A new method for projecting how global temperatures respond to human influences supports the outlook for substantial global warming throughout this century, but the method also indicates that, in many regions, warming patterns are likely to vary significantly from those estimated by widely used computer models, according to McGill University in Montreal, Que.

The new method, outlined by McGill University researchers in Geophysical Research Letters, is based on historical temperature increases in response to rising greenhouse gas concentrations and other climate influences. This approach could be used to complement the complex global climate models, filling a need for more reliable climate projections at the regional scale, the researchers said.

"By establishing a historical relationship, the new method effectively models the collective atmospheric response to the huge numbers of interacting forces and structures, ranging from clouds to weather systems to ocean currents," said McGill physics professor Shaun Lovejoy, a senior author of the study.

"Our approach vindicates the conclusion of the Intergovernmental Panel on Climate Change (IPCC) that drastic reductions in greenhouse gas emissions are needed in order to avoid catastrophic warming," he added. "It also brings some important nuances and underscores a need to develop historical methods for regional climate projections in order to evaluate climate change impacts and inform policy."

In particular, the new approach suggests that for more than 39% of the globe, the computer models either overestimate or underestimate the pace of warming significantly, according to Lovejoy and his co-author, doctoral student Raphaël Hébert (who is now at the Alfred-Wegener-Institut in Potsdam, Germany).

"Global climate models are important research tools, but their regional projections are not yet reliable enough to be taken at face value," Hébert and Lovejoy said. "Historical methods for regional climate projections should be developed in parallel to traditional global climate models. An exciting possibility for further improvements will be the development of hybrid methods that combine the strengths of both the historical and traditional approaches."