Differences between downscaling with spectral and grid nudging using WRF

P Liu,A G Russell,A P Tsimpidi,B Stone,A Nenes,Y Hu

doi:10.5194/acp-12-3601-2012

P Liu, A G Russell + Show 4 more

Open Access

https://doi.org/10.5194/acp-12-3601-2012

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Abstract

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

Highlights

Global climate models (GCMs) serve as the primary tool to understand how climate will respond to emission changes (IPCC, 2007)
As to the “small scale”, instead of the Weather Research and Forecasting (WRF) resolution of 36 km, 300 km is chosen in order to capture features that occur at multiple grids, which are more reliably captured by regional climate models (RCMs) than individual grid cells
The performance of two nudging techniques, grid and spectral nudging are examined in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model

Summary

Introduction

Global climate models (GCMs) serve as the primary tool to understand how climate will respond to emission changes (IPCC, 2007). Downscaling of global model results has been used to address this issue by bridging the gap of scales between the global and regional climate information. Dynamical downscaling has been at the forefront of model development of regional climate models (e.g., Dickinson et al, 1989), and now is being used to address how regional air quality would change in future climate. Dynamical downscaling typically starts with a set of coarse-resolution large-scale fields, which are used as the initial conditions (ICs) and lateral and surface boundary conditions (LBCs) for the RCMs. As the simulation evolves, the internal solution developed by RCMs may be affected by the size of domain, the spin-up period and update frequency of LBCs. A good summary of such issues are provided by Warner et al (1997), Giorgi and Mearns (1999) and Denis et al (2002)

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