Abstract
Modelling the response of agricultural and natural ecosystems to climate forecasts requires daily data at local and regional scales. General circulation models (GCMs) provide reasonable simulations of atmospheric fields at the synoptic scale. However, they tend to over-estimate the frequency and under-estimate the intensity of daily precipitation. Stochastic downscaling techniques provide a means of linking the synoptic scale with local scales. They can be used to quantify the relation of climate variables at small space scales to the larger scale atmospheric patterns produced by GCMs. This paper reviews downscaling techniques from an applications perspective. It then presents a case study involving the use of a downscaling technique known as the nonhomogeneous hidden Markov model (NHMM). A NHMM fit to a 15-year record of daily atmospheric-precipitation data is used to downscale GCM atmospheric fields for South-West Western Australia. We compare the downscaled and observed ‘winter’ precipitation statistics at six stations near Perth, Western Australia. The results show that a downscaled GCM simulation provides credible reproductions of observed precipitation probabilities and the frequencies of wet and dry spells at each station.
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Bates, B., Charles, S., Hughes, J. (2000). Stochastic Down-Scaling of General Circulation Model Simulations. In: Hammer, G.L., Nicholls, N., Mitchell, C. (eds) Applications of Seasonal Climate Forecasting in Agricultural and Natural Ecosystems. Atmospheric and Oceanographic Sciences Library, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9351-9_9
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DOI: https://doi.org/10.1007/978-94-015-9351-9_9
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