to the aerosol-induced \r signal suggested to Salzmann et  al. (2014) that internal variability \r may also play a  role over regions such as northern-central India. \r Further uncertainty surrounds the level of radiative forcing. Dittus \r et al. (2020) forced a GCM with historical aerosol emissions scaled \r [...]"


id: 64145738227e60bb9d6f9d7bf2ac379
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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content:
"[...] between 0.2  and 1.5 times their observed values, representing the \r spread in CMIP5 effective radiative forcing. The strongest forcing led \r to around 0.5 mm day-1 less late-20th century Indian monsoon rainfall \r than the weakest (Shonk et  al., 2020). Meanwhile, the uncertainty \r surrounding aerosol-cloud interactions could change the sign of \r long-term precipitation trends (Takahashi et al., 2018)."


id: 39b6de02bf7f6186e2ec7f32c8a562db
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 7365
content:
"before a rainfall recovery (Krishnan et al., 2016). By holding aerosol \r emissions at 2005 levels, lower monsoon rainfall is found throughout \r the 21st  century than in a  standard RCP8.5 scenario (Zhao et  al., \r 2019), suggesting that the timing of the recovery will be partially \r controlled by the rate at which aerosol emissions decline. The spread \r in spatial distribution of aerosol emissions in SSPs may also play \r a role in near-term projections (Samset et al., 2019). Under divergent \r air-quality policies, SSP3 features a  dipole of declining sulphate \r emissions for China but increases over India, leading to suppression \r of GHG-related precipitation increases there (Wilcox et  al., 2020). \r For the near-term future around the mid-21st century, the interplay \r between internal variability and external forcing must be considered \r (Singh and AchutaRao, 2019). Huang et al. (2020a) used two SMILEs \r to show that internal variability related to PDV could potentially \r [...]"


id: 6924a57201bf0bea589d945eca9a474a
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 15765
content:
"[...] overcome the GHG-forced upward trend in Indian monsoon rainfall, \r consistent with assessments of the global monsoon for the near \r term (Section  4.4.1.4). Emergence of the anthropogenic signal for \r South Asian precipitation is shown from the 2050s onwards in CMIP6 \r (Figure 10.15b)."


id: ff43d9266462f07e0af87d06f65343df
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
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vector_id: 42313
content:
"Figure 10.19 (continued): (a) Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05deg subgrid boxes containing at least one gauge) \r in the APHRO-MA 0.5deg daily precipitation dataset for June to September 1956. (b) Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September \r precipitation (mm day-1) compared to CRU TS observations for the 1985-2010 period. (c) Maps of rainfall trends (mm day-1 per decade) in CRU TS observations (1950-2000), \r the CMIP6 MME-mean of SSP5-8.5 future projections for 2015-2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. (d) Low-pass \r filtered time series of June to September precipitation anomalies (%, relative to 1995-2014 baseline) averaged over the central India box shown in panel (b). The averaging \r [...]"


id: 4dc2610665aeedc3e9341cbb88fe4092
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 10954
content:
"[...] region (20degN-28degN, 76degE-87degE) follows other works (Bollasina et al., 2011; Jin and Wang, 2017; Huang et al., 2020b). Time series are shown for CRU TS (brown), GPCC (dark \r blue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models \r for the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean \r change in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that \r used in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081-2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some \r [...]"


id: 96c30b64815c8e925b102ef72f6a24ae
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 23725
content:
"[...] models exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G). (e) Precipitation linear trend \r (% per decade) over Central India for historical 1950-2000 (left) and future 2015-2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the \r CMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue \r triangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots \r show the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout Chapter 10 and follow the methodology used \r [...]"


id: cca4a574b028ca3238f2b7efdcbdce04
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 33329
content:
"[...] in Figure 10.6. (f) Example spread of trends (mm day-1 per decade) for the period 2016-2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference \r between the three driest and three wettest trends among ensemble members over central India. All trends are estimated using ordinary least-squares regression. Further details \r on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: 85e3245ed7a3a0fec0642cd3bcc16434
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 20698
content:
"consistent improvement other than for spatial patterns (e.g., rainfall \r close to better-resolved orography); some characteristics were made \r worse. Both the rainfall pattern and its bias were worsened in CORDEX \r compared to CMIP5 in Mishra et al. (2018). In contrast, Varikoden \r et  al. (2018) found improved representation of historical rainfall \r patterns, such as over the Western Ghats mountains (consistent with \r Singh et  al., 2017), reducing the dry bias; improvements were not \r found over the northern plains, which are dominated by synoptic \r variability known as monsoon depressions. Similarly, Sabin et  al. \r (2013) compared a  uniform 1deg resolution model ensemble with \r another zoomed to about 35 km over South Asia. Local zooming \r improved simulation of orographic precipitation and the monsoon \r trough. For the future, a  surrogate approach (like pseudo-global \r warming, see Section  10.3.2.2) was used in an RCM to test the \r [...]"


