Contributors: S. Pelt (DMI), E. Støylen (MET Norway), H. Schyberg (MET Norway), X. Yang (DMI), P. Dahlgren (MET Norway), M. A. Ødegaard Køltzow (MET Norway), J. Bojarova (SMHI), K. P. Nielsen (DMI), C. Peralta (DMI)

Issued by: Sebastian Pelt (DMI)

Issued Date: Last updated on 28 Feb 2025 

Official reference number service contract: 2022/C3S2_361a_METNorway

Introduction

This user guide describes the dataset released from the second generation Copernicus pan-Arctic regional reanalysis,  which is part of the Copernicus Climate Change Service (C3S).  We will refer to the dataset as CARRA2 (Copernicus Arctic Regional ReAnalysis, 2^nd generation). The dataset includes the actual grid point reanalysis information on different levels (vertical atmospheric levels and surface levels including soil). Uncertainty information will be added later to the data set.

First in this user guide you will find sections on the data availability, formats and data content. For a short description of the principles and methods behind this reanalysis, numerical weather prediction and data assimilation, see the Annex at the end of this document.

Data availability

The Arctic regional reanalysis data can be downloaded from the Copernicus Climate Data Store (CDS) at the following locations:

• Sub-daily data for single, pressure, height and model levels: https://cds.climate.copernicus.eu/datasets/reanalysis-pan-carra
• Daily and monthly means for single, pressure, height and model levels: https://cds.climate.copernicus.eu/datasets/reanalysis-pan-carra-means

The data are freely available; the only requirement is to register as a CDS user. There are several options to download and visualize the data. An introduction to the CDS, the CDS web interface, and the CDS Application Programming Interface (API) is available here (registration is needed): Climate Data Store (CDS) documentation. An overview linking to tutorials providing examples on downloading data from CDS is found on this link: https://ecmwf-projects.github.io/copernicus-training-c3s/intro.html.

Data can be obtained by:

1. Open one of the CDS Arctic Reanalysis data pages listed above in your browser and click the Download tab.
2. Select the variables and analysis times that you want and click Show API request code at the bottom of the page.
3. Install the python package cdsapi and download the data directly with python scripts. The first API code from step 2 as can be used as a template.

cdsapi is the choice of the advanced user, who needs to download larger amounts of data. It can be installed on Linux, Windows and Mac following the instructions here: https://cds.climate.copernicus.eu/how-to-api

After installing the CDS API you can execute python scripts to retrieve the reanalysis data. In section 2.1 examples are given for how to retrieve the CARRA2 2 meter temperature 6 hour forecast and 24 hour precipitation analysis data. The request in section 2.1 is based on code generated by the CDS web form (see option 1 above). In practice, option 1 is an easy way to make a template script.

The scripts in section 2.1 are also available for download via the open GitHub at  https://github.com/metno/carra_cds

CDS API example, C3S pan-Arctic reanalysis

The example below shows a script for retrieving a subset of the C3S pan-Arctic reanalysis data from CDS for the 28th of February 2001. Such script can be derived from the download form (see "Show API request" at the bottom of the form).

import cdsapi

dataset = "reanalysis-pan-carra"
request = {
    "level_type": "pressure_levels",
    "level_location": ["850"],
    "variable": ["graupel"],
    "product_type": "analysis",
    "time": [
        "00:00", "03:00", "06:00",
        "09:00", "12:00", "15:00",
        "18:00", "21:00"
    ],
    "year": ["2001"],
    "month": ["02"],
    "day": ["28"],
    "data_format": "grib",
    "area": [81, 15, 76, 35]
}

client = cdsapi.Client()
client.retrieve(dataset, request).download()

The 'area': [81, 15, 76, 35] created a geographic subset of the data. Specifically, this line defines an area around the Svalbard archipelago: 76-81 degrees N and 15-35 degrees E.

CARRA2 covers the pan-Arctic region as shown in Figure 1. The model grid has a horizontal grid distance of 2.5 km and is defined using a polar stereographic map projection with 2880x2880 grid points in the horizontal.

Figure 1: The CARRA2 pan-Arctic domain.

The dataset includes data from different level types. We have the following output levels included in the CDS catalogue entry: 

• Single levels
• Model levels
• Pressure levels
• Height levels
• (Soil levels will be provided later.)

The variables available at these levels will be detailed in section 3. The single level data include surface data, data at diagnostic levels (such as 2-metre temperature and 10-metre wind speed), precipitation and cloud cover. They also include accumulated energy fluxes at the surface and at the top of the atmosphere. Soil level data are data from below the surface. They are also included in the single level catalogue entry.

Model level data are data from the 65 (hybrid) model levels. Level 1 is at 10 hPa and level 65 is approx. 12 meters above the surface. They are called hybrid levels since they gradually change from the uppermost levels, which are at surfaces of constant pressure, to the lowermost levels, which follow the topography at the surface. See section 7.3 for the precise equations defining the model levels.

Data at 20 pressure levels and 18 height levels is interpolated from model level data to specific pressure or height values above the surface.

Data formats

The reanalysis data is available in two formats: GRIB2 and NetCDF. Please note that the native output format is GRIB2 and the netcdf data are produced with a converter, which is still experimental. This means that it is advised to use the GRIB2 data, if that is possible.

