About the MarkSim™ DSSAT weather file generator

General Circulation Models (GCMs) use columns of atmosphere covering about 200 by 300 km at ground level. So they do not simulate the weather on the ground at a particular place very accurately. To get back to what is expected in detail, we have to use downscaling. The mean deviation from the baseline for each atmospheric column (pixel) is re-evaluated to take into account the ground terrain and the characteristics of the expected weather.

This is often done with statistical downscaling, which uses the output of the GCM to compute a statistical relationship with existing meteorological data from a met station. That is then used to scale the results of the GCM to that of the station. This won’t work on the global scale that we’ve used for MarkSimGCM simply because there are not enough met stations in the world.

There are two aspects to downscaling: one is to interpolate the results of the GCM spatially; and the other is to ensure that the results are relevant to the local climate. The spatial downscaling is the easiest part. This is usually done with a convolution algorithm—in our case it is the simple inverse squared distance measure.

Wilby et al. (2009) called this ‘unintelligent downscaling’ and we would agree, if this were all that we were doing. Our algorithm is based on the well-tried weather simulator, MarkSim.

MarkSim is a weather generator that uses 720 classes of weather, worldwide, to calculate the coefficients of a third order Markov rainfall generator. This constitutes ‘stochastic downscaling’ as it fits a Markov model to the GCM output and uses it to generate weather data for the site indicated.

The third weapon in MarkSimGCM is built into the weather typing in the clustering process. The 720 classes of world weather define each set of regression equations that MarkSim uses to determine the coefficients for the modelling process. When a climate changes, such that it no longer applies to the original class, then the whole regression structure changes.

This means that a changing climate will be modelled by the one most like it in the real world. The only drawback is that the system cannot model completely new climates except by extrapolation of the regression models from the nearest cluster. But then we are still to see a new climate in sufficient detail to fit the model to it and GCM results are not precise enough to do this for the future. In the meantime, we will have to wait.

To allow you to choose any year of the prediction period, the GCM differential results are fitted to a fourth order polynomial regression by time slices. The regressions are fitted to every pixel of an interpolated grid at ½ degree intervals.

If you want to read about the application in more detail, see the temporary documentation at Jones, Thornton & Heinke 2011.

Jones P.G., Thornton P.K. and Heinke J., 2011. Generating characteristic daily weather data using downscaled climate model data from the IPCC Fourth Assessment. https://hc.box.net/shared/f2gk053td8

Wilby R.L., Troni J., Biot Y., Tedd L., Hewitson B.C., Smith D.M. and Sutton R.T., 2009. A review of climate risk information for adaptation and development planning. Int. J. Climatol. 29, 1193-1215.

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