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".

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

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 into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

ERA5 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 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. 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.

Data for Figure 3.3 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.3 shows the global annual-mean surface (2 m) air temperature (°C) and the model bias to ERA5. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has six panels, with data provided for four panels in subdirectories named panel_a, panel_b, panel_c and panel_d. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains: - Global modelled annual-mean surface (2 m) air temperature (°C) of CMIP6 for the period 1995–2014 - Global bias of modelled annual-mean surface (2 m) air temperature (°C) of CMIP6 for the period 1995–2014 to reanalysis ERA5 - Global root mean square bias of modelled annual-mean surface (2 m) air temperature (°C) of CMIP6 for the period 1995–2014 to reanalysis ERA5 - Global bias of modelled annual-mean surface (2 m) air temperature (°C) of CMIP5 for the period 1985–2004 to reanalysis ERA5 CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. ERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/tas_mean_cmip6.nc; global map - panel_b/tas_bias_cmip6.nc; global map - panel_c/tas_rms_bias_cmip6.nc; global map - panel_d/tas_bias_cmip5.nc; global map --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

Global ERA5 monthly averaged reanalysis Tmax data on single levels from 1940 to 2022 (0.25 degree)Please cite

Yearly citation counts for the publication titled "How do IMERG V07, IMERG V06, and ERA5 Precipitation Products Perform Over Snow-ice-free and Snow-ice-covered Surfaces at a Range of Near Surface Temperatures?".

The historical climate reanalysis data from ERA5 are offered at 0.25 x 0.25-degree resolution over the entire globe. ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to the present. ERA5 uses a broad collection of observational data, including various satellite-derived products in multivariate data assimilation mode to capture global variability and change. The data are offered through the Copernicus Climate Change Service (C3S) as a public good and are updated operationally. Data are updated annually.

Presented at monthly, seasonal, and annual scale Spatial resolution: 0.25o x 0.25o Historical Climatologies (20-year or 30-year periods used for climatologies and natural variability): 1986-2005, 1991-2020, 1995-2014
Decadal trends calculated for: 1951-2020, 1971-2020, 1991-2020

Recommended Use: ERA5 is considered one of the top reanalysis products. It provides consistent coverage of all variables found in climate models, making it a valuable reference. In areas with good station coverage, ERA5 closely aligns with CRU data, while in regions lacking stations, it offers reliable estimates and minimizes false trends from short satellite records. Temperature data from ERA5 is highly reliable, but for precipitation, it’s recommended to use multiple datasets due to the challenges in accurately measuring and modeling it.

If you use this dataset please cite the accompanying paper (Lea et al., 2024)

Maps of key (bio-)climatic variables derived from all currently available ERA5-Land reanalysis data (Muñoz Sabater et al., 2019). These have been calculated for:

1. All possible World Meteorological Organisation (WMO) 30 year climate baseline periods, including: 1951 to 1980; 1961 to 1990; 1971 to 2000; 1981 to 2010; and 1991 to 2020 (this dataset).

2. Annual timescales from 1951-2022 (see here).

Annual timescale data are calculated using monthly statistics using calendar months that account for leap years. WMO baseline maps are calculated by taking the mean of all annual timescale ERALClim maps that fall within the time periods stated above (inclusive). Image bands are named to map onto equivalent BioClim variables (Fick and Hijmans, 2017).

Global data are provided here in GeoTIFF format as multiband images (where each band represents a different year/variable depending on the data downloaded) at a spatial scale of 0.1 degrees within a WGS84 grid (EPSG:4326). If users require data from point locations and/or subset regions for a specific time range or for a custom range of variables, these can be easily accessed using the Google Earth Engine Climate Tool (GEEClimT). Access to this tool requires a Google Earth Engine account, and is free to use for academic research and education purposes, and users who access data through the tool should cite Lea et al., 2024.