GRIB

The GRIB data format is a standard binary format for modelled meteorological data governed by the World Meteorological Organisation (WMO). This is a very compact format that has the metadata contained within the files. It can be read with the tool eccodes, that is available from the European Centre for Medium-range Weather Forecasts (ECMWF): https://confluence.ecmwf.int/display/ECC/GRIB+tools. National weather services and weather companies use a wide array of graphical tools to display GRIB data. The GRIB data format is recommended for users comfortable and familiar with this format.

NetCDF

NetCDF (Network Common Data Form, https://www.unidata.ucar.edu/software/netcdf/docs/index.html ) is an open standard data format developed and supported by the American University Corporation for Atmospheric Research (UCAR). It is a widely used format for geoscientific data. A large array of access libraries and applications for reading and plotting NetCDF data exist: https://www.unidata.ucar.edu/software/netcdf/software.html.

Data content

Data are provided based on 3-hourly cycling interval with long forecasts for two cycles, and short forecast for the remaining ones. Thus, analysis parameters are available at 00, 03, 06, 09, 12, 15, 18 and 21 UTC.

Long forecasts include forecast lengths of 1, 2, 3, .. , 17 and 18 hours for cycles 00 and 12 UTC, and short forecasts of 1, 2 and 3 hours are made for the forecasts initiated at 03, 06, 09, 15, 18 and 21 UTC.

Output variables can be either instantaneous, accumulated, or maxima/minima from a given period. This is specified for each of the variables listed below (see below tables).

Single level variables

For most of the variables the shortest forecast data are recommended to use. In general the data quality decreases with forecast length. On the other hand, for variables that are affected by spin-up effects - that is the model needs to run for a certain number of hours before these variables have an optimal quality - the longer forecasts can be better to use. The cloud and precipitation variables are directly affected by spin-up. For time integrated quantities such as precipitation, accumulation over 12 hours between +6 and +18 h forecasts are recommended. If consistent cloud cover is needed, this should be chosen from similar forecast lengths. Likewise 24 hour accumulation can be obtained from a combination of 12 hour accumulation from two successive forecasts. However, if accuracy in timing of precipitation events is of very high importance in the application, an option could be to combine hourly forecasts from each of the 8 analysis times (00, 03, 06, 09, 12, 15, 18, 21 UTC) for lead times 1, 2 and 3, which then will be slightly affected by spin-up. It is not possible to make general recommendations on this issue, therefore users are advised to make their own choices based on the general guidelines described here. (On spin-up of precipitation, see also here and here.) 

Table 1: Overview of single level variables. The rightmost column refers to the daily and monthly accumulated or mean quantities provided for each parameter, for which more details are provided in Section 6.

a) This parameter is in fact the same as Maximum 10 metre wind gust since previous post-processing used in CARRA1, but has a different param ID due to GRIB encoding issues.

b) Fog (lowest model level cloud) is not available at analysis time. However by using lowest model level cloud, it is possible to get same values as fog.

c) Please note that snow density is not produced. Snow depth can be used in combination with snow depth water equivalent to get the snow density.

d) Snow cover is written as fraction although the param ID indicates it should have been written as per cent

e) The "sea ice surface temperature variable is encoded by paramid = 260649 for the sub-daily data (as indicated in the table above). However the daily/monthly means of this variable is encoded as paramid = 263006 and called "Time-mean sea ice surface temperature

f) The static fields except Land-sea mask and Orography are in a separate NetCDF file (168MB) here: fractions_carra2.nc 

g) These parameters can be calculated from other parameters in the data set. Guidelines for calculating them will be provided at a later stage.

Soil level variables

Please note that soil level variables are not yet available in the first CARRA2 data publication, but it is expected to be published later.

The following soil levels are archived for CARRA2: 1, 2, 3, 5 and 8. They represent depth layers below the surface, and the corresponding depths are:
  Level 1: 0-1 cm,
  Level 2: 1-4 cm,
  Level 3: 4-10 cm,
  Level 5: 20-40 cm
  Level 8: 80-100 cm. 

Soil parameters are archived on analysis times.  Diagnostic output at soil levels is available in three hourly intervals at 00, 03, 06, 09, 12, 15, 18 and 21 UTC.

Table 2: Overview of soil level variables

*  Soil level variables are produced, but are not part of this initial data release and will only be made available at a later stage.

Model level variables

Model level variables are output at 65 hybrid model levels of the HARMONIE-AROME model. The equations defining the model levels are provided in section 7.3 below. The levels follow the surface at the lowest levels and are gradually evolved into pure pressure levels at the highest levels. These are the levels at which the model computations are done. The height level and pressure level variables are interpolated from these data.

Diagnostic model level output is available at analysis time steps only, every three hours.

Table 3: Overview of model level variables

¹⁾ Degree "true" means geographic direction relative North, not relative to the orientation of the rotated model grid.

Pressure level variables

Pressure level variables are interpolated to 20 specific pressure levels: 1000, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70,  and 50 hPa. Thus, they are on isobaric surfaces.

Pressure level variables are available at analysis and forecast time steps.

Table 4: Overview of pressure level variables

Height level variables

Height level variables are interpolated to 18 specific height levels: 15, 30, 50, 75, 100, 150, 200, 250, 300, 400, 500, 750, 1000, 1250, 1500, 2000, 2500 and 3000 metres above the surface.

Height level variables are available at analysis and forecast time steps.

