24 Jul 2023
 | 24 Jul 2023
Status: this preprint is currently under review for the journal TC.

Reducing the High Mountain Asia cold bias in GCMs by adaptingsnow cover parameterization to complex topography areas

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

Abstract. The influence of topography on the snow cover fraction (SCF) is investigated in this study with 5 different parameterizations. These SCF parameterizations are evaluated using the High Mountain Asia Snow Reanalysis (HMASR). Then, they are implemented in the ORCHIDEE land surface model (LSM) of the Institut Pierre Simon Laplace (IPSL) general circulation model (GCM) to quantify their skill in global land-atmosphere coupled simulations. SCF varies as a function to snow depth (SD), with a relationship that differs between flat and mountainous areas in HMASR. SCF parameterizations that do not include a dependency on the topography lead to large snow cover overestimations. Furthermore, a hysteresis between SCF and SD is found in HMASR, with a rapid snow cover increase during accumulation and a slower retreat of patchy snow occurring during ablation periods, discarding parameterizations not considering this effect. The application of the parametrizations in global simulations shows contrasting results depending on the location because other processes also explain the snow biases. Nevertheless, the snow cover overestimation in mountain areas is reduced by about 5 to 10 % on average when we include a dependency on the subgrid topography in our SCF parameterizations, which in turn allows to decrease the surface cold bias from −1.8 °C to about −1 °C in the High Mountain Asia (HMA) region. However, persisting snow cover biases remain in these experiments, with a SCF overestimation in HMA, as well as a SCF underestimation in several other regions (e.g., the Rockies mountains). Further calibration considering other regions and multiple datasets would allow to improve the SCF parameterizations. Assessing SCF parameterizations is challenging as it requires both realistic snowfall and snowpack in model experiments, and combined SCF, SD, and SWE – or snow density – observations, that are generally limited and uncertain in mountainous regions.

Mickaël Lalande et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-113', Anonymous Referee #1, 01 Sep 2023
  • RC2: 'Comment on tc-2023-113', Anonymous Referee #2, 08 Sep 2023
    • AC1: 'Reply on RC2', Mickaël Lalande, 08 Sep 2023

Mickaël Lalande et al.

Mickaël Lalande et al.


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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, reducing in turn 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.