Editing IPCC:AR6/WGI/Chapter-10 (section)

=== 10.1.3 Sources of Regional Climate Variability and Change ===

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Variability in regional climate arises from natural and anthropogenic forcings, internal variability including the local expression of large-scale remote drivers (also known as teleconnections), and the feedbacks between them. Due to the many possible drivers of variability and change (Figure 10.3), quantifying the interplay between internal modes of decadal variability and any externally forced component is crucial in attempts to attribute causes of regional climate changes (e.g., [[#Hoell--2017|Hoell et al., 2017]] ; [[#Nath--2018|Nath et al., 2018]] ). A regional climate signal could arise purely due to some anthropogenic influence or conversely, entirely due to internal variability, but it is most likely the result of a combination of both ( [[#10.4|Section 10.4]] ). This section briefly introduces these sources of regional variability and should be read along with corresponding sections in Chapters 3, 6 and 7. [[#10.3|Section 10.3]] assesses their representation in climate models, [[#10.4|Section 10.4]] discusses their relevance for the attribution of multi-decadal trends and [[#10.6|Section 10.6]] refers to them as sources in specific examples where regional climate information is built. [[IPCC:Wg1:Chapter:Chapter-8#8.2|Section 8.2]] offers a companion discussion focussing on changes in the water cycle. An example of how changes in one region could act as a source for changes in a neighbouring one is assessed in the Cross-Chapter Box 10.1 for the linkages between polar and mid-latitude regions, an interaction that has led to substantial recent research. This section also introduces the sources of uncertainty in model-derived regional climate information and how the quantification of the uncertainties influences the confidence of the regional climate information.

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==== 10.1.3.1 Forcings Controlling Regional Climate ====

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There are important differences in the processes affected by greenhouse gases (GHGs) over land and ocean. Notably, this leads to preferential warming of the land regions, which are themselves skewed towards the Northern Hemisphere (NH).

Variations in solar forcing ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ) could influence regional climate through its modulation of circulation patterns, although this research field is still hampered by large observational and modelling uncertainties. The 11-year solar cycle has been suggested to affect the leading atmospheric circulation modes of the North Atlantic region in model-based studies ( [[#Gray--2013|Gray et al., 2013]] ; [[#Thiéblemont--2015|Thiéblemont et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ). In particular the solar cycle has been suggested as an important source of near-term predictability of the North Atlantic Oscillation (NAO; [[#Kushnir--2019|Kushnir et al., 2019]] ), while other studies have not found evidence for links between the solar cycle and NAO in observational records ( [[#Ortega--2015|Ortega et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ; [[#Chiodo--2019|Chiodo et al., 2019]] ). On centennial time scales, solar fluctuations were found to be correlated with the Eastern Atlantic Pattern ( [[#Sjolte--2018|Sjolte et al., 2018]] ). Possible influences on winter circulation and temperature over Eurasia ( [[#Chen--2015|Chen et al., 2015]] ) and North America ( [[#Liu--2014|Liu et al., 2014]] ; [[#Li--2018|Li and Xiao, 2018]] ) have also been identified.

An updated assessment of past changes in stratospheric ozone can be found in [[IPCC:Wg1:Chapter:Chapter-2#2.2.5.2|Section 2.2.5.2]] . The AR6 assesses that both GHG and stratospheric ozone depletion have contributed to the expansion of the zonal mean Hadley cell in the Southern Hemisphere (SH) for the period 1981–2000 with ''medium confidence'' [[IPCC:Wg1:Chapter:Chapter-3#3.3.3|Section 3.3.3]] ; [[#Garfinkel--2015|Garfinkel et al., 2015]] ; [[#Waugh--2015|Waugh et al., 2015]] ; [[#Grise--2019|Grise et al., 2019]] ). There is ''medium confidence'' that stratospheric ozone depletion contributed to the strengthening trend of the summer Southern Annular Mode (SAM) for the period 1970–1990, but this influence has been weaker since 2000 ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). The poleward shift of the SH westerlies has also been explained by stratospheric ozone depletion ( [[#Solman--2016|Solman and Orlanski, 2016]] ). [[#10.4|Section 10.4]] assesses its role in the multi-decadal increase of rainfall in south-eastern South America and [[#10.6.2|Section 10.6.2]] does so for the occurrence of the Cape Town drought.

