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Introduction

The Copernicus Climate Change Service (C3S) 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. The land surface reanalysis (CERRA-Land) covers the period 1984-2021 and has a horizontal resolution of 5.5km. The dataset 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.

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

History of modifications to this Product User Guide

Acronyms

Introduction

The Copernicus Climate Change Service (C3S) 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 produced, under the contract C3S_322_Lot1. The land surface reanalysis dataset, CERRA-Land, spans the period September 1984 to April 2021 and has been produced at a horizontal spatial resolution of 5.5km. The dataset 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.

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Figure 1: 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 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.

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 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 , 24-h, accumulated surface precipitation (Soci et al., 2016). The accumulated precipitation analysis 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 brief description of the CERRA-Land system is available in Documentation tab.

Details about the data fields

The outputs of the land surface model were archived as forecast type at hourly time step and the precipitation analysis was archived at daily time step (as analysis type). 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 metre 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.

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The available CERRA-Land variables in the CDS are eitherinstantaneous, 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 in the tables below.

Overview of variables calculated as a mean value of a

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grid box

Table 1: Overview of variables calculated as a mean value of a grib grid box.

Albedo

The albedo [0-100%] is the total reflectance of downward solar radiation at the surface over the grid box. The albedo is the ratio of one hour time-integrated surface solar radiation upward by the one hour time-integrated surface solar radiation downward. Multiplying the albedo with the one hour accumulated downward solar radiation gives the one hour accumulated upward solar radiation.

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The soil heat flux is the energy receive by the soil to heat it per unit of surface and time. The Soil soil heat flux is positive when the soil receives energy (warms) and negative when the soil loses energy (cools). It is an instantaneous variable.

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

Table 2: Overview of variables available for the natural land fraction.

 Fraction of snow cover

It represents the fraction (0-1) occupied of natural land covered by snow for nature fraction only. It is an instantaneous variable and takes values between 0 and 1.

 Snow depth

Snow thickness on the ground. It is an instantaneous variable.

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The mass per unit area of water that drains below the deepest soil level in the model. The percolation (or drainage) is accumulated from the initial time of the forecast to the forecast time step. This variable is calculated for the natural land, including soil, vegetation and snow . (not for urban and water bodies fraction). It is an accumulated variable calculated for the natural land, including soil, vegetation and snow whereas.

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The prognostic variables of soil temperature and soil moisture are represented in the model by a diffusive approach. Such a method proposes a discretization discretisation 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 discretisation (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.

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The soil temperature is provided for each soil layer. The SURFEXmodel has 14 soil layers. The vertical discretization discretisation (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. Soil temperature is an instantaneous variable.

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The volume concentration of liquid and ice water. The vertical discretization discretisation (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. The volumetric soil moisture is available for each soil layer. It is an instantaneous variable.

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It is the volume concentration of liquid water only. The vertical discretization discretisation (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. A liquid volumetric soil moisture is available for each soil layer. It is an instantaneous variable.

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Table 3: Overview of variables available for the inland water fraction.

Lake bottom temperature

Temperature of water at the bottom of inland water bodies (lakes). The model keeps lake depth and surface area (or fractional cover) constant in time. It is an instantaneous variable.

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Static variable 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 %: (1) the fraction of inland water (lakes and rivers), (2) the fraction of urban areas, and (3) 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.

Table4: Static  Static variable. The variables marked with * and labelled with TBD ("To Be Determined") do not have yet short

names in the WMO GRIB2 code definitions and have not been uploaded to the CDS in the first batch of released data.

The  fraction data can be downloaded as netcdf file format: CERRALand_tiles_fraction.nc

Lake depth

Depth of inland water. It is defined for positive fractions only. It is a static fieldvariable.

Volumetric wilting point

Model soil water content at which the vegetation wilts and can no longer recover. When the soil moisture reaches the wilting point, the vegetation is not able to extract the soil water. The soil moisture content is too low to be absorbed by the vegetation.

Volumetric transpiration stress-onset (soil moisture)

The vertical discretisation (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. It is a static variable.

Volumetric transpiration stress-onset (soil moisture)

The soil moisture is the water content of a soil after gravitational drainagThe soil moisture is the water content of a soil after gravitational drainage. When the water content of the soil reaches this value, the water cannot drain any more by gravity.e. When the water content of the soil reaches this value, the water cannot drain any more by gravity. The vertical discretisation (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. It is a static variable.

Land-sea mask

The land-sea mask is a field that contains, for every grid point, the proportion of land (including inland water) in the grid box. It is the sum of the three fractions - natural land, urban and inland water. The variable is dimensionless and takes values between 0 (sea) and 1 (land). It is a static variable.

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This variable represents the fraction of water (e.g lakes, rivers) in the grid-box. It takes values between 0 and 1. It is a static variable.

Urban tile fraction

This variable represents the urban (e.g town) in the grid - box. It takes values between 0 and 1. It is a static variable.

Nature tile fraction

This variable represents the fraction of natural land areas that are neither inland water nor urban in the grid - box. It takes values between 0 and 1. It is a static variable.

Accumulated surface fluxes

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Soci C., Bazile E., Besson F., and Landelius T. (2016). High-resolution precipitation re-analysis system for climatological purposes. Tellus A, Dynamic Meteorology and Oceanography, 68:1, DOI: 10.3402/tellusa.v68.29879

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