Abstract
To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and spectral nudging) are debated. Moreover, dynamical downscaling is now performed at the convection-permitting scale to reduce the parameterization uncertainty and obtain the finer resolution. To compare the performances of the two nudging techniques in this study, three sensitivity experiments (with no nudging, analysis nudging, and spectral nudging) covering a period of two months with a grid spacing of 6 km over continental China are conducted to downscale the 1-degree National Centers for Environmental Prediction (NCEP) dataset with the Weather Research and Forecasting (WRF) model. Compared with observations, the results show that both of the nudging experiments decrease the bias of conventional meteorological elements near the surface and at different heights during the process of dynamical downscaling. However, spectral nudging outperforms analysis nudging for predicting precipitation, and analysis nudging outperforms spectral nudging for the simulation of air humidity and wind speed.
Highlights
General circulation models (GCMs) are primary tools for studying the Earth's climate system and understanding climate changes in the future and responses to climate changes with emissions in terms of the simulation of different climate system components, averages, variability, and extremes
Dynamical downscaling ability may be affected by many factors, which can be classified into two types
Among the factors which cause errors and uncertainties in the dynamical downscaling process, we focus on a strategy that balances the performance of regional climate models (RCMs) in adding small-scale features while retaining large-scale features
Summary
General circulation models (GCMs) are primary tools for studying the Earth's climate system and understanding climate changes in the future and responses to climate changes with emissions in terms of the simulation of different climate system components, averages, variability, and extremes. Dynamical downscaling, which is an extremely important and broadly used method, is based on the physical and dynamical framework of regional climate models (RCMs) and became a common approach for obtaining highresolution regional climate information It is forced by largescale circulation of the GCM results or global reanalysis and adds regional detailed representation of local processes, topography, land cover, and other features that shape the regional climate [5,6,7]. Among the factors which cause errors and uncertainties in the dynamical downscaling process, we focus on a strategy that balances the performance of RCMs in adding small-scale features while retaining large-scale features This strategy is called nudging, which provides a method for constraining the RCMs and keeps them from diverging too far from the coarse-scale fields.
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