Table 5: Overview of height level variables

Details about the data fields

All data fields are model grid box (2.5 km X 2.5 km = 6.25 km²) averages. When output data are compared to local data near the coast or close to a glacier boundary, it should be remembered that the model variables represent such averages. For instance, to compute the surface forcing at specific glacial sites near a glacier boundary, it will be most representative to choose output from a model grid box that is fully on the glacier, rather than the closest model grid box if this only partially covers the glacier. Also, very local rain shower intensity can be higher than the modelled average grid box intensity.

As mentioned above, output variables can be either instantaneous, accumulated, or maxima/minima from a given period. This is specified for each of the variables listed.

Precipitation and water fluxes

Precipitation at the surface is output as two separate types: Rain and total solid precipitation. These have the unit kg/m², which for rain with a density of 1000 kg/m³ is the same unit as mm. For the solid precipitation, using this unit removes confusion between mm water equivalent and mm thickness. Total solid precipitation is the sum of the model variables snow and graupel. Total precipitation is the sum of all three precipitation types. The precipitation species include both convective and stratiform precipitation and are available only for the forecast time steps. They are accumulated variables meaning that they are accumulated from the beginning of the forecast. For instance, the 12h-forecast includes accumulated precipitation over 12 hours. Hourly precipitation can be retrieved by subtracting two accumulated precipitation fields with one-hour separation. Examples on extraction procedures for retrieving accumulated precipitation in different time intervals are provided in this page: The use of precipitation information from the Copernicus Arctic Regional Reanalysis (CARRA).

Water fluxes at the surface are output as surface runoff, percolation (drainage), water evaporation and snow sublimation (evaporation). All these variables have units of kg/m². Surface runoff occurs, when the model soil is saturated with water and more precipitation comes. (But do note that over glaciers this runoff estimate will in many cases be low and unrealistic, as the glacial ice melt is not represented in the NWP surface modeling.) Percolation is drainage of water below the deep soil level in the model. The water evaporation can be both positive and negative, where negative values signify condensation. This is also true for the snow sublimation.

Cloud and water vapour integrated variables

All cloud and water vapour variables are instantaneous, i.e. they are given for the time step at which they are output. Vertically integrated water vapour is given in units of kg/m². It is vertically integrated from the surface to the top of the atmosphere. In practice it is computed from the specific water vapour on the 65 model levels (see section 4.3). Likewise, integrated cloud liquid water, integrated cloud ice water, and integrated graupel are computed from the specific cloud liquid water, cloud ice and graupel on the 65 model levels.

Total cloud cover, as given in the output, is computed from model level cloud cover with the nearly maximum-random cloud overlap assumption with a scaling coefficient of 0.8. If this had been 0.0 random cloud overlap would be assumed, which means that the cloud covers at the model levels are assumed to be independent. If the scaling coefficient had been 1.0 maximum-random cloud overlap is assumed, which means that all vertically connected cloud layers are assumed to overlap perfectly. Note that this definition of cloud cover is not consistent with maximum-random cloud cover used within the model for computing radiative fluxes! The same nearly maximum-random cloud overlap assumption is used to compute high, medium and low cloud covers. Following the WMO definitions high cloud cover is above 5 km height, while low cloud cover is below or at 2 km height. Medium cloud cover is in between. Note that height here is considered relative to the surface! 

Cloud base height and cloud top height are output in units of m above the surface. They are defined for the highest and lowest model level with more than 4/8 cloud cover.

Variables at 10-metre height

All 10-metre height wind variables are instantaneous, i.e. they are given for the time step at which they are output. Diagnosed u and v wind components [m/s] as well as wind speed [m/s] and wind direction  [degrees from North] are output at the WMO standard height of 10 m above the surface. While the wind direction  variable provided is relative North, the u- and v-components follow the direction of the Polar stereographic model grid with the u-component being directed 90 degrees clockwise relative to the v-component. The diagnosed winds are computed from the winds at the lowest full model level (see section 4.3). The 10-metre u wind component (u_10m) in the case of a stable or neutral boundary layer is calculated as

$$ u_{10m} = u_{lml} \left[ ln \left( 1 + \frac{10}{z_{lml}} \left( e^{\kappa / \sqrt{C_{dn}}} - 1 \right) \right) - \frac{10}{z_{lml}} \left( \frac{\kappa}{\sqrt{C_{dn}}} - \frac{\kappa}{\sqrt{C_{d}}} \right) \right] \frac{\sqrt{C_{d}}}{\kappa},  (1) $$ 

where u_lml is the wind speed at the lowest model level, 10 is the diagnostic height in metres, z_lml is the height of the lowest full model level in metres, κ is the von Karman constant, C_dn is the momentum drag coefficient in neutral conditions, and C_d is the momentum drag coefficient. For the case of an unstable boundary layer it is calculated as

$$ u_{10m} = u_{lml} \left[ ln \left( 1 + \frac{10}{z_{lml}} \left( e^{\kappa / \sqrt{C_{dn}}} - 1 \right) \right) - ln \left( 1 + \frac{10}{z_{lml}} \left( e^{\kappa / \sqrt{C_{dn}} - \kappa / \sqrt{C_{d}}} - 1 \right) \right)  \right] \frac{\sqrt{C_{d}}}{\kappa},  (2) $$

We are now using "Instantaneous gust" instead of "10m wind gust since previous post-processing", but it is the same parameter in the model.

