Climate intelligence for charting sustainable development
Predicting the climate a decade in advance
Our prediction system, the Norwegian climate prediction model, combines the Norwegian Earth system model with Ensemble Kalman filter data assimilation. The former accurately simulate climate and anthropogenically driven global warming. While the assimilation of ocean and sea ice data is used to synchronize the model with the observed shorter-term fluctuations in climate. Thus, we can skilfully predict the climate out to a decade in advance.
The Norwegian climate prediction model is particularly skilful on multi-annual timescales in the North Atlantic to Arctic Sector. Paramount here is the transport of heat by the ocean to high latitudes and its influence on sea ice and the atmosphere. It gives rise to pronounced decadal shifts in climate in the region. Our model is one of a few that is predicting a shift to cooler conditions in the North Atlantic that could temporarily offset the effects of global warming in our region and parts of the Arctic (see figure below).
IPCC climate report contribution
Our numerical predictions are being used globally and locally. We were part of the decadal climate prediction project that contributed data for the Intergovernmental Panel Climate Change (IPCC) Assessment Report Six. We contribute quasi-operational forecasts to the World Meteorological Global Annual to Decadal Climate Update Reports. At the national level, our climate predictions are available to more than 20 user partners from agriculture, renewable energy, shipping, and insurance sectors through Climate Futures—a recently funded centre for innovation-based research.
A prediction of surface temperature for 2021-2025 based on an ensemble of different models including NorCPM.
Source:
WMO Lead Centre for Annual-to-Decadal Climate Prediction
About Bjerknes
The Bjerknes Climate Prediction Unit is a team of around 20 core researchers based at the Bjerknes Centre for Climate Research in Bergen. We have been funded through grants from the Trond Mohn Foundation, European Union H2020 programmes, the Research Council of Norway, and NordForsk.