the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
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.
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Mickaël Lalande et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2023-113', Anonymous Referee #1, 01 Sep 2023
Reducing the High Mountain Asia cold bias in GCMs by adapting snow cover parameterization to complex topography areas
Tibetan Plateau has a most complex topography across the globe, and it has a large area of seasonal snow cover. In climate models, the influence of complex topography on snow cover distribution cannot be ignored. In this manuscript, the authors calibrated and evaluated different snow cover fraction schemes over the Tibetan Plateau using the HMASR reanalysis data. The results indicate that ignoring the topographic effect can lead to overestimation of snow cover fraction. Furthermore, the authors test the snow cover fraction schemes in the coupled model. The improvement of snow cover distribution simulation contributes to reduction of “cold bias” over the Tibetan Plateau through affecting the surface energy budget and balance. This work highlights the influence of topography on the simulations of snow cover and surface temperature, which offers an effective perspective to improve the climate simulation.
I only have some minor comments as follows.
Minor Comments:
1. The standard deviation of topography is the key factor in this study, and the authors need to explain in detail the meaning and calculation of this parameter. For example, what is the elevation data used for the calculation? As I know, the resolution can lead to difference of the calculation of standard deviation of topography. Please add a sensitivity test about the grid resolution.
2. In the abstract, the authors need to highlight more the significance and application of this study, which is different from writing conclusions. The abstract and conclusions need to streamline to focus on core content. Furthermore, the citation of Jiang et al. (2020) in Line 35 and the contents about SWE data in Lines 58-72 tend to disrupt the logical sequence in the introduction.
3. Why choose 0.3° as the resolution of comparison test?
4. The SL12 scheme didn't perform too badly, and it is physically based. I would suggest that more discussion and work could be done about the SL12 scheme. However, it is not necessary in this work.Citation: https://doi.org/10.5194/tc-2023-113-RC1 -
RC2: 'Comment on tc-2023-113', Anonymous Referee #2, 08 Sep 2023
An accurate representation of the snow conditions over the Tibetan Plateau is crucial not only for local but also global climate. Large overestimates of the snow cover fraction exist in modern land surface modeling and climate modeling, which can lead to profound uncertainties. This manuscript evaluates five snow cover fraction parameterization schemes implemented in the ORCHIDEE land surface model (LSM) of the Institute Pierre Simon Laplace (IPSL) general circulation model (GCM). The snow cover overestimation in Mountain areas was found to be reduced by about 5 to 10% on average in the High Mountain Asia (HMA) region. This study is valuable since it extends snow cover simulations from the offline to the coupled mode, where land-atmosphere interrelations are taken into account. I have some suggestions in the following.
Since only 5 - 10 % of the overestimation is reduced, and a significant percentage of overestimation still remains, the title appears overly confident. I suggest changing it to an interrogative sentence or slightly reduce the certainty.
All the analysis are based on SCF simulations. Offline simulations conducted by Jiang et al (2020) could isolate the impacts of the snow scheme from circulation changes; however, this method was limited by forcing uncertainties (Gao et al. 2020). Therefore, in this study, authors utilized the online mode as an alternative approach. It is valuable as interpreted in section 5.4. However, it should be noted that biases may be influenced by uncertainties arising from other physics schemes in the climate model. Can you effectively isolate the impacts of the SCF scheme?
The authors mention the effect of clouds in the reference dataset during the validation process. Using a cloud-free dataset or processing the MODIS data using a cloud removal procedure as might alleviate this effect from the cloud. Jiang et al. (2019) used a four-step cloud removal approach to generate cloud-free dataset. That might provide some insights.
The logical coherence between sentences needs improvement or clarification since some citations appears abruptly without proper introduction. Enhancing smooth transition and providing clarifications and conciseness would greatly improve readability.
Citation: https://doi.org/10.5194/tc-2023-113-RC2 -
AC1: 'Reply on RC2', Mickaël Lalande, 08 Sep 2023
Thank you very much for your review. We will address your comments within the coming weeks. We would be grateful if you have time to provide us a few examples about your last comment on the logical coherence between sentences and citations, so we can address them more easily. Many thanks in advance, and otherwise, we'll do our best to improve it!
Citation: https://doi.org/10.5194/tc-2023-113-AC1
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AC1: 'Reply on RC2', Mickaël Lalande, 08 Sep 2023
Mickaël Lalande et al.
Mickaël Lalande et al.
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