id: 3c2a1fbe378d93533ccdf929f5c1e5b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 7774
content:
"[...] impacts of warming or moistening on monsoon depressions (Sorland \r and Sorteberg, 2016; Sorland et  al., 2016). The depressions  are \r found to give more rainfall in future, dominated by strengthened \r synoptic circulation from the warming perturbation. By forcing an \r RCM with a  perturbed parameter ensemble of a  GCM, Bal et  al. \r (2016) made projections under SRES A1B for the 2020s, 2050s and \r 2080s. They noted increases in rainfall of 15-24% for India. Finally, \r evidence from a single CORDEX South Asia RCM showed a mixed \r signal for changes in peak season rainfall under RCP2.6 and RCP8.5 \r (Ashfaq et al., 2021)."


id: d4b130ff7cf1c255a74afb3625ce4d5c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 34737
content:
"While several of the above studies selected model subsets to \r constrain future projections based on standard performance metrics \r of the historical period, such as pattern correlation and root-mean\u0002square error, Latif et al. (2018) included a performance measure based \r on agreement with historical rainfall trends. This is an unproven \r constraint for regional projections (Section  10.3.3.9), since the \r 20th-century rainfall trend over India is assessed to have been driven \r chiefly by aerosol and other factors such as PDV (Sections 8.3.2.4.1 \r and 10.6.3.5), while the dominant late-21st century forcing is GHG \r emissions. Modern emergent-constraint techniques (Section 10.3.4.2) \r are being applied to the Indian monsoon such as G. Li et al. (2017), \r who found that models with excessive tropical western Pacific rainfall \r tend to project a greater Indian monsoon rainfall change in future, \r due to an exaggerated cloud-radiation feedback. Correcting for this \r [...]"


id: c6610c60bf75b976f05bf361a147059b
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 32212
content:
"Statistical downscaling and other post-processing require calibration \r in historical conditions (e.g., Akhter et  al., 2019) and assessment \r of fitness-for-purpose (Section  10.3.3.9) before use for future \r projections. Given the noted biases in GCM monsoon simulation \r (Section  10.6.3.5), Vigaud et  al. (2013) used a  variant of quantile \r mapping to bias adjust (Section  10.3.1.3.2 and Cross-Chapter \r Box 10.2) GCM outputs. For the historical period, the pattern, mean \r and seasonal cycle of rainfall versus the input GCMs were improved. \r Increased future monsoon rain, albeit in older SRES A2 projections, \r was found for southern India. Salvi et  al. (2013) used regression\u0002based perfect prognosis (Section 10.3.1.3.1) for the whole country at \r 0.5deg resolution based on five ensemble members of a GCM in SRES \r scenarios. They noted increases over rainy regions of west coast and \r north-east India, but decreases in the north, west and south-east. \r [...]"


id: cc7bdffc7fc8e9520458730821dd60db
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 33295
content:
"[...] Madhusoodhanan et  al. (2018) statistically downscaled 20 CMIP5 \r models to 0.05deg resolution. While the global models projected "


id: 60e2b54d9de41fa6d74dfcc3df0ff3be
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 14343
content:
"The storylines terminology could be used to loosely describe \r the interplay between internal variability and forced change \r (see Section 10.6.3.6), such as considering the difference between \r groups of wettest and driest ensemble members of a SMILE for the \r near-term future in Figure  10.19f. However, given the interest in \r low-likelihood high-impact scenarios (Sutton, 2018), we can also \r consider possible storylines for the Indian monsoon constructed \r from evidence in paleoclimate records and modelling. For example, \r a  future AMOC collapse could cause reduced monsoon rainfall \r (Section  8.6.1; Liu et  al., 2017), offsetting increases expected \r due to GHG. Large tropical volcanic eruptions are also known to \r weaken the Asian summer monsoon, in observations and model \r simulations over the last millennium (Section 8.5.2.3; Zambri et al., \r 2017), although a hemispheric dependence is found, with Southern \r Hemisphere eruptions even strengthening the monsoon around \r [...]"


id: c19f7116e977e8c75af98086d104dd3c
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 31370
content:
"[...] India (Zuo et al., 2019). Typically, future climate projections do not \r consider plausible eruption scenarios and their mitigating effects \r on greenhouse warming (see also Cross-Chapter Box 4.1). A single\u0002model ensemble (Bethke et al., 2017) demonstrates a future drier \r Indian monsoon relative to conditions in which volcanic eruptions \r are not considered, although the effects of GHG warming dominate \r beyond the mid-term."


