Preprints
https://doi.org/10.5194/hess-2021-305
https://doi.org/10.5194/hess-2021-305

  14 Jun 2021

14 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

Improved parameterization of snow albedo in WRF + Noah. Part II: Applicability to snow estimates for the Tibetan Plateau

Lian Liu1,2, Yaoming Ma1,2,3,4, Massimo Menenti5,6, Rongmingzhu Su1,2,4, Nan Yao1,2,4, and Weiqiang Ma1,2,3 Lian Liu et al.
  • 1Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
  • 2LandAtmosphere Interaction and its Climatic Effects Group, State Key Laboratory of Tibetan Plateau Earth System Science, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
  • 3CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101, China
  • 4University of Chinese Academy of Sciences, Beijing 100049, China
  • 5State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China
  • 6Delft University of Technology, Delft, Netherlands

Abstract. Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land-atmosphere interactions estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the WRF and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates, by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged RMSE relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27 % (5 %), 32 % (69 %), 13 % (17 %) and 21 % (108 %) respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

Lian Liu et al.

Status: open (until 09 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-305', Anonymous Referee #1, 19 Jun 2021 reply
    • AC1: 'Reply on RC1', Lian Liu, 21 Jun 2021 reply
  • RC2: 'Comment on hess-2021-305', Enda Zhu, 21 Jul 2021 reply

Lian Liu et al.

Lian Liu et al.

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Short summary
Albedo is a key factor of land surface energy balance, which is difficulte to successfully reproduce by model. Here, we select eight snow events on the Tibetan Plateau to evaluate the universal improvements of our improved albedo scheme. The RMSE relative reductions for temperature, albedo, sensible heat flux and snow depth reach 27 %, 32 %, 13 % and 21 % respectively, with remarkable increases of the correlation coefficients. It presents a strong potential of our scheme for modeling snow events.