Researchers find that a new climate model version does not produce enough low-level clouds.
Low clouds remain the largest source of uncertainty in the cloud-climate feedback. The main reason is that cloud processes and their feedbacks are not fully understood and are poorly represented in contemporary climate models.
Intensive cloud and radiation observations obtained by the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility in the Azores provide a unique opportunity to assess whether new climate model parameterizations more realistically represent cloud processes and cloud radiative effects for low clouds.
The current generation of the Community Atmosphere Model (CAM), a widely used community climate model funded by the National Science Foundation and DOE, tends to underestimate low cloudiness and shortwave cloud radiative forcing, producing unrealistic cloud transition in low clouds. While the next generation of CAM represents low clouds and rain processes seamlessly and with greater sophistication, there is the question of whether the new CAM parameterizations more realistically represent cloud processes and cloud radiative effects for low clouds. To address this question, a recent study conducted CAM short-term global hindcasts using the Regional Global Climate Modeling (RGCM)/Atmospheric System Research (ASR)-supported Cloud-Associated Parameterizations Testbed (CAPT) approach with different versions of cloud parameterization schemes. The model results were compared with ARM observations from the Azores. The assessments identified the different low-cloud biases in the different versions of CAM cloud parameterization schemes. Specifically, CAM5 with new cloud parameterization schemes better represents low cloud processes, but does not improve the surface shortwave cloud radiative effect mainly due to its low-level cloud cover bias. The “too few, too bright” cloud problem becomes a “not enough” cloud problem in the newer CAM version.
Contacts (BER PM)
ASR Program Manager, SC-23.1
RGCM Program Manager, SC-23.1
ARM Program Manager
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory
This work was funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Regional and Global Climate Modeling and Atmospheric System Research programs as part of the CAPT program under contract DE-AC52- 07NA27344 and grants DE-SC0008323 (Scientific Discovery through Advanced Computing, SciDAC) and LLNLJRNL- 680743. Additional funding support was from the National Science Foundation Climate Process Team under grant 0968657 and grant AGS-0968640.
Zheng, X., S. A. Klein, H.-Y. Ma, P. Bogenschutz, A. Gettelman, and V. E. Larson. 2016. “Assessment of Marine Boundary Layer Cloud Simulations in the CAM with CLUBB and Updated Microphysics Scheme Based on ARM Observations from the Azores,” Journal of Geophysical Research Atmospheres, DOI: 10.1002/2016JD025274. (Reference link)
SC-33.1 Earth and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
Mar 23, 2021
Molecular Connections from Plants to Fungi to Ants
Lipids transfer energy and serve as an inter-kingdom communication tool in leaf-cutter ants&rsqu [more...]
Mar 19, 2021
Microbes Use Ancient Metabolism to Cycle Phosphorus
Microbial cycling of phosphorus through reduction-oxidation reactions is older and more widespre [more...]
Feb 22, 2021
Warming Soil Means Stronger Microbe Networks
Soil warming leads to more complex, larger, and more connected networks of microbes in those soi [more...]
Jan 27, 2021
Labeling the Thale Cress Metabolites
New data pipeline identifies metabolites following heavy isotope labeling.
Aug 31, 2020
Novel Bacterial Clade Reveals Origin of Form I Rubisco
List all highlights (possible long download time)