ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31 kilometer (km) grid and resolve the atmosphere using 137 levels from the surface up to a height of 80 km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world …

This dataset contains daily histograms of wind speed at 100m ("WS100"), wind direction at 100 m ("WD100") and an atmospheric stability proxy ("STAB") derived from the ERA5 hourly data on single levels [1] accessed via the Copernicus Climate Change Climate Data Store [2]. The dataset covers six geographical regions (illustrated in regions.png) on a reduced 0.5 x 0.5 degrees regular grid and covers the period 1994 to 2023 (both years included). The dataset is packaged as a zip folder per region which contains a range of monthly zip folders following the convention of zarr ZipStores (more details here: https://zarr.readthedocs.io/en/stable/api/storage.html). Thus, the monthly zip folders are intended to be used in connection with the xarray python package (no unzipping of the monthly files needed).Wind speed and wind direction are derived from the U- and V-components. The stability metric makes use of a 5-class classification scheme [3] based on the Obukhov length whereby the required Obukhov length was computed using [4]. The following bins (left edges) have been used to create the histograms:Wind speed: [0, 40) m/s (bin width 1 m/s)Wind direction: [0,360) deg (bin width 15 deg)Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)Main Purpose: The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).References:[1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)[2] Copernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)'[3] Holtslag, M. C., Bierbooms, W. A. A. M., & Bussel, G. J. W. van. (2014). Estimating atmospheric stability from observations and correcting wind shear models accordingly. In Journal of Physics: Conference Series (Vol. 555, p. 012052). IOP Publishing. https://doi.org/10.1088/1742-6596/555/1/012052[4] Copernicus Knowledge Base, ERA5: How to calculate Obukhov Length, URL: https://confluence.ecmwf.int/display/CKB/ERA5:+How+to+calculate+Obukhov+Length, last accessed: Nov 2024

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 DAILY provides aggregated values for each day for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, daily minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Daily total precipitation values are given as daily sums. All other parameters are provided as daily averages. ERA5 data is available from 1979 to three months from real-time. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Daily aggregates have been calculated based on the ERA5 hourly values of each parameter.

Yearly timeseries (.csv) of basic-ecvs spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data: Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c. Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac Acknowledgement also to: DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (https://portal.azores.gov.pt/en/web/draac) SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1

Abstract

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. The full dataset is available from 1940 onwards at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview. This version only contains hourly measures of solar radiation, temperature and wind speeds, as well as monthly measures for sea surface temperature for 1950-2020.

Methodology

Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.

ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.

Usage

Downloaded Using: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form

These datasets contains ERA-5 data for the entirety for CONUS for the following temporal resolutions and fields:

Hourly Resolution Data

The following fields are available at an hourly resolution.

1. solar_radiation - Surface solar radiation downwards

2. temperature - 2m temperature

3. wind_speeds - 100m u-component of wind and 100m v-component of wind

Note:- Within each field xxxx.nc denotes the hourly data for xxxx year. The data span from 1950-2020.

###Monthly Resolution Data###

1. sst - Available at two resolutions.

preliminary_sst --%3E Data from 1950-1978.

sst --%3E Data from 1979-2020.

Additionally the sst field contains Sea Surface Temperature across the globe.

Data processing: Monthly mean of the daily mean (1-hour sampling interval); computed as the arithmetic mean of the values belonging to the same day. "Day" is defined as a function of the time zone UTC + 00:00 (i.e., the “day” starts from 00:00:00 in UTC + 00:00 time zone). Times are sampled starting from 00:00 in the selected time zone. Then extraction of the nearest cell daily value (i.e., nearest neighbour method) to each pond location (data source for pond location: PONDERFUL_PondID_20230601.csv) using the package stars in R. Variable: 2m temperature (units: K); this parameter is the temperature of air at 2m above the surface of land sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Raw data source: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed on 20-11-2024)

Climate maps (raster layers .tif) of derived-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data: Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c. Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac Acknowledgement also to: DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (https://portal.azores.gov.pt/en/web/draac) SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1