id: ae01ca5f5c6bf4b26324d35e4a5f0135
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 27856
content:
"There is very high confidence (robust evidence, high agreement) of \r a negative trend of summer monsoon rainfall over the second half \r of the 20th  century averaged over all of India. There is medium \r agreement over trends at the regional level owing to uncertainty \r among observational products, which hinders model evaluation, \r downscaling and assessment of changes to extremes. There is high \r confidence (robust evidence, medium agreement) that anthropogenic \r aerosol emissions over the Northern Hemisphere and internal \r variability have contributed to the negative trend, while there is \r high confidence (robust evidence, medium agreement) that Indian \r summer monsoon rainfall will increase at the end of the 21st century \r in response to increased GHG forcing, due to the dominance of \r thermodynamic mechanisms. No contradictory evidence is found from \r downscaling methods. The contrast between declining rainfall in the \r [...]"


id: f03a57653b3486eb071978078ccd00d5
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 39652
content:
"[...] observational record and long-term future increases can be explained \r using multiple lines of evidence. They are not contradictory since they \r are attributable to different mechanisms (primarily aerosols and \r greenhouse gases, respectively). The long-term future changes are \r generally consistent across global (including at high resolution) and \r regional climate models, and supported by theoretical arguments. \r Furthermore, while there are subtle differences found in past periods \r with a  climate similar to the future climate (the mid-Holocene), \r different physical mechanisms at play suggest that paleoclimate \r evidence does not reduce confidence in the future projections. \r In the near term, there is high confidence that internal variability \r will dominate."


id: 5313c44c52b6caa4076c7d5ee196c2a2
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
vector_id: 11884
content:
"The Mediterranean summer climate is affected by large-scale modes \r of natural variability, the most dominant being the NAO (Annex IV) \r in winter and the summer NAO in summer (Folland et al., 2009; Blade \r et al., 2012), although regional differences exist. The influence of those \r modes of variability over the eastern Mediterranean is recognized by \r some studies (Chronis et al., 2011; Kahya, 2011; Black, 2012; Blade \r et al., 2012), but disputed by others (Ben-Gai et al., 2001; Ziv et al., 2006; \r Donat et al., 2014; Turki et al., 2016; Zamrane et al., 2016; Han et al., \r 2019). During positive summer NAO phase, associated with an upper\u0002level trough over the Balkans, the Mediterranean is anomalously wet \r (Blade et al., 2012). Drivers of Mediterranean climate variability include \r modes of variability such as the AMV (Sutton and Dong, 2012) and the \r Asian monsoon (Rodwell and Hoskins, 1996; Logothetis et al., 2020). \r [...]"


id: 605b1c07744afce1b42676c50d483bb8
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index: climate_gpt_v2
vector_id: 14259
content:
"[...] In addition, the increase of GHGs (e.g., Zittis et al., 2019), the decrease \r of anthropogenic aerosols over Europe and the Mediterranean since \r the 1980s resulting from air pollution policies (Turnock et al., 2016), \r and anthropogenic land-use change (Millan, 2014; MedECC 2020) \r have been shown to be linked to the regional warming. The role of the \r zonal averaged circulation as a driver for the Mediterranean climate \r has been stressed by (Garfinkel et al., 2020)."


id: bee1e6ae059731c77b5d92b707bb3352
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
content_type: text
index: climate_gpt_v2
vector_id: 30848
content:
"The Mediterranean has a heterogeneous climate that is partly semi-arid, \r especially along the southern coast of the Mediterranean Sea (Lionello \r et al., 2012). It is characterized by mild humid winters and dry warm or \r hot summers, which are associated with large scale subsidence that is \r partly related to the downward branch of the Hadley circulation. Other \r factors affecting the Mediterranean circulation include the monsoon \r heating over Asia (Rodwell and Hoskins, 1996; Cherchi et al., 2014; Osso \r et al., 2019) and circulation anomalies induced by topography (Simpson \r et al., 2015). Seasonal and interannual variability is strongly linked to \r natural modes of variability (Section  10.6.4.4). The Mediterranean \r Sea acts as an evaporation source that dominates the  regional \r hydrological cycle, which is characterized by local cyclogenesis and \r a separate branch of the mid-latitude storm track (Lionello et al., 2016). \r [...]"


id: 4aef547053fd68e67a3e163b663fa661
created_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"[...] It also affects remote locations such as the Sahel (Park et al., 2016; \r Section 10.4.2.1). Strong storms can develop over the Mediterranean. \r Among these, Medicanes are particularly destructive and exhibit \r several similarities with tropical cyclones (Cavicchia et  al., 2014; \r Kouroutzoglou et al., 2015; Gaertner et al., 2018). The Mediterranean \r region is also characterized by strong land-atmosphere coupling and \r feedbacks (Seneviratne et  al., 2006) generating prolonged droughts \r and intense heatwaves, which can also affect continental Europe \r (Zampieri et al., 2009). Other aspects of Mediterranean climate include \r regional winds, which can be very strong due to the channelling effect \r (Obermann et al., 2018) and extreme rainfall during autumn (Ducrocq \r et al., 2014; Ribes et al., 2019)."