Descriptions of each band within the dataset are listed below:

bio1 - Mean 2 m air temperature derived from hourly data (units: degrees C).

bio2 - Annual mean of monthly mean diurnal 2 m air temperature ranges (units: degrees C).

bio3 - Isothermality (100 * bio2 / bio7) (no units).

bio4 - Standard deviation of monthly mean 2 m air temperatures (units: degrees C).

bio5 - Mean of maximum 2 m air temperature for the warmest month (units: degrees C).

bio6 - Mean of minimum 2 m air temperature for the coldest month (units: degrees C).

bio7 - Annual range of 2 m air temperature (bio5 - bio6) (units: degrees C).

bio8 - Mean 2 m air temperature of wettest 3 month period (units: degrees C).

bio9 - Mean 2 m air temperature of driest 3 month period (units: degrees C).

bio10 - Mean 2 m air temperature of warmest 3 month period (units: degrees C).

bio11 - Mean 2 m air temperature of coldest 3 month period (units: degrees C).

bio12 - Total annual precipitation (units: mm).

bio13 - Total precipitation of wettest month (units: mm).

bio14 - Total precipitation of driest month (units: mm).

bio15 - Precipitation Seasonality (Coefficient of Variation, based on monthly total precipitation data) (no units).

bio16 - Total precipitation in wettest 3 month period (units: mm).

bio17 - Total precipitation in driest 3 month period (units: mm).

bio18 - Total precipitation in warmest 3 month period (units: mm).

bio19 - Total precipitation in coldest 3 month period (units: mm).

This dataset provides daily and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.

All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.

Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). Monthly means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081721).

The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find. Please cite the reference publication when using this dataset.

Known issues:

• All TEM terms divided by (\cos(\phi)), where (\phi) is latitude, diverge at the north and south poles (where (\phi = \pm \pi/2)), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.

Reference evapotranspiration per day with a spatial resolution of 0.1 degree. Unit: mm day-1. The dataset contains daily values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters (for example, mean rates and accumulations) that are not available from the analyses.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

This experiment comprises data that have been used in Hagemann et al. (submitted). It comprises daily data of surface runoff and subsurface runoff from HydroPy and simulated daily discharges (river runoff) of the HD model. The discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region.

a) HD5-ERA5 ERA5 is the fifth generation of atmospheric reanalysis (Hersbach et al., 2020) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides hourly data on many atmospheric, land-surface, and sea-state parameters at about 31 km resolution. The global hydrology model HydroPy (Stacke and Hagemann, 2021) was driven by daily ERA5 forcing data from 1979-2018 to generate daily input fields of surface and subsurface runoff at the ERA5 resolution. It uses precipitation and 2m temperature directly from the ERA5 dataset. Furthermore, potential evapotranspiration (PET) was calculated from ERA5 data in a pre-processing step and used as an additional forcing for HydroPy. Here, we applied the Penman-Monteith equation to calculate a reference evapotranspiration following (Allen et al., 1998) that was improved by replacing the constant value for albedo with a distributed field from the LSP2 dataset (Hagemann, 2002). In order to initialize the storages in the HydroPy model and to avoid any drift during the actual simulation period, we conducted a 50-years spin-up simulation by repeatedly using year 1979 of the ERA5 dataset as forcing. To generate river runoff, the Hydrological discharge (HD) model (Hagemann et al., 2020; Hagemann and Ho-Hagemann, 2021) was used that was operated at 5 arc minutes horizontal resolution. The HD model was set up over the European domain covering the land areas between -11°W to 69°E and 27°N to 72°N. First, the forcing data of surface and sub-surface runoff simulated by HydroPy were interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

b) HD5-EOBS The E-OBS dataset (Cornes et al., 2018) comprises several daily gridded surface variables at 0.1° and 0.25° resolution over Europe covering the area 25°N-71.5°N x 25°W-45°E. The dataset has been derived from station data collated by the ECA&D (European Climate Assessment & Dataset) initiative (Klein Tank et al., 2002; Klok and Klein Tank, 2009). In the present study, we use the best-guess fields of precipitation and 2m temperature of vs. 22 (EOBS22) at 0.1° resolution for the years 1950-2018. HydroPy was driven by daily EOBS22 data of temperature and precipitation at 0.1° resolution from 1950-2019. The potential evapotranspiration (PET) was calculated following the approach proposed by (Thornthwaite, 1948) including an average day length at a given location. As for HD5-ERA5, the forcing data of surface and sub-surface runoff simulated by HydroPy were first interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

