Editing IPCC:AR6/WGI/TS (section)

==== TS.4.2.3 Interplay Between Drivers of Climate Variability and Change at Regional Scales ====

<div id="h3-16-siblings" class="h3-siblings"></div>

Anthropogenic forcing has been a major driver of regional mean temperature change since 1950 in many sub-continental regions of the world ( ''virtually certain'' ). At regional scales, internal variability is stronger, and uncertainties in observations, models and external forcing are all larger than at the global scale, hindering a robust assessment of the relative contributions of greenhouse gases, stratospheric ozone, and different aerosol species in most of the cases. Multiple lines of evidence, combining multi-model ensemble global projections with those coming from single-model initial-condition large ensembles, show that internal variability is largely contributing to the delayed or absent emergence of the anthropogenic signal in long-term regional mean precipitation changes ( ''high confidence'' ). Internal variability in ocean dynamics dominates regional patterns on annual to decadal time scales ( ''high confidence'' ). The anthropogenic signal in regional sea level change will emerge in most regions by 2100 ( ''medium confidence'' ). Links to chapters 9.2.4, 9.6.1, 10.4.1, 10.4.2, 10.4.3

Regional climate change is subject to the complex interplay between multiple external forcings and internal variability. Time evolution of mechanisms operating at different time scales can modify the amplitude of the regional-scale response of temperature, and both the amplitude and sign of the response of precipitation, to anthropogenic forcing ( ''high confidence'' ). These mechanisms include non-linear temperature, precipitation and soil moisture feedbacks; slow and fast responses of SST patterns; and atmospheric circulation changes to increasing GHGs. Land-use and aerosol forcings and land–atmosphere feedback play important roles in modulating regional changes, for instance in weather and climate extremes ( ''high confidence'' ). These can also lead to a higher warming of extreme temperatures compared to mean temperature ( ''high confidence'' ), and possibly cooling in some regions ( ''medium confidence'' ). The soil moisture–temperature feedback was shown to be relevant for past and present-day heatwaves based on observations and model simulations. Links to chapters 10.4.3, 11.1.6, 11.3.1

South-Eastern South America (SES) is one of the AR6 WGI reference regions (outlined with black thick contour in Figure TS.21a), and it is used here as an illustrative example of the interplay between drivers of climate variability and change at regional scale. Austral summer (DJF) precipitation positive trends have been observed over the region during 1950–2014. Drivers of this change include MoVs, such as AMV, ENSO, and PDV, as well as external forcing, like GHG increases and ozone depletion together with aerosols (as illustrated in Figure TS.21a). Modes of variability and external forcing collectively affect climate phenomena, such as the Hadley cell width and strength, Rossby waves activity emerging from the large-scale tropical SST anomalies, and the Southern Hemisphere polar vortex, which are relevant for the region. In fact, local changes over SES in terms of moisture convergence, ascending motion and storm-track locations depend on these climate phenomena, and they are overall responsible for the observed precipitation trends. Projections suggest continuing positive trends in rainfall over SES in the near-term in response to GHG emissions scenarios. Multi-model mean and ensemble spread are not sufficient to characterize situations where different models simulate substantially different or even opposite changes ( ''high confidence'' ) ''.'' In such cases, physical climate storylines addressing possible outcomes for climate phenomena shown to play a role in the variability of the region of interest can aid the interpretation of projection uncertainties. In addition, single-model initial-condition large ensembles of many realizations of internal variability are required to separate internal variability from forced changes ( ''high confidence'' ) and to partition the different sources of uncertainties as a function of future assessed periods. Links to chapters 10.3.4, 10.4.2, Figure 10.12a

<div id="_idContainer054"></div>

[[File:ae3c9ed6ba2c701d8034d0df82fcecd9 IPCC_AR6_WGI_TS_Figure_21.png]]
<div id="_idContainer053" class="Basic-Text-Frame"></div>

'''Figure TS.21 |''' '''Example of the interplay between drivers of climate variability and change at regional scale to understand past and projected changes.''' ''The figure intent is to show an illustrative pathway for understanding past, and anticipating future, climate change at regional scale in the presence of uncertainties.'' '''(a)''' Identification of the climate drivers and their influences on climate phenomena contributing through teleconnection to South-Eastern South America (SES) summer (December–January–February; DJF) precipitation variability and trends observed over 1950–2014. Drivers (red squares) include modes of variability as well as external forcing. Observed precipitation linear trend from GPCC is shown on continents (green-brown colour bar in mm month <sup>–1</sup> per decade) and the SES AR6 WGI reference region is outlined with the thick black contour. Climate phenomena leading to local effects on SES are schematically presented (blue ovals). '''(b)''' Time series of decadal precipitation anomalies for DJF SES simulated from seven large ensembles of historical plus RCP8.5 simulations over 1950–2100. Shading corresponds to the 5–95th range of climate outcomes given from each large ensemble for precipitation (in mm month <sup>–1</sup> ) and thick coloured lines stand for their respective ensemble mean. The thick time series in white corresponds to the multi-model multi-member ensemble mean, with model contribution being weighted according to their ensemble size. GPCC observation is shown in the light black line with squares over 1950–2014, and the 1995–2014 baseline period has been retained for calculation of anomalies in all datasets. '''(c)''' Quantification of the respective weight (in percent) between the individual sources of uncertainties (internal in grey, model in magenta and scenario in green) at near-term, mid-term and long-term temporal windows defined in AR6 and highlighted in (b) for SES DJF precipitation. All computations are done with respect to 1995–2014, taken as the reference period, and the scenario uncertainty is estimated from Coupled Model Intercomparison Project Phase 5 (CMIP5) using the same set of models as for the large ensembles that have run different Representative Concentration Pathway (RCP) scenarios. Links to chapters Figure 10.12a

