Checkerboard Patterns in E3SMv2 and E3SM-MMFv2
- 1Lawrence Livermore National Laboratory, Livermore, CA
- 2Pacific Northwest National Laboratory, Richland, WA
- 3Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY
- 4NASA Goddard Institute for Space Studies, New York, NY
- 1Lawrence Livermore National Laboratory, Livermore, CA
- 2Pacific Northwest National Laboratory, Richland, WA
- 3Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY
- 4NASA Goddard Institute for Space Studies, New York, NY
Abstract. An unphysical checkerboard pattern is identified in E3SMv2 and E3SM-MMF that is detectable across a wide range of time scales, from instantaneous snapshots to multi-year averages. A detection method is developed to quantify characteristics of the checkerboard signal by cataloguing all possible configurations of the 8 adjacent neighbors for each cell on the model's cubed sphere grid using daily mean data. The checkerboard pattern is only found in cloud related quantities, such as precipitation and liquid water path. Instances of pure and partial checkerboard are found to occur more often in E3SMv2 and E3SM-MMF when compared to satellite data regridded to the model grid. Continuous periods of partial checkerboard state are found to be more persistent in both models compared to satellite data, with E3SM-MMF exhibiting more persistence than E3SMv2. The checkerboard signal in E3SMv2 is found to be a direct consequence of the recently added deep convective trigger condition based on dynamically generated CAPE (DCAPE). In E3SM-MMF the checkerboard signal is found to be associated with the "trapping" of cloud scale fluctuations within the embedded CRM. Solutions to remedy this issue are discussed.
Walter M. Hannah et al.
Status: open (until 07 Jun 2022)
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RC1: 'Comment on gmd-2022-35', Anonymous Referee #1, 12 May 2022
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Review Comments For GMD-2022-35: Checkerboard Patterns in E3SMv2 and E3SM-MMFv2
This work identifies checkerboard patterns in the E3SMv2 and E3SM-MMF simulations. The pattern is mostly prominent in the precipitation and liquid water path and shows persistence in various timescales. The authors identified the DCAPE convection trigger as the primary cause of such a pattern in E2SMv2 by showing a diminished pattern when turning off the trigger. They also provided an hypothesis that the pattern in E3SM-MMF is caused by the fluctuation trapping. Satellite data are used as a validation reference. Potential solutions for both model configurations are discussed at the end.
This paper is well written. It brings up the non-negligible persistent and non-physical patterns in the model results, and logically tracks down the causes for the patterns. It points out the potential problems with the physical parameterization (i.e., convection trigger) and the design of CRM configurations of E3SMv2. I recommend acceptance with minor revision.
A general question for curiocity: Have you performed any sensitivity tests on various resolution setups? It would be interesting to see whether the patterns occurs at all or whether they are more/less frequent under different resolutions.
Minor comments:
- Lines 182 - 186 and Figs 4-5: As the authors pointed out, the E3SMv2 shows less obvious patterns. It will be better to show 1) a map of difference between with/without DCAPE, and 2) maps of the differences between simulations and satellite regrided results.
- Line 196: I believe the author meant to refer to Figure 6b,d here.
- Figure 6: Is there an explanation why the simulation without DCAPE is even smoother than satellite results?
Walter M. Hannah et al.
Data sets
Analysis code and data subset Walter Hannah https://g-c5233.fd635.8443.data.globus.org/publications/2022_GMD_chx_detection.tar.gz
Model code and software
DOE Energy Exascale Earth System Model version 2 DOE https://zenodo.org/record/6407199
Walter M. Hannah et al.
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