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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-25
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-25
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 27 Mar 2019

Methods for assessment of models | 27 Mar 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Detecting causality signal in instrumental measurements and climate model simulations: global warming case study

Mikhail Y. Verbitsky1,a,*, Michael E. Mann2, Byron A. Steinman3, and Dmitry M. Volobuev4 Mikhail Y. Verbitsky et al.
  • 1Gen5 Group, LLC, 275 Grove Street, Suite 2-400, Newton, MA, USA
  • 2Department of Meteorology, ThePennsylvania State University, University Park, PA, USA
  • 3Large Lakes Observatory and Department of Earth and Environmental Sciences, University of Minnesota Duluth, Duluth, MN, USA
  • 4The Central Astronomical Observatory of the Russian Academy of Sciences at Pulkovo, Saint Petersburg, Russia
  • aformerly at: Yale University, Department of Geology and Geophysics, New Haven,CT, USA
  • *retired

Abstract. Detecting the direction and strength of the causality signal in observed time series is becoming a popular tool for exploration of distributed systems such as Earth's climate system. Here we suggest that in addition to reproducing observed time series of climate variables within required accuracy a model should also exhibit the causality relationship between variables found in nature. Specifically, we propose a novel framework for a comprehensive analysis of climate model responses to external natural and anthropogenic forcing based on the method of conditional dispersion. As an illustration, we assess the causal relationship between anthropogenic forcing (i.e., atmospheric carbon dioxide concentration) and surface temperature anomalies. We demonstrate a strong directional causality between global temperatures and carbon dioxide concentrations (meaning that carbon dioxide affects temperature stronger than temperature affects carbon dioxide) in both the observations and in (CMIP5) climate model simulated temperatures.

Mikhail Y. Verbitsky et al.
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Supplementary code and data to GMD paper "Detecting causality signal in instrumental measurements and climate model simulations: global warming case study" M. Y. Verbitsky, M. E. Mann, B. A. Steinman, and D. M. Volobuev https://doi.org/10.5281/zenodo.2605142

Mikhail Y. Verbitsky et al.
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Short summary
Here we suggest that in addition to reproducing observed time series of climate variables within required accuracy a model should also exhibit the causality relationship between variables found in nature. Specifically, we propose a novel framework for a comprehensive analysis of climate model responses to external natural and anthropogenic forcing based on the method of conditional dispersion.
Here we suggest that in addition to reproducing observed time series of climate variables within...
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