<div id="box-ts.13" class="h2-container box-container"></div>

'''Box TS.13 | Monsoons'''

<div id="h2-33-siblings" class="h2-siblings"></div>

'''Global land monsoon precipitation decreased from the 1950s to the 1980s, partly due to anthropogenic aerosols, but has increased since then in response to GHG forcing and large-scale multi-decadal variability ( ''medium confidence'' ). Northern Hemispheric anthropogenic aerosols weakened the regional monsoon circulations in South Asia, East Asia and West Africa during the second half of the 20th century, thereby offsetting the expected strengthening of monsoon precipitation in response to GHG-induced warming ( ''high confidence'' ).''' '''During the 21st century, global land monsoon precipitation is projected to increase in response to GHG warming in all time horizons and scenarios ( ''high confidence'' ). Over South and South East Asia, East Asia and the central Sahel, monsoon precipitation is projected to increase, whereas over North America and the far western Sahel it is projected to decrease ( ''medium confidence'' ). There is ''low confidence'' in projected precipitation changes in the South American and Australian-Maritime Continent monsoons. At global and regional scales, near-term monsoon changes will be dominated by the effects of internal variability ( ''medium confidence'' ). Links to chapters 2.3, Cross-Chapter Box 2.4, 3.3, 4.4, 4.5, 8.2, 8.3, 8.4, 8.5, Box 8.1, Box 8.2, 10.6''' '''Global Monsoon'''
Paleoclimate records indicate that during warm climates, like the mid-Pliocene Warm Period, monsoon systems were stronger ( ''medium confidence'' ). In the instrumental records, global summer monsoon precipitation intensity has ''likely'' increased since the 1980s, dominated by Northern Hemisphere summer trends and large multi-decadal variability. Contrary to the expected increase of precipitation under global warming, the Northern Hemisphere monsoon regions experienced declining precipitation from the 1950s to 1980s, which is partly attributable to the influence of anthropogenic aerosols ( ''medium confidence'' ) (Box TS.13, Figure 1). Links to chapters 2.3.1, Cross-Chapter Box 2.4, 3.3.2, 3.3.3

<div id="_idContainer122" class="•-Blue-box--full-width-graphic _idGenObjectStyleOverride-1"></div>

[[File:7261034b568a453571edb26c2e4b1187 IPCC_AR6_WGI_TS_Box_13_Figure_1.png]]

'''Box TS.13, Figure 1 |'''

'''Global and regional monsoons: past trends and projected changes.''' ''The intent of this figure is to show changes in precipitation over regional monsoon domains in terms of observed past trends, how greenhouse gases and aerosols relate to these changes, and in terms of future projections in one intermediate emissions scenario in the near, medium and long term.'' (a) Global (black contour) and regional monsoons (colour shaded) domains. The global monsoon ( ''GM'' ) is defined as the area with local summer-minus-winter precipitation rate exceeding 2.5 mm day <sup>–1</sup> (see Annex V). The regional monsoon domains are defined based on published literature and expert judgement (see Annex V) and accounting for the fact that the climatological summer monsoon rainy season varies across the individual regions. Assessed regional monsoons are South and South East Asia ( ''SAsiaM, Jun–July–August–September'' ), East Asia ( ''EAsiaM, June–July–August'' ), West Africa ( ''WAfriM, June–July–August–September'' ) '','' North America ( ''NAmerM, July–August–-September'' ), South America ( ''SAmerM, December–January–February'' ), Australia and Maritime Continent Monsoon ( ''AusMCM, December–January–February'' ). Equatorial South America ( ''EqSAmer'' ) and South Africa ( ''SAfri'' ) regions are also shown, as they receive unimodal summer seasonal rainfall although their qualification as monsoons is subject to discussion. (b) Global and regional monsoons precipitation trends based on DAMIP CMIP6 simulations with both natural and anthropogenic (ALL), greenhouse gas only (GHG), aerosols only (AER) and natural only (NAT) radiative forcing. Weighted ensemble means are based on nine Coupled model Intercomparison Project Phase 6 (CMIP6) models contributing to the MIP (with at least three members). Observed trends computed from CRU, GPCP and APHRO (only for ''SAsiaM'' and ''EAsiaM'' ) datasets are shown as well. (c) Percentage change in projected seasonal mean precipitation over global and regional monsoons domain in the near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) under SSP2-4.5 based on 24 CMIP6 models. Links to chapters Figures 8.11 and 8.22

