Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas

• Published in: The Cryosphere
• Main Authors: Lalande, Mickaël, Ménégoz, Martin, Krinner, Gerhard, Ottle, Catherine, Cheruy, Frédérique
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; The Cryosphere
• Online Access: https://hal.science/hal-04321343; https://hal.science/hal-04321343/document; https://hal.science/hal-04321343/file/tc-17-5095-2023.pdf; https://doi.org/10.5194/tc-17-5095-2023

International audience Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous ...