Overview
This document provides an overview of the snow products of ERA5, in the context of the larger ERA5 dataset. As background, ERA5 is the 5th generation of the global atmospheric reanalysis (the latest – it replaces the ERA-Interim reanalysis) produced by the Copernicus Climate Change Service at ECMWF, covering the period from January 1950 to present. It provides hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty.
Provider's contact information
ERA5 is produced by the Copernicus Climate Change Service (C3S) at ECMWF.
Copernicus User support (copernicus-support@ecmwf.int (external to C3S) ).
Licensing
Licence: Copernicus (Licence agreement information can be found here or here).
Dataset citable as: Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home
Variable name and units:
Several snow-related variables are available as hourly and monthly subsets. The table below provide more details for each of them and the names used in ERA5.
| Name | Units | Description |
|---|---|---|
| Snow depth | m of water equivalent | This parameter is provided by analysis and by the forecast as an instantaneous variable. It is the amount of snow from the snow-covered area of a grid box. The snow may cover all or part of the grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. Therefore, it is in fact the snow water equivalent. Users can use Snow density to convert the metre of equivalent water to metres. |
| Snow density | kg m-3 | This parameter is an instantaneous measure provided by analysis and by the forecast and it is the mass of snow per cubic metre in the snow layer. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. |
| Snow albedo | dimensionless | This parameter is an instantaneous measure provided by analysis and by the forecast and it is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. |
| Snow evaporation | m of water equivalent | This parameter is provided by the forecast only and it is accumulated. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period. For the reanalysis, the accumulation period is over the one hour ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. |
| Snowfall | m of water equivalent | This parameter is provided by the forecast only and it is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In model, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. |
Those products can be found by selecting their name from the snow category on the pages below:
- hourly data on single levels from 1950 to 1978,
- hourly data on single levels from 1979 to present,
- monthly averaged data on single levels from 1950 to 1978,
- monthly averaged data on single levels from 1979 to present,
More broadly, ERA5 provides four main subsets available, with hourly and monthly sampling: pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities.
Note: The convention for accumulations used in ERA5 and in ERA5-Land differs.
ECMWF provides a conversion table for accumulated variables (total precipitation/fluxes) for ERA5-Land and ERA5. The table shows how accumulated variables from a number of C3S and ECMWF datasets should be processed to derive values for an hour, a day, a month and a year. In the documentation, 'total precipitation' and 'solar radiation' are used for illustration, but the same processing should be applied to all precipitation and radiative flux variables.
Spatial coverage and resolution:
ERA5 data, is a global dataset. The atmospheric data is available on a regular latitude-longitude grid at 0.25o x 0.25o resolution (converted from native reduced‐Gaussian grid resolution of approximately 31 km x 31 km), and on 37 pressure levels.
Temporal coverage and resolution:
ERA5 data, is available from 1950 to present (split into two entries: primary from 1979 onwards and a back extension from 1950-1978). The back extension is a preliminary version that has been released in 2020, and an updated version (that corrects some issues in the tropics) will appear around the end of 2021.
The data is available at hourly and monthly sampling (see above).
ERA5 data, is updated daily with a latency of about 5 days in an early product and with a final release 2 to 3 months later.
Information about observations (number, homogeneity)
Like any other climate variable from a reanalysis product, snow is potentially influenced by all observations assimilated into the product. ERA5’s data assimilation uses observations for all geophysical quantities from about 0.75 million observations per day in 1979 and about 24 Million in 2018. The 2D-OI uses surface observations at 'screen level'. The online technical documentation provides tables with the satellite and in-situ observations used as input into ERA5.
The satellite measurements used in ERA5 are: temperature, humidity, ozone, column water vapour, cloud liquid water, precipitation, ocean surface wind speed, wind vector, soil moisture, wave height.
The in-situ data is provided by WMO WIS and consists in measurements for: surface pressure, temperature, humidity, wind, wind profiles and snow depth. Figure 4 from Hersbach et al. (2020) presents the conventional observations assimilated per day in ERA5 during the period 1979–2018.
ERA5 assimilates rain rates from ground-based radar–gauge composite observations from 2009, and snow cover (NH only) from NOAA/NESDIS IMS. The snow analysis is updated based on a two-dimensional optimal interpolation of station observations of snow depth and the IMS 4-km resolution snow cover. The IMS snow cover is not used above 1500 m.
Methodology
Like any other reanalysis, ERA5 data is strongly influenced by the data assimilation methodology. ERA5 is produced using 4D-Var data assimilation with the ECMWF’s Integrated Forecast System (IFS) model (CY41R2). The forecast model has 137 hybrid sigma/pressure (model) levels in the vertical, with the top level at 0.01 hPa. The IFS is coupled to a land-surface model and an ocean wave model. The model uses as boundary conditions the sea surface temperature, the sea ice cover, the greenhouse gases, the aerosols, and the total solar irradiance. Climate variables are offered from the atmospheric model, the surface model and the wave model.
