The main objective is to provide reliable and accurate probabilistic predictions of the state of the Arctic oceans and sea ice up to 2035 and to assess the value of this information for informed decisions on economic activities in the region. We will combine probabilistic near-term climate predictions of the Arctic with economic analyses to explore the value of this information for transport, fishing and exploration activities in the region
Hypothesis 1: Climate in the Arctic region is predictable on monthly to decadal time scales
Slow variations in the ocean characteristics, sea ice thickness/extent, snow cover and anthropogenic emissions provide probabilistic predictability beyond weather time scales. Recently generated retrospective re-forecasts with IPCC-class climate models from 1960-2010 will be used to assess and verify forecasts against in situ and satellite observations. We will use the climate model EC-Earth to investigate the main drivers (emissions of greenhouse gases, ship emissions, natural variability etc) of the changes as function of forecast lead time.
Hypothesis 2: Forecast quality depends on the simulated climate processes in the Arctic
Climate models contain systematic errors due to lack of knowledge and numerical representation of the physical processes. As a consequence seasonal to decadal forecast systems, initialized from the observed climate state, show large initial drift. It is hypothesized that calibration with mean state errors improves the forecast quality. New results even suggest an interplay between feedback strength of processes in the Arctic and the initial state. The IPCC-class models, including EC-Earth, will be used to investigate the impact of representation of Arctic climate feedbacks and model error on forecast quality in the region.
Hypothesis 3: Probabilistic climate predictions have economical value for activities in the Arctic (exploration, ship routing, fishing)
There is little scientific research on the value of probabilistic climate information in operational planning of economic activities in the Arctic region. Such information, which implicitly translates uncertainty into risk, can increase the benefits of various economic activities in the region. Although the value of probabilistic forecasts have been assessed for various activities, such as wind power generation (Pinson, 2006) and irrigation scheduling (Wilks and Wolfe, 1998, Cai et al., 2011), there is currently a lack of insight into the value of probabilistic climate information for operational planning of economic activities in the Arctic region. The melting of Arctic sea ice will not only enable the extraction of more fossil fuels, but will also provide a boost to fisheries and will allow shipping across the Arctic’s generally frozen north-west and north-east passages, thus linking the Atlantic and Pacific oceans. Transportation costs could for instance be saved each year by using the north-west Passage as a shipping route as long as possible, which requires logistical planning on the basis of the probabilistic climate predictions. We will investigate the value of probabilistic climate information on the basis of the increase in benefits from shipping routing, exploration and fishing as a result of better operational planning. The value is expected to differ for each of these sectors because of the differences in planning horizons for logistics, investments and risks.