Variables at 2-metre height

2-metre temperature (T_2m) is diagnosed from the so-called skin temperature at 0 metre height (T_0m) Since the 2-metre temperature is often referred to as the surface temperature we here, in order to avoid misunderstandings, explicitly use 2-metre and 0-metre subscripts. and the temperature at the lowest model level (T_lml). For stable and neutral boundary layers it is calculated as

$$ T_{2m} = T_{0m} + \left[ ln \left( 1 + \frac{2}{z_{0h}} - \frac{2}{z_{lml}} \right) - \frac{2}{z_{lml}} \left( ln\frac{z_{lml}}{z_{0h}} - \frac{\kappa \sqrt{C_{d}}}{C_{h}} \right) \right] \frac{C_{h}}{\kappa \sqrt{C_{d}}} \left( T_{lml} - T_{om} \right),  (5) $$

where z_0h is the heat roughness length in metres, 2 is the diagnostic height in metres, z_lml is the height of the lowest model level in metres, κ is the von Karman constant, C_d is the momentum drag coefficient, and C_h is the heat drag coefficient. For the case of an unstable boundary layer it is calculated as

$$ T_{2m} = T_{0m} + \left[ ln \left( 1 + \frac{2}{z_{0h}} - \frac{2}{z_{lml}} \right) - ln \left( 1+ \frac{2}{z_{lml}} \left( exp \left( ln\frac{z_{lml}}{z_{0h}} - \frac{\kappa \sqrt{C_{d}}}{C_{h}} \right) - 1 \right) \right) \right] \frac{C_{h}}{\kappa \sqrt{C_{d}}} \left( T_{lml} - T_{om} \right).  (6) $$

Note that the 2-metre temperature is commonly referred to as the surface temperature even though it is not at the actual surface! The 2-metre height temperature is instantaneous at the hourly time steps and has the unit K. In addition to these, 6-hour maximum and minimum temperatures are also output variables. These values are computed from temperature values at each model time step of 75 s.

Furthermore, notice that the CARRA2 reanalysis dataset can exhibit warmer 2-metre temperatures when compared to other models or independent datasets. These warm temperatures are associated with the high-vegetation patch in the model and thus appear in regions dominated by forests. The 2-metre temperature is an average over four model tiles: sea, inland water, nature and urban, where the nature tile is further sub-divided into two patches. The first of these is high vegetation. The second is low vegetation and bare land. Since 2-metre temperatures are observed over grass or bare land, our average temperature is different from these observed temperatures - particularly in areas with high vegetation, urban areas and grid boxes that partly cover sea or inland water. 

Given that some user  applications can benefit from having an alternative representation of 2-metre temperature in high-vegetation dominated areas, we provide a framework for recalculating 2-metre temperatures in these areas. See this document (https://docs.google.com/document/d/1NHiAHRc4kpyglpw0BbPm3k4ThfiI4j6H2iO7LNQYbkw) for further details on this! ← UPDATE LINK!

2-metre specific humidity in units of kg/kg is diagnosed in the same way as the 2-metre temperature, only from the specific humidity at 0 metre height and at the lowest model level rather than the temperatures at these levels. From this and the saturation specific humidity the 2-metre relative humidity in % is computed.

The HARMONIE-AROME model operates with 4 different surface tiles for which the surface physical variables are all computed independently. These are: sea, inland water, urban and nature, i.e. all land areas that are not urban or inland waters. Inland waters are lakes and rivers. Each grid box contains specific fractions of these 4 surface tiles. The tile-specific 2-metre temperatures are particularly important in coastal regions, where the sea and land temperatures can differ with many degrees. Output quantities on tiles are planned, but those will only become available at a later stage.

Variables at 0-metre height

The surface pressure is given in units of Pascal (Pa). From this the mean sea level pressure [Pa] is computed by reducing the surface pressure to the mean sea level. To avoid confusion with the 2-metre “surface” temperature, the 0-metre temperature is referred to as the skin temperature. It is given in units of K. Both the pressure and temperature variables are instantaneous, i.e. they are given for the time step at which they are output. The surface is the lowest model half-level. Thus, the variables at this level are explicitly calculated on each model iteration.

All energy fluxes at the surface are output as accumulated variables from the initial time of the forecast to the forecast hour in question with the unit J/m². As for the accumulated precipitation variables, it is best to use the accumulated surface fluxes forecast lengths from +6h to +18 h. This is explained in Section 4.1. Note that a different accumulation applies to the albedo, see below. The other accumulated variables are considered positive downward to the surface. Energy fluxes are not output variables at the analysis times. Average hourly energy fluxes in W/m² can be computed by subtracting two successive hourly accumulated variables and dividing by 3600 s. The solar radiation variables at the surface are accumulated surface solar radiation downward, direct, direct normal and net solar radiation. Direct normal solar radiation has the shortName "direct solar radiation" and is considered on a plane perpendicular to the direction to the sun, while the other solar variables are considered on a horizontal surface. Direct horizontal solar radiation has the shortName "Time-integrated surface direct short wave radiation flux".  Multiplying this with the accumulated downward solar radiation gives the accumulated upward solar radiation. The net solar radiation is the difference between the downward solar radiation and the upward solar radiation.  The variable accumulated net clear sky solar radiation is the net solar radiation of a cloud free atmosphere. Dividing this with one minus the albedo gives the accumulated downward clear sky solar radiation. The thermal radiance variables at the surface are accumulated downward, net, and net clear sky thermal radiation. The thermal radiation variables are all considered on a horizontal surface. 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. The latent heat due to evaporation and sublimation are given as individual output variables. The accumulated latent heat flux can be calculated as the sum of these two.

