Workshop in June
On June 5th-7th we hosted the third workshop on Climate Prediction in the Arctic and North Atlantic sector – see our Events page for agenda and streaming links.
Will European winters be increasingly mild and wet in the coming years? Will there be more extreme precipitation? Will climatic conditions be beneficial for Norwegian fisheries and hydroelectric power production?
Such questions of large societal importance are at the heart of the emerging scientific field ofclimate prediction. Yet we may know more about global warming in a 100-year perspective than how climate in Norway will be in a decade.
The Bjerknes Climate Prediction Centre (BCPC) aims to bridge this gap between weather forecast and long-term climate change projections, to develop skilful climate prediction.
The prediction centre will capitalise on promising initial results from a team of world-class scientists at the Bjerknes Centre for Climate Research – model developers, observationalists, theoreticians, and forecasters – to develop the World’s most skilfull prediction system for northern climate.
Our empirically based predictions corroborates the great potential to forecast Norwegian surface temperature and Arctic sea ice extent up to a decade in advance.
The Bjerknes Climate Prediction Unit (BCPU) primary objective is to enhance climate prediction to the level where it benefits society, and thus facilitate the needed transition to operational forecasts. The centre focuses on predicting climate in the Atlantic-to-Arctic sector and surrounding continents from a season to a decade and beyond.
To develop a mechanistic understanding of predictable relations in observations and models, as means to improve models, inform data assimilation and verify predictions
To develop novel data assimilation approaches suitable for climate prediction, so as to best use all available, and constantly growing, observations of the Earth system
To explore the limits of climate prediction, by devising innovative approaches to assess and attribute predictive skill and quantify the impact of systematic model errors