Insights of warm cloud biases in CAM5 and CAM6 from the single-column modeling framework and ACE-ENA observations
Abstract. There has been a growing concern that most climate models predict too frequent precipitation, likely due to lack of reliable sub-grid variability and vertical variations of microphysical processes in low-level warm clouds. In this study, the warm cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the DOE ARM Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. Eight-month SCM simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary-layer cloud structure, major macrophysical properties, and their transition. The improvement of warm cloud properties from CAM5 to CAM6 physics can be found compared to the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content, but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft observed ones in the summer but overestimated by a factor of two in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations of droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of sub-grid variations of cloud properties in CAM6 cloud microphysics, which further suppresses the scheme sensitivity to individual warm rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces too strong aerosol indirect effect. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.
Yuan Wang et al.
Status: final response (author comments only)
RC1: 'Comment on egusphere-2023-587', Anonymous Referee #1, 02 May 2023
- AC1: 'Reply on RC1', Yuan Wang, 31 May 2023
RC2: 'Comment on egusphere-2023-587', Anonymous Referee #2, 05 May 2023
- AC2: 'Reply on RC2', Yuan Wang, 31 May 2023
Yuan Wang et al.
Yuan Wang et al.
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Review of “Insights of warm cloud biases in CAM5 and CAM6 from the single-column modeling framework and ACE-ENA observations” by Wang et al.
In this study, model biases in aerosol and warm cloud simulations are examined in two versions of the NCAR CAM model using a single-column model framework (SCAM5 and SCAM6) for the ACE-ENA field campaign. The authors analyze differences between simulated cloud and aerosol properties and ACE-ENA observational data.
The paper is well organized and written, but lacks clarity and important information. My general comments reflect this issue.
Please comment on these and adjust section 3.1 and Figure 1 accordingly.
P4 L93–94: I can’t find the reference to Wang et al. (2016) and Zhang et al. (2020) in the References list.
P4 L93–94: Please consider adding at least one more reference per reference set, and add “e.g.,” before each reference set since there are too many available references to include all.
P4 L111–112: Please define explicitly the acronym MAM.
P6 L150: Please include the estimated median uncertainties also for Nc and CLWC.
P7 L167: The sentence “To make better… only selects the research flights with an “L” shape pattern center at the ARM-ENA site” may require additional context for readers who are not familiar with the flight sampling configuration used during ACE-ENA and its rationale. How does this pattern help improve comparisons between observations and simulations?
P10 L256: Could the authors provide more information on the physics used in the SCAM5 version in this study? In CAM6, CLUBB is responsible to diagnose the cloud macrophysical properties. To improve clarity, it would be helpful to include further information about the differences in the physics between the SCAM5 and SCAM6 versions used here; this ties in with my “Major comment 1”. This should probably go in section 2.1.
P10 L264: Could you please confirm whether these in-situ profiles represent an average of data from the 17 flights? Also, could you clarify what the SCAM6 profiles correspond to? Are they averages of the 17-flight time-stamps, or do they represent something else?
P11 L308: In section 4, the results show that the retuned KK scheme improves cloud micro- and macrophysics in SCAM5, but “as expected” it doesn’t lead to improvements in SCAM6 relative to the default MG2 (if I understood correctly). Thus, I was left at the end of section 4, questioning its purpose. I’m not suggesting removing it, but consider clarifying what this section adds to the paper.
P16 L423: The website link to where the data is stored is currently not working.
Figure 5: This is just a suggestion: Use the x-axis labels on only the bottom row or use the same labels on both rows. Currently, the bottom and upper rows have different x-axis labels even though they represent the same variable, which is a bit inconsistent.