This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset Paper (Open Access)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the ESA CCI Soil Moisture science data records community

f09.C6.B-hist.SON_ANN.tar.gz

CESM2-CAM6 with falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.

The data includes with netcdf self description.

f09.C6.B-hist.h01_AWNC_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_CLDHGH_ANN_climo-CDO.nc
f09.C6.B-hist.h01_CLDLIQ_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_CLDLOW_ANN_climo-CDO.nc
f09.C6.B-hist.h01_CLDMED_ANN_climo-CDO.nc
f09.C6.B-hist.h01_CLDTOT_ANN_climo-CDO.nc
f09.C6.B-hist.h01_CLOUD_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_CONCLD_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_FREQL_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_ICWMR_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_NUMLIQ_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_OMEGA_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.h01_PRECC_ANN_climo-CDO.nc
f09.C6.B-hist.h01_PRECL_ANN_climo-CDO.nc
f09.C6.B-hist.h01_SST_ANN_climo-CDO.nc
f09.C6.B-hist.h01_tauy_ANN_climo-CDO.nc

f09.C6.B-hist.NOS_ANN.tar.gz

CESM2-CAM6 without falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.

f09.C6.B-hist.nos81_AWNC_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_CDNUMC_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_CLDHGH_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_CLDLIQ_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_CLDLOW_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_CLDMED_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_CLDTOT_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_CLOUD_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_CONCLD_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_FREQL_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_ICWMR_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_NUMLIQ_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_OMEGA_ANN_15L-CDO_1x1.nc
f09.C6.B-hist.nos81_PRECC_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_PRECL_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_SST_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_taux_ANN_climo-CDO.nc
f09.C6.B-hist.nos81_tauy_ANN_climo-CDO.nc

AdriaClim Indicators | adriaclim_WRF | yearly | anomaly adriaclim_dataset=indicator adriaclim_model=WRF - V3.5.1 adriaclim_scale=adriatic adriaclim_timeperiod=yearly adriaclim_type=anomaly CDI=Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo) Conventions=CF-1.6, COARDS, ACDD-1.3 Easternmost_Easting=21.98158 geospatial_lat_max=46.97328 geospatial_lat_min=37.00147 geospatial_lat_units=degrees_north geospatial_lon_max=21.98158 geospatial_lon_min=10.0168 geospatial_lon_resolution=0.062316562500000006 geospatial_lon_units=degrees_east history=Fri Jun 09 11:49:38 2023: cdo merge yearly_consecutive_dry_days_index_per_time_period.nc yearly_consecutive_summer_days_index_per_time_period.nc yearly_consecutive_wet_days_index_per_time_period.nc yearly_fg.nc yearly_heat_wave_duration_index_wrt_mean_of_reference_period.nc yearly_heat_waves_per_time_period.nc yearly_number_of_cdd_periods_with_more_than_5days_per_time_period.nc yearly_number_of_csu_periods_with_more_than_5days_per_time_period.nc yearly_number_of_cwd_periods_with_more_than_5days_per_time_period.nc yearly_precipitation_percent_due_to_R95p_days.nc yearly_rx1day.nc yearly_rx5day.nc yearly_simple_daily_intensity_index_per_time_period.nc yearly_summer_days_index_per_time_period.nc yearly_tg.nc yearly_tropical_nights_index_per_time_period.nc yearly_txn.nc yearly_txx.nc yearly_very_wet_days_wrt_95th_percentile_of_reference_period.nc adriaclim_WRF_anomaly_yearly_adriatic.nc Fri Jun 09 11:46:29 2023: cdo chname,XLONG,lon,XLAT,lat 1.nc yearly_consecutive_dry_days_index_per_time_period.nc Fri Jun 9 11:46:29 2023: ncks -A -v XLAT lat1.nc 1.nc Fri Jun 9 11:46:29 2023: ncks -A -v XLONG lon1.nc 1.nc Fri Jun 9 11:46:29 2023: ncks -C -O -x -v XLAT,XLONG yearly_consecutive_dry_days_index_per_time_period.nc 1.nc Fri Jun 09 11:45:21 2023: cdo splitvar adriaclim_WRF_anomaly_yearly_adriatic.nc yearly_ Fri Jun 09 11:27:32 2023: cdo merge cdd.nc csu.nc cwd.nc fg.nc hwdi.nc r95p.nc r95ptot.nc rx1day.nc rx5day.nc sdii.nc su.nc tg.nc tr.nc txn.nc txx.nc adriaclim_WRF_anomaly_yearly_adriatic.nc Fri Jun 9 11:16:19 2023: ncdiff mean_proj_adriaclim_WRF_cdd_yearly_1991_2050.nc mean_hist_adriaclim_WRF_cdd_yearly_1991_2050.nc cdd.nc Fri Jun 09 11:14:15 2023: cdo -timmean proj_adriaclim_WRF_cdd_yearly_1991_2050.nc mean_proj_adriaclim_WRF_cdd_yearly_1991_2050.nc Fri Jun 09 11:12:23 2023: cdo selyear,2031/2050 adriaclim_WRF_cdd_yearly_1991_2050.nc proj/proj_adriaclim_WRF_cdd_yearly_1991_2050.nc history_of_appended_files=Thu Dec 22 16:29:53 2022: Appended file lat1.nc had following "history" attribute: Thu Dec 22 16:29:52 2022: ncrename -O -d south_north,lat lat1.nc Thu Dec 22 16:29:52 2022: ncwa -a west_east lat.nc lat1.nc Thu Dec 22 16:29:51 2022: ncks -v XLAT adriaclim_WRF_cdd_hist_yearly_1991_2020.nc lat.nc Indicator provided by CMCC Foundation Thu Dec 22 16:29:53 2022: Appended file lon1.nc had following "history" attribute: Thu Dec 22 16:29:52 2022: ncrename -O -d west_east,lon lon1.nc Thu Dec 22 16:29:51 2022: ncwa -a south_north lon.nc lon1.nc Thu Dec 22 16:29:51 2022: ncks -v XLONG adriaclim_WRF_cdd_hist_yearly_1991_2020.nc lon.nc Indicator provided by CMCC Foundation infoUrl=https://cmcc.it institution=CMCC NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=46.97328 sourceUrl=(local files) Southernmost_Northing=37.00147 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2041-07-01T21:00:00Z time_coverage_start=2041-07-01T21:00:00Z Westernmost_Easting=10.0168

