The climate in the Arctic is changing faster than in other regions of the Earth. At the same time, the scientific understanding on processes and feedbacks causing this rapid change is poor and climate modeling in the Arctic remains a challenge. Therefore we focus on the identification of regional feedback mechanisms between atmosphere, ocean, and sea ice in the Arctic with the aim to quantify their role in recent climate change, exploiting recent measurements at AWIPEV, ERA Interim- and satellite data, and studies with global and regional climate models. This includes the statistical and dynamical analyses of atmospheric measurements and their reproduction in the models to advance our knowledge on the Arctic climate system towards broader understanding of interactions and feedbacks involved in recent sea-ice reduction and connected changes in baroclinic cyclones and snow cover in the Arctic land areas.
Current numerical weather and climate models have significant problems in reproducing the current state and are unable to simulate known system interactions. There is therefore a need for observations to constrain new process-based sub-grid scale parameterisations for improving the basic tools for prediction of weather and sea-ice, as well as for climate projections.The synthesis of observations and model results for the Arctic region will enable us to distinguish variability arising from Arctic-internally generated and externally forced climate variability and to estimate the uncertainty range for future regional climate changes. The synthesis of measurements and model approaches will improve the understanding of Arctic feedback processes, reduce the biases of regional and global weather forecast and climate models and contribute to improve the predictive skills as described in the attached Figure Observational data analysis is needed for model calibration, evaluation, and development, and therefore we apply a hierarchy of regional and global climate models. Regional atmospheric models based on HIRHAM are applied in climate and forecast modes and coupled Atmosphere-Ocean-Ice (A-O-I) models and Atmosphere-Land surface-Soil (A-L-S) models of the regional Arctic climate system have been developed. This approach delivers hints for improved process descriptions in global climate models and help to improve the predictive skills of regional and global models.