This dataset contains values calculated using existing data produced by Environment and Climate Change Canada (ECCC). Freezing degree-days (FDD) correspond to the negative difference between the average daily temperature and the freezing point of seawater (Tf = -1.8°C). For example, if for one day the average temperature is -21.8°C, it raises the annual FDD value by 20.0 FDD. When the daily averaged temperature is above Tf, the FDD value is negative. FDDs are summed starting on September 1st each year. When the cumulative number of FDDs (CFDD) becomes negative, it is reset to zero. The start of winter corresponds to the first time the CFDD is and remains above 0. In this data set, the daily temperature averaged over the entire marine domain of the Gulf of St. Lawrence is used. The data comes from surface temperature forecasts (T2m) from ECCC's High Resolution Deterministic Prediction System (HRDPS). cdm_data_type=Other comment=Data from 2024-9-01 to 2025-01-22 are transiently not from the HRDPS model but from the Copernicus ERA5 model. HRDPS data will replace ERA5 data when historical HRDPS data become available.

  Data prior to September 1, 2024 are temporarily calculated over a different period from November 1 to September 1 of each year. These data will soon be updated with the new period (i.e. September 1 to August 31)

contributor_institution=(a) Université du Québec à Rimouski, (b) Université du Québec à Rimouski, (c) Service Hydrographique et Océanographique de la Marine, (d) Fisheries and Oceans Canada, (e) St. Lawrence Global Observatory contributor_name=(a) Dany Dumont, (b) Sébastien Dugas, (c) Eliott Bismuth, (d) Peter Galbraith, (e) Antoine Biehler contributor_role=(a) Metadata Custodian, Author, (b) Coauthor, Contributor, (c) Coauthor, Contributor, (d) Contributor, (e) Metadata Custodian, Contributor, Editor Conventions=COARDS, CF-1.12, ACDD-1.3, NCCSV-1.2 data_source_01=Environment and Climate Change Canada - HRDPS model - https://eccc-msc.github.io/open-data/msc-data/nwp_hrdps/readme_hrdps_en/ data_source_02=From 2024-09-01 to 2025-01-22 only Copernicus Climate Change Service, Climate Data Store, (2023) - ERA5 hourly data on single levels from 1940 to present - https://doi.org/10.24381/cds.adbb2d47 dataset_status=OnGoing defaultGraphQuery=&time>=max(time)-1year&.bgColor=0xffffffff DOI=A VERIFIER geospatial_lat_max=52.2 geospatial_lat_min=45.1 geospatial_lat_units=degrees_north geospatial_lon_max=-55.2 geospatial_lon_min=-71.3 geospatial_lon_units=degrees_east grid_mapping_epsg_code=EPSG:4326 grid_mapping_epsg_code_url=https://epsg.io/4326 grid_mapping_geographic_crs_name=WGS 84 grid_mapping_inverse_flattening=298.2572236 grid_mapping_name=latitude_longitude grid_mapping_prime_meridian_name=Greenwich grid_mapping_semi_major_axis=6378137 infoUrl=https://ogsl.ca/cartesglacesstlaurent/ institution=Institut des sciences de la mer de Rimouski keywords_fr=glace de mer, température de l'air, changement climatique keywords_vocabulary=NASA Global Change Master Directory (GCMD) Science Keywords and homemade keywords licenseUrl=https://creativecommons.org/licenses/by/4.0/ marine_region=Gulf of St. Lawrence marine_region_identifier=http://marineregions.org/mrgid/4290 publisherID=https://ror.org/03wfagk22 sourceUrl=(local files) standard_name_nerc_vocabulary=The NERC Vocabulary Server (NVS) standard_name_other_vocabulary=dwc: Darwin Core List of Terms (v 2023-09) standard_name_vocabulary=CF Standard Name Table v86 summary_fr=Ce jeu de données contient des valeurs calculées à partir de données existantes produites par Environnement et changement climatique Canada (ECCC). Un degré-jour de gel (DJG) correspond à la différence négative entre la température moyenne journalière et le point de congélation de l'eau de mer (Tf = -1.8°C). Si pour un jour la température moyenne est de -21.8°C, par exemple, il élève la valeur DJG annuelle de 20.0 DJG. Les jours où la température moyenne est supérieure Tf, la valeur de DJG diminue. Les DJG sont calculés à partir du 1er septembre. Lorsque le nombre cumulé de DJG (DJGC) devient négatif, il est remis à zéro. Le début de l’hiver correspond au moment où les DJG commencent à augmenter de manière persistante, donc au premier moment où DJGC est plus grand que 0. Dans ce jeu de données, on utilise la température journalière moyennée sur l'ensemble du domaine marin du golfe du Saint-Laurent. Les données sont issues des prévisions de température de surface (T2m) du système de prévision déterministe à haute résolution (HRDPS) d'ECCC. time_coverage_end=2025-05-01 time_coverage_start=1996-11-01