file created by T. CHAU (trang.chau@lsce.ipsl.fr) CDI=Climate Data Interface version 1.9.9 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.9 (https://mpimet.mpg.de/cdo) Conventions=CF-1.10, COARDS, ACDD-1.3 description=file created by T. CHAU (trang.chau@lsce.ipsl.fr) Easternmost_Easting=359.875 geospatial_lat_max=89.875 geospatial_lat_min=-89.875 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=359.875 geospatial_lon_min=0.125 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east history=Thu Mar 20 01:51:19 2025: cdo chvar,pCO2_std,spco2_uncertainty /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_4.nc /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_5.nc Thu Mar 20 01:51:19 2025: cdo chvar,pCO2_mean,spco2 /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_3.nc /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_4.nc Thu Mar 20 01:51:19 2025: cdo chvar,fCO2_std,fgco2_uncertainty /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_2.nc /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_3.nc Thu Mar 20 01:51:19 2025: cdo chvar,fCO2_mean,fgco2 /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502.nc /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502_2.nc Thu Mar 20 01:50:52 2025: cdo merge /data/eclaudel/CMEMS_LSCEv2023/Data/fluxCO2_FFN_1step/fluxCO2_model_v2024_r025_202502.nc /data/eclaudel/CMEMS_LSCEv2023/Data/pCO2_FFN_1step/pCO2_model_v2024_r025_202502.nc /scratchu/eclaudel/Data/MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411/cmems_obs-mob_glo_bgc-car_nrt_irr-i_202502.nc infoUrl=??? institution=LSCE.IPSL Northernmost_Northing=89.875 sourceUrl=(local files) Southernmost_Northing=-89.875 standard_name_vocabulary=CF Standard Name Table v70 testOutOfDate=now-108days time_coverage_end=2025-02-15T00:00:00Z time_coverage_start=2024-01-15T00:00:00Z Westernmost_Easting=0.125

The database contains a time series of daily data on climate projections of the daily highest air temperature in the period 1981-2100 for the scenario of greenhouse gas emissions RCP8.5 above Slovenia in the correct grid, resolution 0.125°.

Simulations of six regional climate models are available for the RCP8.5 scenario. The simulations are the result of regional models of the EURO-CORDEX project. Their resolution is 0.11°. The data are corrected according to measurements in Slovenia in the period 1981-2010 (the so-called bias correction). Find out more about the EURO-CORDEX project through additional links.

The data is in NetCDF format files, which are divided into 30-year periods due to their size. There are four files available for each model. Each 30-year period is marked with the year of the last year of the period.

Model projection files have names that contain information about the name of the variable, models, projection time, version, etc. separated by underscores. The components of the names are: variable name (tasmax: daily maximum air temperature), model resolution (12 km: 0.125°), the name of the global climate model that gave marginal conditions to the regional, abbreviated greenhouse gas emissions scenario (rcp85: RCP8.5), ensemble parameters (e.g. r1i1p1), regional climate model name, projection version, projection time step (day: 1 day) and start and end dates of the projection (as YYYYMMDD where YYYY is year, MM month and DD day).

The model results represent the physically possible states of the climate system in the future relative to the day, which in this case is the path of greenhouse gas concentration. As greenhouse gas concentrations cannot be predicted, the International Panel on Climate Change (IPCC) in its fifth report from 2014 produced four plausible scenarios for it, given the socio-economic evolution of humanity in the future. Since model results differ from one another and each of them represents a possible state of the climate, their results must be statistically processed. The differences between them form the basis for assessing the uncertainty of the projections. A summary of analyses of climate change by the end of the century in Slovenia can be found among additional links. There is also the first synthesis report on climate change in Slovenia, which contains a more detailed description of the methodology and results of climate projections. About climate projections are discussed in Chapter 1, on regional climate models, Chapter 3.2, and on the correction of errors or bias of models Chapter 4.3.

