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

=== Atlas.1.4 Combining Multiple Sources of Information for Regions ===

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This section introduces the observational data sources and reanalyses that are used in the assessment of regional climate change and for evaluating and bias adjusting the results of models (more information on observational reference datasets is available in Annex I). It also introduces the different global and regional climate model outputs that are used for regional climate assessment considering both historical and future climate projections (Annex II). Many of these models are run as part of coordinated Model Intercomparison Projects (MIPs), including CMIP5, CMIP6 and CORDEX, described below. Combining information from these multiple data sources is a significant challenge (see [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] for an in-depth treatment of the problem) though if they can be used to generate robust information on regional climate change it can guide policy and support decisions responding to these changes. An important and necessary part of this process is to check for consistency amongst the data sources.

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==== Atlas.1.4.1 Observations ====

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There are various sources of observational information available for global and regional analysis. Observational uncertainty is a key factor when assessing and attributing historical trends, so assessment should build on integrated analyses from different datasets (disparity, inadequacy and contradictions in existing datasets are assessed in [[IPCC:Wg1:Chapter:Chapter-10#10.2|Section 10.2]] ). The Atlas chapter can supplement and complement [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] by providing the opportunity to visualize and expand on its assessment. This includes displaying maps of density of stations’ observations (including those that are used in the different datasets) and assessing observational uncertainty by using multiple datasets.

Two of the most commonly used variables in climate studies are gridded surface air temperature and precipitation. There are many datasets available (Annex I) and [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] provides an assessment of key global datasets, including blended land-air and sea surface temperature datasets to assess global mean surface temperature (GMST). The Atlas separately analyses atmospheric and oceanic variables, and for the former a number of common global datasets supporting the assessment done in other chapters is used, including those selected in Chapter 2, but considering land-only information for the blended products. In particular, for air temperature the Atlas uses CRUTEM5 – the land component of the HadCRUT5 dataset – ( [[#Osborn--2021|Osborn et al., 2021]] ), Berkeley Earth ( [[#Rohde--2020|Rohde and Hausfather, 2020]] ) and the Climatic Research Unit CRU TS4 (version 4.04 used here; [[#Harris--2020|Harris et al., 2020]] ). For precipitation the Atlas includes CRU TS4, the Global Precipitation Climatology Centre (GPCC, v2018 used here; [[#Schneider--2011|Schneider et al., 2011]] ), and Global Precipitation Climatology Project (GPCP; monthly version 2.3 used here; [[#Adler--2018|Adler et al., 2018]] ). Although the ultimate source of these datasets is surface-station reported values (GPCP also includes satellite information), each has access to different numbers of stations and lengths of records and employs different ways of creating the gridded product and ensuring quality control. For oceanic variables, the most widely used sea surface temperature (SST) datasets are HadSST4 ( [[#Kennedy--2019|Kennedy et al., 2019]] ), which is the oceanic component of the HadCRUT5 dataset, ERSST ( [[#Huang--2017|]] [[#Huang--2017|B. Huang et al., 2017]] ), and KaplanSST ( [[#Kaplan--1998|Kaplan et al., 1998]] ).

Figure Atlas.5 shows the spatial coverage of the total number of observation stations for different periods (1901–1910, 1971–1980, and 2001–2010) for two illustrative datasets: the CRU TS4 dataset for precipitation and the SST data in HadSST4. The former illustrates spatially the declining trend of station observation data used in the precipitation datasets for certain regions (South America and Africa) after the 1990s. This demonstrates the regional inhomogeneity and temporal change in station density, which is in part a consequence of many stations not reporting to the WMO networks and their data being held domestically or regionally. During early years (before 1950) a limited number of observations are available. This information is used in the Interactive Atlas to blank out regions not constrained with observations in those datasets providing station density information.

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[[File:ffa10a6a7fa959dcec69ae1d98683dcc IPCC_AR6_WGI_Atlas_Figure_5.png]]

'''Figure Atlas.5''' '''|''' '''Number of stations per 0.5° × 0.5° gridcell reported over the periods of 1901–1910, 1971–1980, and 2001–2010 (rows 1–3), and the global total number of stations reported over the entire globe (bottom row) for precipitation in the CRU TS4 dataset (left) and the HadSST4 dataset (right).''' Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

In addition to surface observations, satellites have been widely used to produce rainfall estimates. The advantage of satellite-based rainfall products is their global coverage including remote areas but there is significant uncertainty in these products over complex terrain ( [[#Rahmawati--2018|Rahmawati and Lubczynski, 2018]] ; [[#Satgé--2019|Satgé et al., 2019]] ). Another recent development has been on gridded datasets for climate extremes based on surface stations, such as HadEX3 ( [[#Dunn--2020|Dunn et al., 2020]] ), as described in [[IPCC:Wg1:Chapter:Chapter-11#11.2.2|Section 11.2.2]] .

