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

Journal metrics

IF value: 4.713
IF4.713
IF 5-year value: 4.927
IF 5-year
4.927
CiteScore value: 8.0
CiteScore
8.0
SNIP value: 1.425
SNIP1.425
IPP value: 4.65
IPP4.65
SJR value: 2.353
SJR2.353
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 53
h5-index53
Preprints
https://doi.org/10.5194/tc-2019-15
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2019-15
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  11 Feb 2019

11 Feb 2019

Review status
This preprint has been withdrawn by the authors.

Uncertainty in predicting the Eurasian snow: Intercomparison of land surface models coupled to a regional climate model

Da-Eun Kim1,3,4 and Seon Ki Park1,2,3,4 Da-Eun Kim and Seon Ki Park
  • 1Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Republic of Korea
  • 2Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
  • 3Severe Storm Research Center, Ewha Womans University, Seoul, Republic of Korea
  • 4Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, Republic of Korea

Abstract. Variability of large and synoptic scale circulations in Asia is strongly affected by the winter and spring Eurasian snow. Therefore, an accurate prediction of the Eurasian snow is of the utmost importance in predicting the climate and weather phenomena in Asia. Most global/regional models are coupled with several land surface models (LSMs) in which the land surface process parameters are calculated under their own physical principles and parameterization schemes. In this study, using the Weather Research and Forecasting (WRF) model, we make intercomparision of LSMs in terms of simulating the Eurasian snow. Simulations are carried out from 1 June 2009 to 31 August 2010, including a spin-up time of 6 months, by employing four different LSMs – the Unified Noah LSM, the Noah LSM with multiparameterization options (Noah-MP), the Rapid Update Cycle (RUC) LSM, and the Community Land Model version 4 (CLM4). The NCEP Final (FNL) Operational Global Analysis data are used as initial and boundary conditions. The LSM results are evaluated using the Canadian Meteorological Centre Daily Snow Depth Analysis Data, the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Snow Cover Monthly L3 Global 0.05Deg Climte Modeling Grid (CMG) Version 6, and the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo Product. Although all the LSMs represent reasonable results, the Noah-MP represents the most accurate predictions in all three variables (snow depth, fractional snow cover, and albedo), in terms of not only quantitative aspects but also spatial correlation patterns. Our results indicate that prediction of the Eurasian snow cover is sensitive to the choice of LSMs coupled to the global/regional climate models, and hence the future climate projections.

This preprint has been withdrawn.

Da-Eun Kim and Seon Ki Park

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Da-Eun Kim and Seon Ki Park

Da-Eun Kim and Seon Ki Park

Viewed

Total article views: 679 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
432 208 39 679 34 43
  • HTML: 432
  • PDF: 208
  • XML: 39
  • Total: 679
  • BibTeX: 34
  • EndNote: 43
Views and downloads (calculated since 11 Feb 2019)
Cumulative views and downloads (calculated since 11 Feb 2019)

Viewed (geographical distribution)

Total article views: 445 (including HTML, PDF, and XML) Thereof 441 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 27 Sep 2020
Publications Copernicus
Download
Withdrawal notice

This preprint has been withdrawn.

Short summary
An accurate prediction of the Eurasian snow is essentially important in predicting the climate and weather phenomena in Asia. Regional climate models are mostly coupled with several land surface models (LSMs) in which the land surface process parameters are calculated under their own physical principles and parameterization schemes. We show that prediction of the Eurasian snow cover is sensitive to the choice of LSMs coupled to regional climate models, and hence the future climate projections.
An accurate prediction of the Eurasian snow is essentially important in predicting the climate...
Citation