One of the options for working with NetCDF files (extraction, aggregation, etc.) is the CDO program. Reading and statistical processing on NetCDF files are also possible with the statistical package R, especially its ncdf4 and raster packages. Links to CDO and R programs can be found in additional links.

Users are recommended to consult the author (malcolm.mistry@unive.it / malmistry1977@gmail.com) for an updated version which includes more recent years.71 core and non-core climate extreme indices (CEIs) based on the Expert Team on Climate Change Detection and Indices (ETCCDI), and the Expert Team on Sector-specific Climate Indices (ET-SCI). The indices are computed using R ClimPACT2 package. The dataset does not include two indices (Heating and Cooling Degree Days) as these are computed separately for various baseline temperature (thresholds) -See 'Historical Global-Gridded Degree-Days: A High Spatio-Resolution Database of CDD and HDD'-. All indices are computed using daily near-surface maximum and minimum temperature (deg C), and near-surface precipitation (mm/day) variables from Global Land Data Acquisation System (GLDAS) ver. 2 (@ 0.25 degree)Important: GLDAS ver-2 comprises of two sub-versions (ver. 2.0 for period 1970-2010 and ver 2.1 for period 2000-present day). The CEIs computed using the input temperature and precipitation variables may report a break in time-series at a few locations around the years 2010-11. Users are therefore advised caution when using the data for trend analysis for instance. Further details on the merging of the two versions can be found below:https://disc.gsfc.nasa.gov/information/faqs?title=Should%20I%20use%20GLDAS%20Version%202.0%20(GLDAS-2.0)%20or%20GLDAS%20Version%202.1%20(GLDAS-2.1)%3F Data are in netCDF-4 format, at a spatial resolution of 0.25 degree by 0.25 degree (latitude by longitude)on a regular lon-lat grid. Some indices have data both at monthly and annual time-steps (as identified by the netCDF file name). Missing values are identified by values '1.e+20f'. Further details of the variables in the individual netCDF files can be checked using either NCO or CDO command line utilites as follows: "ncdump -h netcdf_file_name", "cdo sinfo netcdf_file_name". The developement of this dataset has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 756194 (ENERGYA). Supplement to: Mistry, Malcolm Noshir (2019): A high resolution global gridded historical dataset of climate extreme indices. Data, 4(1)

The collection contains the results of analyses of the basic variables of regional climate models above Slovenia until the end of the 21st century in resolutions of 0.125° and 1 km. 30-year averages of variables at annual and seasonal levels are given, together with the ensemble range. The variables analysed are reference evapotranspiration (evspsblpot), precipitation level (pr) and average (tass), peak (tasmax) and lowest (tasmin) air temperature.

The results are given in the form of a deviation of the selected variable relative to the average of the period 1981-2010. For temperature, deviation is given in °C and for precipitation and reference evapotranspiration as a relative change in percentages. The current situation (reference period 1981-2010) is contained in the directory ‘Current status (1981-2010)’. The values in the files are absolute and derived from interpolated values of ARSO network measurements. The results for each resolution are collected in the ZIP file. Within it, they are organised in NetCDF format files according to directories with names and structure {resolution}/{scenarij (or current state)}/{variable}. Files have names that contain information about the name of the variable, time of year, etc. separated by underscores. The components of the names are: variable name, type of data (deviation or absolute: absolute value), model resolution (12 km: 0.125°oz. 1 km), institution (ARSO), abbreviation of the greenhouse gas emissions scenario or measurements (rcp26: RCP2.6, rcp45: RCP4,5 or rcp85: RCP8.5 and measurements), the initial and final year of the period and the indication of the season or all-year period. The 30-year period is three: the near future (2011-2040), the middle of the century (2041-2070) and the end of the century (2071-2100). The seasons are marked “MAM” (spring), “JJA” (summer), “SON” (autumn) and “DJF” (winter), and the annual period is “year”. Each of the NetCDF files contains four levels: the first denotes the maximum deviation value of the model ensemble during that period (“Maximum”), the second median or the mean of the deviation of the model ensemble of the selected period from the reference period 1981-2010 (“Mediana”), the third minimum deviation value of the model ensemble during that period (“Minimum”) and the last compliance of the model simulations (“Reliability of change”). The consistency of the model simulations is described in section 4.8 of the first synthesis report between additional links. Its values are 1: high reliability, 0: no change and -1: low reliability.

A summary of analyses of climate change by the end of the century in Slovenia can be found among additional links. There is also the first synthesis report on climate change in Slovenia, which contains a more detailed description of the methodology and results of climate projections. The analysis of model data is discussed in section 4.3.5 and by section 4.8 on the compliance of model simulations. A snapshot of all results is given in the addendum to the synthesis report or in the Atlas climate projections web application (addressed between additional links).

