American Geophysical Union Become an AGU Member
Subscribe to AGU Journals
AGU Home AGU Publications

Subscriber Access to Full Article (Nonsubscribers may purchase for $9.00, Includes print PDF, file size: 2383044 bytes)

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D09112, doi:10.1029/2007JD009216, 2008

Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model

Jeff Chun-Fung Lo

Department of Geological Sciences, The Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, USA


Zong-Liang Yang

Department of Geological Sciences, The Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, USA


Roger A. Pielke Sr.

Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA


Abstract

The common methodology in dynamical regional climate downscaling employs a continuous integration of a limited-area model with a single initialization of the atmospheric fields and frequent updates of lateral boundary conditions based on general circulation model outputs or reanalysis data sets. This study suggests alternative methods that can be more skillful than the traditional one in obtaining high-resolution climate information. We use the Weather Research and Forecasting (WRF) model with a grid spacing at 36 km over the conterminous U.S. to dynamically downscale the 1-degree NCEP Global Final Analysis (FNL). We perform three types of experiments for the entire year of 2000: (1) continuous integrations with a single initialization as usually done, (2) consecutive integrations with frequent re-initializations, and (3) as (1) but with a 3-D nudging being applied. The simulations are evaluated in a high temporal scale (6-hourly) by comparison with the 32-km NCEP North American Regional Reanalysis (NARR). Compared to NARR, the downscaling simulation using the 3-D nudging shows the highest skill, and the continuous run produces the lowest skill. While the re-initialization runs give an intermediate skill, a run with a more frequent (e.g., weekly) re-initialization outperforms that with the less frequent re-initialization (e.g., monthly). Dynamical downscaling outperforms bi-linear interpolation, especially for meteorological fields near the surface over the mountainous regions. The 3-D nudging generates realistic regional-scale patterns that are not resolved by simply updating the lateral boundary conditions as done traditionally, therefore significantly improving the accuracy of generating regional climate information.

Received 24 July 2007; accepted 7 January 2008; published 10 May 2008.

Keywords: Weather research and forecasting model; regional climate downscaling.

Index Terms: 3355 Atmospheric Processes: Regional modeling; 3305 Atmospheric Processes: Climate change and variability (1616, 1635, 3309, 4215, 4513); 3309 Atmospheric Processes: Climatology (1616, 1620, 3305, 4215, 8408).


Subscriber Access to Full Article (Nonsubscribers may purchase for $9.00, Includes print PDF, file size: 2383044 bytes)

Citation: Lo, J. C.-F., Z.-L. Yang, and R. A. Pielke Sr. (2008), Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model, J. Geophys. Res., 113, D09112, doi:10.1029/2007JD009216.