Spatial downscaling of precipitation from GCMs for climate change projections using random cascades: A case study in Italy

摘要: [1] We present a Stochastic Space Random Cascade (SSRC) approach to downscale precipitation from General Circulation Models (GCMs), developed for the assessment of water resources under climate change scenarios Oglio river (1440 km2), in Italian Alps. The snow-fed displays complex physiography and high environmental gradient statistical downscaling methods are required assessment. First, back cast analysis is carried out evaluate most representative within set four available GCMs (R30, ECHAM4, PCM, HadCM3). Monthly window 1990–2000 270 gauging stations (one every 25 km2) northern Italy used scores objective indicators calculated. SSRC model then tuned upon catchment spatial (2 daily NCAR Parallel Climate Model, giving comparatively best results area. Scale Recursive Estimation coupled with Expectation Maximization algorithm estimation. seasonal parameters multiplicative cascade accommodated by distributions conditioned climatic forcing, based on regression analysis. reproduces well clustering, intermittency, self-similarity, correlation structure fields, relatively low computational burden. Downscaling future (A2 scenario Model) some preliminary conclusions drawn.