Editing IPCC:AR6/WGI/TS (section)

== Box TS.12 | Multiple Lines of Evidence for Assessing Regional Climate Change and the Interactive Atlas ==

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'''A key novel element in the AR6 is the Working Group I Atlas, which includes the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] (https://interactive-atlas.ipcc.ch/). The Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] provides the ability to explore much of the observational and climate model data used as lines of evidence in this assessment to generate regional climate information. Links to chapters Atlas.2'''

A significant innovation in the AR6 WGI Report is the Atlas. Part of its remit is to provide region-by-region assessment on changes in mean climate and to link with other WGI chapters to generate climate change information for the regions. An important component is the new online interactive tool, the Interactive Atlas, with flexible spatial and temporal analyses of much of the observed, simulated past and projected future climate change data underpinning the WGI assessment. This includes the ability to generate global maps and a number of regionally aggregated products (time series, scatter plots, tables, etc.) for a range of observations and ensemble climate change projections of variables (such as changes in the climatic impact-drivers summarized in Table TS.5) from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5, CMIP6) and the Coordinated Regional Climate Downscaling Experiment (CORDEX). The data can be displayed and summarized under a range of SSP-RCP scenarios and future time slices and also for different global warming levels, relative to several different baseline periods. The maps and various statistics can be generated for annual mean trends and changes or for any user-specified season. A new set of WGI reference regions is used for the regional summary statistics and applied widely throughout the report (with the regions, along with aggregated datasets and the code to generate these, available at the ATLAS GitHub: https://github.com/IPCC-WG1/Atlas).

Box TS.12, Figure 1 shows how the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] products, together with other lines of evidence, can be used to generate climate information for an illustrative example of the Mediterranean summer warming. The lines of evidence include the understanding of relevant mechanisms, dynamic and thermodynamic processes and the effect of aerosols in this case (Box TS.12, Figure 1a); trends in observational datasets (which can have different spatial and temporal coverage; Box TS.12, Figure 1b, c); and attribution of these trends and temperature projections from global and regional climate models at different resolutions, including single-model initial-condition large ensembles (SMILEs; Box TS.12, Figure 1d, e). Taken together, this evidence shows there is ''high confidence'' that the projected Mediterranean summer temperature increase will be larger than the global mean, with consistent results from CMIP5 and CMIP6 (Box TS.12, Figure 1e). However, CMIP6 results project both more pronounced warming than CMIP5 for a given emissions scenario and time period and a greater range of changes (Box TS.12, Figure 1d). Links to chapters 10.6.4, Atlas.2, Atlas.8.4

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'''Box TS.12, Figure 1 |'''

'''Example of generating regional climate information from multiple lines of evidence for the case of Mediterranean summer warming.''' Box TS.12 ''The intent of this figure is to provide an example of using different lines of evidence to assess the confidence in or likelihood of a projected change in regional climate and which of these lines of evidence are available to view and explore in the Interactive Atlas.'' '''(a)''' Mechanisms and feedbacks involved in enhanced Mediterranean summer warming. '''(b)''' Locations of observing stations from different datasets. '''(c)''' Distribution of 1960–2014 summer temperature trends (°C per decade) for observations (black crosses), CMIP5 (blue circles), CMIP6 (red circles), HighResMIP (orange circles), CORDEX EUR-44 (light blue circles), CORDEX EUR-11 (green circles), and selected single model initial-condition large ensembles (SMILEs; grey boxplots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF). '''(d)''' Time series of area averaged (25°N–50°N, 10°W–40°E) land point summer temperature anomalies (°C, baseline period is 1995–2014): the boxplot shows long term (2081–2100) temperature changes of different CMIP6 scenarios in respect to the baseline period. '''(e)''' Projected Mediterranean summer warming in comparison to global annual mean warming of CMIP5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and CMIP6 (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) ensemble means (lines) and spread (shading). Links to chapters Figure 10.20, Figure 10.21, Figure Atlas.8
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=== TS.4.2 Drivers of Regional Climate Variability and Change ===

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'''Anthropogenic forcing, including GHGs and aerosols, but also regional land use and irrigation have all affected observed regional climate changes (''high confidence'') and will continue to do so in the future (''high confidence''), with various degrees of influence and response times, depending on warming levels, the nature of the forcing and the relative importance of internal variability.'''

'''Since the late 19th century, major modes of variability (MoVs) exhibited fluctuations in frequency and magnitude at multi-decadal time scales, but no sustained trends outside the range of internal variability (Table TS.4). An exception is the Southern Annular Mode (SAM), which has become systematically more positive (''high confidence'') and is projected to be more positive in all seasons, except for December–January–February (DJF), in high CO <sub>2</sub> emissions scenarios (''high confidence''). The influence of stratospheric ozone forcing on the SAM trend has been reduced since the early 2000s compared to earlier decades, contributing to the weakening of its positive trend as observed over 2000–2019 (''medium confidence'').'''