id: 4e9991ac611ff7ba724f445b812d0d9d
created_at: 2023-04-19 13:22:41
updated_at: 2023-04-19 13:22:41
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index: climate_gpt_v2
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content:
"Over the period 1960-2014, observed trends over land are consistent \r with those of most of the multi-model or SMILEs ensembles \r (Figure 10.20f), although large differences exist for individual models \r and ensemble members. The modelled ensemble-mean trends show \r large geographical variations. Generally, both global and regional \r models often underestimate the observed trend especially over parts \r of North Africa, Italy, the Balkans and Turkey. The cold bias in global \r models is related to simulated SLP trends that are anti-correlated \r to the observed trend, which is probably due to systematic model \r errors (Boe et al., 2020b). Biases in the simulation of soil-moisture \r and cloud-cover might also have contributed to the underestimation \r of the warming trend in GCMs (van Oldenborgh et  al., 2009). \r The CORDEX results (at both 0.44deg and 0.11deg resolution) show \r consistently smaller values than those in global models and the \r [...]"


id: 5402a0203e7da6d90a5a979e26f61b1f
created_at: 2023-04-19 13:22:41
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vector_id: 12054
content:
"[...] available datasets (Figure 10.20g; Vautard et al., 2021). This is partly \r due to the overestimation in the temperature evolution before 1990 \r (Figure 10.20e), possibly because of differences in the aerosol forcing \r (Boe et  al., 2020a), although the driving global models also have \r a cold bias (Vautard et al., 2021). Cold biases for recent decades are \r also found in Med-CORDEX simulations (Dell'Aquila et al., 2018) and \r by RCM simulations over the southern part of the Mediterranean, \r Middle East and North Africa region (Almazroui, 2016; Almazroui \r et al., 2016a, b; Zittis and Hadjinicolaou, 2017; Ozturk et al., 2018), \r although higher resolution, new bare soil albedo and modified aerosol \r parametrization significantly improve the results (Bucchignani et al., \r 2016a, b, 2018). Despite large differences in the multi-model mean \r trend (Figure  10.20g), in most of the land points the observed \r trend lies within the model range in all ensembles. For the SST bias \r [...]"


id: ea7286a36d4b6cb582576abbbf1abcfa
created_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 38610
content:
"[...] exhibited by coupled RCMs the choice of driving global model has \r the largest impact (Darmaraki et al., 2019; Soto-Navarro et al., 2020)."


id: c537cb43234ec3a12a44042dfe789bd0
created_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 32003
content:
"The Mediterranean is expected to be one of the most prominent \r and vulnerable climate change hotspots (Diffenbaugh and Giorgi, \r 2012). CMIP5, CMIP6, HighResMIP and CORDEX (Section 10.6.4.7) \r simulations all project a  future warming for the 21st  century that \r ranges between 3.5degC and 8.75degC for RCP8.5 at the end of this \r century for those ending at 2100 (Figure 10.21a, b). CMIP6 results \r project more pronounced warming than CMIP5 for a given emissions \r scenario and time period (Figure  10.21c; Coppola et  al., 2020). \r However, when analysing the Mediterranean warming in terms \r of mean global warming levels, the two ensembles largely agree, \r showing that summer warming is projected to reach values up to \r 40-50% larger than the global annual warming, largely independent \r of models and emissions scenarios (Figure 10.21d). Large regional \r differences exist, with enhanced warming projected over Turkey, \r the Balkans, the Iberian Peninsula and North African regions \r [...]"


id: 185927715a0610ec82bd4b69f64e9a35
created_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 1489
content:
"[...] (Figures 10.14a, 10.21c; Almazroui et al., 2020a) and reaching, locally, \r values of up to double the global mean (Lionello and Scarascia, \r 2018). The enhanced summer warming also increases the amplitude \r of the seasonal cycle (Yettella and England, 2018)."


id: a48f51e667a523d5c222f626002ae2a
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 26166
content:
"atmospheric circulation depend, among other factors, on how these \r drivers have changed and will change. Zappa and Shepherd (2017) \r have analysed this for the Mediterranean region and developed \r a set of storylines based on different plausible evolutions of those \r drivers and their impact on the Mediterranean winter climate. \r Important identified drivers during winter are tropical and polar \r amplification of global warming and the polar stratospheric vortex \r (Manzini et  al., 2014; Simpson et  al., 2018), with implications \r for precipitation. Zappa (2019) discusses the relative amplitude \r of tropical and Arctic warming, response of the AMOC, patterns of \r Pacific SST change, and changes in stratospheric vortex strength as \r possible drivers of the Mediterranean summer climate and stresses \r that given the present state of knowledge, alternative storylines \r based on these drivers should be considered as equally plausible \r [...]"


