Published April 1, 2026 | Version v1
Software Open

Code for Barents and Kara Seas model and reanalysis comparision analysis

Authors/Creators

  • 1. ROR icon University of Helsinki

Description

This is the code for producing the results for the manuscript "Sea ice in the Barents and Kara seas: models versus reanalyses" (to be submitted). The package also contains a subset of the data needed for producing the plots. To run the code, first set the paths to the code and the data folder in python_code/utility_code/data_paths.py DATA_PATH (point to folder data_for_plots) and PROJ_PATH (point to code folder root)
The Python scripts for producing the figures and printing out the results for the tables are listed below:
 
Table 1     -
Table 2     python_code/sea_ice/calc_mean_trend_and_print.py
Table 3     python_code/sea_ice/plot_iiee_timeseries.py
Table 4     python_code/ocean/calc_and_print_ocean_mean_and_trend_ease2g.py
Table 5     python_code/ocean/area_mean_temp_correlation_comp_halfyear.py
Fig 1       python_code/sea_ice/sea_ice_area_timeseries_2d.py
Fig 2       python_code/sea_ice/sea_ice_area_timeseries_2d.py, python_code/call_combine_plots.py
Fig 3a-d    python_code/sea_ice/plot_iiee.py
Fig 3e,f    python_code/sea_ice/plot_iiee_timeseries.py
           python_code/call_combine_plots.py
Fig 4       python_code/sea_ice/plot_osidiff_significance_whole_MAM_ASO_new_order.py
Fig 5, 6    python_code/sea_ice/plot_sic_MAM_ASO_trends_new_order.py
Fig 7       python_code/ocean/mld_2d_timeseries_diff_easeg.py, python_code/call_combine_plots.py
Fig A1, A2  python_code/ocean/plot_ocean_mld_temp_mean_and_trend_ease2g_new_order.py
 
Some of the scripts are somewhat inefficient and require a significant amount of memory.
 
Data sources:
 
  • RARE 1.15.2 (Carton and Chepurin, 2023) data was acquired through the UMD Ocean Climate Lab https://www2.atmos.umd.edu/~carton/index_files2/rare1.15.2_download.htm.
  • TOPAZ4b data were acquired from the CMEMS website https://doi.org/10.48670/moi-00007.
  • ORAS5 (Zuo et al., 2019) was acquired from the Universität Hamburg website https://www.cen.uni-hamburg.de/icdc/data/ocean/easy-init-ocean/ecmwf-oras5.html.
  • The NorESM2-MM and CNRM-ESM2-1 simulations are part of the World Climate Research Programme (WCRP)’s CMIP6 archived simulations, which can be found on the Earth System Grid Federation (ESGF, https: //pcmdi.llnl.gov/CMIP6/, Lawrence Livermore National Laboratory, 2025).
  • OSI-SAF data (EUMETSAT, 2022) is available at EUMETSAT https://doi.org/10.15770/EUM_SAF_OSI_0013.
  • HIRHAM–NAOSIM data are available at the tape archive of the German Climate Computing Center (DKRZ) via https://hdl.handle.net/21.14106a6d312c42d4501e75bd9de9186b323206ff5a65b (Dorn, 2024, dataset DKRZ_LTA_049_ds00009).
  • RASM data was acquired from Naval Postgraduate School, Monterey, CA, United States
 
Acknowledgements:
 
The data_for_plots data was generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00007
The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
ChatGPT and Microsoft Copilot assisted with some of the initial data visualisation code. All code was reviewed and further refined by CÄ.

 

 

Files

BK_Sea_comparision_code.zip

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Additional details

Funding

European Commission
PolarRES - Polar Regions in the Earth System 101003590

Software

Programming language
Python

References

  • Dorn, W.: HIRHAM–NAOSIM simulations for the period 1979–2021 with ice–ocean initialization from ORAS5 (P3), DOKU at DKRZ [data set], https://hdl.handle.net/21.14106/a6d312c42d4501e75bd9de9186b323206ff5a65b, 2024
  • TOPAZ 4b https://doi.org/10.48670/moi-00007
  • Carton, J. A. and Chepurin, G. A.: RARE: The Regional Arctic Reanalysis, Journal of Climate, 36, 2333–2348, https://doi.org/10.1175/JCLI- D-22-0340.1, 2023
  • Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Science, 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019
  • Cassano, J. J., DuVivier, A., Roberts, A., Abel, M. R., Seefeldt, M., Brunke, M., Craig, A., Fisel, B., Gutowski, W., Hamman, J., Higgins, M., Maslowski, W., Nijssen, B., Osinski, R., and Zeng, X.: Development of the Regional Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity, Journal of Climate, 30, 5729–5753, https://doi.org/10.1175/JCLI-D-15-0775.1, 2017.
  • EUMETSAT: OSI SAF Global sea ice concentration climate data record 1978-2020, https://doi.org/10.15770/EUM_SAF_OSI_0013, 2022.
  • Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate, Journal of Advances in Modeling Earth Systems, 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019.
  • Seland, , Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.