Main reference: Hagemann, S., Stacke, T. (2022) Complementing ERA5 and E-OBS with high-resolution river discharge over Europe. Oceanologia 65: 230-248, doi:10.1016/j.oceano.2022.07.003

Reference evapotranspiration per dekade with a spatial resolution of 0.1 degree. Unit: mm dekad-1. The dataset contains dekadal values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

This experiment comprises data that have been used in Hagemann et al. (submitted). It comprises daily data of surface runoff and subsurface runoff from HydroPy and simulated daily discharges (river runoff) of the HD model. The discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region.

a) HD5-ERA5 ERA5 is the fifth generation of atmospheric reanalysis (Hersbach et al., 2020) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides hourly data on many atmospheric, land-surface, and sea-state parameters at about 31 km resolution. The global hydrology model HydroPy (Stacke and Hagemann, 2021) was driven by daily ERA5 forcing data from 1979-2018 to generate daily input fields of surface and subsurface runoff at the ERA5 resolution. It uses precipitation and 2m temperature directly from the ERA5 dataset. Furthermore, potential evapotranspiration (PET) was calculated from ERA5 data in a pre-processing step and used as an additional forcing for HydroPy. Here, we applied the Penman-Monteith equation to calculate a reference evapotranspiration following (Allen et al., 1998) that was improved by replacing the constant value for albedo with a distributed field from the LSP2 dataset (Hagemann, 2002). In order to initialize the storages in the HydroPy model and to avoid any drift during the actual simulation period, we conducted a 50-years spin-up simulation by repeatedly using year 1979 of the ERA5 dataset as forcing. To generate river runoff, the Hydrological discharge (HD) model (Hagemann et al., 2020; Hagemann and Ho-Hagemann, 2021) was used that was operated at 5 arc minutes horizontal resolution. The HD model was set up over the European domain covering the land areas between -11°W to 69°E and 27°N to 72°N. First, the forcing data of surface and sub-surface runoff simulated by HydroPy were interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

b) HD5-EOBS The E-OBS dataset (Cornes et al., 2018) comprises several daily gridded surface variables at 0.1° and 0.25° resolution over Europe covering the area 25°N-71.5°N x 25°W-45°E. The dataset has been derived from station data collated by the ECA&D (European Climate Assessment & Dataset) initiative (Klein Tank et al., 2002; Klok and Klein Tank, 2009). In the present study, we use the best-guess fields of precipitation and 2m temperature of vs. 22 (EOBS22) at 0.1° resolution for the years 1950-2018. HydroPy was driven by daily EOBS22 data of temperature and precipitation at 0.1° resolution from 1950-2019. The potential evapotranspiration (PET) was calculated following the approach proposed by (Thornthwaite, 1948) including an average day length at a given location. As for HD5-ERA5, the forcing data of surface and sub-surface runoff simulated by HydroPy were first interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

Main reference: Hagemann, S., Stacke, T. (2022) Complementing ERA5 and E-OBS with high-resolution river discharge over Europe. Oceanologia 65: 230-248, doi:10.1016/j.oceano.2022.07.003

Data for Figure 3.19 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.19 shows long-term mean (thin black contour) and linear trend (colour) of zonal mean DJF zonal winds over 1985-2014 in the SH.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.

List of data provided

This dataset contains:

• ERA5 zonal-mean wind (1985-2014)
• ERA5 zonal-mean wind trend (1985-2014)
• CMIP6 zonal-mean wind (1985-2014)
• CMIP6 zonal-mean wind trend (1985-2014)

ERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.

Data provided in relation to figure

• panel_a/fig_3_19_a_mean.nc (contour, ERA5 mean)
• panel_a/fig_3_19_a_trend.nc (color, ERA5 trend)
• panel_b/fig_3_19_b_mean.nc (contour, CMIP6 mean)
• panel_b/fig_3_19_b_trend.nc (color, CMIP6 trend)

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

Input data for Figure 2.16 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 2.16 provides global precipitation minus evaporation trend maps and time series from a variety of data sources

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi:10.1017/9781009157896.004.