The u and v components of the accumulated surface momentum flux are given as output variables in units of kg m/s. The surface roughness length (for momentum) and the surface roughness length for heat as used in the model are given as output in units of m. 

The albedo can be calculated from the formula: albedo = 1 - SW_net/SW↓, where SW↓ is the downward solar flux at the surface, and SW_net is the net (downward minus upward) solar flux at the surface. The solar fluxes are accumulated over a one hour time interval. So these albedos represent averages over hourly periods prior to the forecast time. Instantaneous albedos at the output times would be less precise than using these accumulated variables, see also section 4 in the note by Hogan, 2015  (https://www.ecmwf.int/sites/default/files/elibrary/2015/18490-radiation-quantities-ecmwf-model-and-mars.pdf). The problem with this procedure is that in some cases unphysical albedo values (>100%) may occur due to rounding errors. 

Variables at the top of the atmosphere

At the top of the atmosphere (TOA) the accumulated solar net TOA radiation and the accumulated thermal net TOA radiation are output variables in units of J/m². These are both considered on a horizontal surface and are both positive in the downward direction. Since the downward solar TOA radiation is always larger than the upward solar TOA radiation, the solar net TOA radiation is always positive. Since there is virtually no downward thermal TOA radiation, the thermal net TOA radiation is always negative. The TOA is the highest model half-level. Thus, the variables at this level are explicitly calculated.

Snow variables

When snow is present, a snow model, the explicit snow scheme (doi: 10.5194/tc-10-853-2016), computes the snow variables for 12 different layers. The snow variables are given as instantaneous values from the most recent model time step relative to the output time. The snow depth output unit is m, the snow depth water equivalent (SWE) output is in units of kg/m², and the snow cover output has percentage units in the range from 0 to 100%. By combining snow depth and snow depth water equivalent one can compute snow density (kg/m³, though that variable is not provided directly).

Sea and sea ice variables

For the sea the sea surface temperature is output in units of K. For areas partially or completely covered with sea ice, the following variables are output: sea ice area fraction [-], upper layer sea ice temperature [K], sea ice thickness [m], and sea ice snow thickness [m]. When ice is snow-covered, the upper layer sea ice temperature  represents the temperature of the ice surface under the snow. For sea ice thickness please note that the routine that computes this variable does not reproduce the evolution of ice thickness with all its complexity. Rather this variable should be treated as a rough estimate in order to get reasonable estimations for the energy fluxes. The sea ice fraction and sea surface temperatures are in fact interpolated input data and are only updated once every day. During the course of a forecast they are kept constant. All other sea and sea ice variables are given as instantaneous values from the most recent model time step relative to the output time.

Soil level variables

The soil level variables are computed by the diffusion soil scheme ISBA-DIF (doi: 10.1029/2011JD016002). The soil columns for each patch, forest and open land, are treated separately. Soil temperature is represented for 14 different layers in this scheme, thin at the top and thicker at the bottom, while the volumetric soil moisture and volumetric soil ice are represented at the same levels but until the root depth only, below the root depth they are undefined. The root depth depends on vegetation type and by patch. Only soil levels 1, 2, 3, 5 and 8 are provided as output, where the depth coverage of these layers were listed in Section 4.2. 

The soil moisture affects the near-surface temperature and humidity by altering the evapotranspiration, i.e. the partitioning between the sensible and latent heat fluxes. The vegetation in the forest patch is treated explicitly by the multi-energy balance model (doi: 10.5194/gmd-10-843-2017).

The data come from prognostic equations for soil temperature, water and ice which are located at the center of each of the layers of the ISBA-DIF soil scheme. Details about this scheme can be found in the SURFEX Scientific Documentation (p. 116 and following pages in LeMoigne, 2018). The default layering in ISBA-DIF is 14 layers for thermal computations and the default layer thickness is used everywhere. For soil hydrology the prognostic equations are only defined down to 1 m for bare soil and to the rooting depth for areas with vegetation.  The exception is grid-cells with permafrost where they are defined as deep as the thermal computations (14 layers).

Since we do not provide the full soil profiles, for anyone who wishes to perform more detailed modelling of processes below the surface we recommend using near-surface variables from our reanalysis dataset as forcing for offline surface models.

Please note that the soil/surface level variables are listed in the "single level" catalogue entry.

Model level variables

The model level variables are computed at the full model levels, and are given as instantaneous values from the most recent model time step relative to the output time. There are 65 vertical model levels in HARMONIE-AROME.  These full model levels are hybrid-sigma coordinates that are counted from the model top towards the surface. They go from being pure pressure levels, i.e. levels with constant pressure starting at 10 hPa, 30 hPa, etc. to being relative to the surface topography in height. Level 64 is at approximately 30 m height and level 65 is at approximately 12 m height above the surface. For a more detailed description of the vertical model layers, see section 7.3 below. The following dynamic and thermodynamic variables are the output variables at model levels: Temperature [K], wind speed [m/s], wind direction [degrees from North], turbulent kinetic energy [J/kg]. Here turbulent kinetic energy is the mean kinetic energy per unit mass from eddies in turbulent flow. Note that the HARMONIE-AROME weather forecasting model with 2.5 x 2.5 km² resolution does not explicitly resolve this turbulent energy. 