All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".
While most of the code is a frozen clone of the original Repository, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.

The dataset for training and evaluation can be found at data/nextsim, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the SASIP shared data OpenDAP server:

• The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework" (Boutin et al., 2023) and available as Zenodo dataset (Boutin et al., 2022).
• The forcing data is based on the ERA5 reanalysis dataset published in the paper: "The ERA5 global reanalysis" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the hourly reanalysis data on single levels and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. Disclaimer: The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

The neural network weights are included under data/models and split into weights for the deterministic models and the diffusion models.
These neural network weights have been used to generate the results presented in the paper.

In this capsule, the notebooks folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.

Generally, we recommend to just download the data.tar.gz file and use otherwise the original Repository, since the here included code can be outdated. We further refer to the repository for additional information.

Contained in this capsule:

• configs.tar.gz: The configuration files for the experiments.
• data.tar.gz: The dataset and neural network weights.
• diffusion_nextsim.tar.gz: The main code for the neural network etc.
• environment.yaml: The anaconda environment file, can be used to install the needed packages.
• notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.
• readme.md: The readme file from the repository.
• scripts.tar.gz: The scripts used for the experiments.
• setup.py: the file to install the diffusion_nextsim package in a python environment.

References:

Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523

Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.

Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47.

Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999–2049. https://doi.org/10.1002/qj.3803