One of the options for working with NetCDF files (extraction, aggregation, etc.) is the CDO program. Reading and statistical processing on NetCDF files are also possible with the statistical package R, especially its ncdf4 and raster packages. Links to CDO and R programs can be found in additional links.

This data set consists of monthly long-term mean temperature data (degrees C) for the last glacial maximum (21 ka) downscaled to a 10-km grid of North America. The 10-km data were derived using simulated temperature data from 10 general circulation models (GCMs; CCSM4, CNRM-CM5, COSMOS-ASO, FGOALS-g2, GISS-E2-R, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P-OA, MPI-ESM-P-OAC, and MRI-CGCM3) run under the PMIP3/CMIP5 (Paleoclimate Modelling Intercomparison Project phase 3 / Coupled Model Intercomparison Project phase 5) “lgm” and “piControl” experiments. The lgm and piControl data are available from the Earth System Grid - Center for Enabling Technologies (ESG-CET; https://esgf-node.llnl.gov/projects/esgf-llnl/). Additional information about the data is available from the CMIP5 (https://pcmdi.llnl.gov/mips/cmip5/) and PMIP3 (https://pmip3.lsce.ipsl.fr/) web sites. The names of the lgm and piControl files we used are listed in the “source_file” global attribute of each GCM temperature netCDF file in this data release. For each GCM, the PMIP3/CMIP5 lgm temperature data were bias corrected using long-term mean differences calculated as the lgm long-term mean minus the piControl long-term mean. These long-term mean differences were regridded to a North America 10-km Lambert azimuthal equal-area grid using the CDO (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo) bilinear interpolation function “remapbil”. We used ICE-5G (VM2) data (Peltier, 2004, https://doi.org/10.1146/annurev.earth.32.082503.144359) to identify grid cells with ice cover at 21 ka. The interpolated long-term mean differences were applied to CRU CL 2.0 (1961-1990 30-year mean) climate data (New et al., 2002, https://doi.org/10.3354/cr021001). The CRU CL 2.0 data were also regridded to the 10-km grid using local lapse-rate adjusted interpolation (Praskievicz and Bartlein, 2014, https://doi.org/10.1016/j.jhydrol.2014.06.017). The ensemble mean data were calculated using the bias corrected temperature data from each of the 10 GCM simulations.

This datasets contains netcdf output files of the simulations analyzed in Colin et al. "Groundwater feedbacks on climate change in the CNRM global climate model" submitted to Journal of Climate.

1. Monthly and daily outputs file names

Monthly outputs : SimulationName_arpsfx_monthly_VariableName_year1-year2.nc
Daily outputs : SimulationName_arpsfx_daily_VariableName_year1-year2_JJAS.nc

Note : the daily outpus are available only for the "extended boreal summer" months, i.e. June, July, August, September

2. Simulation names

PIC_FLD_632_NCB : named PIr in the paper,
without groundwater, under Pre-industrial conditions,
run over the 1850-1939 (year1-year2) period.

PIC_GWFLD_632_NCB_clean : named PIa in the paper,
with groundwater, under Pre-industrial conditions,
run over the 1880-1969 (year1-year2) period.

4xCO2_FLD_632_NCB : named C4r in the paper,
without groundwater, under 4xCO2 conditions (after stabilization),
run over the 1850-1939 (year1-year2) period.

4xCO2_GWFLD_632_NCB_clean : named C4a in the paper,
with groundwater, under 4xCO2 conditions (after stabilization),
run over the 1880-1969 (year1-year2) period.

Note : The years' numbering does not matter as it does not correspond to actual years. Each simulation was run for 90 years (after the spin-up procedures) in a stabilized climate under pre-industrial and 4xCO2 conditions.

3. Variable names

et : evapotranspiration
inflitr : liquid water inflitration in the soil
lai : Leaf Area Index
pr : precipitation
rzwc : root zone water content
tas_max : maximum daily 2-meter air temperature
tas_min : minimum daily 2-meter air temperature
tran : plants transpiration
vegstress : Soil Wetness Index computed with a weighting of soil water content with root density along vertical layers
wg : liquid soil water content in each vertical layers (sdepth)
wtd : water table depth

Note : wtd is defined on the river routing model (CTRIP) grid, at 0.5° resolution. All the other variables are defined on the grid common to the atmospheric model (Arpege-Climat) and land surface model (ISBA), at ~1° resolution.

The variables units and any further needed informations are included in files metadata.

4. Other files (fixed fields)

areacella.nc : grid cell areas of the ISBA/Arpege-Climat grid (Land Surface model and atmospheric model),
areacellr.nc : grid cell areas of the CTRIP grid (River Routing model),
land_area_fraction.nc : fraction of land surface in each cell of the the ISBA/Arpege-Climat grid,
groundwater_mask.nc : groundwater mask, defined on the CTRIP grid,
groundwater_mask_on_Isba-Arpege_grid.nc : groundwater mask interpolated onto the ISBA/Arpege-Climat grid (using the "remapcon" function of CDO),
root_depth.nc : root depth, defined on the ISBA/Arpege-Climat grid,
root_depth_on_CTrip_grid.nc : root depth interpolated onto the CTRIP grid (using the "remapcon" function of CDO).

