Articles | Volume 18, issue 9
https://doi.org/10.5194/tc-18-4089-2024
https://doi.org/10.5194/tc-18-4089-2024
Research article
 | 
10 Sep 2024
Research article |  | 10 Sep 2024

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

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

Data sets

A high-resolution near-surface meteorological forcing dataset for the Third Pole region (TPMFD, 1979-2022) K. Yang et al. https://doi.org/10.11888/Atmos.tpdc.300398

High Mountain Asia UCLA Daily Snow Reanalysis, Version 1 Y. Liu et al. https://doi.org/10.5067/HNAUGJQXSCVU

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

ERA5-Land hourly data from 1950 to present J. Muñoz Sabater https://doi.org/10.24381/cds.e2161bac

JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data Japan Meteorological Agency/Japan https://doi.org/10.5065/D6HH6H41

NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010 S. Saha et al. https://doi.org/10.5065/D69K487J

NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products S. Saha et al. https://doi.org/10.5065/D61C1TXF

MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4 Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/RKPHT8KC1Y1T

MERRA-2 statD_2d_slv_Nx: 2d,Daily,Aggregated Statistics,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4 Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/9SC1VNTWGWV3

NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.1 H. Beaudoing and M. Rodell https://doi.org/10.5067/E7TYRXPJKWOQ

NASA/GSFC/HSL GLDAS Noah Land Surface Model L4 3 hourly 1.0 x 1.0 degree V2.1 H. Beaudoing and M. Rodell https://doi.org/10.5067/IIG8FHR17DA9

NASA/GSFC/HSL GLDAS VIC Land Surface Model L4 3 hourly 1.0 x 1.0 degree V2.1 H. Beaudoing and M. Rodell https://doi.org/10.5067/ZOG6BCSE26HV

NASA/GSFC/HSL GLDAS Catchment Land Surface Model L4 3 hourly 1.0 x 1.0 degree V2.1 B. Li et al. https://doi.org/10.5067/VCO8OCV72XO0

Model code and software

edwardbair/SPIRES E. Bair https://github.com/edwardbair/SPIRES

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