Table of Contents

This page includes a list of  scientific work that fully or partly include diagnostics and verification and/or description of the Copernicus Arctic Regional Reanalysis (CARRA) data set. The list includes links, abstract of peer-reviewed work and some details to provide initial insight on what part of the CARRA data is evaluated. The list is not necessarily complete in terms of including all published work on and with the CARRA data set, but provides a starting point to seek information on the quality of different aspects of the data set in the literature. 

Peer-reviewed studies

Schyberg et al. in preparation:  The Copernicus Arctic regional reanalyis
Type of study: System description

Parameters2m air temperature and humidity, 10m wind speed, precipitation, Mean sea Level Pressure

Comparison against: synop observations,  ERA5

Region and time period: 1991-2020, both CARRA domains (sub-regions: Svalbard, coast, inland etc…)

Features: Summary statistics, trends, case-studies

Summary/abstract (paper):

This paper is in preparation and will provide a full scientific description and evaluation of the Copernicus Arctic Regional Reanalysis. Abstract and link to published paper will be provided when published.

Køltzow M., Schyberg H., Støylen E., & Yang X. (2022). Value of the Copernicus Arctic Regional Reanalysis (CARRA) in representing near-surface temperature and wind speed in the north-east European Arctic. Polar Research, 41.
Type of study: Evaluation/verification

Parameters2m air temperature, 10m wind speed

Comparison against: synop observations,  ERA5

Region and time period: 1998-2018, CARRA-East (sub-regions: Svalbard, coast, inland etc…)

Features: Summary statistics, spatial and temporal variability, extremes/ high-impact

Summary/abstract (paper):

The representation of 2-m air temperature and 10-m wind speed in the high-resolution (with a 2.5-km grid spacing) Copernicus Arctic Regional Reanalysis (CARRA) and the coarser resolution (ca. 31-km grid spacing) global European Center for Medium-range Weather Forecasts fifth-generation reanalysis (ERA5) for Svalbard, northern Norway, Sweden and Finland is evaluated against observations. The largest differences between the two reanalyses are found in regions with complex terrain and coastlines, and over the sea ice for temperature in winter. In most aspects, CARRA outperforms ERA5 in its agreement with the observations, but the value added by CARRA varies with region and season. Furthermore, the added value by CARRA is seen for both parameters but is more pronounced for temperature than wind speed. CARRA is in better agreement with observations in terms of general evaluation metrics like bias and standard deviation of the errors, is more similar to the observed spatial and temporal variability and better captures local extremes. A better representation of high-impact weather like polar lows, vessel icing and warm spells during winter is also demonstrated. Finally, it is shown that a substantial part of the difference between reanalyses and observations is due to representativeness issues, that is, sub-grid variability, which cannot be represented in gridded data. This representativeness error is larger in ERA5 than in CARRA, but the fraction of the total error is estimated to be similar in the two analyses for temperature but larger in ERA5 for wind speed.

Isaksen, K., Nordli, Ø., Ivanov, B. et al. Exceptional warming over the Barents area. Sci Rep 12, 9371 (2022).

Type of study:

Investigation of temperature trends


2m air temperature

Comparison against:

ERA5, dependent and in-dependent in-situ/synop 

Region and time period:

1991-2020, Svalbard and Barents Sea area


Trends, summary statistics (bias, standard deviation of error, RMSE), sea-ice dependency


In recent decades, surface air temperature (SAT) data from Global reanalyses points to maximum warming over the northern Barents area. However, a scarcity of observations hampers the confidence of reanalyses in this Arctic hotspot region. Here, we study the warming over the past 20–40 years based on new available SAT observations and a quality controlled comprehensive SAT dataset from the northern archipelagos in the Barents Sea. We identify a statistically significant record-high annual warming of up to 2.7 °C per decade, with a maximum in autumn of up to 4.0 °C per decade. Our results are compared with the most recent global and Arctic regional reanalysis data sets, as well as remote sensing data records of sea ice concentration (SIC), sea surface temperature (SST) and high-resolution ice charts. The warming pattern is primarily consistent with reductions in sea ice cover and confirms the general spatial and temporal patterns represented by reanalyses. However, our findings suggest even a stronger rate of warming and SIC-SAT relation than was known in this region until now.

Moore, G. W. K., & Imrit, A. A. (2022). Impact of resolution on the representation of the mean and extreme winds along Nares Strait. Journal of Geophysical Research: Atmospheres, 127, e2022JD037443.

Type of study:

Investigation of wind speed in complex terrain


10 m wind speed

Comparison against:

ERA5, ECMWF Operational analysis and in-situ/synop 

Region and time period:

2016-2019, Nares strait


General statistics and extremes

Summary/abstract (paper):

Nares Strait is the long and narrow strait bounded by steep topography that connects the Arctic Ocean's Lincoln Sea to the North Atlantic's Baffin Bay. The winds that blow along the strait play an important role in modulating ice and water exports from the Arctic Ocean as well as in helping to establish the Arctic's largest and most productive polynya that forms at its southern terminus. However, its remote location has limited our knowledge of the winds along the strait. Here we use weather station data from the region as well as two reanalyses and an operational analysis with nominal horizontal resolutions that vary from ∼30 to ∼2.5 km to characterize the wind field in the vicinity of the strait. The strait has a width that varies from ∼40 to ∼100 km and as such the wind field is typically ageostrophic and controlled by the pressure gradient in the along-strait direction. We show that model resolution plays a role in the representation of both the mean and extreme winds along the strait through the ability to represent this ageostrophic flow. Higher windspeeds occur in the vicinity of Smith Sound and are associated with a left-hand corner jet. Kane Basin, the widest section of the strait, is characterized by a gradient in windspeed with higher speeds in the center of the basin and lower winds in the eastern basin that is the result of sheltering by the steep topography of the upstream Washington Land peninsula.