'''In the near term, projected changes in most of the MoVs and related teleconnections will ''likely'' be dominated by internal variability. In the long term, it is ''very likely'' that the precipitation variance related to El Niño–Southern Oscillation will increase. Physical climate storylines, including the complex interplay between climate drivers, MoVs, and local and remote forcing, increase confidence in the understanding and use of observed and projected regional changes. Links to chapters 2.4, 3.7, 4.3, 4.4, 4.5, 6.4, 8.3, 8.4, 10.3, 10.4, 11.3'''
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==== TS.4.2.1 Regional Fingerprints of Anthropogenic and Natural Forcing ====

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While anthropogenic forcing has contributed to multi-decadal mean precipitation changes in several regions, internal variability can delay emergence of the anthropogenic signal in long-term precipitation changes in many land regions (''high confidence''). At the regional scale, the effect of human-induced GHG forcing on extreme temperature is moderated or amplified by soil moisture feedback, snow/ice-albedo feedback, regional forcing from land-use/land-cover changes, forcing from aerosol concentrations, or decadal/multi-decadal natural variability. Changes in local and remote aerosol forcings lead to south–north gradients of the effective radiative forcing (hemispherical asymmetry). Along latitudes, it is more uniform, with strong amplification of the temperature response towards the Arctic (''medium confidence''). The decrease of SO <sub>2</sub> emissions since the 1980s reduces the damping effect of aerosols, leading to a faster increase in surface air temperature that is most pronounced at mid- and high latitudes of the Northern Hemisphere, where the largest emissions reductions have taken place (''medium confidence''). Links to chapters 1.3, 3.4.1, 6.3.4, 6.4.1, 6.4.3, 8.3.1, 8.3.2, Box 8.1, 10.4.2, 10.6, 11.1.6, 11.3

Multi-decadal dimming and brightening trends in incoming solar radiation at Earth’s surface occurred at widespread locations (''high confidence''). Multi-decadal variation in anthropogenic aerosol emissions are thought to be a major contributor (''medium confidence''), but multi-decadal variability in cloudiness may also have played a role. Volcanic eruptions affect regional climate through their spatially heterogeneous effect on the radiative budget as well as through triggering dynamical responses by favouring a given phase from some MoVs, for instance. Links to chapters 1.4.1, Cross-Chapter Box 1.2, 2.2.1, 2.2.2, 3.7.1, 3.7.3, 4.3.1, 4.4.1, 4.4.4, Cross-Chapter Box 4.1, 7.2.2, 8.5.2, 10.1.4, 11.1.6, 11.3.1

Historical urbanization affects the observed warming trends in cities and their surroundings (''very'' ''high confidence''). Future urbanization will amplify the projected air temperature under different background climates, with a strong effect on minimum temperatures that could be as large as the global warming signal (''very high confidence'') (Box TS.14). Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as central North America (''medium confidence'') (Box TS.6). Links to chapters Box 10.3, 11.1.6, 11.3

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==== TS.4.2.2 Modes of Variability and Regional Teleconnections ====

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Modes of variability (Annex IV, Table TS.4) have existed for millennia or longer (''high confidence''), but there is ''low confidence'' in detailed reconstructions of most of them prior to direct instrumental records. MoVs are treated as a main source of uncertainties associated with internal dynamics, as they can either accentuate or dampen, even mask, the anthropogenically forced responses. Links to chapters 2.4, 8.5.2, 10.4, 10.6, 11.1.5, Atlas.3.1

Since the late 19th century, major MoVs (Table TS.4) show no sustained trends, exhibiting fluctuations in frequency and magnitude at multi-decadal time scales, except for the Southern Annular Mode (SAM), which has become systematically more positive (''high confidence'') (Table TS.4). It is ''very likely'' that human influence has contributed to this trend from the 1970s to the 1990s, and to the associated strengthening and southward shift of the Southern Hemispheric extratropical jet in austral summer. The influence of stratospheric ozone forcing on the SAM trend has been reduced since the early 2000s compared to earlier decades, contributing to the weakening of its positive trend observed over 2000–2019 (''medium confidence''). By contrast, the cause of the Northern Annular Mode (NAM) trend toward its positive phase since the 1960s and associated northward shifts of Northern Hemispheric extratropical jet and storm track in boreal winter is not well understood. The evaluation of model performance on simulating MoVs is assessed in Section TS.1.2.2. Links to chapters 2.3.3, 2.4, 3.3.3, 3.7.1, 3.7.2