The database contains a time series of daily data for measurements of average, highest and lowest temperature and precipitation level and reference evapotranspiration in the period 1981-2010 above Slovenia in the correct grid, resolution 0.125°. The data are derived from point measurements of the ARSO meteorological network, which are calculated over the territory of the entire country using spatial interpolation methods.

The data is in NetCDF format files. Measurement files have names that contain information about the name of the variable, time of measurement, version, etc. separated by underscores. The components of the names are: variable name (evspsblpot: reference evapotranspiration, pr: precipitation level, tas: average air temperature, tasmax: maximum air temperature and tasmin: minimum air temperature), net resolution (12 km: 0,125°), institution (ARSO), interpolation version, time step of measurement (day: 1 day) and start and end dates of measurements (as YYYYMMDD where YYYY is year, MM month and DD day).

One of the options for working with NetCDF files (extraction, aggregation, etc.) is the CDO program. Reading and statistical processing on NetCDF files are also possible with the statistical package R, especially its ncdf4 and raster packages. Links to CDO and R programs can be found in additional links.

Model output used to produce the graphics of the manuscript Caldas-Alvarez and Khodayar (2020). Observational data are not included as they have been obtained from other institutions. The model output used to produce each graph is labeled "Fig_number_subnumber". The data is readable using MATLAB (files of the format .mat and .nc) and any program able to work with NetCDF data (ncview, ncl, cdo, etc.) for the *.nc files. In the nomenclature, there are 4 simulation types (CTRL-7, NDG-7, CTRL-2.8 and NDG-2.8). The numbers account for the horizontal resolution of the model (7 km or 2.8 km). At times these might also be referred to as EU (for the 7 km) and DE (for the 2.8 km). CTRL refers to the reference runs and the runs including the nudging (see Caldas-Alvarez and Khodayar, 2020) are indistinctly referred to as NDG or AS.

Model output used to produce the graphics of the manuscript Caldas-Alvarez and Khodayar (2019). Observational data are not included as they have been obtained from other institutions. The model output used to produce each graph is labeled "Fig_number_subnumber". The data is readable using MATLAB (files of the format .mat and .nc) and any program able to work with NetCDF data (ncview, ncl, cdo, etc.) for the *.nc files. In the nomenclature, there are 4 simulation types (CTRL-7, NDG-7, CTRL-2.8 and NDG-2.8). The numbers account for the horizontal resolution of the model (7 km or 2.8 km). At times these might also be referred to as EU (for the 7 km) and DE (for the 2.8 km). CTRL refers to the reference runs and the runs including the nudging (see Caldas-Alvarez and Khodayar, 2019) are indistinctly referred to as NDG or AS.

AdriaClim RESM - NEMO - historical, day - W subgrid 4D adriaclim_dataset=model adriaclim_model=NEMO forcing by WRF adriaclim_scale=adriatic adriaclim_timeperiod=day CDI=Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo) Conventions=CF-1.6, COARDS, ACDD-1.3 dataset=NEMO output files forced by AdriaClim WRF and WRFHydro and by Medcordex ocean NEMOMED8 description=ocean W grid variables Easternmost_Easting=20.97917 geospatial_lat_max=45.875 geospatial_lat_min=39.0 geospatial_lat_resolution=0.020833333333333332 geospatial_lat_units=degrees_north geospatial_lon_max=20.97917 geospatial_lon_min=12.0 geospatial_lon_resolution=0.020833341067285384 geospatial_lon_units=degrees_east history=Thu Feb 16 18:56:50 2023: cdo chname,nav_lon,lon,nav_lat,lat,depthw,depth,depthw_bnds,depth_bnds /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND_ERDDAP/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc.31973 /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND_ERDDAP/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc Thu Feb 16 18:56:50 2023: ncks -A -v nav_lat /tmp/nemo_lat_W.nc /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND_ERDDAP/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc.31973 Thu Feb 16 18:56:50 2023: ncks -A -v nav_lon /tmp/nemo_lon_W.nc /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND_ERDDAP/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc.31973 Thu Feb 16 18:56:50 2023: ncrename -O -d x,lon -d y,lat /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND_ERDDAP/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc.31973 Thu Feb 16 18:56:49 2023: ncks -C -O -x -v nav_lon,nav_lat /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc /work/opa/wrf_cmcc/Dedy/NEMO_NEW_WIND_ERDDAP/historical/day/2016/12/ADRIACLIM2_1d_20161226_grid_W.nc.31973 history_of_appended_files=Tue Oct 11 16:47:28 2022: Appended file /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lat.nc had following "history" attribute: Tue Oct 4 14:21:24 2022: ncrename -O -d y,lat /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lat.nc Tue Oct 4 14:21:24 2022: ncwa -a x /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lat.nc.1 /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lat.nc Tue Oct 4 14:21:24 2022: ncks -v nav_lat /data/products/ADRIACLIM_RESM/NEMO/historical/3h/1992/01_spinup/ADRIACLIM2_3h_19920129_grid_V.nc /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lat.nc.1 Tue Oct 11 16:47:28 2022: Appended file /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lon.nc had following "history" attribute: Tue Oct 4 14:21:24 2022: ncrename -O -d x,lon /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lon.nc Tue Oct 4 14:21:24 2022: ncwa -a y /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lon.nc.23608 /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lon.nc Tue Oct 4 14:21:24 2022: ncks -v nav_lon /data/products/ADRIACLIM_RESM/NEMO/historical/3h/1992/01_spinup/ADRIACLIM2_3h_19920129_grid_V.nc /work/opa/wrf_cmcc/Dedy/NEMO/nemo_lon.nc.23608 infoUrl=https://cmcc.it institution=CMCC keywords_vocabulary=GCMD Science Keywords name=ADRIACLIM2_1d_20161221_20161227_grid_W NCO=netCDF Operators version 4.9.3 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=45.875 project=AdriaClim Project-correspondence to CMCC Foundation OPA Division - giorgia.verri@cmcc.it sourceUrl=(local files) Southernmost_Northing=39.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2020-12-30T12:00:00Z time_coverage_start=1992-01-01T12:00:00Z timeStamp=2023-Feb-01 03:11:20 GMT uuid=6ee3a8d3-fc48-4767-ab79-98fd13868cdc Westernmost_Easting=12.0

