Abstract
Abstract. Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational data sets, which are usually gridded data sets. Models have traditionally met this condition because their spatial resolution was coarser than the observational grids. But recent climate simulations use higher resolution and the models are likely to systematically produce fewer rain days than the gridded observations. In this study, model outputs from a simulation at 2 km resolution are compared with gridded and in situ observational data sets to determine whether the new scenario calls for revised methodologies. The gridded observations are found to be inadequate to correct the high-resolution model at daily timescales, because they are subjected to too frequent low intensity precipitation due to spatial averaging. A histogram equalisation bias correction method was adapted to the use of station, alleviating the problems associated with relative low-resolution observational grids. The wet-day frequency condition might not be satisfied for extremely dry biases, but the proposed approach substantially increases the applicability of bias correction to high-resolution models. The method is efficient at bias correcting both seasonal and daily characteristic of precipitation, providing more accurate information that is crucial for impact assessment studies.
Highlights
Regional climate models (RCMs) are outstanding tools for studying the mechanisms of climate at scales that are not yet resolved by general circulation models (GCMs)
The monthly climatologies of Australian Water Availability Project (AWAP) precipitation averaged over the grid points from each of the regions are illustrated in Fig. 4 to show how different their rainfall regimes are, during the first half of the year
Bias correction has traditionally relied on the assumption that models produce more rain days than the reference observations, which are usually gridded data sets due to their spatial and temporal characteristics
Summary
Regional climate models (RCMs) are outstanding tools for studying the mechanisms of climate at scales that are not yet resolved by general circulation models (GCMs). If any method is to be applied to model output with fewer rain days, it is necessary to introduce additional precipitation events (e.g. through Frequency Adaptation as in Themeßl et al, 2012) otherwise daily intensity might be unrealistically corrected to match, for example, the monthly means This situation has rarely arisen and RCMs have traditionally met the aforementioned condition, partly because their spatial resolution is coarser than the observational gridded data set to which they are compared. RCM simulations that exceed the spatial resolution of most gridded products have become possible due to improvements in computational resources Such RCMs are likely to produce systematically less rain days than the gridded observations and the existing bias correction methodologies have to be revised. We propose an alternative approach to the use of gridded observations for this purpose
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