期刊名称：Journal of Advances in Modeling Earth Systems
电子版ISSN：1942-2466
出版年度：2019
卷号：11
期号：12
页码：4147-4166
DOI：10.1029/2019MS001825
出版社：John Wiley & Sons, Ltd.
摘要：There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model that combines the fully coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly coupled data assimilation (wCDA) when assimilating ocean hydrography (by updating the ocean alone) and strongly coupled data assimilation when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of the Norwegian Climate Prediction Model, the first climate prediction system using strongly coupled data assimilation, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand the origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data exhibits skillful hydrography in the upper Arctic Ocean and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10–12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial seaice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialized through wCDA of only ocean data. Particularly high skill is found for July‐initialized autumn SIE predictions. Plain Language Abstract The declining Arctic sea ice entails both risks and opportunities for the Arctic ecosystem, communities, and economic activities. Reliable seasonal predictions of the Arctic sea ice could help to guide decisionmakers to benefit from arising opportunities and to mitigate increased risks in the Arctic. However, despite some success, seasonal prediction systems in the Arctic have not exploited their full potential yet. For instance, so far only a single model component, for example, the ocean, has been updated in isolation to derive a skillful initial state, though joint updates across model components, for example, the ocean and the sea ice, are expected to perform better. Here, we introduce a system that, for the first time, deploys joint updates of the ocean and the sea ice state, using data of the ocean hydrography and sea ice concentration, for seasonal prediction in the Arctic. By comparing this setup with a system that updates only the ocean in isolation, we assess the added skill of facilitating sea ice concentration data to jointly update the ocean and the sea ice. While the update of the ocean alone leads to skillful winter predictions only in the North Atlantic, the joint update strongly enhances the overall skill.
