2005 .

(2 publications)

N. Rimbu, M. Dima, G. Lohmann, and I. Musat. Seasonal prediction of Danube flow variability based on stable teleconnection with sea surface temperature. Geophysical Research Letters, 32:L21704, November 2005. [ bib | DOI | ADS link ]

It is shown that spring Danube flow anomalies are significantly related to winter SST anomalies from several key regions. These areas are identified through stable teleconnections between flow and SST. A forecast scheme is developed and applied to predict flow anomalies using SST anomalies from these key regions. Small potential predictability of winter flow anomalies from autumn sea surface temperature anomalies was also detected. The predictability of the flow anomalies from summer and autumn using SST from the previous seasons is limited by the instability of teleconnections.

M. H. Zhang, W. Y. Lin, S. A. Klein, J. T. Bacmeister, S. Bony, R. T. Cederwall, A. D. Del Genio, J. J. Hack, N. G. Loeb, U. Lohmann, P. Minnis, I. Musat, R. Pincus, P. Stier, M. J. Suarez, M. J. Webb, J. B. Wu, S. C. Xie, M.-S. Yao, and J. H. Zhang. Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. Journal of Geophysical Research (Atmospheres), 110:D15S02, August 2005. [ bib | DOI | ADS link ]

To assess the current status of climate models in simulating clouds, basic cloud climatologies from ten atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are also, however, large uncertainties in satellite high thin clouds to effectively constrain the models. The majority of models only simulated 30-40% of middle-top clouds in the ISCCP and CERES data sets. Half of the models underestimated low clouds, while none overestimated them at a statistically significant level. When stratified in the optical thickness ranges, the majority of the models simulated optically thick clouds more than twice the satellite observations. Most models, however, underestimated optically intermediate and thin clouds. Compensations of these clouds biases are used to explain the simulated longwave and shortwave cloud radiative forcing at the top of the atmosphere. Seasonal sensitivities of clouds are also analyzed to compare with observations. Models are shown to simulate seasonal variations better for high clouds than for low clouds. Latitudinal distribution of the seasonal variations correlate with satellite measurements at 0.9, 0.6-0.9, and -0.2-0.7 levels for high, middle, and low clouds, respectively. The seasonal sensitivities of cloud types are found to strongly depend on the basic cloud climatology in the models. Models that systematically underestimate middle clouds also underestimate seasonal variations, while those that overestimate optically thick clouds also overestimate their seasonal sensitivities. Possible causes of the systematic cloud biases in the models are discussed.