Santander Meteorology Group

A multidisciplinary approach for weather & climate

Atmospheric circulation models are the main tool for weather simulation and forecast. These models consist of the physical equations governing the atmosphere dynamics and are numerically solved on appropriate grids covering the globe using high-performance computers. Although weather forecast was originally concerned with short-range prediction (1-3 days), this discipline has greatly evolved and nowadays the forecast horizons expand to seasonal time scales (predicting climate anomalies months in advance) and climate change projections (decades); in this case, the atmospheric models are coupled with oceanic ones, with slower dynamics, including also the effects of the cryosphere, biosphere (mainly land use), etc.

When solving the quations at a global scale the boundaries are periodic and the problem is an initial value problem. The initial conditions are integrated forward in time to obtain future states of the system. Due to the nonlinear nature of the climate system, the information of the initial condition is lost within a few days and the exact state of the system (weather) becomes unpredictable. However, statistics of the state of the system (climate) can still be obtained. When interested in climate simulations, the initial conditions become unimportant and the problem becomes a boundary value problem. Only the response of the climate to external forcings such as changes in solar radiation, GHG concentrations, etc., is of interest.

Due to computational restrictions the distance between grid points in a Global Climate Model (GCM) is of the order of hundreds of kilometers. Using this coarse resolution, many regional scale features (orography, lakes, coastlines, vegetation distribution, etc.) become too crudely represented to be useful in the assessment of regional climates. Sub-grid scale processes are parameterized to account for their impact at grid point scale. Those processes include radiation, cloud formation, boundary layer processes, etc.

In order to improve the resolution, Regional Climate Models (RCMs) are nested into GCMs to act as a magnifying glass over an area of interest. The governing equations are solved over a limited domain and, in addition to the initial conditions, boundary conditions from a global simulation are required. RCM resolutions are of the order of tens of kilometers.

Some basics of regional climate modeling are introduced in these slides presented in the Summer Course on Regional projection of climate change scenarios held in Suances on August 2007

Basic reading:

A brief overview of both global and regional numerical modelling is given by H. von Storch (2004) "Models of Global and Regional Climate" in the Earth and Hidrological Sciences Encyclopedia. pdf

Review articles on regional climate modelling:

• Laprise (2008) Regional Climate Modelling. J Comp Phys 227(7):3641-3666. link
• Wang et al. (2004) Regional Climate modelling: Progress, Challenges and Prospects. J Met Soc Jap 82(6):1599-1628. pdf
• Leung et al. (2003) Regional climate research: Needs and opportunities. Bull Am Met Soc 84(1):89-95. pdf
  The presentations of the workshop Regional climate research: Needs and opportunities are also of interest.
• Giorgi and Mearns (1999) Introduction to special section: Regional Climate Modelling revisited. J Geophys Res 104:6335. pdf

Related to climate change and including more up-to-date references is the chapter on Regional Climate Projections of the IPCC 4th Assessment Report (AR4).

State of the Art: Workshop Report World Modelling Summit for Climate Prediction Reading, UK, 6-9 May 2008 pdf [Presentations]

Activities of the Santander Meteorology Group:

• Regional weather simulation with MM5 and WRF models.
• Muti-physic experiments: Sensitivity to parameterizations.
• Case studies in the Cantabric coast (precipitation, wind, etc.).
• Adaptation of the WRF model for GRID computing: WRF4G.
• Adaptation of the WRF model for climate simulations.
• Regional climate change scenarios over Iberia.
• CORDEX: Coordinated regional climate downscaling experiment.
• Error growth in simplified and global models.