There are some studies assessing observational datasets globally ( [[#Beck--2017|Beck et al., 2017]] ; Q. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ) and regionally ( [[#Manzanas--2014|Manzanas et al., 2014]] ; [[#Salio--2015|Salio et al., 2015]] ; [[#Prakash--2019|Prakash, 2019]] ), reporting large differences among them and stressing the importance of considering observational uncertainty in regional climate assessment studies. Uncertainty in observations is also a key limitation for the evaluation of climate models, particularly over regions with low station density ( [[#Kalognomou--2013|Kalognomou et al., 2013]] ; [[#Kotlarski--2019|Kotlarski et al., 2019]] ). More detailed information on these issues is provided in [[IPCC:Wg1:Chapter:Chapter-10#10.2|Section 10.2]] .

For regional studies, observational datasets with global coverage are complemented by a range of regional observational analyses and gridded products, such as E-OBS ( [[#Cornes--2018|Cornes et al., 2018]] ) over Europe, Daymet ( [[#Thornton--2016|Thornton et al., 2016]] ) over North America, or APHRODITE ( [[#Yatagai--2012|Yatagai et al., 2012]] ) over Asia. These are highlighted in various other chapters and the Atlas expands on their treatment, complementing discussions on discrepancies/conflicts in observations presented in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] and expanding on and replicating their results for other regions. In particular, the Interactive Atlas includes the global and regional observational products described here to assess observational uncertainty over the different regions analysed.

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==== Atlas.1.4.2 Reanalysis ====

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There are currently many atmospheric reanalysis datasets with different spatial resolution and assimilation algorithms (see [[IPCC:Wg1:Chapter:Annex-i|Annex I]] and [[IPCC:Wg1:Chapter:Chapter-1#1.5.2|Section 1.5.2]] ). There are also substantial differences among these datasets due to the types of observations assimilated into the reanalyses, the assimilation techniques that are used, and the resolution of the outputs, amongst other reasons. For example, 20CR ( [[#Slivinski--2019|Slivinski et al., 2019]] ) only assimilates surface pressure and sea surface temperature (SST) to achieve the longest record but at relatively low resolution, while ERA-20C ( [[#Poli--2016|Poli et al., 2016]] ) only assimilates surface pressure and surface marine winds. At the other extreme, very sophisticated assimilation systems using multiple surface, upper air and Earth observation data sources are employed, for example ERA5 ( [[#Hersbach--2020|Hersbach et al., 2020]] ) and JRA-55 ( [[#Harada--2016|Harada et al., 2016]] ), which also have much higher resolutions. Most reanalysis datasets cover the entire globe, but there are also high-resolution regional reanalysis datasets which provide further regional detail ( [[#Kaiser-Weiss--2019|Kaiser-Weiss et al., 2019]] ).

The Atlas and Interactive Atlas use information from ERA5 and from the bias-adjusted version WFDE5 ( [[#Cucchi--2020|Cucchi et al., 2020]] ) which is combined with ERA5 information over the ocean and used as the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) observational reference dataset W5E5 ( [[#Lange--2019b|Lange, 2019b]] ). This reference is also used in the Atlas for model evaluation ( [[#Atlas.1.4.4|Atlas.1.4.4]] ) and for bias-adjusting model outputs ( [[#Atlas.1.4.5|Atlas.1.4.5]] ).

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==== Atlas.1.4.3 Global Model Data (CMIP5 and CMIP6) ====

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The Atlaschapter (and the Interactive Atlas) uses global model simulations from both CMIP5 and CMIP6, mainly historical and future projections performed under ScenarioMIP ( [[#O’Neill--2016|O’Neill et al., 2016]] ). This facilitates backwards comparability and thus the detection of new salient features and findings from recent science and the latest CMIP6 ensemble. The selection of the models is based on availability of scenario data for the variables assessed in the Atlas chapter and for those included in the Interactive Atlas ( [[#Atlas.2.2|Atlas.2.2]] ). In particular, in order to harmonize the results obtained from the different scenarios as much as possible, only models providing data for the historical scenario and at least two emissions scenarios, RCP2.6, RCP4.5 and/or RCP8.5 (for CMIP5), and SSP1-2.6, SSP2-4.5, SSP3-7.0 and/or SSP5-8.5 (for CMIP6), were chosen, resulting in 29 and 35 models, respectively (see Cross-Chapter Box 1.4 for a description of the scenarios). In the Atlas chapter (similarly to the regional Chapters 11 and 12) a single simulation is taken from each model (see [[#Atlas.12|Atlas.12]] for limitations of this choice). Since the RCP and SSP emissions scenarios are not directly comparable due to different regional forcing ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.2|Section 4.2.2]] ), the Atlas includes GWLs as an alternative dimension of analysis (Cross-Chapter Box 11.1), which allows intercomparison of results from different scenarios as an alternative to the standard analysis based on time slices for particular scenarios ( [[#Atlas.1.3.1|Atlas.1.3.1]] ). This dimension allows for enhanced comparability of CMIP5 and CMIP6, since it constrains the regional patterns to the same global warming level for both datasets.

