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Microsoft says the answer to bad models is not to give up on modeling, but to develop better models

New York City is home to some of the world's most attractive models;  it is also home to some of the least attractive ones, presented yearly to the United Nations' International Panel on Climate Change (IPCC).  The current state of global warming modeling has been rather poor, detracting both from research indicating anthropogenic influence and that which contraindicates it.  The result is that the debate about climate control, an issue which effects major economic policy decisions, is monopolized by this distraction.

Microsoft Research ecologist Drew Purves acknowledges that this problem is one of the largest ones confronting global warming researchers.  He and researchers at Princeton University and universities in Madrid, Spain are calling on the international research community to not throw out modeling or focus on the poor current models, but rather to develop new, better models.  In particular, they point out a rather common sense start point -- as forests and other plant populations form the crux of the carbon balance, a better understanding of their effects and how to model them needs to be developed and needs to help form the foundation of future models.

Examining deforestation, forest populations and how they effect the carbon balance is both essential and possible with current technology, believes Purves.  While atmospheric equations are important, it's illogical to leave out one of the most important carbon utilities on Earth, forests.  Atmospheric dynamics are well known, but forests, with over 1 trillion trees, from 100,000 species, are still a mystery for lack of knowledge.  What we do know is that these trees hold as much carbon as is currently in the atmosphere, and additionally support two-thirds of the planet's biodiversity.

Purves and Princeton's Stephen Pacala published a paper "Predictive Models of Forest Dynamics", which explores a new branch of modeling dynamic global vegetation models (DGVMs), which simulates forests in the past, present, and future and their effects on climate. Purves states:

[DGVMs] 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.  This kind of uncertainty helps climate skeptics, who erroneously conclude that because the Earth is a complex but poorly understood system, we should not change our behavior. However, we suggest that the convergence of recently developed mathematical models, improved data sources and new methods in computational data analysis could produce more realistic models. That would give us truly invaluable information to help manage the world's forests and understand their impact on our climate.

Indeed, climate change skeptics are quick to pounce on such models.  However, Purves aptly points out that it is counterproductive to merely blast deficient models, rather it is favorable to acknowledge the deficiency and work towards remedying it.

Says Pacala, "Until now, one of the most important pieces of the climate change jigsaw has been missing.  We argue that we can significantly further our understanding of forest dynamics if scientists work together to use new computational techniques and data sources — provided governments and others make more data available in useful forms. We feel that these discoveries could unlock the climate change mysteries of forests on a global scale in as little as five years."

The pair's paper appears in the journal Science.  Also appearing in the journal is a joint study entitled "Animal vs Wind Dispersal and the Robustness of Tree Species to Deforestation," written by Daniel Montoya from the Universidad de Alcalá in Madrid and Purves in Cambridge, with Miguel A. Rodríguez of the Universidad de Alcalá and Miguel A. Zavala of Centro de Investigación Forestal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CIFOR) in Madrid.  Both papers are available here, from Microsoft.

The new study provides intriguing insight into forest growth and resiliency based on vast data sets collected from 90,000 tree plots in Spain.  It found that three common species of tree that are wind pollinated are far more vulnerable to deforestation than others.  Also it found that no animal seed disperser existed in the ecosystem anymore, leaving several animal dispersed species very vulnerable.

Montoya explains how this research could be applied to smarter conservation efforts, stating, "By applying various methods in computational data analysis to a large source of forest data, we have confirmed that, in Spain at least, plants with animal-dispersed seeds are less vulnerable to habitat loss, because animals provide trees with an intelligent dispersal mechanism, traveling and distributing seeds between areas of remaining forest. In contrast, a wind dispersal method is more susceptible to habitat loss, as seeds are more likely to fall in inhospitable environments. Using methods like this, conservationists can identify the species at most risk following deforestation, and use this knowledge to develop new strategies to mitigate the effects of widespread habitat loss and help to protect species diversity."

Microsoft's Purves says it's not just about the trees and animals either; he states, "It is imperative that we create the tools and science to accurately understand the reaction of ecosystems to climate change and other forces — not just for plants and animals, but for our children and succeeding generations."

Purves is the leader of the Computational Science Research at Microsoft Research Cambridge.  His multidisciplinary team features ecologists, biologists, neuroscientists, mathematicians and computer scientists.  Their goals is to develop novel theories, better models, and better computational resources to tackle societal challenges such as climate change, declining biodiversity, and gaining an understanding of how life functions on a most basic scale.





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