This dataset on sea ice production (SIP) in Antarctic coastal polynyas offers crucial insights into these regions' roles in sea ice and dense water formation. Using Earth Observation data and atmospheric reanalysis, the dataset employs a heat budget method to estimate SIP. It incorporates sea ice concentration (SIC) from passive microwave sensors and near-surface wind speed and surface air temperature from ECMWF ERA5 reanalysis. Comparative analysis with previous studies confirms the dataset's accuracy in depicting spatial patterns and the magnitude of ice production, though some variations exist across larger polynyas. While simplifications may increase uncertainties in absolute SIP values, this dataset is a valuable resource for understanding the dynamics and variability of Antarctic polynya SIP from 1992 to 2023. acknowledgements=EU OCEAN:ICE Deliverable D1.4 (June 2024) Gridded European circumpolar sea ice production fluxes OCEAN:ICE is co-funded by the European Union, Horizon Europe Funding Programme for research and innovation under grant agreement Nr. 101060452 and by UK Research and Innovation CDI=Climate Data Interface version 2.3.0 (https://mpimet.mpg.de/cdi) cdm_data_type=Grid CDO=Climate Data Operators version 2.3.0 (https://mpimet.mpg.de/cdo) citation=Kaleschke, L. (2024). EU project OCEAN ICE Deliverable: D1.4 Gridded European circumpolar sea ice production fluxes [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11652686 contributors_name=Kaleschke, Lars contributors_orcid=https://orcid.org/0000-0001-7086-3299 Conventions=CF-1.10, COARDS, ACDD-1.3 conventions=CF-1.6 data_creation=2024.07.31 data_doi=https://zenodo.org/records/11652686 data_format_original=netCDF Description=OCEAN ICE - European circumpolar sea ice production fluxes Easternmost_Easting=3950.0 geospatial_lat_max=4350.0 geospatial_lat_min=-3950.0 geospatial_lat_resolution=12.518853695324283 geospatial_lat_units=degrees_north geospatial_lon_max=3950.0 geospatial_lon_min=-3950.0 geospatial_lon_resolution=12.519809825673534 geospatial_lon_units=degrees_east grid=EPSG:3412 # NSIDC Polar Stereographic for South history=Thu Apr 18 20:46:13 2024: cdo -f nc2 mergetime SIP_199201.nc SIP_199202.nc SIP_199203.nc SIP_199204.nc SIP_199205.nc SIP_199206.nc SIP_199207.nc SIP_199208.nc SIP_199209.nc SIP_199210.nc SIP_199211.nc SIP_199212.nc SIP_199301.nc SIP_199302.nc SIP_199303.nc SIP_199304.nc SIP_199305.nc SIP_199306.nc SIP_199307.nc SIP_199308.nc SIP_199309.nc SIP_199310.nc SIP_199311.nc SIP_199312.nc SIP_199401.nc SIP_199402.nc SIP_199403.nc SIP_199404.nc SIP_199405.nc SIP_199406.nc SIP_199407.nc SIP_199408.nc SIP_199409.nc SIP_199410.nc SIP_199411.nc SIP_199412.nc SIP_199501.nc SIP_199502.nc SIP_199503.nc SIP_199504.nc SIP_199505.nc SIP_199506.nc SIP_199507.nc SIP_199508.nc SIP_199509.nc SIP_199510.nc SIP_199511.nc SIP_199512.nc SIP_199601.nc SIP_199602.nc SIP_199603.nc SIP_199604.nc SIP_199605.nc SIP_199606.nc SIP_199607.nc SIP_199608.nc SIP_199609.nc SIP_199610.nc SIP_199611.nc SIP_199612.nc SIP_199701.nc SIP_199702.nc SIP_199703.nc SIP_199704.nc SIP_199705.nc SIP_199706.nc SIP_199707.nc SIP_199708.nc SIP_199709.nc SIP_199710.nc SIP_199711.nc SIP_199712.nc SIP_199801.nc SIP_199802.nc SIP_199803.nc SIP_199804.nc SIP_199805.nc SIP_199806.nc SIP_199807.nc SIP_199808.nc SIP_199809.nc SIP_199810.nc SIP_199811.nc SIP_199812.nc SIP_199901.nc SIP_199902.nc SIP_199903.nc 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SIP_202212.nc SIP_202301.nc SIP_202302.nc SIP_202303.nc SIP_202304.nc SIP_202305.nc SIP_202306.nc SIP_202307.nc SIP_202308.nc SIP_202309.nc SIP_202310.nc SIP_202311.nc SIP_202312.nc ../1992_2023.nc infoUrl=https://gitlab.awi.de/ocean-ice/deliverable institution=Alfred-Wegener-Institut Helmholtz-Zentrum Für Polar- Und Meeresforschung (AWI) institution_country=DE institution_edmo_code=1368 keywords_vocabulary=GCMD Science Keywords metadata_url=https://zenodo.org/records/12166097 naming_authority=OCEAN ICE netCDF_created_by=Lars.Kaleschke@AWI.de Northernmost_Northing=4350.0 references=ASI SSM/I sea ice concentration data used as input are available at IFREMER/CERSAT ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-concentration ECMWF ERA5 hourly data on single levels from 1940 to present https://doi.org/10.24381/cds.adbb2d47 source=Reanalysis sourceUrl=(local files) Southernmost_Northing=-3950.0 spatial_resolution=12.5 km standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2023-12-31T00:00:00Z time_coverage_resolution=monthly time_coverage_start=1992-01-31T00:00:00Z time_resolution=monthly variables=Latitude, Longitude, Sea Ice Production Westernmost_Easting=-3950.0

The ice_type dataset, provided by ORAS5, is the Meereisalter für die Arktis von NSIDC dataset, updated weekly. It covers a geographical range from 48.4°N to 90°N and utilizes the EASE projection coordinate system. In this study, the dataset is used to classify sea ice into multi-year ice, first-year ice, and open water. The SIC dataset, also released by ORAS5, is the Polare Meereiskonzentration von ASI-SSMI Daten dataset, which is updated daily. It covers latitudinal regions approximately above 45°N and 45°S, using a polar stereographic grid coordinate system based on the NSIDC polar projection. The Climate dataset consists of ERA5 hourly data on single levels from 1940 to present, provided by the Copernicus Climate Change Service (C3S) of ECMWF, including 2m temperature and sea surface temperature. All datasets have been resampled to a spatial resolution of 0.25°. The selected study period is from 2001 to 2020, with sea ice data extending to include January to March of 2021.

