Towards Better Integration of Climate Models and Models for the Terrestrial Cryosphere (Permafrost and Glaciers)

Monday, 15 December 2014
Bernd Etzelmuller1, Sebastian Westermann2, Kjersti Gisnas1, Kjetil Schanke Aas1, Thomas Schuler1, Thorben Dunse3, Torbjorn Ostby1, Terje Berntsen1, Jon Egill Kristjansson1 and Frode Stordal1, (1)University of Oslo, Department of geosciences, Oslo, Norway, (2)University of Oslo, Oslo, Norway, (3)University of Oslo, Department of Geosciences, Oslo, Norway
Predictions of the future climate are based on Earth System Models operating on coarse grids, while the impact of a changing climate on most elements of the terrestrial cryosphere is strongly heterogeneous. This scale discrepancy hampers realistic predictions of the development of permafrost landscapes or glacier mass balances. At the University of Oslo, Norway, meteorologists and glaciologists/permafrost scientists working on the terrestrial cryosphere, have since 2011 collaborated on approaches trying to overcome such scaling problems. For intermediate spatial scales of 1-3 km we use two approaches:

First, we apply the Weather Research and Forecasting model (WRF) to dynamically downscale climate parameters for a period of 10 years. The results are validated against continuous energy balance measurements at Ny-Ålesund and against meteorological and in-situ mass-balance observations from the Austfonna ice cap, both at Svalbard. Those data sets feed into simple permafrost modelling schemes and glacier mass balance models, respectively.

Secondly, for permafrost we combine multi-temporal remote sensing products and thermal ground modeling, compiling maps of permafrost temperatures and thaw depth. Such a “permafrost re-analysis” has significant potential for validation of large-scale models by delivering a statistical distribution of ground parameters for coarse modeling grid cells.

However, a spatial scale of 1km is still too coarse to resolve the spatial heterogeneity of especially permafrost properties because of the large heterogeneity of e.g. snow cover, but also surfical material and/or vegetation cover. For scales below 1 km we propose to describe this variability in a statistical way by distribution functions rather than a deterministic representation on refined grids. We demonstrate that the concept facilitates modeling of the transition from continuous over discontinuous to sporadic permafrost along the climatic gradient from Svalbard to Southern Scandinavia, which is not possible without subgrid representation of snow depths.

Finally, we evaluate the possibility to improve simulations of surface energy fluxes also in atmosphere and climate models, through better representation of sub-grid scale variability of variables relevant for atmosphere-cryosphere interactions.