Both natural and anthropogenic aerosols are often emitted at a regional scale, have a short atmospheric lifetime (from a few hours to several days; Section 6.1), are dispersed regionally and affect climate at a regional scale through radiative cooling/heating and cloud microphysical effects (Chapter 8; [[#Rotstayn--2015|Rotstayn et al., 2015]] ; [[#Sherwood--2015|Sherwood et al., 2015]] ). The majority of aerosols scatter solar radiation, but with strong regional variations ( [[#Shindell--2009|Shindell and Faluvegi, 2009]] ) that lead to regional radiative effects of up to two orders of magnitude larger than the global average ( [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|B. Li et al., 2016]] ; K. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Mallet--2016|Mallet et al., 2016]] ). Black carbon, instead, is known to absorb solar radiation, leading to regional atmospheric warming patterns due to its inhomogeneous spatial distribution ( [[#Gustafsson--2016|Gustafsson and Ramanathan, 2016]] ). Patterns of forcing generally follow those of aerosol burden. However, temperature and precipitation responses are both local and remote (Z. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Kasoar--2018|Kasoar et al., 2018]] ; L. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Samset--2018|Samset et al., 2018]] ; [[#Thornhill--2018|Thornhill et al., 2018]] ; [[#Westervelt--2018|Westervelt et al., 2018]] ). For instance, changes in aerosol concentrations in the NH have been reported to modulate monsoon precipitation in West Africa and the Sahel ( [[#Undorf--2018|Undorf et al., 2018]] ; [[#10.4.2.1|Section 10.4.2.1]] ) and in Asia (H. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#10.6.3|Section 10.6.3]] ).

Natural aerosols include mineral dust, volcanic aerosol and sea salt. The feedback processes between climate and mineral dust as well as sea salt are assessed in Section 6.4, while the volcanic aerosol is dealt with in Cross-Chapter Box 4.1. Mineral dust created by wind erosion of arid and semi-arid surfaces dominates the aerosol load over some areas. The major sources of contemporary dust are located in the arid topographic basins of northern Africa, Middle East, Central and south-west Asia, the Indian subcontinent, and East Asia ( [[#Prospero--2002|Prospero et al., 2002]] ; [[#Ginoux--2012|Ginoux et al., 2012]] ) and emissions are controlled by changes in surface winds, precipitation, and vegetation ( [[#Ridley--2014|Ridley et al., 2014]] ; W. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#DeFlorio--2016|DeFlorio et al., 2016]] ; [[#Evan--2016|Evan et al., 2016]] ; [[#Pu--2018|Pu and Ginoux, 2018]] ). Dust both scatters and absorbs radiation and serves as a nuclei of warm and cold clouds (Chapter 6). The surface direct radiative effect is likely negative over land and ocean, especially when the assumed solar absorption by dust is large ( [[#Miller--2014|Miller et al., 2014]] ; [[#Strong--2015|Strong et al., 2015]] ). Surface temperature and precipitation adjust to the direct radiative effect with both sign and magnitude depending on the dust absorptive properties. Dust often cools the surface, but in regions such as the Sahara surface air temperature increases as the shortwave absorption by dust is increased, leading to increases of surface temperature over the major reflective dust sources ( [[#Miller--2014|Miller et al., 2014]] ; [[#Solmon--2015|Solmon et al., 2015]] ; [[#Strong--2015|Strong et al., 2015]] ; [[#Jin--2016|Jin et al., 2016]] ; [[#Sharma--2017|Sharma and Miller, 2017]] ).

Volcanic eruptions load the atmosphere with large amounts of sulphur, which is transformed through chemical reactions and micro-physics processes into sulphate aerosols (Cross-Chapter Box 4.1; [[#Stoffel--2015|Stoffel et al., 2015]] ; [[#LeGrande--2016|LeGrande et al., 2016]] ). If the plume reaches the stratosphere, sulphate aerosols can remain there for months or years (about two to three for large eruptions) and can then be transported to other areas by the Brewer-Dobson circulation. If the eruption occurs in the tropics, its plume is dispersed across the Earth in a few years, while if the eruption occurs in the high latitudes, aerosols mainly remain in the same hemisphere ( [[#Pausata--2015|Pausata et al., 2015]] ). The global temperature response observed after the last five major eruptions of the last two centuries is estimated to be around –0.2°C ( [[#Swingedouw--2017|Swingedouw et al., 2017]] ), in association with a general decrease of precipitation ( [[#Iles--2017|Iles and Hegerl, 2017]] ). Nevertheless, the statistical significance of the regional response remains difficult to evaluate over the historical era ( [[#Bittner--2016|Bittner et al., 2016]] ; [[#Swingedouw--2017|Swingedouw et al., 2017]] ) due to the small sampling of large volcanic eruptions over this period and the fact that the signal is superimposed upon relatively large internal variability ( [[#Gao--2018|Gao and Gao, 2018]] ; [[#Dogar--2019|Dogar and Sato, 2019]] ). Evidence from paleoclimate observations is therefore crucial to obtain a sufficient signal-to-noise ratio ( [[#Sigl--2015|Sigl et al., 2015]] ). Reconstructed modes of climate variability based on proxy records allow evaluation of the influence on those modes ( [[#Zanchettin--2013|Zanchettin et al., 2013]] ; [[#Ortega--2015|Ortega et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ; [[#Michel--2020|Michel et al., 2020]] ).