cdo -yearmax RR.nc out.nc Highest single day precipitation: Let RRt be the daily precipitation amount on day t. For Rx1day search the maximum value of RRt in one year. Climate Modell Data More information about the climate model data source and methods can be found in the text files of the head data set (DOI: 10.58160/gGzexcbDikobkyvK, see "IsPartOf-DOI").

EMODPACE - Absolute sea level trend (1993 - 2019) - derived from CMCC-CGLORSv7 reanalysis CDI=Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo) Conventions=COARDS, CF-1.6, ACDD-1.3 Easternmost_Easting=130.0 geospatial_lat_max=45.0 geospatial_lat_min=-7.0 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=130.0 geospatial_lon_min=30.0 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east history=Wed Mar 09 16:37:59 2022: cdo -mulc,12000 slope_199301_201912_CMCC--ASLV-CGLORSv7-GLO.nc trend_199301_201912_CMCC--ASLV-CGLORSv7-GLO_v2.nc Mon Nov 22 18:09:17 2021: ncatted -a ,global,d,, /work/opa/das/Ree/EMOD-PACE/WP5/data_mod/trend/slope_199301_201912_CMCC--ASLV-CGLORSv7-GLO.nc 2022-04-07T08:41:53Z (local files) 2022-04-07T08:41:53Z https://erddap.emodnet-physics.eu/erddap/griddap/EMODNET_CMCC_ASLV_CGLORSv7_GLO_TREND.nc?zos%5B(2019-12-16T00:00:00Z):1:(2019-12-16T00:00:00Z)%5D%5B(-7):1:(45)%5D%5B(30):1:(130)%5D infoUrl=https://www.ec.gc.ca/scitech/default.asp?lang=En&n=61B33C26-1#cmc institution=Canadian Meteorological Centre keywords_vocabulary=GCMD Science Keywords NCO=netCDF Operators version 4.9.3 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=45.0 sourceUrl=(local files) Southernmost_Northing=-7.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2019-12-16T00:00:00Z time_coverage_start=2019-12-16T00:00:00Z Westernmost_Easting=30.0

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB" ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). It contains information on the Root-Zone Soil Moisture (RZSM) content at different depth layers as derived from Surface SM satellite observations of the ESA CCI SM products. The RZSM estimates and relative uncertainties are derived using the method of Pasik et al. (2023) forced with observations of the ESA CCI SM Combined product (Dorigo et al., 2017; Gruber et al., 2019; Preimesberger et al., 2021). Technical details The dataset provides global daily estimates for the 1978-2023 period at 0.25° (~25 km) horizontal resolution. The compressed downloadable rzsm_v09.1_1978_2023.tar.gz file is structured in sub-directories each including all files for a specific year. Each netCDF file contains the data of a specific day (DD), month (MM), and year (YYYY) in a 2-dimensional (longitude, latitude) grid system. The file name has the following convention: ESA_CCI_RZSM-YYYYMMDD000000-fv0.9.1.nc The RZSM data reflects the estimates calibrated for 4 depth layers: rzsm1: 0-10 cm rzsm2: 10-40 cm rzsm3: 40-100 cm rzsm4: 0-100 cm A package is available in python for reading the data as daily images and converting these images to time series and reading them. The source code for our python package and installation instructions are available here: https://github.com/TUW-GEO/esa_cci_sm The package can be installed via pip using "pip install esa_cci_sm" The documentation for this package is available here: https://esa-cci-sm.readthedocs.io/en/latest/ The "parameter" argument (e.g., https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39) can be specified to any of the layer variables (rzsm1, rzsm2, ...) Any software that can handle CF conform data should be able to import the raw netCDF files (e.g. CDO, NCO, QGIS, ArCGIS, Matlab, R, ...). You can also use the GUI software Panoply to view each file. Reference Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, 2023 Additional citations Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019 Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W. (2021). Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record, in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896. Related Records The following records are all part of the Soil Moisture Climate Data Records from satellites community 1 ESA CCI SM MODELFREE Surface Soil Moisture Record 10.48436/rqfmp-jp420 2 ESA CCI SM GAPFILLED Surface Soil Moisture Record 10.48436/hcm6n-t4m35