Building on this information, the Interactive Atlas displays a number of (mean and extreme) indices and climatic impact-drivers (CIDs), considering both atmospheric and oceanic variables ( [[#Atlas.2.2|Atlas.2.2]] ). Some of these indices have been selected in coordination with Chapters 11 and 12, in order to support and extend the assessment performed in these chapters (see [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details on the indices). In order to harmonize this information, the indices have been computed for each individual model on the original model grids and the results have been interpolated to a common 2° (for CMIP5) and 1° (CMIP6) horizontal resolution grids. In addition, for the sake of comparability with CMIP6 results (in particular when using baselines going beyond 2005), the historical period of the CMIP5 and CORDEX datasets has been extended to 2006–2014 using the first years of RCP8.5-driven transient projections ( [[#Atlas.1.3.1|Atlas.1.3.1]] ). Tables listing the CMIP5 and CMIP6 models used in the Atlas and in the Interactive Atlas for different scenarios and variables are included as Supplementary Material (Tables Atlas.SM.1 and Atlas.SM.2, respectively); moreover, full inventories including details on the specific Earth System Grid Federation (ESGF) versions are given in the Atlas GitHub repository ( [[#Iturbide--2021|Iturbide et al., 2021]] ).

[[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] and [[#Flato--2013|Flato et al. (2013)]] describe the evaluation of CMIP6 and CMIP5 models, respectively, assessing surface variables and large-scale indicators. [[IPCC:Wg1:Chapter:Chapter-10#10.3.3|Section 10.3.3]] assesses the general capability of GCMs to produce climate output for regions.

Information from the existing CMIP5 and CMIP6 datasets is supplemented with downscaled regional climate simulations from CORDEX. This facilitates an assessment of the effects from higher resolution, including whether this modifies the projected climate change signals compared to global models and adds any value, especially in terms of high-resolution features and extremes.

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==== Atlas.1.4.4 Regional Model Data (CORDEX) ====

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Global model data,as generated by the CMIP ensembles, although available globally, have spatial resolutions that are limited for reproducing certain processes and phenomena relevant for regional analysis (around 2° and 1° for CMIP5 and CMIP6, respectively). The Coordinated Regional Climate Downscaling Experiment (CORDEX; [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ) facilitates worldwide application of Regional Climate Models (RCMs, see [[IPCC:Wg1:Chapter:Chapter-10#10.3.1.2|Section 10.3.1.2]] ), focusing on a number of regions (Figure Atlas.6) with a typical resolution of 0.44° (but also at 0.22° and 0.11° over some domains, such as Europe). However, only a few simulations are available for some domains (Annex II, Table AII.1), thus limiting the level of analysis and assessment that can be performed using CORDEX data in some regions. Moreover, there are regions where several domains overlap, thus providing additional lines of evidence. The use of multi-domain grand ensembles to work globally with CORDEX data have recently been proposed ( [[#Legasa--2020|Legasa et al., 2020]] ; [[#Spinoni--2020|Spinoni et al., 2020]] ). Ongoing efforts, such as the multi-domain CORDEX-CORE simulations are promoting more homogeneous coverage and thus more systematic treatment of CORDEX domains (Box Atlas.1).

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[[File:ef03a1f34ddd4ff9d8eb0722558a8ac8 IPCC_AR6_WGI_Atlas_Figure_6.png]]

'''Figure Atlas.6''' '''|''' '''CORDEX domains showing the curvilinear domainboundaries resulting from the original rotated domains.''' The topography corresponding to the standard CORDEX 0.44° resolution is shown to illustrate the orographic gradients over the different regions.

A lot of progress has been made by the regional climate modelling community since AR5 (Table AII.1) to produce and make available evaluation (reanalysis-driven) simulations over the different CORDEX domains along with downscaled CMIP5 historical and future climate projection information under a range of emissions scenarios, mainly RCP2.6, RCP4.5 and RCP8.5 (Tables AII.3 and AII.4). However, these ensembles cover only a fraction of the uncertainty range spanned by the full CMIP5 ensemble in the different domains (e.g., Figures Atlas.16, Atlas.17, Atlas.21, Atlas.22, Atlas.24, Atlas.26, Atlas.28 and Atlas.29; [[#Ito--2020b|Ito et al., 2020b]] ). Therefore, comparison of CMIP5 and CORDEX results should be performed carefully, providing results not only for the full CMIP5 ensemble but also for the sub-ensemble formed by the driving models since results can diverge ( [[#Fernández--2019|Fernández et al., 2019]] ; [[#Iles--2020|Iles et al., 2020]] ).