In the near term, the forced change in SAM in austral summer is ''likely'' to be weaker than observed during the late 20th century under all five SSPs assessed. This is because of the opposing influence in the near to mid-term from stratospheric ozone recovery and increases in other greenhouse gases on the Southern Hemisphere summertime mid-latitude circulation (''high confidence''). In the near term, forced changes in the SAM in austral summer are therefore ''likely'' to be smaller than changes due to natural internal variability. In the long term (2081–2100) under the SSP5-8.5 scenario, the SAM index is ''likely'' to increase in all seasons relative to 1995–2014. The CMIP6 multi-model ensemble projects a long-term (2081–2100) increase in the boreal wintertime NAM index under SSP3-7.0 and SSP5-8.5, but regional associated changes may deviate from a simple shift in the mid-latitude circulation due to a modified teleconnection resulting from interaction with a modified mean background state. Links to chapters 4.3.3, 4.4.3, 4.5.1, 4.5.3, 8.4.2

Human influence has not affected the principal tropical modes of interannual climate variability (Table TS.4) and their associated regional teleconnections beyond the range of internal variability (''high confidence''). It is ''virtually certain'' that the El Niño–Southern Oscillation (ENSO) will remain the dominant mode of interannual variability in a warmer world. There is no consensus from models for a systematic change in amplitude of ENSO sea surface temperature (SST) variability over the 21st century in any of the SSP scenarios assessed (''medium confidence''). However, it is ''very'' ''likely'' that rainfall variability related to ENSO will be enhanced significantly by the latter half of the 21st century in the SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, regardless of the amplitude changes in SST variability related to the mode. It is ''very likely'' that rainfall variability related to changes in the strength and spatial extent of ENSO teleconnections will lead to significant changes at regional scale. Links to chapters 3.7.3, 3.7.4, 3.7.5, 4.3.3, 4.5.3, 8.4.2, 10.3.3

Modes of decadal and multi-decadal variability over the Pacific and Atlantic Ocean exhibit no significant changes in variance over the period of observational records (''high confidence''). There is ''medium confidence'' that anthropogenic and volcanic aerosols contributed to observed temporal evolution in the Atlantic Multi-decadal Variability (AMV) and associated regional teleconnections, especially since the 1960s, but there is ''low confidence'' in the magnitude of this influence and the relative contributions of natural and anthropogenic forcings. Internal variability is the main driver of Pacific Decadal Variability (PDV) observed since the start of the instrumental records (''high confidence''), despite some modelling evidence for potential external influence. There is ''medium confidence'' that the AMV will undergo a shift towards a negative phase in the near term. Links to chapters 2.4, 3.7.6, 3.7.7, 8.5.2, 4.4.3

'''Table TS.4 |''' '''Summary of the assessments on modes of variability (MoVs) and associated teleconnections.''' '''(a)''' Assessments on observed changes since the start of instrumental records, Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) model performance, human influence on the observed changes, and near-term (2021–2040) and mid- to long-term (2041–2100) changes. Curves schematically illustrate the assessed overall changes, with the horizontal axis indicating time, and are not intended to precisely represent the time evolution. '''(b)''' Fraction of surface air temperature (SAT) and precipitation (pr) variance explained at interannual time scale by each MoV for each AR6 region (numbers in each cell; in percent). Values correspond to the average of significant explained variance fractions based on HadCRUT, GISTEMP, BerkeleyEarth and CRU-TS (for SAT) and GPCC and CRU-TS (for precipitation). Significance is tested based on F-statistics at the 95% level confidence, and a slash indicates that the value is not significant in more than half of the available data sets. The colour scale corresponds to the sign and values of the explained variance as shown at the bottom. The corresponding anomaly maps are shown in Annex IV. DJF: December–January–February. MAM: March–April–May. JJA: June–July–August. SON: September–October–November. In (b), Northern Annular Mode (NAM) and El Niño–Southern Oscillation (ENSO) teleconnections are evaluated for 1959–2019, Southern Annular Mode (SAM) for 1979–2019, Indian Ocean Basin (IOB), Indian Ocean Dipole (IOD), Atlantic Zonal Mode (AZM) and Atlantic Meridional Mode (AMM) for 1958–2019, and Pacific Decadal Variability (PDV) and Atlantic Multi-decadal Variability (AMV) for 1900–2019. All data are linearly detrended prior to computation. (Section TS.1.2.2) Links to chapters 2.4, 3.7, 4.3.3, 4.4.3, 4.5.3, Table Atlas.1, Annex IV

(a) Assessments on MoV.

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'''Table TS.4 (continued): (b) Regional climate anomalies associated with MoV.'''

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

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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

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'''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

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