The following moisture, cloud and precipitation variables are outputs at model levels: specific humidity, specific cloud liquid water, specific cloud ice water content, specific cloud rain water content, specific cloud snow water content, and graupel. All of these variables are given in units of kg/kg. Additionally, the cloud cover in % is output on model levels. The model level cloud covers can be used to compute the total cloud cover based on other cloud cover overlap assumptions than the one described in section 5.2.

Pressure level variables

Pressure level variables are output at the pressure levels: 1000, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, and 50 hPa, and are given as instantaneous values from the most recent model time step relative to the output time. The model level variables described in section 4.3 are vertically interpolated to fixed pressure levels. The following thermodynamic and dynamic variables are output at pressure levels: temperature [K], wind speed [m/s], wind direction [degrees from North], geometric vertical velocity [m/s], geopotential [m²/s²], pseudo-adiabatic potential temperature [K], and potential vorticity [(K m²)/(kg s)]. Here the geopotential is the work required to lift an air parcel of unit mass from mean sea level to the given pressure level. The pseudo-adiabatic potential temperature is the temperature that an air parcel would have if it were first expanded through a pseudo-adiabatic process to 0 hPa pressure and thereafter compressed to a pressure of 1000 hPa through a dry-adiabatic process. The potential vorticity is a measure of the capacity for air to rotate in the atmosphere.

In addition the following moisture, cloud and precipitation variables are output at pressure levels: relative humidity [%], cloud cover [%], specific cloud liquid water content [kg/kg], specific cloud ice water content [kg/kg], specific cloud rain water content [kg/kg], specific cloud snow water content [kg/kg], and graupel [kg/kg].

Height level variables

Height level variables are output at the following fixed heights above the surface:  15, 30, 50, 75, 100, 150, 200, 250, 300, 400, 500, 750, 1000, 1250, 1500, 2000, 2500 and 3000m. They are interpolated to these heights from the full model level variables described in section 4.3. The following thermodynamic variables are output at height levels: Temperature [K], pressure [Pa], wind speed [m/s], and wind direction [degrees from North]. Note here that the wind direction is defined as the direction from which the wind comes!

The following moisture, cloud and precipitation variables are output at height levels: relative humidity [%], specific cloud liquid water [kg/kg], and specific cloud ice water content [kg/kg]. 

Static fields

Static fields are output 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 2.5 x 2.5 km² in units of %, and the orography in units of m. There are two more orography-related static parameters: subgrid orography average slope and subgrid orography standard deviation. For each model grid box in HARMONIE-AROME 4 tile fractions are defined in units of fraction. These are: the fraction of sea, 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. The fraction of glaciers is also output. This is assumed to be a constant field with glacier extents representative of the middle of the full reanalysis period (1985-present). Glacier extent in remote Arctic locations is not available as accurately mapped yearly datasets. The official maps are outdated due to major calving events in the recent decades. HARMONIE-AROME has not yet been designed to deal with changing land-sea masks or other surface classifications. Thus, these are static fields.

Climate means and extreme values

While the CARRA2 data set has a high temporal resolution with hourly/three-hourly data, many applications of reanalysis focus on aggregated, time average quantities or climate mean quantities. We therefore provide mean, sum or extreme values for most of the output parameters to save users from retrieving the full time series and calculating those aggregated quantities themselves.

As indicated in the variable tables of Section 4, the different output quantities can be available both as analyses and as forecasts. The output forecast quantities can have either the nature of an instantaneous quantity or as a cumulative quantity, accumulated from the forecast base (analysis) time up to the actual range forecast. (We run a three-hourly "cycling", meaning that we provide an analysis to restart the forecast from every three hours.) We provide the aggregated (means/min/max) variables both as daily data (calendar dates following UTC) and as monthly data.

This results in one the following categories being assigned to each output quantity for the daily and monthly means/sums/min/max: 
Daily means:

• daily_mean_an (daily means based on analysis)
• daily_mean_fc (daily means based on forecasts)
• daily_min_fc (daily minimum based on forecasts)
• daily_max_fc (daily maximum based on forecasts)
• daily_sum_fc (daily sums based on forecast differences)

Monthly means:

• monthly_mean_an (monthly means of the analysis daily means)
• monthly mean_fc (monthly means of the forecast daily means)
• monthly_min_fc (monthly minimum of the daily minima)
• monthly_max_fc (monthly maximum of the daily maxima)
• monthly_daysum_fc (monthly means of the daily sums)

The variables used for computing daily and monthly means for CARRA2 are summarized in the tables of Section 4, Tables 1-8. The rightmost column is used to describe the daily and monthly means, if provided,  to indicate which type of daily/monthly mean/min/max (as in the above bullet points) are provided.  In these tables it is also indicated if the variable is an instantaneous field or is accumulated over a time interval (cumulative).

The sampling used for calculating the means are provided in Table 9 below.

Table 9: General rules adopted for the CARRA2 daily/monthly means/sums/min/max

In Table 9, fc(...) refers to forecasts from a certain forecast base time (in Z time) with range up to a certain number of hours, ana(...) refers to a certain analysis time (Z time). 

For the daily means of instantaneous variables, we note from the table that we base them on a three-hourly sampling, regardless of whether they are found from analyses or from forecasts.

