Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations
<p>There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-08-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/23/8591/2023/acp-23-8591-2023.pdf |
| Summary: | <p>There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability
and vertical variations in 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 Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern
North Atlantic (ACE-ENA) field campaign near the Azores islands during
2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can
generally reproduce marine boundary layer cloud structure, major
macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with 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 are
overestimated by a factor of 2 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 in 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 subgrid variations
in cloud properties in CAM6 cloud microphysics, which further suppresses the
scheme's 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 an
aerosol indirect effect that is too strong. The present study emphasizes the
importance of understanding biases in cloud physics parameterizations by
combining SCM with in situ observations.</p> |
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| ISSN: | 1680-7316 1680-7324 |