Statistical and dynamical downscaling predictions of changes in surface temperature and precipitation for 2080 -2100, relative to pre-industrial conditions, are compared at 976 European observing sites, for January and July. Two dynamical downscaling methods are considered, involving the use of surf
Regional climate model assessment using statistical upscaling and downscaling techniques
β Scribed by Veronica J. Berrocal; Peter F. Craigmile; Peter Guttorp
- Book ID
- 118283493
- Publisher
- John Wiley and Sons
- Year
- 2012
- Tongue
- English
- Weight
- 560 KB
- Volume
- 23
- Category
- Article
- ISSN
- 1180-4009
- DOI
- 10.1002/env.2145
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract Three downscaling models, namely the Statistical DownβScaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARSβWG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled result
## Abstract This study deals with an analysis of the performance of a general circulation model (GCM) (HadCM2) in reproducing the largeβscale circulation mechanisms controlling Swedish precipitation variability, and in estimating regional climate changes owing to increased CO~2~ concentration by us
Due to increasing interest in the prediction of detailed regional climate, a method to deduce local climate distributions from large-scale variables is proposed. Since surface parameters are dqficult to reproduce with the current resolution of General Circulation Models (GCMs), a statistical approac