Thus, only 6.4% of observed altocumulus clouds are simulated or predicted by climate models. Needless to say, clouds have profound effects on Earth's radiative balance and climate; a mere 1-2% change in global cloud cover alone can account for global warming or cooling. Among their many failings, climate models are unable to simulate clouds, ocean oscillations, solar amplification mechanisms, precipitation, sea ice, albedo, convection, etc. etc.
- Climatology of Ac horizontal scale and vertical depth is presented.
- 93.6% of Altocumulus clouds cannot be resolved by GCMs with a grid resolution of 1°.
- Ac scale distributions are related to their formation mechanisms.
- Ac vertical depth is impacted by CTT and environmental humidity.
Altocumulus (Ac) clouds are important, yet climate models have difficulties in simulating and predicting these clouds, due to their small horizontal scales and thin vertical extensions. In this research, 4 years of collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and CloudSat radar measurements is analyzed to study the along-track horizontal scales and vertical depths of Ac clouds. Methodology to calculate Ac along-track horizontal scale and vertical depth using collocated CALIPSO and CloudSat measurements is introduced firstly. The global mean Ac along-track horizontal scale is 40.2 km, with a standard deviation of 52.3 km. Approximately 93.6% of Altocumulus clouds cannot be resolved by climate models with a grid resolution of 1°. The global mean mixed-phase Ac vertical depth is 1.96 km, with a standard deviation of 1.10 km. Global distributions of the Ac along-track horizontal scales and vertical depths are presented and possible factors contributing to their geographical differences are analyzed. The result from this study can be used to improve Ac parameterizations in climate models and validate the model simulations.
Modeling is an art that uses science to create scenarios based partly on simulation and partly on parameterizaton.ReplyDelete
Models do not actually simulate cloud formation. Clouds are parameterized in the models. That means clouds are incorporated by formula, the parameters of which can tune the output to fit what the modelers believe is correct.
What this study reveals is that the cloud forming parameters have not been tuned correctly and therefore their potential as a feedback mechanism may have been underestimated. And since alto-cumulus clouds have potentially a negative feedback effect, correction of the model errors might actually show that clouds counteract the effect of CO2. If CO2 has a warming effect while clouds have a cooling effect, these two effects might work together as a thermostat to keep the Earth''s temperature within a narrow range.
(Alto-cumulus clouds are important for Svensmark's theory of cosmic particles acting as nuclei for cloud formation, but although related, that is a separate issue.)
Correcting the models might eventually force a search for a different mechanism that would explain global warming between 1950 and 2000, the only period when CO2 and other GHG emissions are claimed to have been sufficient to cause AGW.
The AMO (Atlantic Multidecadal Oscillation) is the best candidate for a natural cause for warming after around 1970. (See the graphic in Wikipedia from a US Government source.)
What a travesty of science the AGW "consensus" would turn out to be if it is determined that the "consensus" is based on falsely attributing to GHGs the natural climate warming effect of the positive phase of the AMO.
another paper finding climate models unable to resolve altocumulus cloudsReplyDelete