Woods Hole Research Center

Model results displaying annual net primary production over boreal North America. These results were derived from a production efficiency model (PEM).

Currently, Woods Hole Research Center scientists are focused on explaining how patterns of carbon storage and release are important to boreal forests. This research takes data collected in the field at individual points and uses it to better interpret satellite imagery that covers a much larger area. Specifically, combining field measurements of carbon exchange between the ecosystem and the atmosphere with remote sensing allows researchers to infer patterns of carbon sequestration in different ecosystems that have experienced different kinds of disturbance.

This work improves the understanding of carbon dynamics in current forests, illustrating how forests can recover from fires and disease outbreaks. To know how the patterns of carbon sequestration in boreal forests will respond to change, more about the ecological and physiological processes that control the exchange of carbon between the atmosphere and the ecosystem must be known. Predictive process-based ecosystem modeling, which is a way to simulate the response of the forests to changes to the elements that control plant growth, can accomplish this. Computer models can simulate the response of the forests to changes in the elements that control tree growth, like temperature and soil moisture. This “mechanistic” approach means that the forests are grown using physiological principles and calculations, rather than through statistical inference. This, in turn, allows the prediction of future landscapes by using various projections of future climates outside the range of current data to forecast what will happen to the carbon cycle in the coming decades.

Center researchers are using a biome-specific biogeochemical cycling model (BIOME - BGC) to understand relationships between climate, ecosystem dynamics, and carbon cycling. This is accomplished using a combination of meteorological data (such as solar radiation and precipitation) and physiological parameters specific to different vegetation types. The meteorological data be collected from research sites, or predicted from widely available weather station data using a microclimate model based upon mathematical expressions of physical principles. Physiological parameters are from previously published research studies. BIOME-BGC allows WHRC scientists to change the type of forest simulated (aspen versus spruce, for instance) or even to let multiple forests types compete for resources in order to understand how changes in ecosystem composition impact carbon cycling, or respond to microclimate model changes in climate. The model's skill at capturing boreal carbon dynamics can be tested by comparing the simulated ecosystem processes for the present conditions with field measurements of the real ecosystem.

Schematic illustrating the effects of fire on forest state and carbon exchange in a typical boreal forest.

By running these simulations millions of times with subtle changes in the model inputs, researchers can simulate forest ecosystem processes for many years and under many different climate scenarios. Because forecasting future climates is difficult, a significant advantage of models is that a variety of climate scenarios can be used to simulate the range of outcomes. A key advantage to ecosystem modeling is that it helps systematically organize current knowledge of the ecology of boreal forests and provides quantitative estimates that can be field tested. As independent climate model estimates improve, the new forecasts can identify increasingly probable changes to the carbon cycle in boreal forests.