The image compares observed (left) and modeled (right) temperature trends over 1970-1999. (Arguably a smoothing filter would be put on the data for a comparison.) The spatial patterns are broadly correct, though the model (in this case CCSM) appears over-sensitive.
A University of Arizona press release says that research led by Dr. X. Zeng “has found that climate-prediction models are good at predicting long-term climate patterns on a global scale but lose their edge when applied to time frames shorter than three decades and on sub-continental scales.”
Nothing really surprising there. I’ve said that for years.
The publication, “The hindcast skill of the CMIP ensembles for the surface air temperature trend” is <a href=”http://www.agu.org/pubs/crossref/2012/2012JD017765.shtml”>here</a>
The scientists pointed out that although the IPCC issues a new report every six years, they didn’t see much change with regard to the prediction skill of the different models.
“The IPCC process is driven by international agreements and politics,” Zeng said. “But in science, we are not expected to make major progress in just six years. We have made a lot of progress in understanding certain processes, for example airborne dust and other small particles emitted from surface, either through human activity or through natural sources into the air. But climate and the Earth system still are extremely complex. Better understanding doesn’t necessarily translate into better skill in a short time.”
I’ve said that too, but not in that way. Rather than expressing confidence in the process, it causes me some concern. I am not convinced that the recently announced US NAS “National Strategy for Advancing Climate Modeling” actually provides a promising direction for further progress.