Present and LGM permafrost from climate simulations: contribution of statistical downscaling

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variation between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then, we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with data. In average for the nine PMIP2 models, we measure a global agreement by kappa statistic of 0.80 with CTRL permafrost data, against 0.68 for the GAM method. In both cases, the provided local information reduces the inter-variation between climate models. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs is not significantly better than large-scale fields with 0.46 (GAM) and 0.49 (ML-GAM) of global agreement with LGM permafrost data. At the LGM, both methods do not reduce the inter-variation between climate models. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with permafrost data, than in the CTRL period. These differences reduce the contribution of downscaling and depend on several other factors deserving further studies.