Lundesgaard, Ø., Sundfjord, A., Lind, S., Nilsen, F., and Renner, A. H. H.: Import of Atlantic Water and sea ice controls the ocean environment in the northern Barents Sea, Ocean Sci., 18, 1389–1418,, 2022

Type of study:

Ocean properties


10m wind speed & MSLP

Comparison against:

in-situ/synop, ocean currents

Region and time period:

2018-2020, Northern Barents Sea


Relationship between atmosphere forcing and ocean currents

Summary/abstract (paper):
The northern Barents Sea is a cold, seasonally ice-covered Arctic shelf sea region that has experienced major warming and sea ice loss in recent decades. Here, a 2-year observational record from two ocean moorings provides new knowledge about the seasonal hydrographic variability in the region and about the ocean exchange across its northern margin. The combined records of temperature, salinity, and currents show the advection of warmer and saltier waters of Atlantic origin into the Barents Sea from the north. The source of these warmer water masses is the Atlantic Water boundary current that flows along the continental slope north of Svalbard. Time-varying southward inflow through cross-shelf troughs was the main driver of the seasonal cycle in ocean temperature at the moorings. Inflows were intensified in autumn and early winter, in some cases occurring below the sea ice cover and halocline water. On shorter timescales, subtidal current variability was correlated with the large-scale meridional atmospheric pressure gradient, suggesting wind-driven modulation of the inflow. The mooring records also show that import of sea ice into the Barents Sea has a lasting impact on the upper ocean, where salinity and stratification are strongly affected by the amount of sea ice that has melted in the area. A fresh layer separated the ocean surface from the warm mid-depth waters following large sea ice imports in 2019, whereas diluted Atlantic Water was found close to the surface during episodes in autumn 2018 following a long ice-free period. Thus, the advective imports of ocean water and sea ice from surrounding areas are both key drivers of ocean variability in the region.

A selection of relevant presentations

Box, J., 2022: C3S General Assembly, September 2022: What can the Copernicus Arctic Regional Reanalysis (CARRA) add to the existing reanalysis information in Greenland? (Presentation) Recorded presentations available at:

Box, J., 2022: Copernicus Polar Workshop, September 2022, Extreme Greenland ice sheet climate events in high-detail via the Copernicus Arctic Regional Reanalysis (CARRA). 

Køltzow, M. et al., 2022, C3S General Assembly, September 2022: The strengths and weaknesses of the new Arctic (CARRA) and European (CERRA) regional reanalyses (Presentation) Recorded presentations available at:

Multiple authors, 2020: User workshop on Copernicus regional reanalysis for Europe and the European Arctic, September 2020. Multiple (recorded) presentations.

A selection of relevant conference abstracts and not peer-reviewed literature

Dahlgren, P. and T. Valkonen, 2021: Use of wind retrievals in regional reanalysis, 15th International Winds Workshop, online, abstract available in abstract brochure here:

Landgren, O., Lutz, J., Dobler, A., and Isaksen, K.: Multi-decadal convection-permitting climate simulation over Svalbard and its benefit for assessing the future of cultural heritage sites, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-556,, 2022

Maniktala, D., 2022, Analysing seasonal snow cover trends and patterns on Svalbard, student thesis, Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.

Nielsen, K. P., Schyberg, H., Yang, X., Støylen, E., Dahlgren, P., Amstrup, B., Peralta, C., Køltzow, M., and Bojarova, J.: 24 years of C3S Arctic regional reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15178,, 2021.

Schyberg, H. The Copernicus Arctic Regional Reanalysis, WCRP-WWRP Symposium on Data Assimilation and Reanalysis / 2021 ECMWF Annual Seminar on Observations, 13-18 September 202,

Schyberg, H., Yang, X., Støylen, E., Dahlgren, P., S. Madsen, M., Køltzow, M., and Olesen, M.: Evolution of the Copernicus Arctic Regional Reanalysis, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-535,, 2022.

Relevant CARRA documents

Nielsen, K. P. et al.: Copernicus Arctic Regional Reanalysis (CARRA): Data User Guide. Available at Copernicus Arctic Regional Reanalysis (CARRA): Data User Guide

Yang, X., et al., 2020: C3S Arctic regional reanalysis - Full System documentation. Available at

Yang, X., et al., 2020: Complete test and verification report on fully configured reanalysis and monitoring system. Available at

Bojarova J., 2020: Uncertainty estimation method. Available at

Copernicus Arctic Regional Reanalysis: Added value to the ERA5 global reanalysis. Copernicus Knowledge Base (CKB) article. Copernicus Arctic Regional Reanalysis (CARRA): Added value to the ERA5 global reanalysis

Uncertainty information for the Copernicus Arctic Regional reanalysis. Copernicus Knowledge Base (CKB) article. Copernicus Arctic Regional Reanalysis (CARRA): known issues and uncertainty information#Uncertaintyinformation

Known issues for the Copernicus Arctic Regional reanalysis. Copernicus Knowledge Base (CKB) article. Copernicus Arctic Regional Reanalysis (CARRA): known issues and uncertainty information#Knownissues

This document has been produced in the context of the Copernicus Climate Change Service (C3S).
The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.
The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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