Dynamical Downscaling for Climate Projection with High-Resolution MRI AGCM-RCM

Abstract

High-resolution downscaling is vital to project climate extremes and their future changes by resolving fine topography reasonably well, which is a key to represent local climatology and impacts of weather extremes. A direct dynamical downscaling with a regional climate model (RCM) embedded within an atmosphere-ocean coupled general circulation model (AOGCM) is commonly used but is subject to systematic biases in their present-day simulations of AOGCM, which may cause unexpected effects on future projections and lead to difficult interpretation of climate change. In a high-resolution atmospheric general circulation model (AGCM)-RCM system, the present-day climate in AGCM is forced by observed sea surface temperature (SST) and sea-ice distribution. Then, the future climate is calculated with the "future" boundary conditions (SST and sea-ice), which are created by adding their future changes projected by AOGCM to the observed present-day values, besides the future radiative forcing. This system is one of methods to minimize the effects of such biases. A Meteorological Research Institute AGCM with 20-km grids is successfully applied to project future changes in weather extremes such as tropical cyclones and rain systems that cause heavy rainfall and strong winds. Regional downscaling with 5-km mesh RCM is then performed over certain area to investigate local extreme rainfall events and their future changes. In this paper, we review various downscaling methods and try to rationalize a use of high-resolution AGCM-RCM system. Keywords regional climate information; dynamical downscaling; GCM; RCM Corresponding author: Akio Kitoh, Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan E-mail: kito.akio.ff@u.tsukuba.ac.jp ©2016, Meteorological Society of Japan Journal of the Meteorological Society of Japan Vol. 94A 2 tical downscaling can provide climate information at the smaller scales. Use of high-resolution dynamical models can add values through better representation of topography and coastlines, and also from improved representation of small-scale processes like con vective precipitation.Dynamical downscaling methods include nested regional climate models (RCMs) and high-resolution atmosphere-only general circulation models (AGCMs). Here, we discuss both methods focusing on the latter approach. For the short-and medium-range (up to 2 weeks) numerical weather predictions (NWPs), world centers are now using AGCMs with grid size around 15-30 km. Some NWP centers use a regional model with a few km grids for veryshort-range forecasts. Due to increasing availability of high-performance computers, we are now capable to modify this NWP-type downscaling system for use in climate change projections. In Section 2, various dynamical downscaling methods are discussed. Section 3 shows the global high-resolution Meteorological research Institute (MRI)-AGCM, and Section 4 shows current projects for further dynamical downscaling by the non-hydrostatic regional clim...

Assessing the impacts of global warming on meteorological hazards and risks in Japan: Philosophy and achievements of the SOUSEI program

“…For the probabilistic analyses, the use of the data from the high-performance MRI-AGCM (Kitoh et al, 2016;Kusunoki, 2016) and the downscaled RCM (Murata et al, 2015) is the advantage of this research project, although the ensemble number is limited and the future scenario is only RCP8.5. With the use of the high-performance climate data, we were able to provide the changes in the atmospheric circulation during the Baiu period and the warm-season rainfall.…”

“…Conceptual diagram of the research in the assessment of meteorological hazards and risks under climate change in the SOUSEI program owing to the limitation of computational resources, it is still not possible to meet the needs on both sample size and spatial resolution. Therefore, we currently take priority in high-resolution over sample size by primarily using the 20-km-mesh AGCM simulations (MRI-AGCM version 3.2, Mizuta et al, 2012Mizuta et al, , 2014Kitoh et al, 2016) and the downscaled 5-km-mesh simulations with the Non-Hydrostatic RCM (NHRCM) (Nakano et al, 2012;Murata et al, 2015). High-resolution is important since the representation of topography and rainfall amount/wind speed critically depends on how topography is reproduced at the model resolutions (Takemi, 2009;Oku et al, 2010).…”

Future precipitation changes during summer in East Asia and model dependence in high-resolution MRI-AGCM experiments

“…The global warming experiments analyzed in this study are the same as in the 60km resolution MRI-AGCM (MRI-AGCM3.2H) experiments described as MRI-AGCM3.2H (YS), MRI-AGCM3.2H (KF), and MRI-AGCM3.2H (AS) under the RCP8.5 scenario in Table 1d of Kitoh et al (2016), except for prescribed future SSTs and experiment periods. The future SST increase relied on the CMIP5 multi-model ensemble (MME) for the RCP8.5 scenario in Table 1d of Kitoh et al (2016).…”

“…To consider regional climate change, reliable high-resolution projection using a high-resolution climate model is necessary. Global warming experiments using the high-resolution Meteorological Research Institute atmospheric general circulation model (MRI-AGCM) combined with regional downscaling have contributed to the study of regional climate change (Kusunoki et al, 2006;Kitoh et al, 2016). However, the influence of global warming on precipitation is uncertain Correspondence to: Tomoaki Ose, Climate Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan.…”

A Trade-Off Relation between Temporal and Spatial Averaging Scales on Future Precipitation Assessment

“…On the other hand, the SST for the future experiment, which is shown as the mean change from the present experiment in Fig. 1b, is obtained by superimposing the trend and future changes in the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble mean (Mizuta et al 2014) on the de-trended observational SST explained above (Kitoh et al 2015). The future SST distribution obtained by this method retains the observed inter-annual variability and the spatial patterns that are more likely to be included in observational data than in model data.…”