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

This dataset comprises global simplified Wet-Bulb Globe Temperature (sWBGT) values from 1940 to 2022, calculated at a daily maximum with a spatial resolution of 2°x2°. The sWBGT integrates air temperature, dew point temperature, and surface air pressure to provide a measure of heat stress. The dataset was derived from the ERA5 hourly data on single levels. In addition to the sWBGT values, the dataset includes climate network data and network metrics calculated for selected years (every five years from 1940 through 2020). These data facilitate the analysis of teleconnections, or long-distance correlations in heat stress events, and provide insights into the spatial correlations and propagation of heat stress effects across different regions. The sWBGT data are stored in NetCDF format, making them compatible with commonly used data analysis tools in climate research. The climate network data and network metrics are saved in the pickle format to ensure efficient serialization and deserialization of Python objects used in network analysis. All the Python scripts required to calculate the dataset are also included. These scripts allow users to replicate the dataset or adapt the methodology for their own research needs. This dataset supports researchers in climate science, public health, and environmental policy by enabling the examination of historical trends and the assessment of future risks associated with heat stress.

The atmospheric component of NORA3 is produced by running the non-hydrostatic HARMONIE-AROME model (Seity et al., 2011, Bengtsson et al., 2017, Muller et al., 2017) with 3km horizontal resolution and 65 vertical levels. The model runs 9-hourly forecasts four times a day. Each forecast starts from an assimilated state of the last forecast adapted to surface observations. Model levels are forced with the global ERA-5 data (https://climate.copernicus.eu/climate-reanalysis). A continuous historical time series, i.e. the hindcast, is then concatenated from hour 4 to 9 of each forecast. The 3-hourly atmosphere files contains sea surface temperature and a number of atmospheric parameters calculated and interpolated from the original model levels to selected fixed heights above surface.

This dataset provides climate indicators of windstorms associated with extratropical cyclones, derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5) over a pan-European domain. Developed as part of Copernicus Climate Change Service's (C3S) Enhanced Windstorm Service (EWS), and responding to requirements from users in the insurance sector, this dataset extends the previous C3S Windstorm Service (winter months only) to include the entire year. This dataset is updated on a monthly basis, in near real-time. The catalogue includes windstorms identified using cyclone tracking algorithms that pass the filtering criteria, including mean sea level pressure and 850hPa vorticity (please see the Product User Guide for more information). The dataset includes three main products:

Windstorm track: a sequence of longitude–latitude points which define the trajectory of an extra-tropical windstorm over time. A tracking algorithm is used to select the windstorm track. Two tracking algorithms are available in this dataset: TempestExtremes and Hodges algorithm (also known as TRACK).

Windstorm footprint: the maximum 10m wind gust over a 72-hour time window. The time window is centred on the time step in which the tracking algorithm identifies the maximum 925 hPa wind speed related to the windstorm. Only windstorm footprints that make landfall are considered by the tracking algorithm. The windstorm footprints are identified on the original ERA5 grid (0.25° x 0.25°) as well as statistically-downscaled high-resolution grid (0.016° x 0.016°) to better represent orographic and wind shear effects. Two spatial configurations of the footprints are provided: i) the "full domain" and ii) "windstorm footprint area", also referred to as "decontaminated" (please see the Product User Guide for more information).

Windstorm summary indicators: annual statistics derived from decontaminated windstorm footprints and both tracking algorithms. Four wind gust thresholds (0, 15.6, 20, and 25 m/s) are considered.

This dataset was produced on behalf of the Copernicus Climate Change Service.