Code for Barents and Kara Seas model and reanalysis comparision analysis

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

This is the code for producing the results for the manuscript "Evaluation of sea ice variability in three ocean reanalyses and two regional models in the Barents and Kara Seas in 1991--2020" (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_and_ttest.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_region.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 3 python_code/sea_ice/plot_osidiff_significance_whole_MAM_ASO_new_order.pyFig 4, 5 python_code/sea_ice/plot_sic_MAM_ASO_trends_new_order.pyFig 6 python_code/climate_index_corr/calc_plot_mlt_and_sia_ci_corr_matrix_heatmap.pyFig A1 python_code/sea_ice/plot_SIA_anomaly_timeseries.py, python_code/call_combine_plots.pyFig A2 python_code/sea_ice/plot_iiee.py (a-d), python_code/sea_ice/plot_iiee_timeseries.py (e-f), python_code/call_combine_plots.py Fig A3, A4 python_code/ocean/plot_ocean_mld_temp_mean_and_trend_ease2g_new_order.pyFig A5 python_code/ocean/mld_2d_timeseries_diff_easeg.pyFig A6-A10 python_code/climate_index_corr/plot_cli_corr_to_sic_and_mlt_per_cli.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. 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 Climate index data was aquired from Royal Netherlands Meteorological Institute (KNMI): Climate Explorer, https://climexp.knmi.nl, accessed 2026-06-02 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.

Links and sources

Comments