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Multiscale evaluation of CMIP5 models using wavelet-based descriptive and diagnostic techniques

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  • Marco Gallegati

    (Polytechnic University of Marche)

Abstract

Time–frequency localization of model-data discrepancies may provide useful information for climate models inter-comparison, and especially for the goals of climate model refinement and improvement. CMIP5 models of the long-term historical (1850–2005) run experiment are compared using wavelet-based multiscale descriptive and diagnostic techniques with interesting results. Wavelet coherence maps can visualize the ability of alternative CMPI5 models to capture the observed climate variability at different time scales, while the performance of each CMIP5 model is assessed using goodness of fit relative measures on a scale-by-scale basis. Finally, the plots of wavelet decompositions of CMIP5 models and observed temperature series at different scales can detect and locate model/data disagreements across frequencies and over time, thus providing useful information to researchers for model diagnostic refinement and improvement.

Suggested Citation

  • Marco Gallegati, 2022. "Multiscale evaluation of CMIP5 models using wavelet-based descriptive and diagnostic techniques," Climatic Change, Springer, vol. 170(3), pages 1-16, February.
    • Handle: RePEc:spr:climat:v:170:y:2022:i:3:d:10.1007_s10584-021-03269-9
    DOI: 10.1007/s10584-021-03269-9
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    References listed on IDEAS

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