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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.

In addition to the description of the dataset, we are summarising the fit-for-purpose quality control applied to the datasets, documenting and curating the dataset based on the results from basic products (maps and climate stripes), and highlighting and fixing any issue encountered.

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Some of the datasets available in the CDS are distributed under several catalogues according to different temporal resolutions (hourly, daily, or monthly) or subsets of variables (e.g. single-level or multi-level). To produce the dataset, we used the most convenient catalogue depending on the variable or index (see Table 2) following the main principle that groups of main variables and derived indices (e.g. maximum temperature and number of days with maximum temperature over 35°C) should be based on the same catalogue. For instance, maximum temperature (TX) is required at daily temporal resolution to compute derived indices; therefore, for ERA5, the catalogue with hourly data (Reanalysis-era5-single-levels) was  selected for this variable (and others), whereas the catalogue with monthly data (Reanalysis-era5-land-monthly-means) was selected for other variables, such as RLDS (surface_downwelling_longwave_radiation), which do not require daily data. Color codes in Table 1 indicate different temporal resolutions of the raw datasets available on the CDS catalogues (hourly: green, daily: blue and monthly: purple).

Table 1: Descriptions of the datasets and CDS catalogue entries used. Color codes indicate the different temporal resolutions (hourly: green, daily: blue and monthly: purple) of the datasets available in the CDS catalogues (one or several for each catalogue). The column “horizontal resolution” indicates the grids used for the different datasets (products regridded to the common regular sub-grids: 2°, 1°, 0.5°, 0.25°, 0.125°, are indicated with an asterisk).

Data harmonization across the different datasets and catalogues is crucial in this contract. For instance, ERA5 and ERA5-Land do not provide directly daily data, so this have to be computed aggregating the hourly values. However, both datasets follow different conventions for coding hourly data (ERA5 provides hourly accumulated values whereas ERA5-Land provides accumulated values since the beginning of the forecast, at 00 for a period of 24 hours) and require different post-processing to obtain daily values https://confluence.ecmwf.int/pages/viewpage.action?pageId=197702790 . This needs to be taken into account when harmonizing the different datasets in order to obtain a standard common definition and units for each of the variables (Sec. 2.3).

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 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).

Table 2: Description of the 21 climate variables and indices included in the IPCC Interactive Atlas, as well as the additional 9 new ones for the first version of the C3S Interactive Atlas dataset (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 (green), wind and radiation (black), snow and ice (violet), coastal and open ocean (blue), circulation (grey). Italics indicates the indices derived from other variables (Table 4).

Table 3 shows the variables from Table 2 which are calculated for the different datasets (see Table 1). Note that bias adjusted indices are not computed for reanalysis/observational datasets, but only for climate projections (CMIP6 and CORDEX-EUR-11 by now). In the case of CMIP5, the only variables/indices considered are those included in the IPCC Atlas gridded monthly dataset due to the complex harmonization required for some of the new variables, which makes comparison across datasets difficult and potentially misleading.

Table 3. Description of variables/indices calculated for the different datasets. Data not available is indicated by 'N/A' and data not considered is indicated by 'N/C'. (*) Indicates variables not considered due to lack of data (only available for a reduced subset).

2.3. Harmonization of variables across catalogues

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Besides the name and unit standardization described in Table 2, the harmonization pre-processing implements calendar conversion (standard calendar⁷) and standard coordinate system (using -180 to 180), as described in D381.1.3.1 (Section 2.3). Moreover, the variables and indices calculated from these datasets are interpolated to common grids to form the final dataset.

Table 4: Harmonization work across the different catalogues carried out for the variables of the dataset. Color codes indicate the temporal resolutions used for the different datasets available in the CDS catalogues, which correspond to the colors indicated in Table 1 (green for hourly, blue for daily and purple for monthly). Unit harmonization (when needed) is indicated with "==>".

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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Figure 1: Final ensemble used in the "Copernicus Interactive Climate Atlas gridded monthly dataset" with the different models (in rows) for the different variables (in columns) for the CMIP6 dataset. Colors indicate the different scenarios available in the CDS.

 
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Figure 2: Final ensemble used in the "Copernicus Interactive Climate Atlas gridded monthly dataset" with the different models (in rows) for the different variables (in columns) for the CORDEX-EUR-11 dataset. Colors indicate the different scenarios available in the CDS.

