4.7 Article

Evaluation of Downscaled CMIP5 Coupled with VIC Model for Flash Drought Simulation in a Humid Subtropical Basin, China

Journal

JOURNAL OF CLIMATE
Volume 31, Issue 3, Pages 1075-1090

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-17-0378.1

Keywords

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Funding

  1. National Key R&D Program of China [2017YFA0603804]
  2. National Natural Science Foundation [41771069]
  3. Jiangsu Natural Science Funds for Distinguished Young Scholar [BK20140047]
  4. Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
  5. Research and Innovation Project for College Graduates of Jiangsu Province [1344051501007]

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Compared to traditional drought events, flash droughts evolve rapidly during short-term extreme atmospheric conditions, with a lasting period of one pentad to several weeks. There are two main categories of flash droughts: the heat wave flash drought (HWFD), which is mainly caused by persistent high temperatures (heat waves), and the precipitation deficit flash drought (PDFD), which is mainly triggered by precipitation deficits. The authors' previous research focused on the characteristics and causes of flash drought based on meteorological observations and Variable Infiltration Capacity (VIC) model simulations in a humid subtropical basin (Gan River basin, China). In this study, the authors evaluated the downscaled phase 5 of the Coupled Model Intercomparison Project (CMIP5) models' simulations, coupled with the VIC model (CMIP5-VIC) in reproducing flash droughts in a humid subtropical basin in China. Most downscaled CMIP5-VIC simulations can reproduce the spatial patterns of flash droughts with respect to the benchmarks. The coupled models fail to readily replicate interannual variation (interannual pentad change), but most models can reflect the interannual variability (temporal standard deviation) and long-term average pentads of flash droughts. It is difficult to simultaneously depict both the spatial and temporal features of flash droughts within only one coupled model. The climato-logical patterns of the best multimodel ensemble mean are close to those of the all-model ensemble mean, but the best multimodel ensemble mean has a minimal bias range and relatively low computational burden.

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