The Copernicus Arctic Regional reanalysis (CARRA) dataset is already in use for couple of years and hereafter we gathered some user questions, which were primarily collected from the registration forms of the forthcoming CARRA User Workshop (21 September, 2023, https://climate.copernicus.eu/copernicus-arctic-regional-reanalysis-carra-user-workshop) and also some user questions submitted to the Copernicus User Support. Hereafter we list these questions (some of the questions are edited for clarity) classified into some major categories and we try to give brief answers to them.
QUESTIONS RELATED TO SCIENTIFIC CONTENT OF CARRA
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Regarding the sea ice parametrisation (SICE): is that implemented with a constant sea ice thickness with number of layers or can the ice column actually evolve?
The SICE routine utilises thermodynamic relations to model the evolution of the sea ice thickness. The ice thickness is initialised once per production stream (different streams covering different time periods) with a one year spin up period each. Then the ice cover evolves freely as modelled by SICE without applying any external constraints. There is no consistency for perennial ice across the streams, and there is indeed a jump between the end of one stream and the beginning of a new one. This is only a problem in regions where there is ice all the time. For parts of the domain which are open some times of the year, the thickness will develop anew. Figure 1 below illustrates this. Here we see clear jumps between one stream and the next. These jumps would be more pronounced if only multi-year ice would be included.
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Some general description about the spin-up in CARRA can be found at Copernicus Arctic Regional Reanalysis (CARRA): Data User Guide#Singlelevelvariables
What are major differences between the ERA5 reanalysis and CARRA?
First of all ERA5 is a global reanalysis based on the IFS model and CARRA is a regional reanalysis targeting the European part of the Arctic based on the HARMONIE-AROME model. They are different in terms of horizontal resolution (31 km in ERA5 vs. 2.5 km in CARRA), data assimilation algorithm (4D-Var in ERA5 vs. 3D-Var in CARRA), observation usage etc. In CARRA more emphasis was put on spatial resolution and on extended use of in-situ observation data. CARRA is providing added value with respect to ERA5. This is explained in details at the Copernicus Arctic Regional Reanalysis (CARRA): Added value to the ERA5 global reanalysis page.
What is the accuracy of snow depth estimates?
The snow is an analysed quantity in CARRA, which means that the snow analysis corrects the modelled snow amount using the observed snow.
The following observations are used in the snow analysis:
- Snow depths at synoptic stations and at additional networks of "climate observing stations".
- "Pseudo observations" based on satellite products giving probability of snow in cloud free regions.
The performance of such analysis is good in regions with representative observations. Lack of conventional observations in parts of the domain, e.g. in Greenland and Svalbard, necessitated the use of satellite snow data for CARRA. The use of satellite snow extent in addition to snow depth observations helps to distinguish between snow free and snow covered ground in the melting season and leads also to improved T2m scores.
The snow analysis in CARRA uses (for technical reasons) climatological mean values of snow density in the conversion of observed snow depths to Snow Water Equivalent (SWE), leading to small systematic errors in the snow amounts during the winter.
Validation and qualitative considerations of the CARRA snow on Svalbard is published at Maniktala, D. (2022). Analysing seasonal snow cover trends and patterns on Svalbard, https://www.diva-portal.org/smash/get/diva2:1689663/FULLTEXT01.pdf
How is the solar radiation downward data obtained? What is the basic principle behind it?
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How do the heat flux calculations differ in CARRA from ERA5?
In both cases sensible and latent heat fluxes (H and LE, respectively) formulations are based on aerodynamic resistances. But there are some differences. We describe some of them below.
The expressions of H and LE in ERA5 and CARRA can be described as:
H=rho*Ch*U*Ft and LE=rho*Lv*Ch*U*Fq
where rho is the air density, Ch the turbulent exchange coefficient for heat and vapour, Lv is the latent heat of vaporization, U is the wind speed, Ft is a function of air and surface temperature, and Fq is a function of air and surface specific humidity, respectively. Ft and Fq represent the temperature and specific humidity gradients between surface and the environmental air, respectively.
In ERA5 and CARRA Ch, U, and Ft are different.
- In ERA5 Ch is calculated from the Obukhov length based on an iterative process to compute the Richardson number whereas CARRA uses directly the Richardson number (Louis 1979 formulation).
- In ERA5 U (wind modulus) accounts for free convection velocity w* but not in CARRA, which uses only the wind components u and v.
- In ERA5 Ft uses enthalpy where CARRA uses surface temperature.
Please note that the list of differences above might not be exhaustive.
What are major differences between the ERA5 reanalysis and CARRA?
First of all ERA5 is a global reanalysis based on the IFS model and CARRA is a regional reanalysis targeting the European part of the Arctic based on the HARMONIE-AROME model. They are different in terms of horizontal resolution (31 km in ERA5 vs. 2.5 km in CARRA), data assimilation algorithm (4D-Var in ERA5 vs. 3D-Var in CARRA), observation usage etc. In CARRA more emphasis was put on spatial resolution and on extended use of in-situ observation data. CARRA is providing added value with respect to ERA5. This is explained in details at the Copernicus Arctic Regional Reanalysis (CARRA): Added value to the ERA5 global reanalysis page.
What is the accuracy of snow depth estimates?
The snow is an analysed quantity in CARRA, which means that the snow analysis corrects the modelled snow amount using the observed snow.
The following observations are used in the snow analysis:
- Snow depths at synoptic stations and at additional networks of "climate observing stations".
- "Pseudo observations" based on satellite products giving probability of snow in cloud free regions.
