Atmos. Chem. Phys. Discuss., 11, 14933-14990, 2011
www.atmos-chem-phys-discuss.net/11/14933/2011/
doi:10.5194/acpd-11-14933-2011
© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
Review Status
This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations
Y. Qian1, C. Long1, H. Wang1, J. Comstock1, S. A. McFarlane1, and S. Xie2
1Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
2Lawrence Livermore National Laboratory, Livermore, CA, USA

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM ground measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Our intercomparisons of three CF or sky-cover related datasets reveal that the relative differences are usually less than 10 % (5 %) for multi-year monthly (annual) mean values, while daily differences are quite significant. The results also show that the model-observation and inter-model deviations have similar magnitudes for the total CF (TCF) and the normalized cloud effect, and these deviations are twice as large as the deviations in surface downward solar radiation and cloud transmissivity. This implies that other cloud properties, such as cloud optical depth and height, have a similar magnitude of disparity to TCF among the GCMs, and suggests that the better agreement among the GCMs in solar radiative fluxes is the result of compensating errors in cloud vertical structure, cloud optical depth and cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is very minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation.

Differences are found in GCM performance over the three ARM sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF and cloud transmissivity is less susceptible to the change of TCF than observed. Much larger inter-model deviation and model bias are found over NSA than the other sites, suggesting that the Arctic region continues to challenge cloud simulations in climate models. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high altitude CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating difficulties in the representation of ice cloud associated with convection in the models.


Citation: Qian, Y., Long, C., Wang, H., Comstock, J., McFarlane, S. A., and Xie, S.: Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations, Atmos. Chem. Phys. Discuss., 11, 14933-14990, doi:10.5194/acpd-11-14933-2011, 2011.
 
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