Why global climate models do not give a realistic description of the local climate

Global climate

glasses Global climate statistics, such as the global mean temperature, provide good indicators as to how our global climate varies (e.g. see here). However, most people are not directly affected by global climate statistics. They care about the local climate; the temperature, rainfall and wind where they are. When you look at the impacts of a climate change or specific adaptations to a climate change, you often need to know how a global warming will affect the local climate.

Yet, whereas the global climate models (GCMs) tend to describe the global climate statistics reasonably well, they do not provide a representative description of the local climate. Regional climate models (RCMs) do a better job at representing climate on a smaller scale, but their spatial resolution is still fairly coarse compared to how the local climate may vary spatially in regions with complex terrain. This fact is not a general flaw of climate models, but just the climate models’ limitation. I will try to explain why this is below.

Regional climate characteristics

Most GCMs are able to provide a reasonable representation of regional climatic features such as ENSO, the NAO, the Hadley cell, the Trade winds and jets in the atmosphere. They also provide a realistic description of so-called teleconnection patterns, such as wave propagation in the atmosphere and the ocean. These phenomena, however, tend to have fairly large spatial scales, but when you get down to the very local scale, the GCMs are no longer appropriate.

Minimum scale

Land-sea mask for ECHAM4 There are several reasons why GCMs do not provide a representative description of the local climate (i.e. exactly where I live). For one, the grid mesh, on which they compute the physical quantities relevant for the climate, is too coarse (typically 200km) to capture the local aspects. The figure on the left shows a typical land-sea mask for a GCM.

The distance between two grid points in a GCM (or an RCM) is the minimum scale (~200km). The coarse resolution typically used in the GCMs till now has implied that the topography has been smooth compared to the real landscape and that some countries (e.g. Denmark and Italy) are not represented in the models (one exception is one Japanese GCM with an extremely high spatial resolution).

Sub-grid processes are represented by parameterisation schemes describing their aggregated effect over a larger scale. These schemes are often referred to as ‘model physics’ but are really based on physics-inspired statistical models describing the mean quantity in the grid box, given relevant input parameters. The parameterisation schemes are usually based on empirical data (e.g. field measurements making in-situ observations), and a typical example of a parameterisation scheme is the representation of clouds.

Surface processes

Fjords Climate models need boundary conditions describing the surface conditions (e.g. energy and moisture fluxes) in order to yield a realistic representation of the climate system. Often simple parameterisation schemes are employed to provide a reasonable description, but these do not capture the detailed variations associated with small spatial scales.

Skillful scale

Shortcomings associated with parameterisation schemes and coarse resolution explain why one gridpoint value provided by the GCMs may not be representative for the local climate. A concept called skillful scale has sometimes been employed in the literature, most of which have been linked to a study by Grotch and MacCracken (1991) who found model results to diverge as the spatial scale was reduced. Specifically, they observed that:

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