id: 27dcbb70b7b2b94903fd6bd38912b2da
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 4208
content:
"[...] future manifestations of regional climate change. Brogli et  al. \r (2019a, b) and Kroner et al. (2017) have revealed thermodynamic \r processes, lapse rate, and circulation as important drivers for \r Mediterranean summer climate."


id: 47cc8ab126dd6fa62d412efc218ab5a4
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 9847
content:
"Despite the large efforts of these regional downscaling projects, the \r global model-RCM matrix is still sparse and lacking a systematic \r design to explore the uncertainty sources (e.g.,  global model, \r RCM, scenario, resolution) (Section 10.3). Focusing on the Iberian \r peninsula, Fernandez et  al. (2019) argued that the driving global \r model is the main contributor to uncertainty in the ensemble. \r Physically consistent but implausible temperature changes in \r RCMs can occur. An example is a strong temperature increase over \r the Pyrenees due to excessive snow cover in the present climate \r (Fernandez et al., 2019). Based on an older set of RCM simulations \r (ENSEMBLES), Deque et al. (2012) also argued that the largest source \r of uncertainty in the temperature response over southern Europe \r is the choice of the driving global model (whereas for summer \r precipitation the choice of the RCM dominates the uncertainty). \r [...]"


id: 922322905f29a9a1ea7e47ce209caa01
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 22923
content:
"[...] Finally, Boe et al. (2020a) found that over a large area of Europe, \r including parts of the Mediterranean, RCMs project a  summer \r warming 1.5degC-2degC colder than in their driving global models for \r the end of the 21st century. This is caused by differences in solar \r radiation related to the absence of time-varying anthropogenic \r aerosols in RCMs (Boe et al., 2020a; Gutierrez et al., 2020), which \r also affects the noted differences in cloud cover between global \r models and RCMs (Bartok et al., 2017)."


id: 3c5733b135428b653dba3157ba9f1ce9
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 7805
content:
"Traditionally, the distillation process to produce contextualized, \r policy relevant information has taken place at regional or national \r level. For example, the potential effects of climate change on \r public health are discussed in several national climate change \r and adaptation reports (Bruci et  al., 2016; MoARE, 2016; MoE, \r 2016; MoEP, 2018; MoEU, 2018). Although these reports are \r extremely helpful and widely used for the development of national \r adaptation policies, they are often based on non-comprehensive \r and heterogeneous sources of climate information (e.g.,  MEEN, \r 2018; MoE/UNDP/GEF, 2019). For instance, future climate change \r projections are based on a  limited number of socio-economic \r scenarios and climate model simulations, which are also often \r not evaluated comprehensively (e.g.,  Bruci et  al., 2016; MoARE, \r 2016; MoEU, 2018). In addition, these reports are often not peer\u0002reviewed, not available in English, and mainly limited to the country \r [...]"


id: 4fe84993feef8c7bd47bf2e3ff52f3bc
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index: climate_gpt_v2
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content:
"[...] level, thus making it difficult to compare the details of the climate \r information across them."


id: 8a76f6002f7a8864b6678e7adeda934e
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 21531
content:
"During nighttime, urban centres are often several degrees warmer than the surrounding rural area, a phenomenon known as the \r nighttime canopy urban heat island effect (Bader et al., 2018; Kuang, 2019; Li et al., 2019; Y. Li et al., 2020a). While green and blue \r infrastructures can mitigate the urban heat island effect, three main factors contribute to its development (Hamdi et al., 2020; Masson \r et al., 2020): (i) three-dimensional urban geometry including building density and plan area, street aspect ratio and building height; \r (ii) thermal characteristics of impervious surfaces; and (iii) anthropogenic heat release, either from building energy consumption, \r especially waste heat from air conditioning systems, or as direct emissions from industry, traffic, or human metabolism (Ichinose et al., \r 1999; Sailor, 2011; de Munck et al., 2013; Bohnenstengel et al., 2014; Chow et al., 2014; Salamanca et al., 2014; Dou and Miao, 2017; \r [...]"


id: 346c51050918c595999ce9956d3d4146
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updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
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content:
"[...] Ma et al., 2017a; Chrysoulakis et al., 2018; Takane et al., 2019). Urban heat island magnitude is also affected by aerosols due to air \r pollution in urban areas (Cheng et al., 2020; Han et al., 2020) and by local background climate (Zhao et al., 2014; Ward et al., 2016)."


id: a057fa1a8232b4f989b82da370257fef
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 25420
content:
"Long-term climate datasets (a year or more) at the small spatial scales required to resolve processes of interest for cities (<1 km) \r are scarce (Bader et al., 2018; Caluwaerts et al., 2020). Moreover, urban observation sites often represent only parts of the urban \r environment and are suboptimal for detecting urban effects (e.g., sites in city parks). Recently, city-scale climate monitoring networks \r as well as satellite and ground-based remote sensing are being used (though still missing in Global South cities; Technical Annex I), \r enhancing our understanding of the urban microclimate and its interaction with climate change, and providing key information \r for users (F. Chen et al., 2012; Barlow et al., 2017; Bader et al., 2018). It has been found that harmonization of collection practices, \r instrumentation, station locations, and quality control methodologies across urban environments needs improvement to facilitate \r [...]"