For the daily sums of cumulative variables, we note from Table 9 that those are derived from forecasts, and are based on combining data from three different forecast base times. This combination notes what was  described in Section 4.1 above that spin-up effects which can potentially affect the shortest range forecasts for some of the cumulative variables. As described at that section, 00Z and 12Z are the only base times where long forecasts beyond +3h are calculated. The combination of forecasts used for the daily sums for all cumulative variables is the same as that used for precipitation in Example 2 of this short guide. 

Also note that for cumulative quantities we compute daily sums instead of daily means, but they can be easily converted to daily mean rates, by dividing with the duration of one day.

The provision of maximum and minimum values is limited to the following subset of variables:

• Maximum temperature at 2 metres since previous post-processing (mx2t, parameter ID: 201)
• Minimum temperature at 2 metres since previous post-processing (mn2t, parameter ID: 202)
• Instantaneous 10 metre wind gust (i10fg, parameter ID: 228029) - which in fact is Maximum 10 metre wind gust since previous post-processing as noted in footnote a) of Section 4.1.

It should be noted that the minimum and maximum values for these variables are based on all model time steps of 75 seconds.

Processing in case of missing samples: For some, few, output quantities some samples are missing in the data set (see the CARRA2 known issues list). We only provide daily means/min/max/sums when all samples within a day is available. For the monthly means/min/max we only provide those when full data from 27 or more days in the month are available. 

Annex: Brief outline of the Arctic reanalysis system

The details about the Numerical Weather Prediction model system used can be found in the Full System Documentation: Copernicus pan-Arctic Regional Reanalysis (CARRA2): Full system documentation 

To supplement this we provide in the following a short description of the service as well as a description of basic principles of data assimilation in numerical weather prediction aimed at a non-specialist audience.

The service

This contract of the Copernicus Climate Change Service produces and delivers a regional reanalysis (RRA) for the Arctic including long-term datasets of Essential Climate Variables (ECVs) for the period from September 1985 to close to real-time. The model domain is shown in Figure 1. The modelling system has a resolution of 2.5x2.5 km² and 65 vertical levels in the atmosphere. The produced datasets are freely available and can be used by anyone who wishes detailed long-term atmospheric data, for instance to study typical ranges of meteorological variables, as a reference for climate model runs, or to investigate highly resolved changes in the ECVs during the reanalysis period. The reanalysis model is an adapted version of the weather forecasting model HARMONIE-AROME cy46h1, which has been enhanced with more Arctic input data, and more extensive surface and atmospheric data assimilation. The model formulation has been improved with a specific focus on processes essential in the Arctic. Some basic aspects of the methodology is explained further in section 7.2.

The CARRA2 system builds on CARRA1, where updates in model formulations from CARRA1 include the multi-layer SURFEX surface scheme with a 14-layer diffusion soil scheme,  a 12-layer explicit snow scheme, the multi-energy balance (MEB) for forest canopy and surface assimilation based on the Simplified Extended Kalman Filter (SEKF) technique. CARRA2 uses hydrostatic dynamics, while CARRA1 is non-hydrostatic.

Principles of reanalysis systems

Atmospheric reanalysis is a method to reconstruct the atmospheric states by using historical observations (in situ, surface and satellite remote sensing) together with a weather forecasting model. It mainly provides a physically and dynamically coherent description of the state of the atmosphere. Surface variables are included mainly to the extent that they affect the atmosphere. This synthesis is accomplished by assimilating the observational data into a meteorological model and thereby forcing the model to reproduce the observations as closely as possible and make a gridded dataset that covers also locations from which no observations are available. The advantage of a reanalysis is that it provides a spatially and temporally complete, and consistent record of the atmospheric state. This cannot be achieved with an observational dataset alone, since interpolation methods cannot adequately represent the non-linear variability of the atmosphere. It can be mentioned 
The main advantages of reanalysis dataset are:

• They provide regularly gridded data, even in places where there are no or few observations;
• They provide a coherent, complete set of variables describing the atmospheric state;
• They provide a reconstruction of the record of past weather since it is constrained by observations.

A reanalysis system cannot perfectly recreate all variables, which are very variable in space and time. In particular, precipitation is a challenge. For specific applications, e.g. in hydrology, it is therefore quite common to correct local precipitation data for biases. Other variables, like surface temperature, are generally less variable in space and time and easier to reconstruct by the reanalysis system. Even with 2.5 km horizontal resolution, the results in complex terrain, such as mountainous regions or coastal areas that have considerable sub-grid variability have limitations compared to results over terrains that are more homogeneous. To accurately represent extreme local wind phenomena such as Greenlandic piteraqs sub-kilometer grid resolution is required. 
Weather forecasting is based on an analysis of the current state of the atmosphere and the surface of land and sea. The forecasts are made with mathematical and physical computer models starting from the analysis. Diagnostic model output is a 0 hour forecast from after the model has run for one time step. The temperatures, winds, pressure, moisture, cloud contents and other variables are mapped at regular points in space and time as illustrated in Figure 2 below. 

Figure 2:  Schematic representation of the HARMONIE-AROME grid for model variables (surface pressure, temperature, u and v wind components and geopotential (Z), energy and specific moisture content (q)). Energy is considered per area on horizontal surfaces and is obtained from the time-integrated fluxes that are computed on each model time step. In addition to these variables HARMONIE-AROME includes the variables cloud liquid water, cloud ice, rain, snow, graupel and turbulent kinetic energy at the same vertices for which q is computed.