AdriaClim Simulations | Emilia Romagna | historical tsl CDI=Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo) comment=Data restriction: for academic research use only contact=email: georg.umgiesser@ismar.cnr.it Conventions=CF-1.6, COARDS, ACDD-1.3 Easternmost_Easting=13.36 geospatial_lat_max=45.32 geospatial_lat_min=43.96 geospatial_lat_resolution=0.009999999999999995 geospatial_lat_units=degrees_north geospatial_lon_max=13.36 geospatial_lon_min=12.24 geospatial_lon_resolution=0.009999999999999993 geospatial_lon_units=degrees_east history=Fri Jun 30 18:07:05 2023: ncap2 -O -s water_level=water_level+Tsteric+Ssteric adriaclim_erp_tsl_2020_hist.nc adriaclim_erp_tsl_2020_hist.nc Fri Jun 30 17:38:45 2023: ncks -A -v Tsteric,Ssteric ../steric/spatial_mean/adriaclim_erp_steric_hist_2020.nc adriaclim_erp_tsl_2020_hist.nc Mon Jun 26 15:38:20 2023: cdo splityear adriaclim_erp_ssh_concat.nc adriaclim_erp_ssh_ Mon Jun 26 15:36:52 2023: ncrcat adriaclim_sea_level_hist_0000.ous.nc adriaclim_sea_level_hist_0001.ous.nc adriaclim_sea_level_hist_0002.ous.nc adriaclim_sea_level_hist_0003.ous.nc adriaclim_sea_level_hist_0004.ous.nc adriaclim_sea_level_hist_0005.ous.nc adriaclim_sea_level_hist_0006.ous.nc adriaclim_sea_level_hist_0007.ous.nc adriaclim_sea_level_hist_0008.ous.nc adriaclim_sea_level_hist_0009.ous.nc adriaclim_sea_level_hist_0010.ous.nc adriaclim_sea_level_hist_0011.ous.nc adriaclim_sea_level_hist_0012.ous.nc adriaclim_sea_level_hist_0013.ous.nc adriaclim_sea_level_hist_0014.ous.nc adriaclim_sea_level_hist_0015.ous.nc adriaclim_sea_level_hist_0016.ous.nc adriaclim_sea_level_hist_0017.ous.nc adriaclim_sea_level_hist_0018.ous.nc adriaclim_sea_level_hist_0019.ous.nc adriaclim_sea_level_hist_0020.ous.nc adriaclim_sea_level_hist_0021.ous.nc adriaclim_sea_level_hist_0022.ous.nc adriaclim_sea_level_hist_0023.ous.nc adriaclim_sea_level_hist_0024.ous.nc adriaclim_sea_level_hist_0025.ous.nc adriaclim_sea_level_hist_0026.ous.nc adriaclim_sea_level_hist_0027.ous.nc adriaclim_sea_level_hist_0028.ous.nc adriaclim_erp_ssh_concat.nc Mon Jun 26 12:10:49 2023: ncks -v water_level ac-erm_chunk_0000.ous.nc adriaclim_sea_level_hist_0000.ous.nc created on 2023-06-26 11:47:06 MET history_of_appended_files=Fri Jun 30 17:38:45 2023: Appended file ../steric/spatial_mean/adriaclim_erp_steric_hist_2020.nc had following "history" attribute: Fri Jun 30 17:10:48 2023: cdo splityear temp_steric.nc spatial_mean/adriaclim_erp_steric_hist_ Fri Jun 30 17:03:37 2023: ncwa -a lat,lon -v Tsteric,Ssteric concat_files.nc temp_steric.nc Fri Jun 30 17:00:19 2023: ncrcat Steric_SeaLevel_1992.nc Steric_SeaLevel_1993.nc Steric_SeaLevel_1994.nc Steric_SeaLevel_1995.nc Steric_SeaLevel_1996.nc Steric_SeaLevel_1997.nc Steric_SeaLevel_1998.nc Steric_SeaLevel_1999.nc Steric_SeaLevel_2000.nc Steric_SeaLevel_2001.nc Steric_SeaLevel_2002.nc Steric_SeaLevel_2003.nc Steric_SeaLevel_2004.nc Steric_SeaLevel_2005.nc Steric_SeaLevel_2006.nc Steric_SeaLevel_2007.nc Steric_SeaLevel_2008.nc Steric_SeaLevel_2009.nc Steric_SeaLevel_2010.nc Steric_SeaLevel_2011.nc Steric_SeaLevel_2012.nc Steric_SeaLevel_2013.nc Steric_SeaLevel_2014.nc Steric_SeaLevel_2015.nc Steric_SeaLevel_2016.nc Steric_SeaLevel_2017.nc Steric_SeaLevel_2018.nc Steric_SeaLevel_2019.nc Steric_SeaLevel_2020.nc concat_files.nc created on 2023-06-25 11:54:53 MET infoUrl=https://www.unibo.it institution=UNIBO keywords_vocabulary=GCMD Science Keywords NCO=netCDF Operators version 4.9.3 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=45.32 references=Model info: http://www.ismar.cnr.it/shyfem source=Model data produced by SHYFEM at ISMAR-CNR sourceUrl=(local files) Southernmost_Northing=43.96 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2020-12-30T00:00:00Z time_coverage_start=1992-01-01T01:00:00Z Westernmost_Easting=12.24

