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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model experiment description paper 15 Apr 2020

Submitted as: model experiment description paper | 15 Apr 2020

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This preprint is currently under review for the journal GMD.

A multi-year short-range hindcast experiment for evaluating climate model moist processes from diurnal to interannual time scales

Hsi-Yen Ma1, Chen Zhou2, Yunyan Zhang1, Stephen A. Klein1, Mark D. Zelinka1, Xue Zheng1, Shaocheng Xie1, Wei-Ting Chen3, and Chien-Ming Wu3 Hsi-Yen Ma et al.
  • 1Lawrence Livermore National Laboratory, Livermore, California, USA
  • 2School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • 3Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

Abstract. We present a multi-year short-range hindcast experiment and its experiment procedure for better evaluating both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual time scales to facilitate model development. We use the Community Earth System Model version 1 as the based model and performed a suite of 3-day long hindcasts every day starting at 00Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the Central U.S., precipitation and diabatic heating associated with the Madden-Julian Oscillation propagation, and the response of moist processes to sea surface temperature anomalies associated with the El Niño-Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment design to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment. Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model’s climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated parameterized moist processes usually develop within a few days, and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

Hsi-Yen Ma et al.

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Hsi-Yen Ma et al.

Hsi-Yen Ma et al.


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