Monday, December 10, 2012

New paper finds more evidence of the 'poor performance' of climate models

A new paper published in the Journal of Climate finds there has been "little to no improvement" in simulating clouds by state-of-the-art climate models. The authors note the "poor performance of current global climate models in simulating realistic [clouds]," and that the models show "quite large well as a remarkable degree of variation" with the differences between models remaining "large." 

As Dr. Roy Spencer points out in his book
"The most obvious way for warming to be caused naturally is for small, natural fluctuations in the circulation patterns of the atmosphere and ocean to result in a 1% or 2% decrease in global cloud cover. Clouds are the Earth’s sunshade, and if cloud cover changes for any reason, you have global warming — or global cooling."

This new paper is one of many that demonstrate current climate models do not even approach the level of accuracy [within 1 - 2%] or 'consensus' required to properly model global cloud cover, and therefore cannot be used as 'proof' of anthropogenic global warming, nor relied upon for future projections.

Prior posts on clouds and the abject failure of climate models

Simulating clouds with global climate models: A comparison of CMIP5 results with CMIP3 and satellite data

Axel Lauer1,* and Kevin Hamilton1,2
1 International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii
2 Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii

Clouds are a key component of the climate system affecting radiative balances as well as the hydrological cycle. Previous studies from the Coupled Model Intercomparison Project Phase 3 (CMIP3) showed quite large biases in the simulated cloud climatology affecting all GCMs [global climate models] as well as a remarkable degree of variation among the models, which represented the state-of-the-art circa 2005. Here we measure the progress that has been made in recent years by comparing mean cloud properties, interannual variability, and the climatological seasonal cycle from the CMIP5 models with satellite observations and with results from comparable CMIP3 experiments. We focus on three climate-relevant cloud parameters: cloud amount, liquid water path, and cloud radiative forcing. We show that intermodel differences are still large in the CMIP5 simulations. We find some small improvements of particular cloud properties in some regions in the CMIP5 ensemble over CMIP3. In CMIP5 there is an improved agreement of the modeled interannual variability of liquid water path as well as of the modeled longwave cloud forcing over mid and high latitude oceans with observations. However, the differences in the simulated cloud climatology from CMIP3 and CMIP5 are generally small and there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5.

Comparisons of the results from the coupled CMIP5 models with their atmosphere-only versions run with observed SSTs show remarkably similar biases in the simulated cloud climatologies. This suggests the treatments of subgrid-scale cloud and boundary layer processes are directly implicated in the poor performance of current GCMs [global climate models or general circulation models] in simulating realistic cloud fields.

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