A general method for validating statistical downscaling methods under future climate change

Mathieu Vrac, M. L. Stein, K. Hayhoe, X. Z. Liang

Research output: Contribution to journalArticle

73 Scopus citations

Abstract

Statistical downscaling; methods (SDMs) are often use to increase the resolution of future climate projections from coupled atmosphere-ocean general circulation models (GCMs). However, SDMs are not able to capture small-scale dynamical changes unresolved by GCMs. For this reason, we propose a two-step generalized validation process to evaluate the performance of any statistical downscaling method relative to regional climate model (RCM) simulations driven by the same GCM fields. First, compare historical station-based observations with simulations obtained from a statistical model fitted to and driven by reanalysis fields, and then driven by historical GCM fields. Then, the SDM is required to produce future projections consistent with RCM simulations used as pseudo-observations under future emissions scenarios. Using the climate extension of the fifth generation Penn-State/NCAR Mesoscale Model (CMM5) driven by NCAR/DOE Parallel Climate Model (PCM) simulations, we apply method to identify the strengths/weaknesses of a nonhomogeneous stochastic weather typing method.

Original languageEnglish
Article numberL18701
JournalGeophysical Research Letters
Volume34
Issue number18
DOIs
StatePublished - Sep 28 2007

Fingerprint Dive into the research topics of 'A general method for validating statistical downscaling methods under future climate change'. Together they form a unique fingerprint.

  • Cite this