Fransner F, Counillon F, Bethke I, Tjiputra J, Samuelsen A, Nummelin A, Olsen A. 2020: Ocean Biogeochemical Predictions – Initialization and Limits of Predictability. Front. Mar. Sci. DOI: https://doi.org/10.3389/fmars.2020.00386
Tag: bethke
The change in the ENSO teleconnection under a low global warming scenario and the uncertainty due to internal variability
Michel, C., C. Li, I.R. Simpson, I. Bethke, M.P. King, and S. Sobolowski. 2020: The change in the ENSO teleconnection under a low global warming scenario and the uncertainty due to internal variability. J Clim.
https://doi.org/10.1175/JCLI-D-19-0730.1
Pacific contribution to the early twentieth-century warming in the Arctic
Svendsen, L., N. Keenlyside, I. Bethke, Y. Gao, and N.-E. Omrani, 2018: Pacific contribution to the early twentieth-century warming in the Arctic. Nature Climate Change, 8, 793-797.
DOI: https://doi.org/10.1038/s41558-018-0247-1
Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model
Kimmritz, M., F. Counillon, C. M. Bitz, F. Massonnet, I. Bethke, and Y. Gao, 2017: Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model. Tellus A,, 70.
DOI: https://doi.org/10.1080/16000870.2018.1435945
Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model
Counillon, F., N. Keenlyside, I. Bethke, Y. Wang, S. Billeau, M. L. Shen, and M. Bentsen, 2016: Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model. Tellus A, 68,
DOI: https://doi.org/10.3402/tellusa.v68.32437
Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic
Kimmritz, M., F. Counillon, L. H. Smedsrud, I. Bethke, N. Keenlyside, F. Ogawa, and Y. Wang:. 2019: Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic. JAMES https://doi.org/10.1029/2019MS001825 .
Summary: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.
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