id: bd6b3b5684675fd2be7d1216f13effd7
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 30580
content:
"[...] collaborative research (Muller et al., 2013; Barlow et al., 2017). Real time crowdsourcing data is becoming available (Section 10.2.4). \r The urban climate community is making efforts to understand how these methods can complement traditional datasets (Meier et al., \r 2017; Zheng et al., 2018; Langendijk et al., 2019b; Venter et al., 2020)."


id: a1dba06342d97f5558f8debba7a72e23
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 25697
content:
"Exchanges of heat, water and momentum between the urban surface and its overlying atmosphere are calculated using specific surface\u0002atmosphere exchange schemes. Three different schemes, here in order of increasing complexity, can be distinguished (Masson, 2006; \r Grimmond et al., 2010, 2011; Chen et al., 2011; Best and Grimmond, 2015): (i) in the slab or bulk approach, the three-dimensional city \r structure is not resolved but cities are represented by modifying soil and vegetation parameters within land surface models, increasing \r roughness length and displacement height (e.g., Seaman et al., 1989; Dandou et al., 2005; Best et al., 2006; Liu et al., 2006). The energy \r balance is often modified to account for the radiation trapped by the urban canopy, heat storage, evaporation and anthropogenic \r heat fluxes. (ii) Single-layer urban canopy modules use a simplified geometry (urban canyon, with three surface types: roof, road and \r [...]"


id: e7d6ae4df738ca582b2559e9d604f1fe
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 38097
content:
"[...] wall) that approximately capture the three-dimensional dynamical and thermal physical processes influencing radiative and energy \r fluxes (Masson, 2000; Kusaka et al., 2001). (iii) Multi-layer urban canopy modules compute urban effects vertically, allowing a direct \r interaction with the planetary boundary layer (Brown, 2000; Martilli et al., 2002; Hagishima et al., 2005; Dupont and Mestayer, 2006; \r Hamdi and Masson, 2008; Schubert et al., 2012). Building-energy models that estimate anthropogenic heat from a building for given \r atmospheric conditions can be incorporated. Recent model development has focused on improving the representation of urban \r vegetation (Lee et al., 2016; Redon et al., 2017; Mussetti et al., 2020)."


id: 642603b860bf0c5d33efaf20b9c0562e
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vector_id: 14901
content:
"Global (McCarthy et al., 2010; Oleson et al., 2011; Zhang et al., 2013; H. Chen et al., 2016; Katzfey et al., 2020; Sharma et al., 2020; \r Hertwig et al., 2021) and regional modelling groups (Oleson et al., 2011; Kusaka et al., 2012a; McCarthy et al., 2012; Hamdi et al., \r 2014; Trusilova et al., 2016; Daniel et al., 2019; Halenka et al., 2019; Langendijk et al., 2019a) are beginning to implement these \r urban parametrizations within the land surface component of their models. There is very high confidence (robust evidence and \r high agreement) that while all types of urban parametrizations generally simulate radiation exchanges in a realistic way, they have \r strong biases when simulating latent heat fluxes, though recent research incorporating in-canyon vegetation processes improved their \r performance. There is medium confidence (medium evidence, high agreement) (Kusaka et al., 2012b; McCarthy et al., 2012; Hamdi \r [...]"


id: 1a6d54da968aeb2a99ab65b9a6441243
created_at: 2023-04-19 13:22:42
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content:
"[...] et al., 2014; Trusilova et al., 2016; Janicke et al., 2017; Daniel et al., 2019) that a simple single-layer parametrization, is sufficient for \r the correct simulation of the urban heat island magnitude and its interplay with regional climate change."


id: 7fcfd20f1391aff11731974be1356681
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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content:
"Pepin et al., 2015), leading to warming of 2degC-2.5degC at 5000 m over 1961-2006, but only 0.5degC at sea level (Xu et al., 2016). However, \r EDW behaviour appears to depend on region, time period and elevation (D. Guo et al., 2019; b. Li et al., 2020) and understanding is limited \r by the sparse observational network (You et al., 2020). Observational and model analyses have attributed EDW to GHG and black carbon \r emissions, accelerating warming by snow-albedo feedback (Ming et al., 2012; Gautam et al., 2013; Xu et al., 2016; Yan et al., 2016; Lau \r and Kim, 2018; Y. Zhang et al., 2018), or the more pronounced cooling effect of scattering aerosols at low elevations and stratospheric \r ozone depletion (Guo and Wang, 2012; Zeng et al., 2015). There is high confidence that the eastern and central HKH has exhibited rising \r temperatures (Cross-Chapter Box 10.4, Figure 1), with warming dependent on season and elevation. There is high confidence that much of \r [...]"


