Downscaling means takinginformation known at large scales to make predictions at local scales. Downscaling climate models is an attempt to bridge the gap between global and local effects by layering local-level data over larger-scale climate models. Generally, climate information comes from global climate models.
At this resolution, a whole region may be represented by only a few grid cells, every cell representing a single value. Moreover, GCMs do not adequately account for vegetation variations, complex topography and coastlines, which are important aspects of the physical response governing the regional/local climate change signal.
Downscaled modeling examines relatively small areas in detail—in some cases down to 25 square kilometers, a far higher resolution than that offered by global climate model simulations. information on much smaller scales supporting more detailed impact and adaptation assessment and planning, which is vital in many vulnerable regions of the world.
There are two general strategies for downscaling:
- dynamical downscaling and statistical downscaling.
Dynamical downscaling makes use of a regional climate model (RCM) having higher spatial resolution (typically 10–50 km) over a limited area and ‘fed with large-scale weather’ from the GCM at the boundaries of the domain.
Statistical downscaling first derives statistical relationships between observed small-scale (often station level) variables and larger (GCM) scale variables. Future values of the largescale variables obtained from GCM projections of future climate are then used to drive the statistical relationships and so estimate the smaller-scale details of future climate.