The Atlas chapter and the Interactive Atlas use CORDEX information for the following 11 individual CORDEX domains (out of the 14 domains shown in Figure Atlas.6): North, Central and South America; Europe; Africa; South, East and South East Asia; Australasia; Arctic and Antarctica; in addition, oceanic information has been used from the Mediterranean domain, which provides simulations from coupled atmosphere–ocean regional climate models (RCMs). In order to harmonize the information across domains and to maximize the size of the resulting ensembles, all the available simulations for each individual CORDEX domain (including the standard 0.44° CORDEX and the 0.22° CORDEX-CORE) have been interpolated to a common regular 0.5°-resolution grid to provide a grand ensemble covering the historical and future emissions scenarios RCP2.6, RCP4.5 and RCP8.5, and also the reanalysis-driven simulations for evaluation purposes. In the case of the European domain, the dataset considered is the 0.11° simulations (CORDEX EUR-11, the same dataset as used in Chapter 12), which has been interpolated to a regular 0.25° resolution grid (the same used for the regional observations). In the case of the Mediterranean domain, oceanic information (sea surface temperature, SST) is interpolated to a regular 0.11° grid. In all cases, the indices are computed on the original grids and the interpolation process is applied to the resulting indices. Moreover, for the sake of comparability with CMIP6 results (in particular when using baseline periods beyond 2005), the historical period of the CORDEX datasets has been extended to 2006–2014 using the first years of RCP8.5-driven transient projections in which the emissions are close to those observed (see [[#Atlas.1.3.1|Atlas.1.3.1]] ); note that this procedure is also applied to CMIP5 simulations.

For the different CORDEX domains, the full ensembles of models (GCM-RCM matrix) used in the Atlas for the different scenarios and variables are described in the Supplementary Material (Tables Atlas.SM.3–Atlas.SM.14) and in the Atlas repository ( [[#Iturbide--2021|Iturbide et al., 2021]] ), including full metadata relative to ESGF versions used and the periods with data available for the different simulations. In particular, the historical scenario information is only available from 1970 onwards for some models and therefore the common period 1970–2005 is used for historical CORDEX data in the Atlas. As a result, the WMO baseline period 1961–1990 is not available in the Interactive Atlas for CORDEX data.

Sections [[#Atlas.4|Atlas.4]] to [[#Atlas.11|Atlas.11]] assess research on CORDEX simulations over different regions, analysing past and present climate as well as future climate projections. They also focus on regional model evaluation in order to extend and complement the validation of global models in Chapter 3, considering the specific regional climate and relevant large-scale and regional phenomena, drivers and feedbacks ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3|Section 10.3.3]] ). Besides the literature assessment, some simple evaluation diagnostics have been computed for the simulations used in the Atlas chapter to provide some basic information on model performance across regions. In particular, biases for mean temperature and precipitation have been calculated for the 11 CORDEX domains analysed.

Figure Atlas.7 shows mean temperature and precipitation biases over the North American domain in RCM simulations driven by reanalysis and historical GCM simulations ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.2.5|Section 10.3.2.5]] ). Annual and seasonal (December–January–February (DJF) and June–July–August (JJA)) biases are computed for both the RCMs and driving GCMs. Biases in the reanalysis-driven RCMs result from intrinsic model errors, with the results displayed being spatially aggregated for each reference region. This same analysis is performed for the GCM-driven RCM simulations over the historical period 1986–2005. This allows comparison of the intrinsic bias of the RCMs with the biases resulting when driven by the different GCMs and patterns of behaviour in the RCMs, for example intrinsic warm and dry biases in ENA and WNA respectively or reduced RCM warm biases compared to the CCCma GCM in NEN and ENA. Similar results for the other CORDEX domains are included as Supplementary Material (Figures Atlas.SM.1–Atlas.SM.10).

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[[File:86a17e811403e3604a7566e2b424abf2 IPCC_AR6_WGI_Atlas_Figure_7.png]]

'''Figure Atlas.7''' '''|''' '''Evaluation of annual and seasonal air temperature and precipitation for the six North America sub-regions, NWN, NEN, WNA, CNA, ENA and NCA (land only) for CORDEX-NAM RCM simulations driven by reanalysis or historical GCMs.''' Seasons are June–July–August (JJA) and December–January–February (DJF). Rows represent sub-regions and columns correspond to the models. Magenta text indicates the driving historical CMIP5 GCMs (including ERA-Interim in the first set of slightly separated columns) and the black text to the right of the magenta text represents the driven RCMs. The colour matrices show the mean spatial biases; all biases have been computed for the period 1985–2005 relative to the observational reference (E5W5, see [[#Atlas.1.4.2|Atlas.1.4.2]] ). Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

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==== Atlas.1.4.5 Bias Adjustment ====