With continued global warming, it is ''likely'' that global land monsoon precipitation will increase during this century (Box TS.13, Figure 1), particularly in the Northern Hemisphere, although the monsoon circulation is projected to weaken. A slowdown of the tropical circulation with global warming can partly offset the warming-induced strengthening of precipitation in monsoon regions ( ''high confidence'' ). In the near term, global monsoon changes are ''likely'' to be dominated by the effects of internal variability and model uncertainties ( ''medium confidence'' ). In the long term, global monsoon rainfall change will feature a robust north–south asymmetry characterized by a greater increase in the Northern Hemisphere than in the Southern Hemisphere and an east–west asymmetry characterized by enhanced Asian–African monsoons and a weakened North American monsoon ( ''medium confidence'' ). Links to chapters 4.4.1, 4.5.1, 8.4.1

'''Regional Monsoons'''

Paleoclimate reconstructions indicate stronger monsoons in the Northern Hemisphere but weaker ones in the Southern Hemisphere during warm periods, particularly for the South and South East Asian, East Asian, and North and South American monsoons, with the opposite occurring during cold periods ( ''medium confidence'' ). It is ''very likely'' that Northern Hemispheric anthropogenic aerosols weakened the regional monsoon circulations in South Asia, East Asia and West Africa during the second half of the 20th century, thereby offsetting the expected strengthening of monsoon precipitation in response to GHG-induced warming (Box TS.13, Figure 1). Multiple lines of evidence explain this contrast over South Asia, with the observed trends dominated by the effects of aerosols, while future projections are mostly driven by GHG increases. The recent partial recovery and enhanced intensity of monsoon precipitation over West Africa is related to the growing influence of GHGs with an additional contribution due to the reduced cooling effect of anthropogenic aerosols, emitted largely from North America and Europe ( ''medium confidence'' ). For other regional monsoons, that is, North and South America and Australia, there is ''low confidence'' in the attribution of recent changes in precipitation (Box TS.13, Figure 1) and winds. Links to chapters 2.3.1, 8.3.1, 8.3.2, Box 8.1, 10.6.3

Projections of regional monsoons during the 21st century indicate contrasting (region-dependent) and uncertain precipitation and circulation changes. The annual contrast between the wettest and driest month of the year is ''likely'' to increase by 3–5% per degree Celsius in most monsoon regions in terms of precipitation, precipitation minus evaporation, and runoff ( ''medium confidence'' ). For the North American monsoon, projections indicate a decrease in precipitation, whereas increased monsoon rainfall is projected over South and South East Asia and over East Asia ( ''medium confidence'' ) (Box TS.13, Figure 1). West African monsoon precipitation is projected to increase over the central Sahel and decrease over the far western Sahel ( ''medium confidence'' ). There is ''low confidence'' in projected precipitation changes in the South American and Australian-Maritime Continent regional monsoons (for both magnitude and sign) (Box TS.13, Figure 1). There is ''medium confidence'' that the monsoon season will be delayed in the Sahel and ''high confidence'' that it will be delayed in North and South America. Links to chapters 8.2.2, 8.4.2.4, Box 8.2

'''Building the Assessment from Multiple Lines of Evidence'''
Large natural variability of monsoon precipitation across different time scales, found in both paleoclimate reconstructions and instrumental measurements, poses an inherent challenge for robust quantification of future changes in precipitation at regional and smaller spatial scales. At both global and regional scales, there is ''medium confidence'' that internal variability contributes the largest uncertainty related to projected changes, at least in the near term (2021–2040). A collapse of the Atlantic Meridional Overturning Circulation could weaken the African and Asian monsoons but strengthen the Southern Hemisphere monsoons ( ''high confidence'' ). Links to chapters 4.4.4, 4.5.1, Cross-Chapter Box 4.1, 8.5.2, 8.6.1, 9.2.3, 10.6.3

Overall, long-term (2081–2100) future changes in regional monsoons like the South and South East Asian monsoon are generally consistent across global (including high-resolution) and regional climate models and are supported by theoretical arguments. Uncertainties in simulating the observed characteristics of regional monsoon precipitation are related to varying complexities of regional monsoon processes and their responses to external forcing, internal variability, and deficiencies in representing monsoon warm rain processes, organized tropical convection, heavy orographic rainfall and cloud–aerosol interactions. Links to chapters 8.3.2, 8.5.1, 10.3.3, 10.6.3

<div id="TS.4.3" class="h2-container"></div>

<span id="ts.4.3-regional-climate-change-and-implications-for-climate-extremes-and-climatic-impact-drivers"></span>