New Paper On The Need For Improved Cloud Representation In Climate Models By Wang Et Al 2009

Yesterday, I discussed the issue that water vapor feedbacks are more poorly understood than indicated in the papers by Andrew Dessler (see). Today, I have provided a new paper that discusses one aspect of the current inability of the multi-decadal global climate models to skillfully predict cloud-precipitation feedbacks (and thus their difficulty in accurately representing radiative feedbacks in this models).

The new paper is

Wang, Y., C.N. Long, L.R. Leung, J. Dudhia, S.A. McFarlane, J.H. Mather, S.J. Ghan, and X. Liu. 2009. “Evaluating Regional Cloud-Permitting Simulations of the WRF Model for the Tropical Warm Pool International Cloud Experiment (TWP-ICE), Darwin, 2006.” J. Geophys. Res., 114, D21203, doi:10.1029/2009JD012729

The abstract reads

“Data from the Tropical Warm Pool International Cloud Experiment (TWP-ICE) were used to evaluate Weather Research and Forecasting (WRF) model simulations with foci on the performance of three six-class bulk microphysical parameterizations (BMPs). Before the comparison with data from TWP-ICE, a suite of WRF simulations were carried out under an idealized condition, in which the other physical parameterizations were turned off. The idealized simulations were intended to examine the interaction of BMP at a “cloud-resolving” scale (250 m) with the nonhydrostatic dynamic core of the WRF model. The other suite of nested WRF simulations was targeted on the objective analysis of TWP-ICE at a “cloud-permitting” scale (quasi-convective resolving, 4 km). Wide ranges of discrepancies exist among the three BMPs when compared with ground-based and satellite remote sensing retrievals for TWP-ICE. Although many processes and associated parameters may influence clouds, it is strongly believed that atmospheric processes fundamentally govern the cloud feedbacks through the interactions between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. Based on the idealized experiments, we suggest that the discrepancy is a result of the different treatment of ice-phase microphysical processes (e.g., cloud ice, snow, and graupel). Because of the turn-off of the radiation and other physical parameterizations, the cloud radiation feedback is not studied in idealized experiments. On the other hand, the “cloud-permitting” experiments engage all physical parameterizations in the WRF model so that the radiative heating processes are considered together with other physical processes. Common features between these two experiment suites indicate that the major discrepancies among the three BMPs are similar. This strongly suggests the importance of ice-phase microphysics. To isolate the influence of cloud radiation feedback, we further carried out an additional suite of simulations, which turns off the interactions between cloud and radiation schemes. It is found that the cloud radiation feedback plays a secondary, but nonnegligible role in contributing to the wide range of discrepancies among the three BMPs.”

There is a news release for this paper that is titled

Computer-simulated Thunderstorms with Ice Clouds Reveal Insights for Next-generation Computer Models

Excerpts from the paper are [highlight added]

“Thunderstorms in the tropics generate widespread cirrus clouds that are important in reflecting and absorbing energy. These mixing ratios for granular snow pellets (also called “soft hail”) (shades) and cloud ice (contours) from comparison testing of thunderstorm clouds in the tropics illustrate the wide discrepancy of the ice-phase cloud microphysics in current models. The melting line is marked as a thicker, red line. These types of discrepancies must be resolved for models to more accurately predict cloud influence on climate change.”

Computer simulations of thunderstorms using data from a field campaign in Australia confirm that the “ice-phase” cloud processes in climate models contribute most to the wide discrepancy between model results and actual cloud measurements. This was a key finding from PNNL scientist Dr. Yi Wang and his colleagues from a recent study.”

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