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Regression model for generating time series of daily precipitation amounts for climate change impact studies

TA Buishand, AMG Klein Tank

The precipitation amounts on wet days at De Bilt ( the Netherlands) are linked to temperature and surface air pressure through advanced regression techniques.
Temperature is chosen as a covariate to use the model for generating synthetic time series of daily precipitation in a CO2 induced warmer climate. The precipitation-temperature dependence can partly be ascirbed to the phenomenon that warmer air can contain more moisture. Spline functions are introduced to reproduce the non-monotonous change of the mean daily precipitation amount with temperature.
Because the model is non-linear and the variance of the errors depends on the expected response, an iteratively reweighted least-squares technique is needed to estimate the regression coefficients. A representative rainfall sequence for the situation of a systematic temperature rise is obtained by multiplying the precipitation amounts in the observed record with a temperatue dependent factor based on a fitted regression model. For a temperature change of 3ºC (reasonable quess for a doubled CO2 climate according to the present-day general circulation models) this results in an increase in the annual average amount of 9% (20% in winter and 4% in summer). An extended model with both temperature and surface air pressure is presented which makes it possible to study the additional effects of a potential
systematic change in surface air pressure on precipitation.

Key words: Climate change, daily precipitation modelling, generalized linear models, iteratively reweighted least squares, spline functions.

Bibliografische gegevens

TA Buishand, AMG Klein Tank. Regression model for generating time series of daily precipitation amounts for climate change impact studies
published, Stochastic Hydrology and Hydraulics, 1996

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