Using Downscaling Techniques on GCMs Data for Long Term Forec5asts

GCMs Models are benefit for using in long tenns forecasts. But for using them, we should use some methods for changing their resolution from Global to Regional. Even if global climate models in the future are run at high resolution there will remain .the need to 'downscale' the results from such niodels to individual sites or localities for impact studies. General Circulation Models (GCMs) indicate that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called "downscaling" techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what impact assessors require. Unfortunately, GCMs are restricted in their usefulness for local impact studies by their coarse spatial resolution (typically of the order 50,000 km2) and inability to resolve important sub-grid scale features such as clouds and topography. As a consequence, two sets of techniques have emerged as a means of deriving local-scale surface weather from regional-scale atmospheric predictor variables. Firstly, statistical downscaling is analogous to the "model output statistics" (MOS) and "perfect prog" approaches used for short-range numerical weather prediction. Secondly, Regional Climate Models (RCMs) simulate sub-GCM grid scale climate features dynamically using time-varying atmospheric conditions supplied by a GCM bounding a specified domain. Both approaches will continue to play a significant role in the assessment of potential climate change impacts arising from future increases in greenhousegas concentrations and also for predict weather and climate factors like temperature and precipitation and pressure and etc. in this paper, we discuss about downscaling methods and using them for GCMs data and explain a case study of these that used in part of I.R.of Iran.

Keywords:

Downscaling , Models , GCM , RCM , MOS , Management , Forecast