Observed Global Temperatures (black curve) compared with the spread among model simulations that include only natural factors (blue band) and that include both natural and human factors (red bad). Only the model simulations that include human factors are able to reproduce the observed warming Graphic: Second Assessment of the U.S. Global Change Research Program, 2009

By Michael E. Mann, Ph.D., Distinguished Professor, Pennsylvania State University
[Text excerpted from chapter 2 of The Hockey Stick and the Climate Wars, by Michael E. Mann, Columbia University Press, New York, 2012, 395pp] (wunderground.com) – By the mid-1990s, it was possible to investigate the causal mechanisms behind changes in Earth’s climate using relatively sophisticated mathematical models of Earth’s climate. These models solved the same complex equations of atmospheric physics that numerical weather prediction models did. But they also took into account components of the climate system other than the atmosphere, including the oceans, the continental ice sheets, and even life on Earth (collectively known as the “biosphere”), and they attempted to account for the physical, chemical, and biological interactions among these components. Of course, no theoretical model is ever perfect; even the best model is only an idealization of the actual world. There are always real-world processes that cannot be captured—for example, in the case of a numerical climate model, individual clouds or small-scale air currents like dust devils—that are simply too small for the model to resolve. The key question is, can the model be shown to be useful? Can it make successful predictions? Climate models had passed that test with flying colors by the mid-1990s. James Hansen, in the late 1980s, successfully predicted the continuing warming that would be observed by the mid-1990s. Even something the model couldn’t have predicted in advance—the 1991 eruption of Mount Pinatubo in the Philippines—provided yet another key test. As soon as the eruption occurred, Hansen put what was known about the reflective qualities of volcanic sulfur particulates (known as sulfate “aerosols”) into the simulations. The aerosols cooled surface temperatures for several years in the model by shielding the surface from a fraction of incoming sunlight, leading Hansen to make what turned out to be a successful prediction of the temporary cooling that was seen over the ensuing few years. Finally, perhaps most significant of all, only when human factors were included could the models reproduce the observed warming—both its overall magnitude and, equally important, its geographical pattern over Earth’s surface and its vertical pattern in the atmosphere. The primary such human factor was increasing greenhouse gas concentrations due to fossil fuel burning and other human activities. A secondary human factor, sulfate aerosols emitted from industrial smokestacks, also played a role, however. Like volcanic sulfate aerosols, these industrial aerosols have a cooling effect. Unlike volcanic aerosols, which reach the lower stratosphere, allowing them to spread out into a layer covering the globe, industrial aerosols remain confined to the lower atmosphere, leading to localized patterns of cooling that offset global warming in some regions. The pattern of warming predicted by the models from the combination of these two human effects on the climate provide a unique “fingerprint” of what the human influence on climate should look like if the models were correct, and the fingerprint matched. The surface and lower atmosphere showed an irregular pattern of warming, while the atmosphere aloft was cooling, just as the models indicated it should. The fingerprint predicted for natural factors alone—for example, from fluctuations in solar output—on the other hand, failed to match the observations. [more]

How Do We Know Humans are Responsible for Global Warming?