Stochastic Rainfall Downscaling of Climate Models

Abstract

Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.

Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China

“…In addition, the proposed framework can not only be used to downscale historical precipitation data like in this study, but also for future prediction, e.g., for downscaling the output of Global Circulation Models (GCM) at regional scales [74]. Moreover, it is also applicable in other areas of hydrology such as soil moisture downscaling [75].…”

Assessment of an ensemble of ocean–atmosphere coupled and uncoupled regional climate models to reproduce the climatology of Mediterranean cyclones

“…To address this issue, RCMs have been long ago employed to analyze climate dynamics across different spatial scales (e.g., Giorgi 1990;Deque and Piedelievre 1995). In fact, several recent studies demonstrated the benefits from the use of RCMs in reproducing climate patterns at local scales within the MR and for improving the quality of input data for impact studies in areas of complex geography (Flaounas et al 2013;Guyennon et al 2013;D'Onofrio et al 2014;Calmanti et al 2015). Despite their ability to resolve fine-scale atmospheric features, RCM results are associated with significant uncertainties.…”

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology

“…RnormM provides the monthly precipitation sums over the standard period 1981-2010, calculated from the data of all automatic and manual stations in Switzerland, achieving high accuracy and detailed spatial resolution. The RainFARM procedure is described in detail in Rebora et al (2006) andD'Onofrio et al (2014), and in the present paper we refer to the spatial-only downscaling method described in the latter. The RainFARM method downscales a large-scale spatio-temporal precipitation field P (X, Y, t), which is considered reliable at scales larger than a reliability scale L o (which often may coincide with the spatial resolution of the field).…”