U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Identifying Deficiencies in Climate Model Simulations of Low Clouds
Published: July 19, 2016
Posted: August 12, 2016

Researchers find that a new climate model version does not produce enough low-level clouds.

The Science
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.

The Impact
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)
Shaima Nasiri
ASR Program Manager, SC-23.1
Renu Joseph
RGCM Program Manager, SC-23.1
Sally McFarlane
ARM Program Manager

(PI Contact)
Xue Zheng
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)

Related Links
ASM Highlight

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Atmospheric System Research
  • Cross-Cutting: Scientific Computing and SciDAC
  • Facility: DOE ARM User Facility

Division: SC-23.1 Climate and Environmental Sciences Division, BER


BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)