Snow products of the ECMWF Reanalysis 5th Generation Land (ERA5-Land)

Overview

This document provides an overview of the snow products of ERA5-Land, in the context of the larger ERA5-Land dataset. ERA5-Land is a replay of the land component of the ERA5 atmospheric global reanalysis using a finer spatial resolution and including a series of improvements making it more accurate for all types of land applications. ERA5-Land is produced by ECMWF framed within the Copernicus Climate Change Service (C3S) of the European Commission. The data covers a period from January 1950 to the present. It provides hourly data for many near-surface atmospheric and land-surface parameters.

Provider's contact information

ERA5-Land 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: Muñoz Sabater, J., 2019: ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < DD-MMM-YYYY >), 10.24381/cds.e2161bac

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 albedo	dimensionless	It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow-covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above.
Snow cover	%	It represents the fraction (0-1) of the cell / grid box occupied by snow (similar to the cloud cover fields of ERA5).
Snow density	kg m^-3	Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.
Snow depth	m	This parameter is provided as an instantaneous variable. It is the grid-box average of the snow thickness on the ground (excluding snow on canopy).
Snow depth water equivalent	m of water equivalent	Depth of snow from the snow-covered area of a 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. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.
Snow evaporation	m of water equivalent	This parameter is an accumulated variable. Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step.
Snowfall	m of water equivalent	This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.
Snow melt	m of water equivalent	This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02 m, this parameter would have a value of 0.01 m. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time.

Those products can be found by selecting their name from the snow category on the pages below:

- hourly data from 1950 to present,

- monthly averaged data from 1981 to present,

Note: The convention for accumulations used in ERA5-Land differs with that for ERA5. In ERA5-Land. In ERA5-Land, they are accumulated from the beginning of the forecast to the end of the forecast step.

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-Land is a global land-surface dataset. The atmospheric data is available on a regular latitude-longitude grid at 0.1o x 0.1o resolution (converted from native reduced‐Gaussian grid resolution of approximately 9 km x 9 km), and on 4 surface layers. Oceans have been masked out with data available over landmasses and inland lakes.

Temporal coverage and resolution:

ERA5-Land data is available from 1950 to present at hourly time step. Monthly data is also available from 1981 to present (the 1950 – 1980 back extension is scheduled to be available in 2022).

ERA5-Land data updates are made synchronously with ERA5 updates, approximately 2-3 months behind real time.

Information about observations (number, homogeneity)

ERA5-Land is not directly influenced by observations, but rather, indirectly influenced through the ERA5 atmospheric forcings. 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. Further details can also be found in the ERA5 document previously prepared by the CCCS.

Methodology

ERA5-Land is produced under a single simulation of the land component of the ERA5 climate reanalysis, without coupling to the atmospheric module of the ECMWF's Integrated Forecasting System (IFS) and without data assimilation. The low atmospheric forcing is provided by the ERA5 reanalysis, with additional lapse-rate correction. The core of ERA5-Land is the Tiled ECMWF Scheme for Surface Exchanges over Land incorporating land surface hydrology (H-TESSEL). Because it runs without data assimilation, it makes it computationally affordable for relatively quick updates. For example, if significant improvements of the land surface model are implemented, the whole or part of the dataset can be reprocessed in a relatively short period. Updates are possible in case improved auxiliary datasets are used as input for the production.

Production of ERA5-Land is not produced as a single continuous segment, but instead as three segments: Stream-1 (2001 onwards), Stream-2 (1981-2000), and Stream-3 (1950-1980). This is because it allows parallel production of data enabling sooner public access to the data, and because the atmospheric forcings used by ERA5-Land is derived from ERA5, thus needing corresponding completed ERA5 segment. Each stream is initialized with various meteorological fields from ERA5 (temperature, precipitation, humidity, radiation, etc.). While ERA5-Land does not assimilate observations directly, they are introduced via the ERA5 atmospheric forcings. These forcings are adjusted using ERA5 derived lapse rates before being integrated with the ECMWF Carbon Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) land surface model. This is done in 24-hour cycles, generating hourly outputs and the evolution of the land surface state and water and energy fluxes. For further details of the assimilation system used to obtain the ERA5 atmospheric forcings, please see the ERA5 document previously prepared by the CCCS.

