Marine and atmospheric transport modeling supporting nuclear preparedness in Norway: Recent achievements and...

Numerical transport models are important tools for nuclear emergency decision makers in that they rapidly provide early predictions of dispersion of released radionuclides, which is key information to determine adequate emergency protective measures. They can also help us understand and describe environmental processes and can give a comprehensive assessment of transport and transfer of radionuclides in the environment. Transport of radionuclides in air and ocean is affected by a number of different physico-chemical processes. Along with uncertainty arising from the input data, the model estimates will therefore involve a combination of numerous uncertain factors, caused by knowledge gaps and assumptions in the model system. As discussed in this paper, the major sources to uncertainty affecting the model results are release descriptions, driving data, process descriptions and parameters. Here, we give a synthesis of the most important improvements in atmospheric and marine models achieved through the CERAD programme. In the atmospheric transport model, an important improvement has been inclusion of uncertainties in the dispersion estimates. Recent developments also include adaption to high resolution forcing data and ensemble forecasts, inversion methods and long term analyses. Case studies clearly show improved predictions from ensemble mean values compared to single deterministic runs, and promises for future upgrades of preparedness decision support systems. A major improvement in the marine model system was implementation of dynamic speciation including transformation of species, identifying particle size and parameterizations to be key factors affecting radionuclide distribution. The model system was further developed in a case study involving the impact of changing environmental factors on the transport of aluminium river run-off to an estuary in southeastern Norway. Suggestions for future improvements include implementation of an operational preparedness model for marine transport, better quantification of uncertainties using ensemble methods and improved source identification with further development of inverse transport.