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A substantial part of Arctic climate predictability at interannual timescales stems from the knowledge of the initial sea ice conditions. Among all sea ice properties, its volume, which is a product of sea ice concentration (SIC) and thickness (SIT), is the most responsive parameter to climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study, the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the regional benefits of specific initialized variables, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean only; ocean plus SIC; and ocean plus SIC and SIT initialization. In the Atlantic Arctic, the Greenland–Iceland–Norway (GIN) and Barents seas are the two most skilful regions in SIC prediction for up to 5–6 lead years with ocean initialization; there are re-emerging skills for SIC in the Barents and Kara seas in lead years 7–9 coinciding with improved skills of sea surface temperature (SST), reflecting the impact of SIC initialization on ocean–atmosphere interactions for interannual-to-decadal timescales. For the year 2–9 average, the region with significant skill for SIT is confined to the central Arctic Ocean, covered by multi-year sea ice (CAO-MYI). Winter preconditioning with SIT initialization increases the skill for September SIC in the eastern Arctic (e.g. Kara, Laptev and East Siberian seas) and in turn improve the skill of air surface temperature locally and further expanded over land. SIT initialization outperforms the other initialization methods in improving SIT prediction in the Pacific Arctic (e.g. East Siberian and Beaufort seas) in the first few lead years. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the futur

The Swedish Meteorological and Hydrological Institute used the global climate model EC-Earth3 to perform a large ensemble of simulations (SMHI-LENS). It consists of 50 members, covers the period 1970 to 2100, and comprises the SSP1-1.9, SSP3-3.4, SSP5-3.4-OS, and SSP5-8.5 scenarios. Thus, it is currently the only large ensemble that allows for analyzing the effect of delayed mitigation actions versus no mitigation efforts and versus earlier efforts leading to similar radiative forcing at the year 2100. We describe the set-up of the SMHI-LENS in detail and provide first examples of its application. The ensemble mean future changes in key variables in the atmosphere and ocean are analyzed and compared against the variability across the ensemble members. In agreement with other large-ensemble simulations, we find that the future changes in the near-surface temperature are more robust than those for precipitation or sea level pressure. As an example of a possible application of the SMHI-LENS, we analyze the probability of exceeding specific global surface warming levels in the different scenarios. None of the scenarios is able to keep global warming in the 21st century below 1.5 degrees C. In SSP1-1.9 there is a probability of approximately 70% to stay below 2 degrees C warming, while all other SSPs exceed this target in every single member of SMHI-LENS during the course of the century. We also investigate the point in time when the SSP5-8.5 and SSP5-3.4 ensembles separate, i.e., when their differences become significant, and likewise when the SSP5-3.4-OS and SSP4-3.4 ensembles become similar. Last, we show that the time of emergence of a separation between different scenarios can vary by several decades when reducing the ensemble size to 10 members.

A substantial part of Arctic climate predictability at interannual timescales stems from the knowledge of the initial sea ice conditions. Among all sea ice properties, its volume, which is a product of sea ice concentration (SIC) and thickness (SIT), is the most responsive parameter to climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study, the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the regional benefits of specific initialized variables, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean only; ocean plus SIC; and ocean plus SIC and SIT initialization. In the Atlantic Arctic, the Greenland–Iceland–Norway (GIN) and Barents seas are the two most skilful regions in SIC prediction for up to 5–6 lead years with ocean initialization; there are re-emerging skills for SIC in the Barents and Kara seas in lead years 7–9 coinciding with improved skills of sea surface temperature (SST), reflecting the impact of SIC initialization on ocean–atmosphere interactions for interannual-to-decadal timescales. For the year 2–9 average, the region with significant skill for SIT is confined to the central Arctic Ocean, covered by multi-year sea ice (CAO-MYI). Winter preconditioning with SIT initialization increases the skill for September SIC in the eastern Arctic (e.g. Kara, Laptev and East Siberian seas) and in turn improve the skill of air surface temperature locally and further expanded over land. SIT initialization outperforms the other initialization methods in improving SIT prediction in the Pacific Arctic (e.g. East Siberian and Beaufort seas) in the first few lead years. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the futur

The recent increase in Atlantic and Pacific ocean heat transports has led to a decrease in Arctic sea-ice area and volume. As the respective contributions from both oceans in driving sea-ice loss is still uncertain, our study explores this. We use the EC-Earth3 coupled global climate model and perform different sensitivity experiments to gain insights into the relationships between ocean heat transport and Arctic sea ice. In these model experiments, the sea-surface temperature is artificially increased in different regions of the North Atlantic and North Pacific Oceans and with different levels of warming. All the experiments lead to enhanced ocean heat transport, and consequently to a decrease in Arctic sea-ice area and volume. We show that the wider the domain in which the sea-surface temperature is increased and the larger the level of warming, the larger the increase in ocean heat transport and the stronger the decrease in Arctic sea-ice area and volume. We also find that for a same amount of ocean heat transport increase, the reductions in Arctic sea-ice area and volume are stronger when the sea-surface temperature increase is imposed in the North Pacific, compared to the North Atlantic. This is explained by the lower-salinity water at the Bering Strait and atmospheric warming of the North Atlantic Ocean in the Pacific experiments. Finally, we find that the sea-ice loss is mainly driven by reduced basal growth along the sea-ice edge and enhanced basal melt in the Central Arctic. This confirms that the ocean heat transport is the primary driver of Arctic sea-ice loss in our experiments., DD is supported by the OSeaIce project (https://cordis.europa.eu/project/id/834493), which has received funding from the European Union’s (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 834493. TK and RFF are funded by the EU Horizon 2020 PRIMAVERA project, grant agreement No. 641727. MPK acknowledges support from the NordForsk research program Arctic Climate Predictions: Pathways to Resilient, Sustainable Societies (ARCPATH, No. 76654). YRR was funded by the EU Horizon 2020 research and innovation programme in the framework of the Marie Sklodowska-Curie grant INADEC (grant agreement No. 800154)., Peer Reviewed, Postprint (published version)