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Regional climate model based dynamical downscaling method is one of the important approaches to obtain fine-scale climate information. Many dynamical downscaling methods were developed over past years. A recent paper reviewed these methods with specific focus on their merits and limitations.
The paper entitled “Dynamical downscaling of regional climate: A review of methods and limitations” was published in Science China Earth Sciences, 2018.
The traditional dynamical downscaling (TDD) of future climate employs a continuous integration of regional climate model (RCM) where GCM data are used to provide initial and lateral boundary conditions. It is known that GCMs are not perfect and all simulations suffer from systematic biases to a certain extent. The TDD approach certainly brings GCM biases into RCMs through the lateral boundary of the RCMs and degrades the downscaled simulation.
To constrain model biases, various bias correction methods were employed in dynamical downscaling simulations in recent years. All studies suggested that GCM bias correction can help to improve dynamical downscaling simulations. In the article coauthored by Zhongfeng Xu, Ying Han, and Zong-Liang Yang at the Institute of Atmospheric Physics, Chinese Academy of Sciences reviewed the available dynamical downscaling methods. They grouped these dynamical downscaling methods into four categories: (1) the TDD method, (2) the pseudo global warming method, (3) dynamical downscaling with GCM bias corrections, and (4) dynamical downscaling with both GCM and RCM bias corrections. The merits and limitation of each dynamical downscaling method are discussed in the paper.
Dynamical downscaling has a broad application prospect and needs in meteorology, hydrology, agriculture, ecosystem, wind power, and atmospheric environment studies and projections. The review article summarized the advance of dynamical downscaling methods and discussed the challenges and potential directions of future studies. “We expect the review article can help readers to better understand the advance of dynamical downscaling methods and stimulated dynamical downscaling studies” the researchers said.