id: ee98e7771abe7fc057afde9c3be814c0
created_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 39356
content:
"[...] the warming can be attributed to GHGs, but the effect of albedo has only medium confidence. There is high confidence in more frequent \r extreme warm events and fewer extreme cold events over the eastern Himalayas in the last five decades."


id: 5bb413f1c75b2f06624f7b322741b1c8
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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vector_id: 13387
content:
"Cross-chapter Box 10.4, Figure 1 | Historical annual-mean surface air temperature linear trend (degC per decade) and its attribution over the \r Hindu Kush Himalaya (HKH) region. (a) Observed trends from Berkeley Earth (also showing the HKH outline), CRU TS (also showing the AR6 Tibetan Plateau (TIB) \r outline, for ease of comparison to the Interactive Atlas), APHRO-MA and JRA-55 datasets over 1961-2014. (b) Models showing the coldest, median and warmest \r HKH temperature linear trends among the CMIP6 historical ensemble over 1961-2014. (c) Low-pass-filtered time series of annual-mean surface air temperature \r anomalies (degC, baseline 1961-1980) over the HKH region as outlined in panel (a), showing means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings \r sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue). Observed datasets are Berkeley Earth (dark blue), CRU (brown), \r [...]"


id: c958419e5b7e7bc1c7ac414ffab192c4
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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content:
"[...] APHRO-MA (light green) and JRA-55 (dark green). The filter is the same as that used in Figure 10.10. (d) Distribution of annual mean surface air temperature trends \r (degC per decade) over the HKH region from 1961 to 2014 for ensemble means, the aforementioned observed and reanalysis data (black crosses), individual members \r of CMIP6 hist all-forcings (red circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs used throughout \r Chapter 10 (grey shading). Ensemble means are also shown. All trends are estimated using ordinary least-squares regression and box-and-whisker plots follow the \r methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: d875c72bfc914dfc4e119b6509ed31ff
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 35380
content:
"Annual and summer precipitation over the central-eastern HKH show decreasing trends over 1979-2010 in multiple observed datasets, \r attributable to a weakening South Asian monsoon (Yao et al., 2012a; Palazzi et al., 2013; Roxy et al., 2015). There are contradictory \r trends in the western HKH (Azmat et al., 2017; Yadav et al., 2017; H. Li et al., 2018; Meher et al., 2018), where most precipitation is \r associated with western disturbances on the subtropical westerly jet, but trends in western disturbance activity are unclear (Kumar \r et al., 2015; Hunt et al., 2019; Krishnan et al., 2019a). There has been an increased frequency and intensity of extreme precipitation \r over the central-western HKH but contrasting evidence in the east (Sheikh et al., 2015; Talchabhadel et al., 2018). The number of \r consecutive wet days has increased over 1961-2012, but with no uniform trend in consecutive dry days (Zhan et al., 2017). There is \r [...]"


id: 96785f98a66265d5303dc66d9e6871d5
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 23676
content:
"[...] medium confidence that the eastern-central HKH has experienced decreased summer precipitation (Section 10.6.3). There is medium \r confidence in the increase of summer extreme precipitation over the western HKH."


id: 8517cf26c19f3e75eebf01c5b923b8be
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 20559
content:
"The SROCC assessed that snow cover has declined in duration, depth and accumulated mass at lower elevations in mountain regions, \r including the HKH (high confidence). Glaciers are losing mass (very high confidence) and permafrost is warming (high confidence) \r over high mountains in recent decades, and it is very likely that atmospheric warming is the main driver. A significant reduction in \r HKH glacier area has been observed since the 1970s, with smaller glaciers generally shrinking faster (e.g., Bolch et al., 2019). HKH \r glacier mass loss took place at the lowest rate among high mountain areas in the last 20 years, although with one of the largest \r total losses (Section 9.5.1.1 and Figure 9.20; Shean et al., 2020). The highest mass-loss rates occurred in the eastern and northern \r HKH, while gains occurred in the west (e.g., Shean et al., 2020). Glacier mass gain has been coined as the 'Karakoram anomaly' \r [...]"


id: 67672ea0d1b63af6c75be0ef636997bd
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
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vector_id: 15462
content:
"[...] (Sections 8.3.1.7.1 and 9.5.1), explained by a combination of low temperature sensitivity of debris-covered glaciers, a decrease in \r summer air temperatures, and increased snowfall possibly linked to evapotranspiration from irrigated agriculture (You et al., 2017; \r Bolch et al., 2019; de Kok et al., 2020a; Farinotti et al., 2020). Meanwhile, increased air temperature and decreased snowfall explain \r the glacier mass decrease elsewhere (Bonekamp et al., 2019; de Kok et al., 2020b; Farinotti et al., 2020; Shean et al., 2020). There is \r high confidence that glaciers in most HKH regions have thinned, retreated and lost mass since the 1970s."


