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 | ||||
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Type of study: System description | Parameters: 2m 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. https://doi.org/10.33265/polar.v41.8002 | ||||
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Type of study: Evaluation/verification | Parameters: 2m 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. https://polarresearch.net/index.php/polar/article/view/8002/14479 |
Hansche, I., Shahi, S., Abermann, J., & Schöner, W. (2023). The vertical atmospheric structure of the partially glacierised Mittivakkat valley, southeast Greenland. Journal of Glaciology, 1-12. doi:10.1017/jog.2022.120 | ||||
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Type of study: Investigation of atmospheric temperature inversions | Parameters: air temperature | Comparison against: UAV-observations compared with CARRA, ERA5, ERA-interim and radiosondes | Region and time period: Mittivakkat valley (southeast Greenland), field campaign summer 2019 | Features; Inversion characteristics, comparisons by correlation and RMSE |
Summary/abstract(paper): |
Isaksen, K., Nordli, Ø., Ivanov, B. et al. Exceptional warming over the Barents area. Sci Rep 12, 9371 (2022). https://doi.org/10.1038/s41598-022-13568-5 | ||||
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Type of study: Investigation of temperature trends | Parameters: 2m air temperature | Comparison against: ERA5, dependent and in-dependent in-situ/synop | Region and time period: 1991-2020, Svalbard and Barents Sea area | Features; Trends, summary statistics (bias, standard deviation of error, RMSE), sea-ice dependency |
Summary/abstract(paper): |
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. https://doi.org/10.1029/2022JD037443 | ||||
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Type of study: Investigation of wind speed in complex terrain | Parameters: 10 m wind speed | Comparison against: ERA5, ECMWF Operational analysis and in-situ/synop | Region and time period: 2016-2019, Nares strait | Features; General statistics and extremes |
Summary/abstract (paper): |
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, https://doi.org/10.5194/os-18-1389-2022, 2022 | ||||
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Type of study: Ocean properties | Parameters: 10m wind speed & MSLP | Comparison against: in-situ/synop, ocean currents | Region and time period: 2018-2020, Northern Barents Sea | Features; Relationship between atmosphere forcing and ocean currents |
Summary/abstract (paper): |
Steffensen Schmidt, L., Schuler, T. V., Thomas, E. E., and Westermann, S.: Meltwater runoff and glacier mass balance in the high Arctic: 1991–2022 simulations for Svalbard, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1409, 2023. | ||||
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Type of study: glacier mass balance, runoff and snow conditions | Parameters: 2m air temperature, 2m relative humidity, 10m wind speed, incoming short- and longwave radiation | Comparison against: Dependent and independent in-situ observations- | Region and time period: 1991-2021, Svalbard | Features; Evaluation of CARRA used as forcing data for relevant parameters. Bias and RMSE are calculated. |
preprint open for discussion Summary/abstract (paper): The Arctic is undergoing increased warming compared to the global mean, which has major implications for fresh-water runoff into the oceans from seasonal snow and glaciers. Here, we present high-resolution (2.5 km) simulations of glacier mass balance, runoff and snow conditions in Svalbard from 1991–2022, one of the fastest warming regions in the Arctic. The simulations are created using the CryoGrid community model forced by both CARRA reanalysis (1991–2021) and AROME-ARCTIC forecasts (2016–2022). Updates to the water percolation and runoff scheme are implemented in the CryoGrid model for the simulations. In-situ observations available for Svalbard are used to carefully evaluate the quality of the simulations and model forcing. The overlap period of 2016–2021, when both CARRA and AROME-ARCTIC data are available, is used to evaluate the consistency between the two forcing datasets. We find a slightly negative climatic mass balance (cmb) over the simulation period of −0.08 m w.e. yr−1, but with no statistically significant trend. The average runoff was found to be 41 Gt yr−1, with an significant increasing trend of 6.3 Gt decade−1. In addition, we find the simulated climatic mass balance and runoff using CARRA and AROME-ARCTIC forcing are similar, and differ by only 0.1 m w.e. in climatic mass balance and by 0.2 m w.e. in glacier runoff when averaged over all of Svalbard. There is, however, a clear difference over Nordenskiöldland, where AROME-ARCTIC simulates significantly higher mass balance and significantly lower runoff. This indicates that AROME-ARCTIC may provide high-quality predictions of the total mass balance of Svalbard, but regional uncertainties should be taken into consideration. The data produced from both the CARRA and AROME-ARCTIC forced CryoGrid simulations are made publicly available, and these high resolution simulation may be re-used in a wide range of applications including studies on glacial runoff, ocean currents, and ecosystems https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1409/ |
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: https://climate.copernicus.eu/5th-c3s-general-assembly
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: https://climate.copernicus.eu/5th-c3s-general-assembly
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: http://cimss.ssec.wisc.edu/iwwg/iww15/index.html
Kallio-Myers, V., Batrak, Y., and Cheng, B.: Comparison of Arctic sea-ice albedo between CARRA and ERA5 reanalyses and satellite based CLARA-A2, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1510, https://doi.org/10.5194/egusphere-egu23-1510, 2023.
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, https://doi.org/10.5194/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. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1689663
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, https://doi.org/10.5194/egusphere-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, https://symp-bonn2021.sciencesconf.org/data/357176.pdf
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, https://doi.org/10.5194/ems2022-535, 2022.
Slättberg, N., Maturilli, M., and Dahlke, S.: Fram Strait Marine Cold Air Outbreaks and associated surface heat fluxes in the ERA5 & CARRA reanalyses, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14048, https://doi.org/10.5194/egusphere-egu23-14048, 2023.
Torres-Alavez, A., Landgren, O., Boberg, F., Christensen, O. B., Mottram, R., Olesen, M., Van Ulft, B., Verro, K., and Batrak, Y.: Assessing Performance of a new High Resolution polar regional climate model with remote sensing and in-situ observations: HCLIM in the Arctic and Antarctica, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14090, https://doi.org/10.5194/egusphere-egu23-14090, 2023.
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 https://datastore.copernicus-climate.eu/documents/reanalysis-carra/CARRAFullSystemDocumentationFinal.pdf
Yang, X., et al., 2020: Complete test and verification report on fully configured reanalysis and monitoring system. Available at https://datastore.copernicus-climate.eu/documents/reanalysis-carra/CARRATestVerificationFinal.pdf
Bojarova J., 2020: Uncertainty estimation method. Available at https://datastore.copernicus-climate.eu/documents/reanalysis-carra/CARRAUncertainty%20estimationFinal.pdf
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