Regional Climate Model Intercomparison Project for Asia

Abstract

Phase one of the Regional Climate Model Intercomparison Project for Asia reveals the capacities of regional climate models (RCMs) for simulating the Asian monsoon climate and extreme events as well. (Mearns et al. 2001;Giorgi et al. 2001). At present, analysis of the coupled atmosphere-ocean GCM (AOGCM) simulations indicates that average biases at regional scales, when simulating present-day climate, are highly variable from region to region and across models. For example, Giorgi and Francisco (2000) find that temperature biases are typically within the range of ±4°C, but exceed ±5°C in some regions, particularly in the winter. They also find that precipitation biases are mostly between -40% and +80%, but exceed 100% in some regions. Thus, a high priority for climate research is to improve the downscaling of GCM climate change to regional scales so that potential impacts can be adequately assessed (Houghton et al. 1995(Houghton et al. , 2001 Simulating regional climate poses difficulties, such as capturing effects of forcing and circulation at the planetary, regional, and local scales, along with teleconnection effects of regional anomalies. These processes are characterized by a range of temporal variability scales and can be highly nonlinear. The East Asian summer monsoon is characterized by marked variability at seasonal, interannual, and interdecadal time scales (Fu and Zheng 1998). The uncertain timing of monsoon onset and the irregular pace of its seasonal, northward progression strongly influence water availability for agriculture and urban consumption (Tao and Chen 1987;Wu and Zhang 1998). Interannual changes, such as those linked with the ENSO cycle, affect the frequency of droughts, floods, and other weather extremes that occur during the summer monsoon (Fu and Teng 1993;Ju and Slingo 1995;Fasullo and Webster 2002). Finally, on decadal-to-century time scales, the rapidly growing economy and population of East Asia presents anthropogenic influences that may also alter monsoon behavior (Fu and Zheng 1998;Quan et al. 2003). However, coarse-resolution climate models generally fail to give satisfactory simulations of the East Asian monsoon (Lau and Yang 1996; Yu et al. 2000).To date, most studies of regional climate change over East Asia have used output of GCMs without applying any downscaling techniques (Hulme et al. 1992;Zhao and Wang 1994). However, a relatively high degree of uncertainty exists in the regional climate change information of East Asia from GCMs, which results from the scenario's construction, such as future emission variations and the GCMs' modeling of the climate responses to a given scenario. Several researchers have used RCMs for simulating the regional climate of East Asia. Many of these studies have shown that RCMs can simulate the spatial detail of monsoon climate better than GCMs (Liu et al. 1994(Liu et al. ,1996Fu et al. 1998;Lee and Suh 2000). However, multiyear simulations must be used to provide meaningful climate statistics and to identify significant model errors. Therefo...

Reconsidering the Quality and Utility of Downscaling

“…In Asia, the Regional Climate Model Intercomparison Project (RMIP; Fu et al 2005) was established to build on the developments in Europe and North America with similar goals, but with a different geographical focus than PRUDENCE and NARCCAP and with quite different climatological drivers. These included, for example, the Asian monsoon and the subcontinent (i.e., the Tibetan Plateau) downwind of the large-scale flows crossing the Eurasian continent.…”

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

“…In order to quantify possible causes of deficiency in downscaling generated by RCM-derived errors, it is useful to adopt reanalysis data as boundary forcing, which is referred to as a perfect boundary experiment (or type 2 experiment in Castro et al 2005). There are many studies evaluating the value added by dynamical downscaling with the perfect boundary experiment (e.g., Castro et al 2005;Fu et al 2005;Xue et al 2007;Gao et al 2011). In addition to errors due to the quality of lateral boundary condition data (e.g., Gong and Wang 2000;Xue et al 2012), known sources of uncertainty generated through RCM experiments are horizontal resolution (e.g., Christensen et al 1998;Mo et al 2000;Wang et al 2003;Zhang et al 2003;Castro et al 2005;Xue et al 2007;Sato et al 2008), domain size and location (e.g., Treadon and Petersen 1993;Xue et al 2007;Gao et al 2011), lateral boundary settings (Denis et al 2003;Giorgi and Mearns 1999;Warner et al 1997), and physics schemes Liang et al 2004;Wang et al 2004).…”

“…Kang and Hong (2008) investigated the sensitivity of simulated climatology to convective parameterizations. Fu et al (2005) conducted the RCM inter-comparison project (RMIP), in which nine RCMs participated, and found that cumulus parameterization is the most important factor that causes diversity of simulated regional climate in East Asia. Ishizaki et al (2012) examined the RCM's ability to simulate the Japanese climate using five RCMs.…”

The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: A case study in 1998

“…The regional climate model RIEMS has been designed within the framework of general monsoon systems (Fu 1995) since 2000 (Fu et al 2000). RIEMS has shown good performance in simulating temperature, precipitation and atmospheric circulation in the Regional Climate Model Intercomparison Project for Asia (Fu et al 2005). The present version 2.0 of RIEMS developed since 2007 with non-hydrostatic dynamics Zhao 2013) is used here to couple the land surface model (LSM) of AVIM.…”