DATE2018-05-21 03:42:38
AUTHORSJ von Hardenberg (1), S Terzago (1), E Palazzi (1)
  1. CNR-ISAC, Torino, Italy
ABSTRACTThe application of climate model projections and forecasts to impact studies at small scales, such as hydrological modeling or ecological modeling, requires to bridge the large gap between the spatial resolution currently achieved by global and regional climate models and the scales necessary for a correct representation of the spatial and temporal structure of precipitation at fine-scales (~1 km) and of the probability of extreme precipitation events. In absence of a dynamical physically based representation, a useful approach to bridge the scale mismatch is the use of stochastic rainfall downscaling techniques. In particular the Rainfall Filtered AutoRegressive Model (RainFARM) method is a weather generator which has only one free parameter (which can be derived from the large scales) and which requires no further calibration. It is currently being implemented for use in climate service applications at temporal scales ranging from seasonal prediction to climate projections, in the framework of different on-going projects (H2020 Ecopotential, Copernicus C3S MAGIC, ERA4CS MEDSCOPE), with a particular focus on the Mediterranean area. Stochastic downscaling techniques usually provide a statistically homogeneous distribution of fine-scale precipitation in each large-scale grid element of the field to downscale, so they usually do not take into account heterogeneities in local precipitation patterns, due for example to orographic effects, at spatial scales finer than those resolved by the large-scale input field. For this reason, stochastic downscaling techniques may be less reliable in areas with complex topography or specific sub-grid precipitation patterns. Here we test a simple method to introduce realistic fine-scale precipitation patterns into the downscaled fields (Terzago et al. 2018). The proposed method relies on the availability of a reference fine-scale precipitation climatology from which corrective weights are derived and used to adjust to the downscaled daily precipitation fields. We demonstrate the method by applying it to the RainFARM algorithm for the Alpine region. The modified RainFARM method has been tested on an area of complex topography encompassing the Swiss Alps, first, in a perfect model experiment in which high resolution (4 km) simulations performed with the Weather Research and Forecasting regional model are aggregated to a coarser resolution (64 km) and then downscaled back to 4 km and compared with the original data. Second, the modified RainFARM is applied to the E-OBS gridded precipitation data (0.25 degrees spatial resolution) over Switzerland, where high-quality gridded precipitation climatologies and accurate in-situ observations are available for comparison with the downscaled data. The results of the perfect model experiment and of the real case experiment are discussed and compared, showing the strengths of the method and providing ideas for possible further developments.