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Bias adjustment is often applied to data from climate model simulations to improve their applicability for assessing climate impacts and risk (e.g., in the Inter-Sectoral Impact Model Intercomparison Project, ISIMIP; [[#Rosenzweig--2017|Rosenzweig et al., 2017]] ). Bias-adjustment approaches ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.1.3|Section 10.3.1.3]] ) are particularly beneficial when threshold-based indices are used, but they can introduce other biases, in particular when applied directly to coarse-resolution GCMs (Cross-Chapter Box 10.2). Bias-adjustment techniques should be chosen carefully for a specific application. In the Atlas, bias adjustment is not applied systematically (in particular, it is not applied for the variables assessed in the Atlas chapter), and only some threshold-dependent extreme indices and climatic impact-drivers (CIDs) included in the Interactive Atlas are bias adjusted (in particular TX35 and TX40 in coordination with Chapter 12). To facilitate integration with WGII, the Atlas uses the same bias-adjustment method as in ISIMIP3 ( [[#Lange--2019a|Lange, 2019a]] ) and the same observational reference (W5E5, see [[#Atlas.1.4.2|Atlas.1.4.2]] ), upscaled to the same resolution as the model to avoid downscaling artefacts (Cross-Chapter Box 10.2). The ISIMIP3 bias-adjustment method is a trend-preserving approach that is recommended for general applications, as it reduces biases while preserving the original climate change signal ( [[#Casanueva--2020|Casanueva et al., 2020]] ). Following the recommendations given in Chapter 10, results in the Interactive Atlas are displayed for both the adjusted and the raw model output.

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'''Box Atlas.1 | CORDEX-CORE'''

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[[File:34dc524f1f3545ee6f9042b6bfa01860 IPCC_AR6_WGI_Atlas_Box_Figure_1.png]]

'''Box Atlas.1, Figure'''

'''1 |'''

'''Temperature and precipitation climate change signals at the end of the century (2070–2099).''' The top panels show climate change signals for '''(a)''' temperature and '''(b)''' precipitation for the entire CMIP5 ensemble (box-whisker plots) and the CORDEX-CORE driving GCMs (grey symbols) of the respective CORDEX-CORE results (non-grey symbols) in the South Asia (SAS) reference region. The shape of the grey symbols represents the climate sensitivity of the driving GCMs: triangles pointing upwards (low equilibrium), circles (medium equilibrium), triangles pointing downwards (high equilibrium). The corresponding RCM results are drawn using the same symbols, but in orange for REMO and in blue for RegCM. The bottom panels show the warming signal by 2070–2099 over the CORDEX regions for RCP2.6 '''(c)''' and RCP8.5 '''(d)''' (Figure from [[#Teichmann--2021|Teichmann et al., 2021]] ).
Box Atlas.1

The main objective of CORDEX-CORE is to provide a global homogeneous foundation of high-resolution regional climate model (RCM) projections to improve understanding of local phenomena and facilitate impact and adaptation research worldwide ( [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ). The experimental framework is designed to produce homogeneous regional projections for most inhabited land regions using nine CORDEX domains at 0.22° resolution (Figure Atlas.6): North, Central and South America (NAM, CAM, SAM); Europe (EUR); Africa (AFR); East, South and Southeast Asia (EAS, WAS, SEA); and Australasia (AUS). Due to computational requirements, three GCMs were selected to drive the simulations, HADGEM2-ES, MPI-ESM and NorESM, covering, respectively, the spread of high-, medium- and low-equilibrium climate sensitivities from the CMIP5 ensemble at a global scale (with MIROC5, EC-Earth and GFDL-ES2M as secondary GCMs), focusing on two scenarios RCP2.6 and RCP8.5 (see Box Atlas.1, Figure 1). Two RCMs have contributed so far to this initiative (REMO and RegCM4) constituting an initial homogeneous downscaled ensemble to analyse mean climate change signals and hazards ( [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ), and there are ongoing efforts to extend the CORDEX-CORE ensemble with additional regional simulations (e.g., the COSMO-CLM community) to increase the ensemble size. CORDEX-CORE simulations are distributed as part of the information available for the different CORDEX domains at the Earth System Grid Federation (ESGF).

CORDEX-CORE spans the spread of the CMIP5 climate change signals for interquartile ranges of annual mean temperature and precipitation for most of the reference regions covered (Box Atlas.1, Figure 1; [[#Teichmann--2021|Teichmann et al., 2021]] ). However, it is still a small ensemble and for other variables like extremes or climatic impact-drivers it has only been partially investigated in [[#Coppola--2021b|Coppola et al. (2021b)]] and needs further analysis.

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'''Cross-Chapter Box Atlas.1 | Displaying Robustness and Uncertainty in Maps'''

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'''Coordinators:''' José Manuel Gutiérrez (Spain), Erich Fischer (Switzerland)

'''Contributors:''' Alessandro Dosio (Italy), Melissa I. Gomis (France/Switzerland), Richard G. Jones (UK), Maialen Iturbide (Spain), Megan Kirchmeier-Young (Canada/USA), June-Yi Lee (Republic of Korea), Stéphane Sénési (France), Sonia I. Seneviratne (Switzerland), Peter W. Thorne (Ireland/UK), Xuebin Zhang (Canada)

