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

• Main Authors: Levavasseur, G., Vrac, M., Roche, D. M., Paillard, D., Martin, A., Vandenberghe, J.
• Other Authors: Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2011
• Subjects: Earth Science; [SDU]Sciences of the Universe [physics]; Gam; permafrost
• Online Access: https://hal.science/hal-04113875; https://doi.org/10.5194/cp-7-1225-201110.5194/cpd-7-1647-2011

International audience 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 variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of 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-systematic 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 CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. 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 (GAM and ML-GAM) is not ...