Climate and climate change in western canada as simulated by the Canadian regional climate model

Abstract

nouveau Mod ele Canadien de Climat Rà egional (MCCR). La formulation du MCCR re- IntroductionThe last quarter of the century has witnessed important progress in the performance and achievements of global forecast and climate models. This progress is based on more accurate and efþcient numerical methods, more comprehensive parametrization of the ensemble effects of unresolved physical processes upon the resolved part of the ow, more sophisticated programming techniques to speed-up execution of computer programs and þnally { but not least { increased resolution afforded by faster computers. Together with improved model initialization and data assimilation methods, this evolution has led to better weather analyses and short-term forecasts. The increasing ability of global models to reproduce complex phenomena that operate on longer timescales, such as the ENSO (El Niño { Southern Oscillation), lends conþdence to the possibility of achieving seasonal dynamical extended-range forecasts. General Circulation Models (GCMs) have evolved from the initial purely atmospheric models into Global Climate Models which include oceans, sea-ice and land-surface processes as well as detailed physical parametrizations of subgrid-scale phenomena. Serving as computerized experimentation laboratories, such models constitute useful tools to further our understanding of climate as a physical system with multiple interacting processes.Numerical Weather Prediction (NWP) and climate models differ in a number of ways, resolution being one of the most obvious. Nowadays NWP centres operate global models with computational meshes of the order of one hundred kilometres, whereas the resolution of GCMs typically used for climate simulations is coarser by about a factor of three or þve or even more. This coarser resolution is imposed by computational constraints associated with the very long simulations (on timescales of decades to centuries) required for climate studies, as opposed to days for forecasts. The computing cost of a global model increases by more than two orders of magnitude for a factor of 5 increase in resolution. Lesser resolution is hence a

Seasonal/Regional Variation of Variability Characteristic of Daily Maximum/Minimum Temperatures in Japan Observed and Reproduced by RegCM Nested in NCAR-CSM

“…As mentioned in their paper, the two 10-year simulations are not intended to provide actual predictions of climate change, but to clarify the problems of this nesting method in the prediction of regional climate change in East Asia. The purpose of the series of studies using RegCM, is almost the same as that of the regional climate change simulation studies recently conducted (e.g., Jones et al 1997;Giorgi et al 1998;Renwick et al 1998;Deque et al 1998;Laprise et al 1998;Leung and Ghan 1999).…”

Assessment of climate change on the Canadian prairies from downscaled GCM data

“…The CRCM of Laprise et al (1998) uses GCMII for the initial input values and then has a separate atmospheric model to downscale to a fine grid. However, our method of downscaling does not change the GCM atmospheric processes, but downscales statistics describing the change of GCM from a 1x CO 2 regime to a 2x CO 2 regime.…”

“…Laprise et al (1998) attributed differences in results from GCMII and the regional climate model (45-km resolution) to a number of factors including a simplified land surface in the GCM, excessive ground water and little seasonal change in groundwater, plus the radiative balance in the models.…”

“…Downscaling GCM data to a regional scale was reported by Laprise et al (1998) who worked with the Canadian Regional Climate Model (CRCM) data. Laprise et al (1998) attributed differences in results from GCMII and the regional climate model (45-km resolution) to a number of factors including a simplified land surface in the GCM, excessive ground water and little seasonal change in groundwater, plus the radiative balance in the models.…”

The Interannual Variability as a Test Ground for Regional Climate Simulations over Japan

“…The RCM methodology was pioneered by Dickinson et al (1989), and can be employed for several purposes. First, RCMs have been utilized for the generation of climate change scenarios (e.g., Giorgi et al, 1992Giorgi et al, , 1994Houghton et al, 1996;Jones et al, 1997, Laprise et al, 1998Machenhauer et al, 1998). For this application, the RCM is driven at its lateral boundaries by a low-resolution global climate model (GCM), which in turn is forced by a prescribed anthropogenic increase of greenhouse gases and/or aerosol concentrations.…”