Spatial information on observed and projected future climate changes has always been a key output of IPCC reports. This information is typically represented in the form of maps of historical trends (from observational datasets) and of projected changes for future reference periods and scenarios relative to baseline periods (from multi-model ensembles). These maps usually include information on the robustness or uncertainty of the results such as the significance of trends or the consistency of the change across models. Visualization of this information combines two aspects that are intertwined: the core methodology (measures and thresholds) and its visual implementation. For observed trends, robustness can be simply ascertained by using an appropriate statistical significance test. However, for multi-model mean changes, the consistency across models for the sign of change (model agreement) and the magnitude of change relative to unforced climate variability (signal-to-noise ratio) provide two complementary measures allowing for simple or more comprehensive approaches to represent robustness and uncertainty. While they can be visually represented in various ways with more or less complexity ( [[#Retchless--2016|Retchless and Brewer, 2016]] ), the most common implementation for maps in the climate science community remains the overlay of symbols and/or masking of the primary variable. This Cross-Chapter Box reviews the approaches followed in previous IPCC reports and describes the methods used across this WGI report, presenting the rationale and discussing its relative merits and limitations.

The objectives in AR6 for representing robustness and uncertainty in maps are: 1) adopting a method that can be as coherent as possible across the different global/regional chapters while accommodating different needs, 2) being visually consistent across WGs, and 3) making the different layers of information on the maps as accessible as possible for the reader. As a result, a single approach is selected for observations and two alternative approaches (simple and advanced) are adopted for projected future changes. It is important to highlight that, as in previous reports, these approaches are implemented in maps at a grid-box level and, therefore, are not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small-scale variability leading to an increase in robustness. This is particularly relevant for the AR6 regional assessments and approaches (e.g., for trend detection and attribution; Cross-Chapter Box 1.4, [[IPCC:Wg1:Chapter:Chapter-11#11.2.4|Section 11.2.4]] ) which are performed for climatological regions and not at grid-box scale (Chapters 11 and 12, and Atlas). Both small and large scales are relevant (e.g., adaptation occurs at smaller scales but also at the level of countries, which are typically larger than a few grid boxes). They are both addressed in the Interactive Atlas, which implements the above approaches for representing robustness in maps at the grid-box level, but also enables the analysis of region-wide signals (e.g., AR6 WGI reference regions, monsoon regions, etc.), helping to isolate background changes happening at larger scales ( [[#Atlas.2.2|Atlas.2.2]] ).

'''Approaches used in previous reports'''
Recent IPCC reports adopted different approaches for mapping uncertainty/robustness, including their calculation method and/or their visual implementation. In AR5 WGI ‘+’ symbols were used to represent significant trends in observations at grid-box level. For future projections, different methods for mapping robustness were assessed (AR5 Box 12.1, [[#Collins--2013|Collins et al., 2013]] ), while proposing as a reference an approach based on relating the multi-model mean climate change signal to internal variability, calculated as the standard deviation of non-overlapping 20-year means in the pre-industrial control runs. Regions where the multi-model mean change exceeded two standard deviations of the internal variability and where at least 90% of the models agreed on the sign of change were stippled (as an indication of a robust signal). Regions where the multi-model change was less than one standard deviation were hatched (small multi-model mean signal). However, this category did not distinguish areas with consistent small changes from areas of significant but opposing/divergent signals. In addition, the unstippled/unhatched areas were left undefined, since the categories were not mutually exclusive.

The AR5 WGII ( [[#Hewitson--2014|Hewitson et al., 2014]] ) used hatching to represent non-significant trends in observations. For future projections, an elaborated approach with four mutually exclusive and exhaustive categories was proposed (to avoid some of the limitations of the AR5 WGI approach): very strong agreement (same as in WGI); strong agreement; divergent change; and little or no change. These depended on the percentage of models showing change greater than the baseline variability and/or agreeing on sign of change (using a 66% agreement threshold). Leaving the robust regions uncovered minimized any interference with the perception of underlying colours that encoded the primary information of the figure.

The two special reports IPCC SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) and SROCC ( [[#IPCC--2019a|IPCC, 2019a]] , c) adopted a simplified approach, using only model agreement (≥66% of models agree on sign of change) to characterize robustness. However, cross-hatching was used in SR1.5 to highlight robust areas where models agree, whereas the SROCC used hatching/shading to represent regions where models disagree. Similarly, stippling was used in SR1.5 to indicate regions with significant trends, whereas it was used in SROCC to represent regions where the trends were not significant.

'''Recent methodologies'''
Since AR5 there has been a growing interest for disentangling small consistent climate change signals from significant divergent opposite changes resulting in conflicting information ( [[#Tebaldi--2011|Tebaldi et al., 2011]] ), and different statistical tests have been applied to assess the significance of signals working with the individual models forming the ensemble ( [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Yang--2018|Yang et al., 2018]] ; [[#Morim--2019|Morim et al., 2019]] ). Moreover, new approaches have been proposed to identify large changes of opposite sign that compensate in the mean ( [[#Zappa--2021|Zappa et al., 2021]] ). Recent literature has also highlighted the respective risksof Type I vs Type II errors, which can be associated with the determination of robustness in analysed signals ( [[#Lloyd--2018|Lloyd and Oreskes, 2018]] ; [[#Knutson--2019|Knutson et al., 2019]] ). Type I errors are identifying signals when there are none, while Type II errors are concluding there is no signal when there is one. In the case of grid-box level analysis, the focus on small-scale features with inherently large signal-to-noise ratio may emphasize noise even though signals are

