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1. Introduction 

This document describes the "Copernicus Interactive Climate Atlas gridded monthly dataset" which constitutes an evolution of the IPCC Atlas gridded monthly dataset recently published in the CDS¹. This dataset has been entirely produced using a workflow ensuring total reproducibility within the CDS. The climate information used to construct the dataset is fully available in C3S CDS. The dataset has a corresponding visualisation tool, which is the Copernicus Interactive Climate Atlas, available at https://atlas.climate.copernicus.eu/atlas.

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2. CDS catalogues, variables and harmonization

2.1. CDS catalogues and datasets used

The Copernicus Interactive Climate Atlas gridded monthly dataset integrates information from several climatic observations, reanalysis and projection datasets. Table 1 shows the datasets and the specific CDS catalogues used. Note that the CORDEX catalogue includes two different datasets of different horizontal spatial resolution²: 1) CORDEX-CORE includes the homogeneous subset of CORE³ simulations at 0.22° and 3) CORDEX-EUROPE covering Europe at 0.11° resolution. Note that in order to harmonize the grids used for the different datasets in the Atlas dataset, a set of common nested regular sub-grids of varying resolutions (2°, 1°, 0.5°, 0.25°, 0.125°) were used to (conservatively) interpolate some of the original datasets, particularly multi-model ensembles (note that some products, e.g. ERA5-Land and ERA5⁴ are maintained in their original regular grids provided in the CDS to preserve their nominal resolution). The reference grids used for the different products included in the datasets are available in the project’s GitLab repository⁵ (access can be granted upon ECMWF request) together with other auxiliary information.

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2.2. Variables and indices

 Table 2 shows the set of 30 variables/indices included in the dataset. This dataset includes the 21 climate variables and indices included in the original IPCC Atlas gridded monthly dataset, as well as 9 additional variables (the new variables are indicated with "*" after the sequence number). Color codes are included for variables/indices characterizing different Climatic Impact Driver (CID) categories: heat and cold (red), wet and dry (blue), wind and radiation (black), snow and ice (violet), coastal and open ocean (blue), circulation (grey). Italics indicates the indices derived from other variables (see Table 4).

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2.3. Harmonization of variables across catalogues

Table 4 shows the CDS particular variables from the different catalogues displayed in Table 1 used to construct the variables/indices displayed in Table 2. Note that Table 4 describes only the raw climate variables and not the derived indices (denoted in italics in Table 2). These raw variables are defined from different variables, temporal resolutions and units available in the different CDS catalogues. This table also describes the harmonization/conversion work carried out to produce the dataset using a standard definition and units for the variables/indices. The first and second columns display the code and the standard name and units. The third column describes the CDS name of the variables and the harmonization work done for the different datasets to obtain the standard variables and units. The colors used correspond to the particular temporal aggregation of the specific catalogue used for the different datasets. N/C and N/A indicate "Not Considered" or "Not Available" variables. 

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2.4. Available ensembles for climate projection products

The climate projection datasets listed in Table 1 are based on multi-model ensembles including simulations from the historical and future scenarios (RCPs or SSPs). In order to compute climate change information, simulations from both historical and future scenarios are required. Therefore, in the Atlas dataset we consider only those models providing simulations for historical period and, at least, for one future scenario. Figures 1, 2 and 3 show the final ensembles computed for the different variables (in columns) for the CMIP6, CORDEX-EUR and CMIP5 datasets. 

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2.5. Mosaic approach for CORDEX-CORE

One of the main novelties of the dataset is related to the CORDEX-CORE project⁹ . CORDEX-CORE is a highly ambitious initiative aimed at providing regional climate projections with global coverage (see Figure 4) and high resolution (0.25°x0.25°) by combining of 2 RCMs nested to 6 GCMs selected to span the widest range of uncertainty (see Table 6). The dataset includes, for the first time, the CORDEX-CORE simulations spatially blended using the mosaic approach described in Diez-Sierra et al. 2022¹⁰ . This dataset addresses two main issues related to the CORDEX-CORE simulations: 1) avoiding domain selection in overlapping areas by choosing the domain that best fits each one of the IPCC AR6 reference regions and 2) avoiding the multiple native projections of the different domains by providing the data in a regular global mesh. Due to its global coverage and higher resolution, this dataset constitutes the main source of information to analyze climate change for megacities globally, so it is a strategic dataset for the Atlas.

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2.6. Software used for index calculation

Most of the variables and indices included in the datasets require the application of simple temporal aggregations or threshold computations, and therefore, we implemented them ourselves to reduce the required computational cost. The unique indices that require more complex implementations are the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI) and the bias adjusted variables (tx35ba and tx40ba). The icclim module ({+}https://pypi.org/project/icclim/) was selected to compute the SPI and the SPEI indices and the ibicus module ({+}https://pypi.org/project/ibicus/) for the bias adjusted variables.

2.7. Bias adjustment

In this first version of the dataset we build on recent work on an intercomparison of different bias adjustment methods¹¹ which shows that a simple linear scaling method is a good choice for threshold-based temperature indices; the adjustment of the mean performed by these methods produces overall good results, as compared with more complex methods such as the ISIMIP3 method used in the IPCC Interactive Atlas. As an example, Figure 6 shows the results for the variable tasmax bias adjusted (tasmaxba) using both the ISIMIP3 and linear scaling methods for a particular model. From top to bottom, the panels display the results for linear scaling, ISIMIP3b and the difference between both methods.

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2.8. File format and archiving

Files have been generated using netcdf-c verion 4.4.1.1 and hdf5 version 1.10.1 libraries using NETCDF4 data model. The resulting files are NetCFD format and metadata is CF1.9¹⁴ compliant allowing for string type NetCDF variables (used to define some attributes in the files, such as members). The attribute convention for data discovery is ACDD-1.3¹⁵ compliant (including reference, geospatial, etc.).
Data is stored in different files for different experiments, scenarios and variables/indices (using the naming convention experiment scenario_index.nc{_}), including all members in the same file using the member attribute and the full time periods mentioned in Table 1. The original data files were compressed aggressively, which means that although the file size looks small, but the necessary memory to read the data from these files might be large.

2.9. Spatial subsetting

The CDS form (and API) offers the possibility to spatially subset the data to be downloaded. The users can select any latitude-longitude area. If the selected area is larger than the region covered by the dataset then the full dataset will be provided. If the selected area includes any data points of the data then those data points will be provided. Requests, which don't include any point in the region of the dataset will fail. 

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In the longer term, we intend to introduce temporal subsetting too easing the selection of the time period and allowing to choose shorter time periods. 

3. Fit-for-purpose quality control 

Some basic quality control procedures were implemented to check the consistency of the data produced for the different indices and variables across datasets. This section describes the tests implemented to analyze and fix issues in the dataset. 

3.1. Tests implemented

An exhaustive quality control of the different variables and indices has been conducted with the aim of identifying potential issues in the original data (units, coordinates, etc.) as well as during the workflow for index generation. This procedure allowed us to detect data issues and to detect and fix metadata issues in the original data, and to identify problems and bugs during data harmonization. The quality control has been carried out based on four different qualitative tests:

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