Figure subpanels

The figure has four panels, with input data provided for all panels in the main directory

List of data provided

The datasets contains:

• Global precipitation and evaporation data from ERA5 reanalysis
• Time series of global, land-only and ocean-only average annual P–E (mm day–1) from the following reanalysis products: 20CRv3, ERA5, ERA20CM, MERRA, CFSR, ERA20C, JRA55 and MERRA2.

Data provided in relation to figure

Panel a: - Data files: IntermediateData_era5_evap_2.nc and era5_tp_2.nc

Panel b: - Data file: GPME2.csv and GPME2.mat

Panel c: - Data file: LPME2.csv and LPME2.mat

Panel d: - Data file: OPME2.csv and OPME2.mat

For panels b to d: I. Column 2: orange solid line II. Column 3: cyan solid line III. Column 4: black solid line IV. Column 5: grey solid line V. Column 6: blue solid line VI. Column 7: dark green solid line VII. Column 8: brown solid line VIII. Column 9: green solid line

20CRv3 is the NOAA-CIRES-DOE Twentieth Century Reanalysis Version 3. ERA5 is a reanalysis of the global climate from 1950 to present, developed by ECMWF. ERA20CM is a twentieth century atmospheric model ensemble developed by ECMWF. MERRA stands for Modern-Era Retrospective analysis for Research and Applications. CFSR stands for Climate Forecast System Reanalysis. ERA20C is the first atmospheric reanalysis of the 20th century, from 1900-2010, developed by ECMWF. JRA55 stands for Japanese 55-year Reanalysis. MERRA2 stands for Modern-Era Retrospective analysis for Research and Applications, version 2.

Notes on reproducing the figure from the provided data

Additional information to correctly reproduce the figure in the corresponding readme files for code archived on Zenodo (see the link to code provided in the Related Documents section of this catalogue record).

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 2) - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1 - Link to the code for the figure, archived on Zenodo.

This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).

Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.

Citation

In order to use these data, you must cite this data set with the following citation:

Obu, J.; Westermann, S.; Barboux, C.; Bartsch, A.; Delaloye, R.; Grosse, G.; Heim, B.; Hugelius, G.; Irrgang, A.; Kääb, A.M.; Kroisleitner, C.; Matthes, H.; Nitze, I.; Pellet, C.; Seifert, F.M.; Strozzi, T.; Wegmüller, U.; Wieczorek, M.; Wiesmann, A. (2021): ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v3.0. NERC EDS Centre for Environmental Data Analysis, 28 June 2021. doi:10.5285/6e2091cb0c8b4106921b63cd5357c97c. https://dx.doi.org/10.5285/6e2091cb0c8b4106921b63cd5357c97c

This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).

Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.

Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.

Citation

In order to use these data, you must cite this data set with the following citation:

Westermann, S.; Barboux, C.; Bartsch, A.; Delaloye, R.; Grosse, G.; Heim, B.; Hugelius, G.; Irrgang, A.; Kääb, A.M.; Matthes, H.; Nitze, I.; Pellet, C.; Seifert, F.M.; Strozzi, T.; Wegmüller, U.; Wieczorek, M.; Wiesmann, A. (2024): ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0. NERC EDS Centre for Environmental Data Analysis, 04 April 2024. doi:10.5285/93444bc1c4364a59869e004bf9bfd94a. https://dx.doi.org/10.5285/93444bc1c4364a59869e004bf9bfd94a

The U.S. Climate Reference Network (USCRN) was designed to monitor the climate of the United States using research quality instrumentation located within representative pristine environments. This heat index product is designed to provide heat index variables calculated hourly for each USCRN station location. Beyond the hourly average air temperature, relative humidity, and global solar radiation observed at the station, an hourly 10 meter wind speed average is estimated from station observations 1.5 meters above ground, and surface atmospheric pressure is extracted from either ERA5 reanalysis historically, or NCEP RUC in near real time. The file contains three heat exposure variables beyond air temperature: heat index (HI), apparent temperature (AT) and wet bulb globe temperature (WBGT). Air temperature and the three heat exposure indices have been provided in both metric (Degrees Celsius) and standard customary units (Degrees Fahrenheit) for convenience of the user. File names are structured at CRNHE0101-STATIONNAME.csv. HE stands for Heat Indices. The first two digits of the trailing integer indicate major version and the second two digits minor version of the product.