Contributors:

Introduction

The Copernicus Climate Change Service (C3S) of the European Commission aims to provide authoritative information about the past, present and future climate in Europe and the rest of the World. It is in this context that the pan-European (Fig. 1) Copernicus European Regional ReAnalysis CERRA has been developed, under the contract C3S_322_Lot1. This land surface reanalysis covers the period 1984-2021 and has a horizontal resolution of 5.5km. The datasets can be used in support of adaptation action and policy development as well as contribute to climate monitoring and research, but also provide valuable information to climate services.

Figure1: CERRA-Land domain. Orography [m] is presented in color. The system used the Lambert Conformal Conic projection.

 The need for precipitation and surface variables at an ever-increasing spatial and temporal resolution is a recurrent demand. These variables allow, among other things, to address water resource management issues and to carry out climate change impact studies. Regional surface reanalyses are a way to reconstruct these variables for past periods covering several decades using state-of-the-art models.

A common way to generate a land surface reanalysis dataset is to run in an open loop a land surface model driven by gridded atmospheric datasets (forcing data) to describe the evolution of water and energy cycles over land. The model is run offline, i.e. without feedback on the atmospheric analysis from the assimilation cycle, to prevent propagation and increase of errors due to surface-atmosphere coupling. The surface observations were only used for the construction of the forcing data. This offline system allows consistency with mass conservation which is essential for conducting climate change impact studies. The quality of the reanalysis depends on both the quality of the forcing data and the parameterization of the surface and ground physical processes. This approach has been used by the ERA5-Land, MERRA-Land, ERA-Interim-Land global land surface reanalysis  (Albergel et al., 2013; Muñoz Sabater et al., 2021), but also by regional reanalysis such as MESCAN-SURFEX (Bazile et al., 2017). The new CERRA-Land dataset follows the same approach.

 CERRA-Land is the result of a unique standalone integration of the SURFEX V8.1 land surface model (Le Moigne et al. 2020) driven by meteorological forcing data from the CERRA atmospheric reanalysis and an offline analysis of daily accumulated surface precipitation (Soci et al., 2016). The accumulated precipitation is created by an optimal interpolation between an initial estimate (first guess) based on CERRA predicted precipitation and daily rain gauge data. Temperature and relative humidity forcing data at 2 metres are from the CERRA surface analysis and the short- and long-wave downwelling radiation, wind speed at 10 metres, and surface pressure are from CERRA forecast data. A description of the CERRA-Land system is available (C3S_D322_Lot1.1.1.12_202110_documentation_CERRA-Land, add link).

Details about the data fields

The outputs were archived at hourly time step and the precipitation analysis was archived at daily time step. The ouputs data are available in the same way as the CERRA dataset as if analysis were done every 3 hours ( at 00, 03, 06, 09, 12, 15, 18 and 21 UTC). The date of the analysis is related to the two metere temperature and humidity forcing fields. The forecast fields are available at three time step ( +1, 2  and 3 hours).

The CERRA-Land system uses the tiling approach where each grid-box (5.5 km X 5.5 km) of the model is divided into three different fractions: urban, lake and natural land.

The surface–atmosphere fluxes are then aggregated for each atmospheric grid box, according to the fraction (frac) of the three types of surface in the cell. The averaged value (F) over the grid cell is thus given by

where the values F_nature, F_urban, and F_lake are calculated by specific physical parametrization. For costal grid point, the positive values of sea fraction less than 1 were set to 0 and frac_nature was set to 1 - (frac_urban + frac_lake). The sum of fractions must always equal 1. It means that the costal aggregated values can be biased because no sea fraction was considered.

The surface pressure, 10-metre wind speed and direction, two metre temperature and relative humidity analysis were not archived into the CERRA-Land dataset, but are available through the CDS from the CERRA on single levels dataset (add link).

The available CERRA-Land variables in the CDS are either instantaneous, accumulated or static. The natural, urban and inland water fraction will be available in netCDF format only. This is specified for each of the variables listed below.

Table1: Overview of Fields calculated as a mean value of a grib box.

Table2: Overview of Fields available for the natural land fraction.

Table3: Overview of Fields available for the inland water fraction.

