Sensitivity of Permafrost in the Arctic - a multiscale perspective

Permafrost regions occupy approximately 24 % of the Northern Hemisphere’s land area; these regions are anticipated to be considerably reduced by climate change. Comprehensive data sets are sparse for the Arctic, yet they are of great value to support modeling efforts on current and future arctic climate and permafrost conditions. The SPARC (Sensitivity of Permafrost in the ARCtic) research group concentrates on examining heat, water and carbon fluxes in the Arctic permafrost system at sites in Siberia, Svalbard and the Canadian Arctic and how these processes vary across multiple spatial and temporal scales. Specifically, our goals are to: (i) quantify water and energy fluxes across a spectrum of scales, (ii) identify environmental factors and processes controlling the fluxes, and (iii) understand the interactions with biochemical processes determing the carbon balance of large Arctic areas. This poster summarizes the recent results of the following topics: land cover characteristics, surface temperature and energy balance. The surface energy budget is the key to process understanding in permafrost areas, since it determines the surface temperature and thus the seasonal thawing of the soil. The land surface temperature is related to all components of the energy balance and is thus a crucial parameter when monitoring the energy budget of permafrost environments. Land cover affects the biogeophysical properties of the surface like surface hydrology, albedo, and biomass which determine the exchange of energy, water and carbon fluxes between the surface and the atmosphere. Our results show that especially small ponds and lakes play a dominant role in the water and energy budget of Arctic permafrost landscapes. This is of particular importance, as such land cover heterogeneities are usually not accounted for in large-scale climate models. Hence, model derived estimats of surface temperature, ground heat flux, evaporation as well as carbon fluxes might be biased