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.
Highlights
Summer sea ice in the Arctic Ocean has lost nearly threequarters of its sea ice volume (SIV) since the 1970s (Kwok, 2018) caused by a reduction of both sea ice extent (SIE) and thickness (SIT)
As the method is developed as a prototype for our initialization strategy implemented for the Coupled Model Intercomparison Project phase 6 (CMIP6) decadal climate prediction project (DCPP) with EC-Earth3, the present study provides a documentation of the new climate prediction system with anomaly initialization including SIT in a multi-category sea ice model framework
For decadal prediction with EC-Earth3-CPSAI, we develop a novel method with (1) a weighting function mapping single-category Aice onto multiple categories; (2) a multi-category thickness distribution depending on concentration levels; or (3) both when converting the initial volume (i.e. V ice,sn) at the grid-cell level to its subgrid, while thickness in the last category is determined with a constraint of V ice,sn being conservative
Summary
Summer sea ice in the Arctic Ocean has lost nearly threequarters of its sea ice volume (SIV) since the 1970s (Kwok, 2018) caused by a reduction of both sea ice extent (SIE) and thickness (SIT). As the method is developed as a prototype for our initialization strategy implemented for the Coupled Model Intercomparison Project phase 6 (CMIP6) decadal climate prediction project (DCPP) with EC-Earth, the present study provides a documentation of the new climate prediction system with anomaly initialization including SIT in a multi-category sea ice model framework. It characterizes the performance with focus on the predictions in the Arctic.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
