the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Intercomparison of the weather and climate physics suites of a unified forecast/climate model system (GRIST-A22.7.28) based on single column modeling
Xiaohan Li
Xindong Peng
Baiquan Zhou
Jian Li
Yiming Wang
Abstract. As a unified weather-forecast/climate model system, Global-to-Regional Integrated forecast SysTem (GRIST-A22.7.28) currently employs two separate physics suites for weather forecast and typical long-term climate simulation, respectively. Previous AMIP-style experiments have suggested that the weather (PhysW) and climate (PhysC) physics suites, when coupled to a common dynamical core, lead to different behaviors in terms of modeling clouds and precipitation. To explore the source of their discrepancies, this study compares the two suites using a single column model (SCM). The SCM simulations demonstrate significant differences in the simulated precipitation and low clouds. Convective parameterization is found to be a key factor responsible for these differences. Compared with PhysC, parameterized convection of PhysW plays a more important role in moisture transport and rainfall formation. The convective parameterization of PhysW also better captures the onset and retreat of rainfall events, but stronger upward moisture transport largely decreases the tropical low clouds in PhysW. These features are in tune with the previous 3D AMIP simulations. Over the typical stratus-to-stratocumulus transition regime such as the Californian coast, shallow convection in PhysW is more prone to be triggered and leads to larger ventilation above the cloud layer, reducing stratocumulus clouds there. These two suites also have intrinsic differences in the interaction between cloud microphysics and other processes, resulting in different time step sensitivities. PhysC tends to generate more stratiform clouds with decreasing time step. This is caused by separate treatment of stratiform cloud condensation and other microphysical processes, leading to a tight interaction with boundary layer turbulence. In PhysW, all the microphysical processes are executed at the same temporal scale, and thus no such time step sensitivity was found.
Xiaohan Li et al.
Status: open (until 05 Apr 2023)
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RC1: 'Comment on gmd-2022-283', Anonymous Referee #1, 06 Mar 2023
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General comments
In this manuscript, two physics suites separately coupled in a unified weather-climate model are described. Three field cases are used to understand the difference in model behaviors between the two physics suites within single column model configuration. The authors provide evidence for contribution of convective parameterization scheme to the major discrepancy in the simulated precipitation and clouds. Model sensitivity to time step is attributable to the interaction between microphysics and other processes. Although the discrepancies between the simulations are clearly illustrated, the underlying reasons need further investigation as suggested in the specific comments. Meanwhile, the roles for the two physics suites in unified weather and climate modeling need to be clarified to provide reference for other modeling centers. I would recommend it for publication in GMD after minor revisions in terms of the specific issues below.
Specific comments
L40 and L46: Please briefly describe the distinct formulation of unified weather-climate modeling (e.g., GRIST) that is different from other weather and climate models.
L133: The statement “… a clear difference…, that is, dynamics and all the microphysical processes are more closely coupled together” could be more specific. A schematic flowchart of the computational procedure, if possible, could be beneficial to illustrating the difference in coupling strategies of dynamics and physics between PhysC and PhysW.
L172: The mentioned citation for DYCOMS experiment (Table 1) is missing in the reference list. In addition, please check the long name of the experiment in Table 1. And the location (lat, lon) should be precise with direction units.
L221: It is found that the ratio of convective precipitation in PhysC is quite smaller during day 2-4 than that during day 0-2 at the peak value time of each event (Figure 1a). What background environment or treatment in model physics contributes to the difference? Does the difference has influence on the mean state of cloud profile during the convection active period (Figure 1c, e)?
L272: DYCOMS shows consistent marine stratocumulus cloud amount generated by different interactions of the physical processes in PhysW and PhysC. In CGILS, however, difference in stratocumulus cloud fraction at S11 between PhysW and PhysC is apparent. Is this difference primarily attributed to the shallow convection or other processes such as PBL turbulence?
L274: Please clarify the “lower levels” with specific pressure layers.
L323: Compared to the convection active period, the increment of middle and low clouds (500-900 hPa) without convection parameterization seems larger in PhysW during the convection suppressed period. Why does the increment become larger when the convection is suppressed rather than active?
L417: What roles do the two physics suites play in the current unified weather and climate modeling? The authors may consider linking the main conclusion to future implication of this study for other modeling centers.
Technical corrections
L90: Section 5 explores
L142: while using
L156: subscript phys
L183: Please check the vector symbol in the equation (4).
Please make sure the reference list includes all the citations in the manuscript.
Citation: https://doi.org/10.5194/gmd-2022-283-RC1 -
RC2: 'Comment on gmd-2022-283', Anonymous Referee #2, 07 Mar 2023
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This paper leverages the single column modeling framework to compare two physics suites in a unified forecast/climate modeling system and performs various sensitivity experiments. I felt the paper was well written and I do recommend publication after the following issues are addressed.
- I felt that all (line) plots could benefit from adding a background grid for more ease in interpretation of results (though this is more of a personal preference and leave it up to the authors).
- It is my viewpoint that SCMs are very useful (even vital) tools for GCMs. However, including a brief discussion on their limitations would be useful for the reader.
- Figure 1: Would it be possible to add profiles of the cloud ice?
- For TWP-ICE is it possible to show the profiles of Temperature and Moisture errors with respect to observations?
- Some figures use Pressure for the y-coordinate while some use kilometers/meters. Please use the same coordinate for all plots so that they can be directly compared to another.
- Regarding the DYCOMS case, it was not clear to me at all if the authors used the research flight 1 (RF01) or RF02. On page 6 it is stated that DYCOMS focuses on “nonprecipitating marine stratocumulus” which suggests RF01, while on page 9 it is stated that DYCOMS is “… with embedded pockets of drizzling open cellular convection” which suggests RF01. Please explicitly state in the document which flight segment was used and the appropriate reference.
- Figure 3: Please add the LES mean and spread for variables where available from the LES intercomparison study. Whether it was for RF01 or RF02 this data is publicly available and would add a nice reference point.
- Regarding the DYCOMS results… 30 layer vertical resolution is quite coarse for this regime. I feel like the paper would be strengthened by adding a vertical resolution sensitivity for the DYCOMS case. Often times parameterizations are tuned to achieve optimal results for stratocumulus to compensate for these very coarse vertical resolutions; which often breaks down when the vertical resolution is increased to something more appropriate for this regime. This would be a nice way to exploit any potential sensitivities to vertical resolution for each physics package and the SCM is the ideal vehicle to do this.
- Figure 6, I feel like panels e) and h) should be made into their own figure. There are shared and conflicting color schemes with the other plots in this panel that makes it very confusing (and easily misleading) to interpret.
- Page 11, lines 314-315. The authors state “the nocu runs of PhysC and PhysW produce highly consistent precipitation evolution…”. This is really hard to tell from this plot to my eye. I think it would be more illuminating to show the evolution of the difference between the two nocu runs and the two base runs.
- Figure7, is there an observational source available to plot here?
- In the conclusions section it would be nice if the authors could expand on how this work would more broadly benefit modeling centers.
Citation: https://doi.org/10.5194/gmd-2022-283-RC2
Xiaohan Li et al.
Xiaohan Li et al.
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