Statistical downscaling relationships for precipitation in the netherlands and north germany

摘要

The statistical linkage of daily precipitation to the National Centers for Environment Prediction (NCEP) reanalysis data is described for De Bilt and Maastricht (Netherlands), and for Hamburg, Hanover and Berlin (Germany), using daily data for the period 1968-97. Two separate models were used to describe the daily precipitation at a particular site: an additive logistic model for rainfall occurrence and a generalized additive model for wet-day rainfall. Several dynamical variables and atmospheric moisture were included as predictor variables. The relative humidity at 700 hPa was considered as the moisture variable for rainfall occurrence modelling. For rainfall amount modelling, two options were compared: (i) the use of the specific humidity at 700 hPa, and (ii) the use of both the relative humidity at 700 hPa and precipitable water. An application is given with data from a time-dependent greenhouse gas forcing experiment using the coupled ECHAM4-OPYC3 atmosphere-ocean general circulation model for the periods 1968-97 and 2070-99. The fitted statistical relationships were used to estimate the changes in the mean number of wet days and the mean rainfall amounts for the winter and summer halves of the year at De Bilt, Hanover and Berlin. A decrease in the mean number of wet days was found. Despite this decrease, an increase in the mean seasonal rainfall amounts is predicted if specific humidity is used in the model for wet-day rainfall. This is caused by the larger atmospheric water content in the future climate. The effect of the increased atmospheric moisture is smaller if the alternative wet-day rainfall amount model with precipitable water and relative humidity is applied. Except for an anomalous change in mean winter rainfall at Hanover, the estimated changes from the latter model correspond quite well with those from the ECHAM4/OPYC3 model. Despite the flexibility of generalized additive models, the rainfall amount model systematically overpredicts the mean rainfall amounts in situations where extreme rainfall could be expected. Interaction between predictor effects has to be incorporated to reduce this bias.