Reanalysis uses a weather forecasting model to create a 'first guess' of the atmospheric state at a certain time. The first guess is then corrected based on observations. This corrective step, referred to as 'data assimilation' (see Figure 3), requires statistical knowledge of the forecast error and the observation error. The procedure also uses physical and statistical relationships of the atmosphere when interpreting the observational data. The result of the data assimilation is called the analysis. The data analysis is run in 3-hourly cycles and hourly forecast data is computed. Thus, the analyses will contain a complete set of values describing the evolution of the atmosphere and the surface over time, also for locations where there are no observations.

This complete estimate of the atmospheric state over time can be of great value to users, for example in assessing the impacts of past weather and climate related events, for statistics of the climate in a location or an area or for running other fine scale models or validating climate models.

An important difference between reanalyses and archived weather analyses from operational forecasting systems is that a reanalysis is produced with a single and improved version of a data assimilation system – including the forecast model used – and is therefore not affected by changes in method. There is also more time to collect observations since the reanalysis does not have the constraint of issuing timely forecasts, and this will improve the quality of the reanalysis. Additionally, re-processed observations can be also used. Reanalysis systems differ in the set of observations that are assimilated, the model that is used, and possibly the way the error statistics are estimated and corrections are applied.

Figure 3: Schematic figure showing the simulation of the atmospheric state (black line) in the reanalysis, which starts from the analysis (green dots) and resulting in the background (blue dots). Note that the data assimilation is run in 3-hourly cycles, and that the background usually does not coincide with the true observed state of the atmosphere.

The Arctic reanalysis system applies the so-called 3D variational data assimilation (3D-VAR) reanalysis method. The 3D-VAR method is depicted schematically in Figure 3. At fixed points in time the model state is adjusted based on the observed state, taking into account the statistics of model and observation errors. The Arctic reanalysis system is run with eight cycles per day performing analyses at 00 UTC, 03 UTC, 06 UTC, 09 UTC, 12 UTC, 15 UTC, 18 UTC and 21 UTC. The forecast lengths vary between 3 and 18 hours as explained in section 4.

 Definition of the 65 vertical layer structure in HARMONIE 

The CARRA vertical coordinate system is a terrain-following hybrid vertical coordinate, which means that it is terrain following at the bottom and pressure based on the top of the atmosphere. It has the advantage to describe the surface terrain properly, but also benefitting the advantage of having the pressure coordinate at the top of the atmosphere.

CARRA uses 65 model levels (level 65 is the surface and level 1 is the top of the atmosphere), which is further splitted into the so called half levels. CARRA has 66 half levels and the pressure of each half level can be obtained by the following formula:

p (k+1/2) = A (k+1/2) + B (k+1/2) * p_s

where k=0.... 65, p_s is the surface pressure and the A and B coefficients (see below) are valid at each half level. 

The full model level pressure [1, 2, ...65] is defined as the mean of the pressure of each pair of neighbouring half levels [0.5, 1.5, ..., 65.5]. The model variables are defined in the full model levels. 

The A and B coefficients are listed below (from the top to the bottom).

A_½[0.5, 1.5, ..., 65.5] = 0.00000000, 2000.00000000, 4000.21287319, 6002.09662113, 7911.25838577, 9633.01049417,11169.37146237, 12522.57753978, 13695.00149653, 14689.11546998, 15507.49052823, 16154.69697732, 16632.12471208, 16940.14949960,  17082.34869816, 17065.28164099, 16898.18367797, 16592.58939571,  16161.90395878,  15620.94340550,  14985.46502362, 14271.70773051, 13495.95994372, 12674.16909910, 11821.60314859, 10952.57042620, 10080.20053763, 9216.28565403, 8371.17893039, 7553.74479607, 6771.35457397, 6029.92021691, 5333.95880836, 4686.68074804, 4090.09511346, 3545.12645110, 3051.73811264, 2609.05813936, 2215.50455766, 1868.90774223, 1566.62821060, 1305.66882073, 1081.85503306, 890.47596795, 727.74548529, 590.17748096, 474.58767980, 378.08857614, 298.07947335, 232.23312781, 178.48015386, 134.99207440, 100.16369201, 72.59529482, 51.07508967, 34.56216490, 22.17022046, 13.15225964, 6.88641310, 2.86306141, 0.67344356, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000

B_½ [0.5, 1.5, ..., 65.5] = 0.00000000,  0.00000000,  0.00000000, 0.00000000, 0.00095468, 0.00382570, 0.00862327, 0.01535782, 0.02404046, 0.03468314, 0.04729839, 0.06195102, 0.07868187, 0.09744325, 0.11815586, 0.14071098, 0.16497348, 0.19078554,   0.21797086, 0.24633925, 0.27569119, 0.30582244, 0.33652825, 0.36760726, 0.39886479, 0.43011564, 0.46118624, 0.49191624, 0.52215946, 0.55178443, 0.58067442, 0.60872709, 0.63585388, 0.66197911, 0.68703898, 0.71098036, 0.73375964, 0.75534143, 0.77569737, 0.79480486, 0.81264598, 0.82920633, 0.84454000, 0.85875505, 0.87191802, 0.88409276, 0.89534045,  0.90571965, 0.91528643, 0.92409452, 0.93219549, 0.93963895, 0.94647277, 0.95274328, 0.95849551, 0.96377340, 0.96862008, 0.97307803, 0.97718944, 0.98099640, 0.98454132, 0.98786727, 0.99102462, 0.99406510, 0.99703923, 1.00000000

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