A New Approach to Extract Monthly High Resolution Information for Precipitation from GCM Scenarios and Predictions
The results reported here include a highlight on the annual cycle evolution of the downscaling relationships. For example, for CIT, circulation anomalies associated with precipitation tend to be larger-scale and somewhat stronger in winter compared to summer. Generally, enhanced precipitation in winter is associated with a stronger push of cyclonic southerly anomalies over Italy (and extending eastward). For NEB, the primary circulation linkages during the wet season (approximately January-May) are consistent with known structures in the tropical Atlantic related to displacement of the ITCZ. During boreal summer, even though mean NEB precipitation is typically <0.5 mm/day, the year-to-year fluctuations of precipitation still contain strong expression in monthly mean circulation, but markedly distinct from those in the wet season. Now, primary circulation anomalies associated with NEB precipitation connect from the equatorial Atlantic to known structures in the tropical North Atlantic (accompanying Sahel rainfall anomalies), leading to strikingly different downscaling models for NEB during boreal summer. Statistical developments are anticipated to further enhance models in such relatively dry climatological situations.
Spatial structure of the downscaling models (as revealed by CCA modes) supports a clear expression of orography in the precipitation anomaly fields. The balance of synoptic and orographic influence on precipitation variability is highlighted in the models. Application of the downscaling models to a GCM climate change scenario (2021-2050) generates plausible downscaled time-series and fields. For CIT, results (skill, spatial structure) are consistent with those produced using the E-OBS station-only gridded (0.25°) set for the extended period 1979-2012. The overall impression gained is that TRMM data enable estimation of skillful downscaling relationships, at least for some locations. Developments drawing on longer datasets to adjust the downscaled fields will likely further increase the utility of a record like TRMM.