New Ensemble Background State Dataset Enables Testing of Error Sources in Climate Models
Errors in physical parameterizations rather than large-scale dynamics appear to cause most model biases.
Clouds represent one of the largest uncertainties in current general circulation model (GCM) simulations. Studies have shown that model discrepancies can come from deficiencies in the physical parameterization and uncertainties in the large-scale atmospheric condition. Unclear, however, is how these discrepancies contribute to model errors.
The newly developed ensemble Atmospheric Radiation Measurement (ARM) constrained analysis of atmospheric forcing data accurately specifies the large-scale dynamics conditions along with uncertainty range over the ARM Southern Great Plains (SGP) site. This analysis makes it easier to isolate errors due to model physics when the ensemble data are used to force models.
An ensemble variationally constrained objective analysis of atmospheric large-scale forcing data has been developed for the March 2000 Intensive Observing Period at the ARM SGP site. The ensemble approach uses the uncertainty information of the background data, error covariance matrices, and constraint variables in the ARM constrained variational analysis. The ensemble forcing data are applied to drive the Community Atmosphere Model 5 (CAM5) single-column model and the simulated clouds are compared with MICROBASE cloud retrievals to diagnose the source of model biases. The results show that most of the model biases are larger than the uncertainty from large-scale forcing data plus uncertainty from observations, pointing the simulated cloud biases to model parameterization deficiencies. Sensitivity studies show that background data, error covariance matrix, and constraint variables all contribute to the uncertainty range of the analyzed state variables and large-scale forcing data, especially to the vertical velocity and advective tendencies. Background data have the largest impact. CAM5 simulations of clouds forced by the ARM ensemble forcing data systematically overestimate high clouds, while underestimating low clouds when compared with ARM MICROBASE cloud retrievals. These model biases cannot be explained by the uncertainty of large-scale forcing data and the uncertainty of observations, which points to the deficiencies of physical parameterizations.
Stony Brook University
The research at Stony Brook University was supported by the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy’s (DOE) Office of Science and the National Science Foundation. Work at Lawrence Livermore National Laboratory was supported by BER’s Atmospheric Radiation Measurement (ARM) program and performed under contract DE-AC52-07NA27344. This study uses ARM data.
Tang, S., M. Zhang, and S. Xie. “An ensemble constrained variation analysis of atmospheric forcing data and its application to evaluate clouds in CAM5.” J. Geophys. Res. Atmos. 121, 33-48 (2016). DOI:10.1002/2015JD024167.
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