average low cloud cover of 3.6 percent between 1952 and 1995. It is not clear what is driving this change; it could potentially be related to aerosol indirect effects.

It appears that the NCAR model may have a problem with predicting cloud optical properties in the upper troposphere and thus be missing additional negative feedbacks from tropical high clouds. Once this bias is corrected for, the model probably will end up with an even stronger net negative cloud feedback.

To address these uncertainties, we need a multifaceted approach to testing models against observations. Two strategies for future diagnosis of cloud parameterizations may involve (1) compositing observed response of low clouds to different meteorological conditions and (2) analysis of high cloud characteristics using Webb-Klein International Satellite Cloud Climatology Project (ISCCP cloud) simulations.5


Venkatchalam Ramaswamy discussed the results of a study to examine water vapor feedbacks in the Hadley and GFDL models, in comparison with available observations (Allan et al., 2002). Both models were input with the same reported sea surface temperatures and run over the specified period. The two models were quite consistent with regard to interannual changes in column water vapor and clear-sky outgoing longwave radiation. However, they appear to have given the same result for different reasons. The GFDL model has a stronger positive lapse rate feedback, while the Hadley model had a stronger water vapor feedback. So there was compensation between these two effects, yielding a similar net sensitivity. The differences between the GFDL and Hadley models have not been evaluated quantitatively. They are probably due primarily to different convection schemes, but possibly to different treatments of advection of water vapor. Regardless, it points to the fact that two models can give the same value for climate sensitivity, for different reasons.

Ramaswamy’s GFDL colleagues evaluated an Atmospheric Model Intercomparison Project (AMIP)6 study of the ability of atmospheric models to simulate interannual variability in tropical hydrological cycle intensity (30 models were included in the study) (Boyle, 1998). The models gave a reasonable simulation of variations in temperature, water vapor, and outgoing longwave radiation, but they underpredicted precipitation variations by a factor of four. Although there are substantial uncertainties associated with the satellite observations themselves, this disparity suggests that the models may be missing some process that causes variations in tropical organized convection and evaporation. This problem must be reconciled in order to have confidence in projecting future changes in hydrological cycle intensity.


Michael Prather, from the University of California, Irvine, discussed feedbacks related to changes in atmospheric composition. The observational record of increasing trace gas concentrations makes it clear that current atmospheric composition is significantly different from the pre-industrial era (e.g., see greenhouse gas atmospheric concentration time series in Figure 4, pg. 30), and it is likely that future atmospheric composition will be much different than today. Thus, we must be careful about extrapolating from one era to another and calibrating models to paleo-era data records.

Paleo-records (such as the Vostok ice core records) make it clear that natural feedback processes can lead to large changes in atmospheric concentrations of CO2, CH4 (methane), N2O (nitrous oxide), and other trace gases. In such a context, these changes can be viewed as climate feedbacks, rather than forcings. The relationships


This is a code developed by M. Webb and S. Klein that can be used to take information from atmospheric models and convert it into something that is comparable to data from the ISCCP. ISCCP is a collection of weather satellite radiance measurements that are used to infer the global distribution and temporal variation of clouds and their properties.


AMIP is a standard experimental protocol for global atmospheric general circulation models, which provides a community-based infrastructure in support of climate model diagnosis, validation, intercomparison, documentation, and data access. The infrastructure is supported by the PCMDI (Program for Climate Model Diagnosis and Intercomparison).

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