Climate Dynamics

, Volume 48, Issue 5–6, pp 1705–1721 | Cite as

Quantification of the relative role of land-surface processes and large-scale forcing in dynamic downscaling over the Tibetan Plateau

  • Yanhong Gao
  • Linhong Xiao
  • Deliang Chen
  • Fei Chen
  • Jianwei Xu
  • Yu Xu


Dynamical downscaling modeling (DDM) is important to understand regional climate change and develop local mitigation strategies, and the accuracy of DDM depends on the physical processes involved in the regional climate model as well as the forcing datasets derived from global models. This study investigates the relative role of the land surface schemes and forcing datasets in the DDM over the Tibet Plateau (TP), a region complex in topography and vulnerable to climate change. Three Weather Research and Forecasting model dynamical downscaling simulations configured with two land surface schemes [Noah versus Noah with multiparameterization (Noah-MP)] and two forcing datasets are performed over the period of 1980–2005. The downscaled temperature and precipitation are evaluated with observations and inter-compared regarding temporal trends, spatial distributions, and climatology. Results show that the temporal trends of the temperature and precipitation are determined by the forcing datasets, and the forcing dataset with the smallest trend bias performs the best. Relative to the forcing datasets, land surface processes play a more critical role in the DDM over the TP due to the strong heating effects on the atmospheric circulation from a vast area at exceptionally high elevations. By changing the vertical profiles of temperature in the atmosphere and the horizontal patterns of moisture advection during the monsoon seasons, the land surface schemes significantly regulate the downscaled temperature and precipitation in terms of climatology and spatial patterns. This study emphasizes the selection of land surface schemes is of crucial importance in the successful DDM over the TP.


DDM Tibet Plateau Temperature Precipitation 



We thank Ruby L. Leung for help with DDM. We appreciate the free access of the CMIP5 datasets, which are provided by the ESGF web portals ( and the observation data provided by the National Climate Center, China Meteorological Administration (CMA). This work is jointly supported by the Ministry of Science and Technology of China (2013CB956004), and National Natural Science Foundation of China (91537211, 91537105, and 41322033). We thank the Supper-Computing Center of Chinese Academy of Science for computing the simulations. Deliang Chen was supported by the Swedish strategic research areas MERGE and BECC, and Swedish Research Council. Fei Chen would also like to acknowledge the support from the NCAR Water System program.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yanhong Gao
    • 1
  • Linhong Xiao
    • 1
  • Deliang Chen
    • 2
  • Fei Chen
    • 3
    • 4
  • Jianwei Xu
    • 1
  • Yu Xu
    • 1
  1. 1.Key Laboratory of Land-surface Process and Climate Change in Cold and Arid Regions, Cold and Arid Regions Environmental and Engineering Research InstituteChinese Academy of SciencesLanzhouPeople’s Republic of China
  2. 2.Department of Earth SciencesUniversity of GothenburgGothenburgSweden
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijingPeople’s Republic of China

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