Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3

Abstract

Abstract. 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 future, the controlling mechanism for decadal predictability may thus shift from sea ice volume to ocean-driven processes.

Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model

“…It is worth mentioning that the variable such as SST with minor additional contributions to the model does not mean that it is a minor contributor since the contributions from different variables to prediction skill partially overlap. In addition, adding SIT to the model has a substantial contribution to the prediction skill in the warm season, indicating that sea ice thickness is a key source of sea ice predictability within the Arctic Basin in the warm season, especially in summer, which is consistent with previous studies (Blanchard-Wrigglesworth et al, 2011;Blockley and Peterson, 2018;Day et al, 2014a;Morioka et al, 2021;Tian et al, 2021;Yuan et al, 2016). However, SIT has a negative contribution to the prediction skill in the cold season (Fig.…”

“…The results show that SIC is highly related to OHC in the upper 300 m, SIT, SST, SAT, surface net radiative flux, surface net turbulent heat flux, and geopotential height (GPH) and wind vector at different levels, including 850 to 200 hPa. Due to the barotropic nature of the polar troposphere (Chen, 2005;Ting, 1994) and the low correlation between sea-level pressure and SIC, we chose GPH and wind vector at 850 hPa to define the lowlevel atmospheric circulation, whose interaction with sea ice is stronger relative to that in higher levels. Therefore, we choose to define the coupled atmosphere-ice-ocean Arctic climate system with nine variables: SIC, OHC in the upper 300 m, SIT, SST, SAT, surface net radiative flux, surface net turbulent heat flux, 850 hPa GPH, and 850 hPa wind vector.…”

Lasting impact of winds on Arctic sea ice through the ocean's memory

“…Sea ice drift pathways changed significantly between different years even for ice floes originating from the same location (Krumpen et al, 2021). The spatial distribution of liquid freshwater and ocean surface circulation in the Arctic Ocean have varied considerably over recent decades (e.g., Timmermans et al, 2011;Wang et al, 2019c;Polyakov et al, 2020), which could have contributed to the changes in sea ice drift pathways, besides the direct impact of winds. Our finding also addresses that the changes in ocean surface geostrophic currents must be taken into account in the calculation of ice-ocean stress.…”

“…Q. Wang et al: Impact of the Arctic Ocean's memory on sea ice improving the prediction of Arctic sea ice and climate (e.g., Tian et al, 2021). Previous analysis of the role of ocean initialization has mainly been focused on the impact of ocean temperature.…”

Added value of assimilating springtime Arctic sea ice concentration in summer-fall climate predictions

“…Assimilation of observed SIC in retrospective forecasts initialized on May 1st improves Arctic sea ice predictions in May-June, and October-November. The largest improvements in the sea ice edge representation are found in the Greenland-Norwegian-Iceland (GIN) Seas, a region where EC-Earth3 is known to have important systematic sea ice biases (Cruz-García et al 2021, Tian et al 2021). Better skill in SIC also leads to improved central North Atlantic SST forecasts already in May via a fast (sub-monthly scale) atmospheric pathway, evidenced by increased prediction skill of GPH500 and T2m in the first weeks of May over the North Atlantic.…”