Uncertainty estimate: Currently, ERA5-Land variable uncertainty estimates are those corresponding to ERA5. ERA5 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.

The original plan was to apply the same methodology ERA5-Land was to provide an estimate of the uncertainty fields as was done for ERA5. However, the uncertainty was estimated to be extremely low, and would have assigned unrealistically high confidence to the ERA5-Land variables. As such, it is recommended to use the corresponding ERA5 uncertainty estimates for the time being until further studies are done.

Information about the technical and scientific quality

ERA5-Land represents one of the products of the latest global atmospheric reanalysis produced by Copernicus Climate Change Service at ECMWF. It is archived at a shorter (hourly) time step, has a fine spatial resolution, uses a more advanced assimilation system and includes more sources of data than previous versions (e.g., ERA-Interim-Land). 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-Land%3A+data+documentation).

Information on land surface model: The land surface model of the ERA5-Land was operational in 2018 with the IFS model cycle 45r1. While most of the changes from the IFS Cy41R2 used in ERA5 are primarily technical, there were a few improvements to various fields: 1) the parameterization of the soil thermal conductivity was updated to take the ice component of frozen soil into consideration, 2) conservation of the soil-water balance was fixed and improved, and 3) rain over snow is now accounted for and is not accumulated in snow pack. Furthermore, a bug exists in IFS Cy41R2, that affects potential evapotranspiration (PET) flux calculations over forests and deserts, has been corrected in ERA5-Land, and unlike ERA5, ERA5-Land PET is an available dataset. However, PET is now determined by assuming a vegetation type of crops and no soil moisture stress. These assumptions may not be always realistic, and therefore PET should be used cautiously. More information on the CHTESSEL land surface model used in ERA5-Land can be found in Muñoz-Sabater et al. (2021, preprint) and the ERA5 document previously prepared by the CCCS

Limitations and strengths for application in the Canadian North

ERA5-Land is a newer land surface reanalysis and there are few available scientific evaluations of the dataset dedicated specifically to northern Canada. However, it should be noted that in northern Canada, there are currently no sub-daily records over a long historical period for many weather stations. Reanalyses data with hourly output cover this gap and, with suitable investigation and calibration (downscaling), could be valuable particularly for hydrological models.

As for all gridded data, observed values of the various snow parameters at local scales can differ from the values provided by the gridded dataset, which represent a statistical summary of the area surrounding a grid point.

Most importantly, in mountainous regions above about 1,500 m, ERA5 snow depth is unrealistically large. In contrast, ERA5-Land snow mass and snow depth are improved for mid-latitude mountains, although ERA5 snow depth estimates match better on mountain heights > 3300 m. Per continent, ERA5-Land snow fields demonstrate the most skill in the United States over complex mountain terrains, implying that that near-surface temperature may be more accurate. Overall, however, ERA5-Land skill for the snow varies depending on the geographic region.

References to documents describing the methodology or/and the dataset

Muñoz Sabater, J., 2019: ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 25-Jun-2021 >), https://doi.org/10.24381/cds.e2161bac

Muñoz-Sabater, J., E. Dutra, A. Agustí-Panareda, C. Albergel, G. Arduini, G., Balsamo, S. Boussetta, M. Choulga, S. Harrigan, H. Hersbach, B. Martens, D. G. Miralles, M. Piles, N. J. Rodríguez-Fernández, E. Zsoter, C. Buontempo, and J.-N. Thépaut, 2021: ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349-4383. https://doi.org/10.5194/essd-2021-82

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.