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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-2019-107
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-107
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 06 May 2019

Methods for assessment of models | 06 May 2019

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

Spatio-temporal consistent bias pattern detection on MIROC5 andFGOALS-g2

Bo Cao, Ying Zhao, and Ziheng Zhou Bo Cao et al.
  • Department of Computer Science and Technology, Tsinghua University, Beijing, China

Abstract. Building climate models is a typical means of studying the dynamics of the climate system and assessing the impacts of climate change. However, model-related biases are common in existing climate models, such as the double ITCZ bias in most CMIP5 models. Recent studies suggest that biases showing distinct spatio-temporal characteristics may involve different mechanisms and sources in climate models. More dedicated studies on bias patterns is important not only for improving model performance, but also for helping modelers to better understand the climate system. In this paper, we focus on detecting spatio-temporal consistent bias patterns from climate model outputs. A spatio-temporal pattern is a bias pattern that is present in contiguous space with significant and coherent biases in continuous time periods. These patterns are ideal for revealing regional and seasonal characteristics of biases and worth further investigation by modelers. Due to the high computation cost, most of the existing analysis methods can only detect bias patterns that are either spatial consistent or temporal consistent, but not both at the same time. We proposed a bottom-up algorithm to tackle this problem. The proposed method first detects regions showing significant and consistent biases at each time slot and then merges them iteratively to form bias instances. The resulting bias instances are further clustered into different families to depict corresponding spatio-temporal consistent bias patterns. The experiments on both MIROC5 and FGOALS-g2 precipitation outputs show that the proposed approach can detect some important bias patterns that are consistent with previous studies and can produce other interesting findings. Modelers can adopt the proposed method as an exploratory tool to develop insights for bias correction and model improvement.

Bo Cao et al.
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Short summary
We propose a method to detect spatio-temporal consistent bias patterns, which are present in contiguous space with significant and coherent biases in continuous time periods, from climate model outputs. These patterns are ideal for revealing regional and seasonal characteristics of biases and worth further investigation by modelers. Experiment results on both MIROC5 and FGOALS-g2 precipitation outputs show that the proposed approach can produce some important findings.
We propose a method to detect spatio-temporal consistent bias patterns, which are present in...
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