Anthropogenic aerosols play a key role in climate change (Chapter 6). Although the global mean optical depth caused by anthropogenic aerosols did not change from 1975 to 2005 (Chapter 6), the regional pattern changed dramatically between Europe and eastern Asia ( [[#Fiedler--2017|Fiedler et al., 2017]] , 2019; [[#Stevens--2017|Stevens et al., 2017]] ). Large regional differences in present-day aerosol forcing exist with consequences for regional temperature, hydrological cycle and modes of variability (Chapter 8, [[#10.6|Section 10.6]] ). Examples of regions with a notable role for anthropogenic aerosol forcing are the Indian monsoon region ( [[#10.6.3|Section 10.6.3]] ) and the Mediterranean basin [[#10.6.4|Section 10.6.4]] ). Anthropogenic aerosols are also very relevant in many urban areas (Box 10.3; [[#Gao--2016|Gao et al., 2016]] ; [[#Kajino--2017|Kajino et al., 2017]] ).

The SRCCL assessed that nearly three-quarters of the land surface is under some form of land use, particularly in agriculture and forest management ( [[#Jia--2019|Jia et al., 2019]] ). The effects of land management on climate are much less studied than land cover effects although net cropland has changed little over the past 50 years, while land management has continuously changed ( [[#Jia--2019|Jia et al., 2019]] ). [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.1|Section 7.3.4.1]] assesses the global influence of both land use and irrigation on the effective radiative forcings. Land cover changes and land management can influence climate locally, such as the urban heat island and non-locally as in the case of increased rainfall downwind of a city ( [[#Jia--2019|Jia et al., 2019]] ; Box 10.3) or the monsoon circulation affected by irrigation ( [[#10.6.3|Section 10.6.3]] ). The influence of land cover changes and land management on regional climate extremes is assessed in [[IPCC:Wg1:Chapter:Chapter-11#11.1.6|Section 11.1.6]] .

It is ''very likely'' that the global land surface air temperature response to urbanization is negligible ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] ). However, there is evidence that urbanization may regionally amplify the air temperature response to climate change in different climatic zones ( [[#Mahmood--2014|Mahmood et al., 2014]] ), either under present ( [[#Doan--2016|Doan et al., 2016]] ; [[#Kaplan--2017|Kaplan et al., 2017]] ; X. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ) or future climate conditions ( [[#Argüeso--2014|Argüeso et al., 2014]] ; [[#Kim--2016|Kim et al., 2016]] ; [[#Kusaka--2016|Kusaka et al., 2016]] ; [[#Grossman-Clarke--2017|Grossman-Clarke et al., 2017]] ; [[#Krayenhoff--2018|Krayenhoff et al., 2018]] ). For instance, in northern Belgium, [[#Berckmans--2019|Berckmans et al. (2019)]] found that including urbanization scenarios for the near future (up to 2035) have a comparable influence on minimum temperature (increasing it by 0.6°C) to that of the GHG-induced warming under RCP8.5.

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==== 10.1.3.2 Internal Drivers of Regional Climate Variability ====

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Internal climate variability on seasonal to multi-decadal temporal scales is substantial at regional scales. This variability arises from internal modes of atmospheric and oceanic variability, intrinsically coupled climate modes, and may additionally be driven by processes other than those originating the modes. It also interacts with the response of the climate system to external forcing. The teleconnections associated with the modes are useful to understand the relationship between large and regional scales (Annex IV: Modes of Variability). A description of various large-scale modes of variability can be found in Chapters 2, 3 and 8, and in Annex IV, while their future projections are assessed in Chapter 4. The specificities of their regional influence are briefly discussed here. More details of their typical temporal scales and regional influences can be found in Annex IV.

Atmospheric modes of variability may have seasonally-dependent regional effects like the North Atlantic Oscillation (NAO) in European winter ( [[#Tsanis--2019|Tsanis and Tapoglou, 2019]] ) and summer ( [[#Bladé--2012|Bladé et al., 2012]] ; [[#Dong--2013|Dong et al., 2013]] ). Even though these modes are internal to the climate system, their variability can be affected by anthropogenic forcings. For instance, the SAM ( [[#Hendon--2014|Hendon et al., 2014]] ) is both internally driven ( [[#Smith--2017|Smith and Polvani, 2017]] ), but also affected by recent stratospheric ozone changes ( [[#Bandoro--2014|Bandoro et al., 2014]] ). The teleconnections between these modes of variability and surface weather often exhibit considerable non-stationarity ( [[#Hertig--2015|Hertig et al., 2015]] ).