Cross-Chapter Box Atlas.1

present when aggregated at larger scale (Sections 11.2.4 and 11.2.5). Consequently, changes averaged over regions or a number of grid boxes emerge from internal variability at a lower level of warming than at the grid-box level (e.g., Cross-Chapter Box Atlas.1, Figure 2). Hence, focus on grid-box significance enhances the risk of Type II errors for overlooking signals significant at the level of AR6 regions. The significance of signals is also affected by the interdependence of single simulations considered in a given ensemble, for example when several come from the same modelling group and share parametrizations or model components ( [[#Knutti--2013|Knutti et al., 2013]] ; [[#Maher--2021|Maher et al., 2021]] ). The risk of Type II errors increases when a model ensemble includes several related simulations showing no signal.

'''The AR6 WGI approach'''
The AR6 WGI adapts the approaches applied in previous IPCC reports into a comprehensive framework based on the two general principles followed by AR5 WGII: 1) not obscuring (with stippling or hatching) the areas where relevant/robust information needs to be highlighted (since stippling and hatching obstruct the visualization of the colours, which can affect the perception/interpretation of the underlying data); 2) using mutually exclusive and exhaustive categories to avoid leaving areas undefined. The three adopted approaches (one for observations and two for model projections) are described in Cross-Chapter Box Atlas.1, Table 1. This framework integrates as much as possible the specificities of each WGI Chapter, proposing in some cases alternative thresholds.

'''Approach A''' is intended for observations and consists of two categories, one for areas with significant trends (colour, no overlay) and one for non-significant ones (coloured areas overlaid with ‘x’), typically using a two-sided test for a significance level of 0.1; [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] and Atlas trends have been calculated using ordinary least squares regression accounting for serial correlation ( [[#Santer--2008|Santer et al., 2008]] ).

'''Approach B''' is the simple alternative for model projections. It consists of two categories, one for model agreement (at least 80% of the models agree on the sign of change; colour, no overlay) and the other one for non-agreement (hatching). It is noted that model agreement is computed using ‘model democracy’ (i.e., without discarding/weighting models), since quantifying and accounting for model interdependence (shared building blocks) still remains challenging ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.6|Section 4.2.6]] ). Different thresholds have been used in previous reports and in the literature. In CORDEX studies, 80% has been widely used ( [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Nikulin--2018|Nikulin et al., 2018]] ; [[#Yang--2018|Yang et al.,2018]] ; [[#Akperov--2019|Akperov et al., 2019]] ; [[#Rana--2020|Rana et al., 2020]] ), partially due to the small ensemble sizes available in some cases; this also helps to reduce the impact of model interdependence in the final results. Although 90% (used in AR5 WGI) provides high confidence on the forced change, it is deemed too stringent for precipitation-like variables and regional assessments and was therefore not included (see Cross-Chapter Box Atlas.1, Figure 1). The 66% threshold, which has been used in previous reports (e.g., SR1.5 and SROCC) and in the literature, is not used to avoid communicating weak confidence. Cross-Chapter Box Atlas.1, Figure 1 illustrates the application of this approach.

'''Approach C''' is a more advanced alternative for model projections, extending the AR5 WGI and simplifying the AR5 WGII approaches (fewer categories). It consists of three categories: ‘robust change’, ‘conflicting change’, and ‘no change or no robust change’ (see the details in Cross-Chapter Box Atlas.1, Table 1). The first two categories can be interpreted as areas where the climate change signal likely emerges from internal variability (i.e., it exceeds the variability threshold in ≥66% of the models). The variability threshold is defined as

γ = <code> </code> √ <code> 2 </code> * 1.645 * δ <sub>20yr</sub>

, where

δ <sub>20yr</sub>

is the standard deviation of 20-year means, computed from non-overlapping periods in the pre-industrial control (after detrending with a quadratic fit as in AR5 WG1); in cases where this information is not available (e.g., for CORDEX or HighResMIP), the following approximation is used instead:

γ = <code> </code> √ <code> 2/20 </code> * 1.645 * δ <sub>1yr</sub>

, where

δ <sub>1yr</sub>

is the interannual standard deviation measured in a linearly detrended modern period (note that for white noise

δ <sub>20yr</sub> = δ <sub>1yr</sub> / √ <code> 20 </code>

). The factor

√ <code> 2 </code>

is used as in the AR5 WGI approach to account for the fact that the variability of a difference in means (the climate change signal) is of interest. This approach is an evolution of the AR5 WGI method with three notable differences: (a) AR6 uses a lower threshold for internal variability (1.645 corresponding to a 90% confidence level, instead of 2 as used in AR5 WG1); (b) the threshold on agreement in sign is lowered from ≥90% to ≥80%, leading to more grid boxes classified as robust as opposed to conflicting signal; (c) the AR6 method compares signal to variability in each individual model and consequently introduces a 66% cut-off on significant changes, implying that the climate change signal ''likely'' emerges from internal variability in the baseline period.
<br/><br/>
Cross-Chapter Box Atlas.1, Figure 1 illustrates the application of this method considering the effect of the baseline period (1850–1900 versus 1995–2014) and shows that it provides similar results to related approaches proposed in the literature ( [[#Zappa--2021|Zappa et al., 2021]] ).

