Preprints
https://doi.org/10.5194/gmd-2020-330
https://doi.org/10.5194/gmd-2020-330

Submitted as: model evaluation paper 26 Oct 2020

Submitted as: model evaluation paper | 26 Oct 2020

Review status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1

Hui Wan1, Shixuan Zhang1, Philip J. Rasch1, Vincent E. Larson2,1, Xubin Zeng3, and Huiping Yan4,1 Hui Wan et al.
  • 1Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory
  • 2Department of Mathematical Sciences, University of Wisconsin – Milwaukee
  • 3Department of Hydrology and Atmospheric Sciences, University of Arizona
  • 4School of Atmospheric Science, Nanjing University of Information Science and Technology

Abstract. This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1-degree horizontal resolution. We show that a factor-of-6 reduction of time step size in all major parts of the model leads to significant changes in the long-term mean climate. Examples of such changes include warming in the lower troposphere, cooling in the tropical and subtropical upper troposphere, as well as decreases of relative humidity throughout the troposphere accompanied by cloud fraction decreases. These changes imply that the reduction of temporal truncation errors leads to a notable although unsurprising degradation of agreement between the simulated and observed present-day climate; the model would require retuning to regain optimal climate fidelity in the absence of those truncation errors.

A coarse-grained attribution of the time step sensitivities is carried out by separately shortening time steps used in various components of EAM or by revising the numerical coupling between some processes. Our analysis leads to the counter-intuitive finding that the marked decreases in the subtropical low-cloud fraction and total cloud radiative effect are caused not by the step size used for the collectively subcycled turbulence, shallow convection and stratiform cloud macro- and microphysics parameterizations but by the step sizes used outside the subcycles. Further analysis suggests that the coupling frequency between the subcycles and the rest of EAM has a substantial impact on the marine stratocumulus decks while the deep convection parameterization has a significant impact on trade cumulus. The step size of the cloud macro- and microphysics subcycles appears to have a primary impact on cloud fraction at most latitudes in the upper troposphere as well as in the mid-latitude near-surface layers. Impacts of step sizes used by the dynamical core and radiation appear to be relatively small. These results provide useful clues to help better understand the root causes of time step sensitivities in EAM. The experimentation strategy used here can also provide a pathway for other models to identify and reduce time integration errors.

Hui Wan et al.

 
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Hui Wan et al.

Model code and software

EAM source codes and scripts for time step sensitivity experiments E3SM developers, Shixuan Zhang, and Hui Wan https://doi.org/10.5281/zenodo.4118705

Hui Wan et al.

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Short summary
Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.