Journal cover Journal topic
Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
Journal topic
Volume 3, issue 2
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93-105, 2017
https://doi.org/10.5194/ascmo-3-93-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93-105, 2017
https://doi.org/10.5194/ascmo-3-93-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Oct 2017

26 Oct 2017

Probabilistic evaluation of competing climate models

Amy Braverman1, Snigdhansu Chatterjee2, Megan Heyman3, and Noel Cressie4 Amy Braverman et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Mail Stop 158-242, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
  • 2University of Minnesota, 313 Ford Hall, 224 Church St. S.E., Minneapolis, MN 55455, USA
  • 3Rose-Hulman Institute of Technology, G-205 Crapo Hall, 5000 Wabash Ave., Terre Haute, IN 47803, USA
  • 4University of Wollongong, Northfields Ave., Wollongong, NSW 2522, Australia

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.

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Short summary
In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
In this paper, we introduce a method for expressing the agreement between climate model output...
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