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Probabilistic evaluation of competing climate models

Journal Article


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Abstract


  • Climate models produce output over decades or longer at high spatial and temporal resolution. Starting

    values, boundary conditions, greenhouse gas emissions, and so forth make the climate model an uncertain

    representation of the climate system. A standard paradigm for assessing the quality of climate model simulations

    is to compare what these models produce for past and present time periods, to observations of the past

    and present. Many of these comparisons are based on simple summary statistics called metrics. In this article,

    we propose an alternative: evaluation of competing climate models through probabilities derived from tests of

    the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals.

    The probabilities are based on the behavior of summary statistics of climate model output and observational data

    over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal

    and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences.

    The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients.

    Here, we compare monthly sequences of CMIP5 model output of average global near-surface temperature

    anomalies to similar sequences obtained from the well-known HadCRUT4 data set as an illustration.

Authors


  •   Braverman, Amy (external author)
  •   Chatterjee, Snigdhansu (external author)
  •   Heyman, Megan (external author)
  •   Cressie, Noel A.

Publication Date


  • 2017

Citation


  • Braverman, A., Chatterjee, S., Heyman, M. & Cressie, N. (2017). Probabilistic evaluation of competing climate models. Advances in Statistical Climatology, Meteorology and Oceanography, 3 (2), 93-105.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1037&context=niasrawp

Ro Metadata Url


  • http://ro.uow.edu.au/niasrawp/42

Number Of Pages


  • 12

Start Page


  • 93

End Page


  • 105

Volume


  • 3

Issue


  • 2

Place Of Publication


  • Germany

Abstract


  • Climate models produce output over decades or longer at high spatial and temporal resolution. Starting

    values, boundary conditions, greenhouse gas emissions, and so forth make the climate model an uncertain

    representation of the climate system. A standard paradigm for assessing the quality of climate model simulations

    is to compare what these models produce for past and present time periods, to observations of the past

    and present. Many of these comparisons are based on simple summary statistics called metrics. In this article,

    we propose an alternative: evaluation of competing climate models through probabilities derived from tests of

    the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals.

    The probabilities are based on the behavior of summary statistics of climate model output and observational data

    over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal

    and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences.

    The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients.

    Here, we compare monthly sequences of CMIP5 model output of average global near-surface temperature

    anomalies to similar sequences obtained from the well-known HadCRUT4 data set as an illustration.

Authors


  •   Braverman, Amy (external author)
  •   Chatterjee, Snigdhansu (external author)
  •   Heyman, Megan (external author)
  •   Cressie, Noel A.

Publication Date


  • 2017

Citation


  • Braverman, A., Chatterjee, S., Heyman, M. & Cressie, N. (2017). Probabilistic evaluation of competing climate models. Advances in Statistical Climatology, Meteorology and Oceanography, 3 (2), 93-105.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1037&context=niasrawp

Ro Metadata Url


  • http://ro.uow.edu.au/niasrawp/42

Number Of Pages


  • 12

Start Page


  • 93

End Page


  • 105

Volume


  • 3

Issue


  • 2

Place Of Publication


  • Germany