Due to the large ocean heat capacity and their long temporal scales, multi-annual to multi-decadal modes of ocean variability such as the Pacific Decadal Variability (PDV; [[#Newman--2016|Newman et al., 2016]] ) and the Atlantic Multi-decadal Variability (AMV; [[#Buckley--2016|Buckley and Marshall, 2016]] ) are key drivers of regional climate change. In the case of the AMV both natural (volcanic) and anthropogenic (aerosol) external forcings are thought to be involved in its timing and intensity ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ). These modes not only affect nearby regions but also remote parts of the globe through atmospheric teleconnections ( [[#Meehl--2013|Meehl et al., 2013]] ; [[#Dong--2015|Dong and Dai, 2015]] ) and can act to modulate the influence of natural and anthropogenic forcings ( [[#Davini--2015|Davini et al., 2015]] ; [[#Ghosh--2017|Ghosh et al., 2017]] ; [[#Ménégoz--2018b|Ménégoz et al., 2018b]] ). The dynamics of the ocean modes is simultaneously affected by other modes of variability spanning the full range of spatial and temporal scales due to non-linear interactions (Figure 10.3; [[#Kucharski--2010|Kucharski et al., 2010]] ; [[#Dong--2018|Dong et al., 2018]] ). This mutual interdependence can result in changing characteristics of the connection over time ( [[#Gallant--2013|Gallant et al., 2013]] ; [[#Brands--2017|Brands, 2017]] ; [[#Dong--2017|Dong and McPhaden, 2017]] ), and of their regional climate impact ( [[#Martín-Gómez--2016|Martín-Gómez and Barreiro, 2016]] , 2017). As with atmospheric modes of variability, the regional influence of ocean modes of variability on regional climates can be seasonally dependent ( [[#Haarsma--2015|Haarsma et al., 2015]] ).

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==== 10.1.3.3 Uncertainty and Confidence ====

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Uncertainty and confidence are treated in the same way in regional climate change information as in larger-scale (continental and global) climate problems ( [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] and [[#10.3.4|Section 10.3.4]] ). The degree of confidence in climate simulations and in the resulting climate information typically depends on the identification of the role of the uncertainties ( [[#10.3.4|Section 10.3.4]] ). Since the direct verification of simulations of future climate changes is not possible, model performance and reliable (i.e., trustworthy) uncertainty estimates need to be assessed indirectly through process understanding and a systematic comparison with observations of past and current climate ( [[#10.3.3|Section 10.3.3]] ; [[#Knutti--2010|Knutti et al., 2010]] ; [[#Eyring--2019|Eyring et al., 2019]] ). The observational uncertainty, which is particularly large at regional scales, also has to be taken into account ( [[#10.2|Section 10.2]] ). These uncertainty estimates are then propagated in the distillation process to generate climate information.

Uncertainties in model-based future regional climate information arise from different sources and are introduced at various stages in the process ( [[#Lehner--2020|Lehner et al., 2020]] ): (i) forcing uncertainties associated with the future scenario or pathway that is assumed; (ii) internal variability; and (iii) uncertainties related to imperfections in climate models, also referred to as structural or model uncertainty. However, the relative role of each of these sources of uncertainty differs between the global and the regional scales as well as between variables and also between different regions ( [[#Lehner--2020|Lehner et al., 2020]] ). One way to address the internal variability and model uncertainties is to consider results from both multiple models and multiple realizations of the same model ( [[#Eyring--2016a|Eyring et al., 2016a]] ; [[#Lehner--2020|Lehner et al., 2020]] ; [[#Díaz--2021|Díaz et al., 2021]] ). These models are at times also combined with different weights that are a function of their performance and independence to increase the confidence of the multi-model ensemble ( [[#Abramowitz--2019|Abramowitz et al., 2019]] ; [[#Brunner--2019|Brunner et al., 2019]] ).

Other elements that play a role are the inconsistency between the global and regional models in dynamical downscaling or the observational and methodological uncertainty in bias-adjustment methods ( [[#Sørland--2018|Sørland et al., 2018]] ). These elements, in addition to those typical of the uncertainty in global and large-scale phenomena (Chapters 1–9), affect the overall confidence of regional climate information. This complex scene with different sources of uncertainty makes the collection of results available from multi-model, multi-member simulations most useful when synthesized through a distillation process ( [[#10.5.3|Section 10.5.3]] ).

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