The two alternative approaches discussed above allow visualization of differentlevels of detail of information on the projected change and are intended for different communication purposes. Approach B just informs on the consistency of the sign of change independent

of its significance relative to internal variability, whereas approach C puts the projected changes into the context of internal variability and allows the highlighting of areas of conflicting signals. It is important to note that different approaches can be applied to the same variable between different chapters for different communication purposes. For example, in maps showing multi-model mean changes of precipitation, [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] adopts approach C but [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] applies approach B.

In terms of visual implementation, the approach follows recommendations resulting from conversations with IPCC national delegations: 1) having a consistent approach across WGs would aid consistency and reduce the risk of confusion; 2) defining ‘hatching’ as ‘diagonal lines’ in the caption would aid accessibility for non-expert audiences; 3) a clear and concise legend that explains what these patterns represent should be included directly in the figure; 4) information about model uncertainty should be overlaid such that it does not detract from the data underneath.

Since stippling is commonly used to represent statistical significance, diagonal lines were chosen to ‘obscure’ the problematic categories in the above approaches; it also facilitates the visualization of uncertainty in the Interactive Atlas when zooming in. To avoid confusion, methods or thresholds that were unrelated to the three approaches hereby presented were visualized with a different pattern (i.e., model improvement between low- and high-resolution simulations in Chapter 3; agreement between observation-based products in Chapter 5; correlation between two variables in Chapter 6).

'''Cross-Chapter Box Atlas.1, Table'''

'''1 |'''

'''Approaches for representing robustness (uncertainty) in maps of observed (approach A) and projected (approaches B and C) climate changes.'''
[[File:387c6676c7b7a374eb2a57f27de08a87 IPCC_AR6_WGI_Atlas_Table_Box_Atlas_1_Table_1.jpg]] '''Uncertainty at the grid-box and regional scales: interpreting areas with diagonal lines'''
There is no one-size-fits-all method for representing robustness or uncertainty in future climate projections from a multi-model ensemble. One of the main challenges is the dependence of the significance on the spatial scale of interest: while a significant trend may not be detected at every location, a fraction of locations showing significant trends can be sufficient to indicate a significant change over a region, particularly for extremes (e.g., it is ''likely'' that annual maximum one-day precipitation has intensified over the land regions globally even though there are only about 10% of weather stations showing significant trends; Figure 11.13). The approach adopted in WGI works at a grid-box level and, therefore, is not informative for assessing climate change signals over larger spatial scales. For instance, an assessment of the amount of warming required for a robust climate change signal to emerge can strongly depend on the considered spatial scale. A robust change in the precipitation extremes averaged over a region or a number of grid boxes emerge at a lower level of warming than at the grid-box level because of larger variability at the smaller scale (Cross-Chapter Box Atlas.1, Figure 2).

[[File:19b5382b14e156b9e70a2eaa74cddf0f IPCC_AR6_WGI_Atlas_CCBox_Figure_1.png]]

'''Cross-Chapter Box Atlas.1, Figure'''

'''1 |'''

'''Illustration of the simple, (a) and (b), and advanced, (c–f), approaches (B and C in Cross-Chapter Box Atlas.1, Table 1) for uncertainty representation in maps of future projections.''' Annual multi-model mean projected precipitation change (%) from CMIP6 for the period 2040–2060 (left) and 2080–2100 (right) relative to the baseline periods 1995–2014 (a–d) and 1850–1900 (e and f) under a high-emissions (SSP3-7.0) future. Diagonal and crossed lines follow the indications in Cross-Chapter Box Atlas.1, Table 1. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).
[[File:a9539dd991d805756f63d4a08243b07f IPCC_AR6_WGI_Atlas_CCBox_Figure_2.png]]

'''Cross-Chapter Box Atlas.1, Figure 2'''

'''|'''

'''Climate change signals are more separable from noise at larger spatial scales.''' The figure shows the global warming level associated with the emergence of a significant increase in the probability, due to anthropogenic forcing, in the 1-in-20-year daily precipitation event. It uses a 500-year sample from the CanESM2 large ensemble simulations. The left panel uses data analysed over a single grid box, with no spatial aggregation, while the right box uses data averaged over 25 grid boxes to represent the regional scale, with moderate spatial aggregation. Aggregation over 25 grid boxes reduces natural variability, resulting in a smaller warming required for a clear separation between the signal and noise (after Kirchmeier‐Young et al., 2019).

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