Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5095-2023
https://doi.org/10.5194/tc-17-5095-2023
Research article
 | 
04 Dec 2023
Research article |  | 04 Dec 2023

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

Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Catherine Ottlé, and Frédérique Cheruy

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Cited articles

Balogh, B., Saint‐Martin, D., and Ribes, A.: How to Calibrate a Dynamical System With Neural Network Based Physics?, Geophys. Res. Lett., 49, 1–9, https://doi.org/10.1029/2022GL097872, 2022. a
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Beljaars, A. C. M., Brown, A. R., and Wood, N.: A new parametrization of turbulent orographic form drag, Q. J. Roy. Meteor. Soc., 130, 1327–1347, https://doi.org/10.1256/qj.03.73, 2004. a, b
Bernus, A. and Ottlé, C.: Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model, Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, 2022. a
Bolibar, J., Rabatel, A., Gouttevin, I., Zekollari, H., and Galiez, C.: Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning, Nat. Commun., 13, 409, https://doi.org/10.1038/s41467-022-28033-0, 2022. a
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Short summary
This study investigates the impact of topography on snow cover parameterizations using models and observations. Parameterizations without topography-based considerations overestimate snow cover. Incorporating topography reduces snow overestimation by 5–10 % in mountains, in turn reducing cold biases. However, some biases remain, requiring further calibration and more data. Assessing snow cover parameterizations is challenging due to limited and uncertain data in mountainous regions.