Microsoft scientist highlights urgent need for new computer models to address clim...( Two papers published in the journal ...)

Two papers published in the journal Science today* by Microsoft Research ecologist Drew Purves together with research colleagues at Princeton University and universities in Madrid, Spain, highlight how an improved understanding of forest dynamics is needed to better predict environmental change. The research suggests that a new generation of realistic forest modelling, which is urgently needed and now within reach, will significantly improve an understanding of how forests work, how tree species respond to deforestation, and how forests impact climate regulation and environmental change.

The research points out that forest dynamics (how populations of trees interact with each other and the environment) remains the single most important outstanding component in fully understanding climate change. There trillions of trees on the planet, made up of more than 100,000 species, which contain as much carbon as is currently in the atmosphere and serve as home to two-thirds of the planet's terrestrial biodiversity. However, while other climate change factors such as ocean dynamics are now well researched, the effects of changes to the world's forests are still largely unknown.

The paper "Predictive Models of Forest Dynamics" by Purves and Princeton's Stephen Pacala explores dynamic global vegetation models (DGVMs), which simulate the reaction of forests to past, present and future climate.

"DVGMs have shown that forests could be a crucial part of the way the Earth's climate responds to man-made CO2 emissions, but insufficient understanding of forests, and insufficient data and computing power, have made their predictions highly uncertain," Purves said. "This kind of uncertainty helps climate sceptics, who erroneously conclude that because the Earth is a complex but poorly understood system, we should not change our behaviour. However, we suggest that the convergence of recently developed mathematical models, improved data sources and new metho
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