id: d9e2fdd4b659e9a82b7731a56e252af1
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 35631
content:
"In AR5, the HKH was projected to continue warming over the 21st century, faster than the likely ranges for the global mean and \r South Asia. New CMIP5 results show temperature increases across mountainous HKH by about 1degC-2degC (in some places in summer \r 4degC-5degC) during 2021-2050 compared to 1961-1990 (Shrestha et al., 2015). Projected warming differs by up to 1degC between east and \r west, with higher values in winter (Sanjay et al., 2017; see Interactive Atlas). Statistically significant mean warming (0.30degC-0.90degC \r per decade until the end of the 21st century) across all RCPs has been projected by CORDEX South Asia (Dimri et al., 2018). CMIP6 \r models report that north-western South Asia, including the western Himalayas, is projected to experience temperature increases \r exceeding 6degC by the end of the 21st century under SSP5-8.5 relative to 1995-2014 (Almazroui et al., 2020b). Results from CMIP5, \r [...]"


id: 714811486556b620b88bfc3cdf448e35
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 17191
content:
"[...] CMIP6 and CORDEX ensembles for different warming levels are shown in the Interactive Atlas and summarized in Figure Atlas.20. \r The HKH will likely continue warming in the coming decades."


id: 61cb2a46ed4da3927818b6e304c4a604
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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vector_id: 14492
content:
"The SR1.5 (IPCC, 2018b) stated that heavy precipitation risk in high-elevation regions is projected to be higher at 2degC compared to \r 1.5degC of global warming (medium confidence). CMIP5 models project increased annual or summer monsoon precipitation over the \r HKH in the 21st century (Palazzi et al., 2015; Kitoh and Arakawa, 2016), intensifying by about 22% in the hilly south-eastern Himalaya \r and TP for the long term in RCP8.5, but with no trends in the western HKH (Rajbhandari et al., 2015; Krishnan et al., 2019a). CMIP6 \r projects an increase of winter precipitation over the western Himalayas, with a corresponding decrease in the east (Almazroui et al., \r 2020b). HKH projections are subject to large uncertainties in CMIP5 and CORDEX (Hasson et al., 2013, 2017; Mishra, 2015; Sanjay \r et al., 2017). CORDEX, in particular, has inherent limitations at reproducing the characteristics of summer monsoon rainfall variability \r [...]"


id: 88804d661577ec5a63841dd70fbe5d00
created_at: 2023-04-19 13:22:42
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vector_id: 21180
content:
"[...] (Singh et al., 2017). There is medium confidence that HKH precipitation will increase in the coming decades."


id: fff90091cdcd38370db904fea2b9606d
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 42427
content:
"The SROCC assessed that glaciers will lose substantial mass (high confidence) and permafrost will undergo increasing thaw and \r degradation (very high confidence) over high mountain regions (including the HKH), with stronger changes for higher emissions \r scenarios. Regional differences in warming and precipitation projections and glacier properties cause considerable differences in \r glacier response within High Mountain Asia (Kraaijenbrink et al., 2017). Glacier mass loss will accelerate through the 21st century, \r increasing with RCP after 2030 (Section 9.5.1.3; Marzeion et al., 2014). Loss of between 40 +- 25% to 69 +- 21 % of 2015 glacier \r volume is expected by 2100 in RCP 2.6 and RCP 8.5, respectively (Section 9.5.1.3 and Figure 9.21). Glacier mass loss is expected due to \r decreased snowfall, increased snowline elevations and longer melt seasons. However, due to projection uncertainties, simplicity of the \r [...]"


id: 1f3813fc29577032f6414bb4333a9767
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 2703
content:
"[...] models, and limited observations, there is medium confidence in the magnitude and timing of glacier mass changes (Section 9.5.1.3). \r Glacier mass in HKH will decline through the 21st century (high confidence), more so under high-emissions scenarios."


id: 32c09ab80002ca554d994414e77ffb50
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 6196
content:
"The development, delivery, and use of climate change information requires engaging all parties involved: \r those producing the climate data and related knowledge, those communicating it, and those who combine \r that information with their knowledge of the community, region or activity that climate change may impact. \r To be successful, these parties need to work together to explore the climate data and thus co-develop the \r climate information needed to make decisions or solve problems, distilling output from the various sources \r of climate knowledge into relevant climate information. Effective partnerships recognize and respond to the \r diversity of all parties involved (including their values, beliefs and interests), especially when they involve \r culturally diverse communities and their indigenous and local knowledge of weather, climate and their society. \r This is particularly true for climate change - a global issue posing challenges that vary by region. By recognizing \r [...]"


id: a4181c91e4021d1147853996e6c9241f
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 26063
content:
"[...] this diversity, climate information can be relevant and credible, most notably when conveying the complexity of \r risks for human systems and ecosystems and for building resilience."


