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https://doi.org/10.5194/egusphere-2024-82
https://doi.org/10.5194/egusphere-2024-82
23 Feb 2024
 | 23 Feb 2024

Which global reanalysis dataset represents better in snow cover on the Tibetan Plateau?

Shirui Yan, Wei Pu, Yang Chen, Yaliang Hou, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, and Xin Wang

Abstract. The extensive snow cover across the Tibetan Plateau (TP) regions has a major influence on the climate and water supply for over one billion downstream inhabitants. However, an adequate evaluation of snow cover fraction (SCF) variability over the TP simulated by global multiple reanalysis datasets has yet to be undertaken. In this study, we examined eight global reanalysis SCF datasets using the Snow Property Inversion from Remote Sensing (SPIReS) product spanning the period 2001–2020. The results reveal that the HMASR generated the best SCF simulations because of its outstanding spatial and temporal accuracy. The GLDAS and CFSR demonstrated acceptable SCF accuracy with respect to spatial variability, but struggled to reproduce the annual trend. Pronounced SCF overestimations were found when using the ERA5, ERA5L, and JRA55, but SCF was underestimated by MERRA2, and CRAL generated poor spatial pattern. Overall biases were related to the combined effect of precipitation forcing, temperature forcing, snow data assimilation, and SCF parameterization methods, with the dominant factor changing across datasets. In ERA5 and ERA5L, temperature and snowfall bias exhibited significant correlations with SCF bias over most TP areas, therefore having a greater impact on the accuracy of SCF in terms of spatial variability and temporal evolution. On the other hand, the impact of snow assimilation was possibly more pronounced in MERRA2 and CRAL. Although parameterization methods can improve SCF simulation accuracy, their influence was weaker than those of other factors, except for JRA55. To further improve the accuracy of SCF simulation, an ensemble average method was developed. The ensemble based on HMASR and GLDAS generated the most accurate SCF spatial distribution, whereas the ensemble containing ERA5L, CFSR, CRAL, GLDAS, ERA5, and MERRA2 proved optimal for capturing the annual trend.

Shirui Yan, Wei Pu, Yang Chen, Yaliang Hou, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, and Xin Wang

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-82', Anonymous Referee #1, 05 Mar 2024
  • RC2: 'Comment on egusphere-2024-82', Anonymous Referee #2, 21 Mar 2024
  • RC3: 'Comment on egusphere-2024-82', Anonymous Referee #3, 24 Mar 2024
Shirui Yan, Wei Pu, Yang Chen, Yaliang Hou, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, and Xin Wang
Shirui Yan, Wei Pu, Yang Chen, Yaliang Hou, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, and Xin Wang

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Short summary
The snow cover over the Tibetan Plateau (TP) plays a role in the climate and hydrological systems, yet there exist uncertainties in snow cover fraction (SCF) estimations within reanalysis datasets. This study utilized the Snow Property Inversion from Remote Sensing (SPIReS) product to assess the accuracy of eight global reanalysis SCF datasets over the TP. Factors contributing to uncertainties were analyzed, and an ensemble averaging method was employed to provide optimized SCF simulations.