GCM grid-box choice and predictor selection associated with statistical downscaling of daily precipitation over Northern Ireland

Abstract

Statistical downscaling bridges the gap between General Circulation Model (GCM) out- put and climate-impacts modellers' requirements. Recent development of user-friendly software has facilitated use of statistical downscaling methods by the wider climate-impacts community. Simplic- ity of use, however, does not imply underlying conceptual simplicity. Ostensibly innocuous choices made when parameterizing such downscaling tools can have major implications for output and the impact study. Issues of predictor variable selection and GCM grid-box choice in downscaling daily precipitation occurrence and amount are investigated using the Statistical DownScaling Model (SDSM) package version 3.1. Choices are discussed within the context of Northern Ireland (NIR), designated an oceanic grid box in GCMs' land -sea mask. Use of the Republic of Ireland (IR) grid box, or combination of IR and Scottish Borders (SB) grid boxes to represent the NIR grid box, are evalu- ated by identifying site-specific optimum predictor sets for a NIR precipitation network. Realism of contrasts evident in dominance and spatial pattern of predictors are discussed with reference to spatial and temporal characteristics of local climate and large-scale atmospheric processes. For example, NIR grid-box predictor output displays weaker spatial contrasts in spring and summer than its IR counterpart. Explained variance associated with grid box/predictor set combinations is ~25%, similar to other precipitation downscaling studies. Differences of explained variance between grid boxes are ~5%, an attractive gain when modelling daily precipitation using statistical downscaling (SD), where 'improvement' of explained variance is at a premium. Future projections indicate more pronounced precipitation intensity using the NIR grid box. Overall, IR grid-box output appears most appropriate for downscaling of NIR daily precipitation. The NIR case study highlights the impact of predictor choice and grid-box selection upon SD output, particularly when the land area is repre- sented by an oceanic grid box in GCM experiments.