Table4: Static fields. Parameters labelled with TBD ("To Be Determined") do not yet have short name and GRIB2 code definitions

Soil parameters

The prognostic variables of soil temperature and soil moisture are represented in the model by a diffusive approach. Such a method proposes a discretization of the soil into 14 layers, resulting in a total depth of 12 m, with a fine description of the subsurface layers to capture the diurnal cycle. The vertical discretization (bottom depth of each layer in metres) is as follows: 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. Heat transfer is resolved over the total depth, while moisture transfer is resolved only over the depth of the roots, which depends on the type of vegetation and its geographical location.

Static fields

Static fields are variables that do not change depending on the model initial time or the forecast length (in other words they are time-independent). These include the land-sea mask, that is the fraction of land in a given model grid box of 5.5 x 5.5 km² in units of %, and the orography in units of m. For each model grid box in CERRA-Land 3 tile fractions are defined in units of %:
the fraction of inland water (lakes and rivers), the fraction of urban areas, and the fraction of nature, i.e. land areas that are not inland water or urban. There is no data available for the fraction of sea. The fraction data will be available directly from the climate data store website in netCDF format.

Accumulated surface fluxes

All energy fluxes at the surface are accumulated variables from the initial time of the forecast to the forecast hour in question with the unit J/m². They are considered positive downward to the surface. Average hourly energy fluxes in W/m² can be computed by subtracting two successive hourly accumulated variables and dividing by 3600 s. The albedo in units of % is defined by the ratio of average hourly upward radiation upward by the average hourly solar radiation downward. The net solar radiation is the difference between the solar radiation downward and the solar radiation upward. The solar radiation upward can be calculated by subtracting the net solar radiation from the downward solar radiation.

The net thermal radiation is the difference between the downward thermal radiation and the upward thermal radiation. The upward thermal radiation can be calculated by subtracting the net thermal radiation from the downward thermal radiation. The accumulated surface sensible heat flux is the conductive energy from the atmosphere to the surface. If this is going from the surface to the atmosphere it has negative values. The accumulated latent heat flux is the sum of all latent energy fluxes that are due to the phase transitions of water. Here condensation causes a positive latent heat flux to the surface, and evaporation causes a negative heat flux from the surface.

Precipitation and water fluxes

The daily surface precipitation analysis is the accumulation of total water (liquid and solid) that fall at ground level during the last 24 hours in kg/m² . The date represents the end of the accumulation period. It is available only at 0600 UTC.

The percolation (or drainage) is the mass per unit area of water that drains below the deepest soil level in the model whereas the surface runoff is the mass per unit area of water at the surface when saturation occurs. Percolation and surface runoff are calculated for the natural land, including soil, vegetation and snow whereas evaporation is available only as a mean value of a grid box. Those two variables can be used as input for hydrological model. Evaporation over the natural land fraction is not available.

Snow variables

The snow variables are related to natural land only. The fraction of snow cover represents the fraction of natural land which has snow on the ground. Snow on urban or lake fraction is not available.

References

Albergel, C., Dorigo, W., Reichle, R. H., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., and Wagner, W.: Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing, Journal of Hydrometeorology, 14, 1259–1277,825
https://doi.org/10.1175/JHM-D-12-0161.1, 2013

Bazile, E; R. Abida , A. Verelle, P. Le Moigne, C. Szczypta (2017): MESCAN-SURFEX surface analysis, deliverable D2.8 of the UERRA project,http://www.uerra.eu/publications/deliverable-reports.html

Le Moigne P., Bazile E., Glinton M. and Verrelle A. (2022) : Documentation of the CERRA-Land system ( C3S delivrable C3S_D322_Lot1.1.1.12_202110_documentation_CERRA-Land)

Le Moigne, P., Besson, F., Martin, E., Boé, J., Boone, A., Decharme, B., Etchevers, P., Faroux, S.,
Habets, F., Lafaysse, M., Leroux, D., & Rousset-Regimbeau, F. (2020). The latest improvements with SURFEX v8.0 of the Safran–Isba–Modcou hydrometeorological model for France. Geoscientific Model Development, 13(9), 3925–3946

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

Schimanke S., Isaksson L and  Edvinsson L.: CERRA data user guide (C3S deliverable C3S_322_Lot1.4.1.3_CERRA_ data_user_guide)

Soci, C., Bazile, E., Besson, F., & Landelius, T. (2016). High-resolution precipitation re-analysis system for climatological purposes. Tellus A, 68

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