For a safe and sustainable Arctic resource use there is a high need for reliable seasonal to decadal forecasts of sea-ice. Therefore Wageningen UR, TripleP@Sea, started research this year to provide reliable seasonal to decadal probabilistic predictions of the Arctic sea ice and to assess the value of this information for economic activities in the Arctic region.
Folmer Krikken, Meteorology and Air Quality Group Wageningen University, is doing his research in close co-orporation with Global Climate (KNMI) and Environmental Economics Group Wageningen University. The research (PhD) is part of the Arctic program TripleP @ Sea. IMARES Wageningen UR is leading the Arctic Program TripleP @ Sea, which consists of three showcases (Arctic, BES, MUP's).
Changing Arctic climate
The Arctic climate is changing. Arctic sea ice extent has reduced substantially in the last few decades and the surface temperatures rise more than anywhere in the world. The Arctic region is therefore seen as being on the forefront of climate change. This enhanced climate change is the result of complex Arctic climate feedbacks which result in a so called ‘Arctic amplification’ of the global trends. Consequently, climate models predict ice-free arctic summers already at the 2nd half of this century.
With the sea ice reducing, economical activities in the Arctic region are expanding and diversifying; the fishing industry grows, the offshore extraction of oil and gas increases and northerly passages for ship are used more frequently, all because of more and longer ice-free seas. However, the variability of the Arctic sea ice offers large uncertainties and risks in operation planning. The unique Arctic ecosystem is extremely vulnerable for pollution (e.g. oil spills), resulting in no margin for error for these increasing economic activities.
Dynamical climate model EC-EARTH
For a safe and sustainable Arctic resource use there is a high need for reliable seasonal to decadal forecasts of sea-ice. Recently, climate models showed that Arctic climate processes and feedbacks provide predictability for longer time scales than normal weather prediction. In this project we will investigate this in more detail with the dynamical climate model EC-EARTH. With this model we will study the feedbacks that influence the natural variability in the Arctic and assess the quality off the probabilistic forecasts. Furthermore, we will investigate if and how the probabilistic climate information can be used to limit uncertainties and risks for operational planning in the Arctic.