No Matter How Well You Model it, Humans are to Blame
New research appearing in the online issue of the Proceedings of the U.S. National Academy of Sciences, Lawrence Livermore National Laboratory scientists and a group of international researchers found that climate model quality does not affect the ability to identify human effects on atmospheric water vapor. Since atmospheric water vapor is an important driver of temperatures and rainfall, the results of this study will help convince skeptics that man's impacts are causing at least part of the problem.
The physics that drive changes in water vapor are very simple and are reasonably well portrayed in all climate models, bad or good.
More water vapor - which is itself a greenhouse gas - amplifies the warming effect of increased atmospheric levels of carbon dioxide.
Previous LLNL research had shown that human-induced warming of the planet has a pronounced effect on the atmosphere's total moisture content. In that study, the researchers had used 22 different computer models to identify a human "fingerprint" pattern in satellite measurements of water vapor changes. Each model contributed equally in the fingerprint analysis. "It was a true model democracy," Santer said. "One model, one vote."
But in the recent study, the scientists first took each model and tested it individually, calculating 70 different measures of model performance. These "metrics" provided insights into how well the models simulated today's average climate and its seasonal changes, as well as on the size and geographical patterns of climate variability.
This information was used to divide the original 22 models into various sets of "top ten" and "bottom ten" models. "When we tried to come up with a David Letterman type 'top ten' list of models," Santer said, "we found that it's extremely difficult to do this in practice, because each model has its own individual strengths and weaknesses."
Then the group repeated their fingerprint analysis, but now using only "top ten" or "bottom ten" models rather than the full 22 models. They did this more than 100 times, grading and ranking the models in many different ways. In every case, a water vapor fingerprint arising from human influences could be clearly identified in the satellite data.
"One criticism of our first study was that we were only able to find a human fingerprint because we included inferior models in our analysis," said Karl Taylor, another LLNL co-author. "We've now shown that whether we use the best or the worst models, they don't have much impact on our ability to identify a human effect on water vapor."
Photo shows total amount of atmospheric water vapor over the oceans on July 4, 2009. These results are from operational weather forecasts of the European Centre for Medium-Range Weather Forecasting.
For more information: https://publicaffairs.llnl.gov/news/news_releases/2009/NR-09-08-01.html