The ERA5 dataset contains one (31 km) high resolution realization (HRES) and a reduced resolution 10-member ensemble (EDA). The model time step is 12 minutes for the HRES and 20 minutes for the Ensemble Data Assimilation (EDA), though occasionally these numbers are adjusted to cope with instabilities. Climate variables result from analyses and short (18 hours) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most of climate variables from the analyses are also available from the forecasts. However, there are several climate variables from forecast, e.g., mean rates and accumulations, that are not available from the analyses. More information on the differences between analysis, forecast, instantaneous, accumulated and mean rate parameters are provided on https://confluence.ecmwf.int/pages/viewpage.action?pageId=85402030.
The ERA5 atmospheric analysis is based on a hybrid incremental 4-dimensional variational data assimilation (4D-Var) system including variational bias correction (VarBias). The method finds the best estimate of the state of the atmosphere/land/surface ocean within an assimilation time window, given a background forecast valid at the start of the window and observations falling within that window. The 4D-Var data assimilation uses 12 hour windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC (the following day).
Uncertainty estimate: An uncertainty estimate is sampled by a 10-member lower-resolution Ensemble Data Assimilation (EDA) which provides background-error estimates for the deterministic HRES 4D-Var Data Assimilation system. The analysis method is the same for each EDA member and follows that of the HRES. Each member (except the control) is run with different random perturbations added to the observations. Likewise, the model physical tendencies are perturbed in the short forecasts that link subsequent analysis windows. 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.
Information about the technical and scientific quality
ERA5 data represents the latest global atmospheric reanalysis produced by Copernicus Climate Change Service at ECMWF. It is archived at a shorter (hourly) time step, has a finer spatial resolution, uses a more advanced assimilation system and includes more sources of data than previous versions. It is accompanied by extensive technical documentation and two principal scientific documentation papers. A list of ‘known issues’ is maintained at the online documentation (https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation#ERA5:datadocumentation-Knownissues).
A prerelease quality control revealed some problems affecting the performance in the tropics (tropical cyclones are too intense) and that the deep soil moisture tends to be too dry for the 1950-1978 dataset. A new version of the data should gradually become available by late 2021. This issue will be of little direct relevance to data in Canada’s north, but the user should be aware of the reason for this update.
Information on model improvement: The forecast model of the ERA5 is the IFS Cycle 41r2. In the ten-year period between ERA-Interim (Cy31r2) and ERA5 (Cy41r2), many significant improvements have been made to the representation of atmospheric physical processes (see Section 4 of Hersbach et al. (2020)). ERA5 snow-related processes will be influenced by several changes to ERA5’s parameterization schemes. ERA5’s HTESSEL land surface scheme (Balsamo et al., 2015) accounts for seasonally varying monthly vegetation maps specified from a MODIS-based satellite dataset. In addition, an enhanced snowpack parameterization allows a more realistic timing of runoff and terrestrial water storage variations and a better match of the albedo to satellite products. The chosen parameterization for lakes (FLake), allows consideration of both subgrid and resolved water bodies, which is potentially relevant for the lake-enriched Canadian sub-Arctic. This series of changes contributes to significant improvements in the soil moisture and land surface fluxes consistency, which allowed for the usage of satellite data in ERA5 to analyze soil moisture. This will influence the surface energy budget. Some important improvements in the wave model include: an updated model bathymetry with a more recent version of ETOPO2 and a revised unresolved bathymetry scheme. Some of these changes are associated with better accounting for wave propagation along coastlines and better modelling of the impact of previously unresolved features like islands and narrow embayments (e.g., Moore et al. in prep).
Limitations and strengths for application in North Canada
ERA5 is a new atmospheric reanalysis and there are not available scientific evaluations of the dataset dedicated specifically to North Canada. However, evaluations in other regions, have shown that in mountainous regions above about 1,500 m, the snow depth is unrealistically large in ERA5.
References to documents describing the methodology or/and the dataset
Hersbach, H., B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, J.-N. Thépaut, 2018: ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 29-Apr-2019 >), https://doi.org/10.24381/cds.adbb2d47
Hersbach, H., B .Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz‐Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R.J. Hogan, E. Hólm, M. Janisková, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. Rosnay, I. Rozum, F. Vamborg, S. Villaume, and J.-N. Thépaut, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803.
Online technical documentation: https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation
Link to download the data and format of data:
Data Access: Copernicus | NCAR | ECMWF
ERA5 is available in GRIB1 and NetCDF formats
Link to download hourly and monthly data on Copernicus:
- hourly data on single levels from 1950 to 1978,
- hourly data on single levels from 1979 to present,
- hourly data on pressure levels from 1950 to 1978,
- hourly data on pressure levels from 1979 to present,
- monthly averaged data on single levels from 1950 to 1978,
- monthly averaged data on single levels from 1979 to present,
- monthly averaged data on pressure levels from 1950 to 1978,
- monthly averaged data on pressure levels from 1979 to present.