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Reliability of CMIP5 GCM simulations in reproducing atmospheric circulation over Europe and the North Atlantic: a statistical downscaling perspective
Author(s) -
Wójcik Robert
Publication year - 2015
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4015
Subject(s) - downscaling , climatology , gcm transcription factors , geopotential height , environmental science , general circulation model , context (archaeology) , atmospheric circulation , precipitation , canonical correlation , climate change , atmospheric sciences , meteorology , geology , mathematics , statistics , geography , paleontology , oceanography
Reliability of GCM (general circulation model) simulations is a significant predictor selection criteria and seems to be essential for outcomes of the statistical downscaling. This article investigates the reliability of a wide ensemble of CMIP5 climate models in reproducing seasonal atmospheric circulation patterns over Europe and the North Atlantic, namely sea level pressure ( SLP ) and 500 hPa geopotential height (G500). GCMs were evaluated with respect to 1971–2000 NCEP / NCAR reanalysis data by means of MAE , correlation coefficient and standard deviation. An attempt was made to determine the range of biases introduced into the statistical models by biases in the GCM simulations. For this purpose, canonical correlation analysis ( CCA ) models linking large‐scale atmospheric circulation and air temperature in Poland were driven by historical GCM simulations expressed as anomalies with reference to the reanalysis data. It was shown that reliability of GCMs varies considerably, both seasonally and on an inter‐model basis, thus becoming unfavourable in the context of statistical downscaling application. Overall, GCM ability to reproduce G500 values is better than for SLP . The most important finding from the statistical downscaling perspective is that SLP and G500 biases may introduce considerable air temperature biases (T‐ GCM biases ) into the downscaled reconstructions or projections of climate change. Seasonally, the highest T‐ GCM biases were found in DJF , when a significant overestimation of air temperature results from a GCM tendency to overestimate the meridional pressure gradient. A similar situation appears to be a significant factor in MAM and SON . Multiyear average GCM biases do not, however, explain a significant part of the inter‐model variability of T‐ GCM bias , thus highlighting the need for a more in‐depth evaluation. Nevertheless, good GCM performance seems to ensure that T‐ GCM bias is not of a considerable value. Overall, the results of the article emphasise the necessity for undertaking an application‐oriented assessment of GCM reliability prior to any statistical downscaling approach.