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Figure 4: Spatial coverage of the CORDEX-CORE simulations (displaying near-term temperature change for RCP8.5 relative to 1961-1990),

Table 5: CORDEX simulations for the different domains used to build the CORDEX-CORE dataset (note that EUR-11 simulations are upscaled to the global 0.22 grid). Empty cells indicate that the default versions for the two RCMs (RegCM4_7 and REMO2015) are used. Different versions and/or backup GCMs are indicated in the table.

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Figure 5: Final ensemble used in the "Copernicus Interactive Climate Atlas gridded monthly dataset" with the different models (in rows) for the different variables (in columns) for the CORDEX-CORE dataset. Colors indicate the different scenarios available in the CDS (see Table 5 for the specific configuration of the global mosaic).

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Most of the variables and indices included in the datasets dataset are fully defined in Table 2 and require the application of simple temporal aggregations or threshold computations, and ; therefore, we implemented them ourselves they have been implemented in Python as part of the Atlas workflow to reduce the required computational cost. The unique indices that require more complex implementations are the Standardized exceptions are detailed below.

The Standardised 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

-6) is a monthly index that compares accumulated precipitation for 6 months with the long-term distribution (reference period: 1971-2010) for the same location and accumulation period, as the number of standard deviations from the median. This index has been calculated using using Python xclim software. 

The Standardised Precipitation Evapotranspiration Index (SPEI-6) is a monthly index that compares accumulated precipitation minus potential evapotranspiration (PET, Thornthwaite definition) for 6 months with the long-term distribution (reference period: 1971-2010) for the same location and accumulation period, as the number of standard deviations from the median. This index has been calculated using using Python xclim software applying the Thornthwaite (TW) version for the calculation of PET (using tasmin and tasmax as input).

Cooling degree-days (CD) and Heating degree-days (HD) have been implemented in Python as part of the Atlas workflow, following the definition used in the IPCC AR6 Atlas (IPCC, 2021: Annex VI: Climatic Impact-driver and Extreme Indices [Gutiérrez J.M., R. Ranasinghe, A.C. Ruane, R. Vautard (eds.)]. Cambridge University Press, Cambridge, pp. 2205–2214, doi:10.1017/9781009157896.020).

2.7. Bias adjustment

In this first version of the dataset we build on recent work on an intercomparison 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.

All calculations are done using the ibicus package¹² which implements a number of bias adjustment methods (including linear scaling and ISIMIP3 among others). In principle, climate indices including absolute thresholds are most sensitive to biases. This is more pronounced as the threshold values become more and more extreme. This is the case of the extreme maximum temperature indices as also reported by Iturbide et al. 2022 (On the need of bias adjustment for more plausible climate change projections of extreme heat, https://doi.org/10.1002/asl.1072). We use the simple linear scaling method for bias adjustment. In this version the two most extreme climate indices were bias adjusted for the CMIP6 and CORDEX-EUR-11 datasets: monthly count of days with maximum temperature above 35°Cand monthly count of days with maximum temperature above 40°C. The proposal for future versions of the Atlas dataset (if computing resources permit) is expanding the results including an additional bias adjustment method (the quantile mapping ISIMIP3 method), allowing users to explore this source of uncertainty. Note that this is aligned with the recommendation of the IPCC AR6 Report¹³.

Figure 6: Illustrative comparison between two commonly used bias adjustment methods of different complexity: ISIMIP3b (upper left) and linear scaling (upper right) for an illustrative model (the ACCESS-CM2 model). The panel in the bottom shows the difference between them.

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Figure 7: Illustrations of the extent of the E-OBS (lefttop) and CORDEX-EUR-11 (rightbottom) European domains

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. 

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Figure 10: Example of box-plots for CMIP6 near surface air temperature (t) for different IPCC AR6 regions (in different panels) for the first version of the Copernicus Atlas (left, in blue) and the IPCC version (right, in green). The gray dashed lines join results from the same model in both datasets; small red squares indicate the ensemble means and the red lines join the medians of both ensembles(in different panels) for the first version of the Copernicus Atlas (left, in blue) and the IPCC version (right, in green). The gray dashed lines join results from the same model in both datasets; small red squares indicate the ensemble means and the red lines join the medians of both ensembles.

4. Versions of the document 

The following table details the changes (and dates, in chronological order) that this document has undergone.

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