Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.713 IF 4.713
  • IF 5-year value: 4.927 IF 5-year
    4.927
  • CiteScore value: 8.0 CiteScore
    8.0
  • SNIP value: 1.425 SNIP 1.425
  • IPP value: 4.65 IPP 4.65
  • SJR value: 2.353 SJR 2.353
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 71 Scimago H
    index 71
  • h5-index value: 53 h5-index 53
Preprints
https://doi.org/10.5194/tc-2020-131
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-131
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 Jun 2020

Submitted as: research article | 15 Jun 2020

Review status
This preprint is currently under review for the journal TC.

Systematic bias of Tibetan Plateau snow cover in subseasonal-to-seasonal models

Shuzhen Hu and Wenkai Li Shuzhen Hu and Wenkai Li
  • Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China

Abstract. Accurate subseasonal-to-seasonal (S2S) atmospheric forecasts and hydrological forecasts have considerable socioeconomic value. This study conducts a multimodel comparison of the Tibetan Plateau snow cover (TPSC) prediction skill using three models (ECMWF, NCEP and CMA) selected from the S2S project database to understand their performance in capturing TPSC variability. S2S models can skilfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Compared with the observational snow cover analysis, all three models tend to overestimate the area of TPSC, especially during winter. Another remarkable issue regarding the TPSC forecast is the increasing TPSC with forecast lead time, which further increases the systematic positive biases of TPSC in the S2S models at longer forecast lead times. The underestimation of TPSC dissipation induces an increase in TPSC with forecast lead time in the models. Such systematic biases of TPSC influence the forecasted surface air temperature in the S2S models. The surface air temperature over the Tibetan Plateau becomes colder with increasing forecast lead time in the S2S models.

Shuzhen Hu and Wenkai Li

Interactive discussion

Status: open (until 10 Aug 2020)
Status: open (until 10 Aug 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Shuzhen Hu and Wenkai Li

Shuzhen Hu and Wenkai Li

Viewed

Total article views: 139 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
96 39 4 139 2 2
  • HTML: 96
  • PDF: 39
  • XML: 4
  • Total: 139
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 15 Jun 2020)
Cumulative views and downloads (calculated since 15 Jun 2020)

Viewed (geographical distribution)

Total article views: 104 (including HTML, PDF, and XML) Thereof 104 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 04 Jul 2020
Publications Copernicus
Download
Short summary
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on the Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skilfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC was found.
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on the Tibetan...
Citation