The performance of such analysis is good in regions with representative observations. Lack of conventional observations in parts of the domain, e.g. in Greenland and Svalbard, necessitated the use of satellite snow data for CARRA. The use of satellite snow extent in addition to snow depth observations helps to distinguish between snow free and snow covered ground in the melting season and leads also to improved T2m scores.
The snow analysis in CARRA uses (for technical reasons) climatological mean values of snow density in the conversion of observed snow depths to Snow Water Equivalent (SWE), leading to small systematic errors in the snow amounts during the winter.
Validation and qualitative considerations of the CARRA snow on Svalbard is published at Maniktala, D. (2022). Analysing seasonal snow cover trends and patterns on Svalbard, https://www.diva-portal.org/smash/get/diva2:1689663/FULLTEXT01.pdf
How is the solar radiation downward data obtained? What is the basic principle behind it?
The solar downward radiation is computed from detailed 2-stream radiative transfer computations for all 66 model half-levels including the surface. For the gas radiative transfer 112RRTM-SW spectral g-pointsare included, and for clouds and aerosols 6 spectral bands. See more details at
Iacono, M.J., Mlawer, E.J. and Clough, S.A., 2001, March. Validation of the RRTM shortwave radiation model and comparison to GCM shortwave models. In Proc. 11th Atmospheric Radiation Measurement (ARM) Science Team Meeting.
Iacono, M.J., Delamere, J.S., Mlawer, E.J., Clough, S.A. and Morcrette, J.J., 2002, April. Cloudy sky RRTM shortwave radiative transfer and comparison to the revised ECMWF shortwave model. In Twelfth ARM Science Team Meeting Proceedings, St. Petersburg, Florida (pp. 8-12)
Fu & Liou (1992): https://doi.org/10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2
Mlawer et al. (1997): https://doi.org/10.1029/97JD00237
I would love to see comparisons of surface winds, temperatures and precipitations to ERA5.
General comparisons between ERA5 and CARRA are provided at Copernicus Arctic Regional Reanalysis (CARRA): Added value to the ERA5 global reanalysis
List of references where such comparisons might be available are at Copernicus Arctic Regional Reanalysis (CARRA): list of references
Particularly, see more details on
- surface winds and temperature: https://polarresearch.net/index.php/polar/article/view/8002
- precipitation: https://rmets.onlinelibrary.wiley.com/doi/10.1002/met.2134?af=R
What would be your guess why CARRA snow thickness (on sea ice) is usually much larger than from altimetry?
It is not clear which altimetry product the question is referring to and how accurate it is. However, a comparison study of such data with CARRA data would be of interest. The modelling of snow on sea ice in CARRA applies the SICE scheme (Batrak et al, 2018, https://doi.org/10.5194/gmd-11-3347-2018). This scheme has been shown to give advances over previous schemes, which don't attempt to take the snow on sea ice into account, however it still does not give a perfect representation of the snow on sea ice. To speculate about sources for possible biases, one could mention the absence of ice dynamics in the scheme: If there is an area of intensive snow accumulation, ice has no chance to transport this snow and it keeps piling up in the same grid cells. Also the absence of snow-ice formation in the model (even though it isn't as important in the Arctic as in the Antarctic). Additionally, the CARRA system doesn't represent the process of snow precipitation loss to the open sea portion of a grid cell when ice concentration is below 100%.
Iacono, M.J., Mlawer, E.J. and Clough, S.A., 2001, March. Validation of the RRTM shortwave radiation model and comparison to GCM shortwave models. In Proc. 11th Atmospheric Radiation Measurement (ARM) Science Team Meeting.
Iacono, M.J., Delamere, J.S., Mlawer, E.J., Clough, S.A. and Morcrette, J.J., 2002, April. Cloudy sky RRTM shortwave radiative transfer and comparison to the revised ECMWF shortwave model. In Twelfth ARM Science Team Meeting Proceedings, St. Petersburg, Florida (pp. 8-12)
Fu & Liou (1992): https://doi.org/10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2
Mlawer et al. (1997): https://doi.org/10.1029/97JD00237
I would love to see comparisons of surface winds, temperatures and precipitations to ERA5.
General comparisons between ERA5 and CARRA are provided at Copernicus Arctic Regional Reanalysis (CARRA): Added value to the ERA5 global reanalysis
List of references where such comparisons might be available are at Copernicus Arctic Regional Reanalysis (CARRA): list of references
Particularly, see more details on
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QUESTIONS RELATED TO THE CARRA DATA
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This is a valid question what we are aware of. First of all we tried to speed up the access to the data, i.e. faster download speeds for the full (whole domain) data files. We think that with the migration of our archiving system to Bologna this gave some improvements. We are also planning to put the most used parts of the CARRA datasets into spinning discs instead of tapes. The next step will be to make possible for the users to have any geographic subset of the CARRA data. This is work in progress (the complications are related to the distorted Lambert projection geometry towards the Pole and the fact that the netcdf files don't have a good data compression capabilities).. We are also planning to put the most used parts of the CARRA datasets into spinning discs instead of tapes. The next step will be to make possible for the users to have any geographic subset of the CARRA data. This is work in progress (the complications are related to the distorted Lambert projection geometry towards the Pole and the fact that the netcdf files don't have a good data compression capabilities).
When will monthly and daily means be available?
The software computing CARRA daily and monthly mean values are ready and will be deployed soon for the entire CARRA dataset. Since we need to get through the entire more than 30-year sub-daily CARRA dataset for the daily/monthly mean computations it will take a bit of time to complete the process and open the dataset for public use. Our latest estimate is that the means will be available for the users around mid-2024. We will give news on our progress in the CARRA User Forum (see at Copernicus Arctic Regional Reanalysis (CARRA)).