Development and application of high-resolution regional climate models

点击翻译按钮获取中文摘要

Regional Climate Models (RCMs) are presently routinely run at resolutions of around 50km per grid box.  The major thrust of this application is to initiate work in the area of RCM physical parameterisations to develop the next generation of RCMs that will perform optimally at significantly higher resolution (~10km). At this scale, RCMs should begin to provide the detailed information required by planners and impact assessors in the areas of climate change and longer-range (seasonal) forecasting. Much of the work will be devoted to developing a better representation of the atmospheric water cycle at high model resolutions. In particular, a unified and physically based approach to the parameterisation of turbulence, convection, cloud formation and precipitation will be pursued. Most of this work will use the GEM (Global Environmental Multiscale) model as the basic modelling tool and apply this in both Regional Climate mode and at Cloud Resolving resolutions. The latter resolutions (~1-2km per grid box) allow many of the key convective and turbulent circulations to be explicitly resolved. Along with a very detailed cloud scheme, this allows the Cloud Resolving Model to provide very accurate simulations of key cloud scale processes, albeit at a very high computational cost. This model version will act as a reference against which parameterisation approaches will be developed and evaluated. In particular, many terms in moist process parameterisation schemes cannot be observed and therefore must be derived from these explicit model simulations. Parallel to this effort, the high-resolution RCM will be applied over sub-Saharan Africa to simulate key regional climate processes controlling the timing, distribution and amount of rainfall over this climate sensitive region. The aim will be to investigate the potential for high-resolution regional downscaling of Global seasonal predictions and climate change scenarios for sub-Saharan Africa. We further aim to link these regional predictions to impact assessment and planning bodies in the region.