AdriaClim Indicators | adriaclim_WRF | seasonal | proj | r95p adriaclim_dataset=indicator adriaclim_model=WRF - V3.5.1 adriaclim_scale=adriatic adriaclim_timeperiod=seasonal adriaclim_type=timeseries CDI=Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo) Conventions=CF-1.6, COARDS, ACDD-1.3 Easternmost_Easting=192.0 geospatial_lat_max=216.0 geospatial_lat_min=0.0 geospatial_lat_resolution=1.0 geospatial_lat_units=degrees_north geospatial_lon_max=192.0 geospatial_lon_min=0.0 geospatial_lon_resolution=1.0 geospatial_lon_units=degrees_east history_of_appended_files=Mon Dec 19 16:09:25 2022: Appended file lat1.nc had following "history" attribute: Mon Dec 19 16:09:15 2022: ncrename -O -d south_north,lat lat1.nc Mon Dec 19 16:09:15 2022: ncwa -a west_east lat.nc lat1.nc Mon Dec 19 16:09:15 2022: ncks -v XLAT adriaclim_WRF_cdd_hist_seasonal_1991_2005.nc lat.nc Indicator provided by CMCC Foundation Mon Dec 19 16:09:25 2022: Appended file lon1.nc had following "history" attribute: Mon Dec 19 16:09:15 2022: ncrename -O -d west_east,lon lon1.nc Mon Dec 19 16:09:15 2022: ncwa -a south_north lon.nc lon1.nc Mon Dec 19 16:09:15 2022: ncks -v XLONG adriaclim_WRF_cdd_hist_seasonal_1991_2005.nc lon.nc Indicator provided by CMCC Foundation infoUrl=https://cmcc.it institution=CMCC NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=216.0 sourceUrl=(local files) Southernmost_Northing=0.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2050-10-15T00:00:00Z time_coverage_start=2031-01-15T00:00:00Z Westernmost_Easting=0.0

AdriaClim Indicators | adriaclim_WRF | monthly | proj | r95p adriaclim_dataset=indicator adriaclim_model=WRF - V3.5.1 adriaclim_scale=adriatic adriaclim_timeperiod=monthly adriaclim_type=timeseries CDI=Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo) Conventions=CF-1.6, COARDS, ACDD-1.3 Easternmost_Easting=21.98158 geospatial_lat_max=46.97328 geospatial_lat_min=37.00147 geospatial_lat_units=degrees_north geospatial_lon_max=21.98158 geospatial_lon_min=10.0168 geospatial_lon_resolution=0.062316562500000006 geospatial_lon_units=degrees_east history_of_appended_files=Mon Dec 19 15:54:02 2022: Appended file lat1.nc had following "history" attribute: Mon Dec 19 15:54:01 2022: ncrename -O -d south_north,lat lat1.nc Mon Dec 19 15:54:01 2022: ncwa -a west_east lat.nc lat1.nc Mon Dec 19 15:53:54 2022: ncks -v XLAT adriaclim_WRF_r95p_proj_monthly_2006_2050.nc lat.nc Indicator provided by CMCC Foundation Mon Dec 19 15:54:02 2022: Appended file lon1.nc had following "history" attribute: Mon Dec 19 15:54:01 2022: ncrename -O -d west_east,lon lon1.nc Mon Dec 19 15:53:56 2022: ncwa -a south_north lon.nc lon1.nc Mon Dec 19 15:53:54 2022: ncks -v XLONG adriaclim_WRF_r95p_proj_monthly_2006_2050.nc lon.nc Indicator provided by CMCC Foundation infoUrl=https://cmcc.it institution=CMCC NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=46.97328 sourceUrl=(local files) Southernmost_Northing=37.00147 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2050-12-01T00:00:00Z time_coverage_start=2031-01-01T00:00:00Z Westernmost_Easting=10.0168

cdo -yearsum -ltc,1 RR.nc out.nc Number of dry days: Let RRt be the daily precipitation amount on day t. For dd count all days in one year where RRt < 1mm. Climate Modell Data More information about the climate model data source and methods can be found in the text files of the head data set (DOI: 10.58160/gGzexcbDikobkyvK, see "IsPartOf-DOI").