Abstract
The study presents a simulation of climate change across Northern Eurasia during the 20th and 21st centuries using two different versions of the Earth system model developed by the Marchuk Institute of Numerical Mathematics at the Russian Academy of Sciences (INMCM). Model version INMCM5 participates in Coupled Model Intercomparison Project Phase 6 (CMIP6) and has the lowest equilibrium climate sensitivity (ECS) among the CMIP6 models. In the next model version, INMCM6, changes in the physical parameterisations lead to an increase in ECS by a factor of 2. Changes in near-surface temperature, precipitation, snow cover area and sea ice extent simulated by both model versions are compared with available observational and reanalysis data. Climate change predictions for the middle and end of the twenty-first century are provided by two model versions. Both model versions simulate similar climate changes for the upcoming two decades. After the middle of twenty-first century, the model version with high equilibrium climate sensitivity simulates stronger climate changes over Northern Eurasia than the model version with low sensitivity. But, in general, the ratio of predicted warming is much less than the ratio of ECS.
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Data availability
Monthly mean ERA5 reanalysis data are available through https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview and https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=form. Precipitation data of GPCP can be downloaded from https://psl.noaa.gov/data/gridded/data.gpcp.html. Satellite observational sea ice extent data of NSIDC can be obtained through https://nsidc.org/data/g10010/versions/2 and sea ice volume data of Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) are available through https://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/. National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR) of Snow Cover Extent (SCE) reanalysis can be downloaded from https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00756. Data of the INM RAS climate model simulations can be obtained via the INM RAS climate data server https://climdat.inm.ras.ru/climate-data/.
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Acknowledgements
The authors are grateful to the two anonymous reviewers for their valuable comments. Numerical experiments with INMCM are obtained using the HPC system of the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Cray XC40-LC HPC system at the MCC of Roshydromet, and supercomputing facilities of the Joint Supercomputer Center of the Russian Academy of Sciences.
Funding
This research was carried out at the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The study in Sects. 3.1, 3.2, 3.3, 3.4, 3.6 is supported by the Russian Science Foundation, grant № 20-17-00190. All INMCM6 future projections and analysis of permafrost and extreme events described in Sects. 3.5, 3.7 were obtained with the support of the grant of the youth laboratory “Supercomputer technologies for mathematical modeling of the Earth system” (Agreement with the Ministry of Education and Science of the Russian Federation № 075-03-2023-509/1).
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Numerical experiments with INMCM5 and INMCM6 were carried out by Evgeny Volodin. The comparison of model climate change based on INMCM5 and INMCM6 with data from available observations and reanalyses was made by Vasilisa Bragina (Vorobyeva) (near-surface temperature, precipitation, Arctic sea ice and the East Atlantic/West Russia (EAWR) index), Alexey Chernenkov (snow extent, permafrost), and Maria Tarasevich (extreme events). The results were discussed with Evgeny Volodin. The paper was written by Vasilisa Bragina (Vorobyeva) and Evgeny Volodin with feedback from Alexey Chernenkov and Maria Tarasevich. All authors read and approved the final manuscript.
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Bragina, V., Volodin, E., Chernenkov, A. et al. Simulation of climate changes in Northern Eurasia by two versions of the INM RAS Earth system model. Clim Dyn 62, 7783–7797 (2024). https://doi.org/10.1007/s00382-024-07306-y
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DOI: https://doi.org/10.1007/s00382-024-07306-y