
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.pyTable 3 python_code/sea_ice/plot_iiee_timeseries.pyTable 4 python_code/ocean/calc_and_print_ocean_mean_and_trend_ease2g.pyTable 5 python_code/ocean/area_mean_temp_correlation_comp_halfyear.py Fig 1 python_code/sea_ice/sea_ice_area_timeseries_2d.pyFig 2 python_code/sea_ice/sea_ice_area_timeseries_2d.py, python_code/call_combine_plots.pyFig 3a-d python_code/sea_ice/plot_iiee.pyFig 3e,f python_code/sea_ice/plot_iiee_timeseries.py python_code/call_combine_plots.pyFig 4 python_code/sea_ice/plot_osidiff_significance_whole_MAM_ASO_new_order.pyFig 5, 6 python_code/sea_ice/plot_sic_MAM_ASO_trends_new_order.pyFig 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.
Ocean Modelling, Python code, Barents Sea, Kara Sea, Reanalysis
Ocean Modelling, Python code, Barents Sea, Kara Sea, Reanalysis
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
