There are over 100 monitoring sites in forests across the globe that continuously measure the rate and magnitude of CO2 exchange between the forest and atmosphere, and some of these sites are now approaching, or have exceeded, ten years of measurements. Computer models of ecosystem processes, focusing on those involved with photosynthesis and respiration, will be used to evaluate the data from 20 of these forest monitoring sites in order to comparatively evaluate which forests have the highest potential to affect the atmospheric CO2 concentration, and why.
An understanding of the processes that result in the accumulation of atmospheric CO2 and thus, impacts to future climate warming, require that we understand how forest ecosystems across the globe exchange CO2 with the atmosphere, and ultimately sequester some of the CO2 in the trees and soils of the forest. The research will facilitate the development of new computer modeling approaches to be applied to CO2 flux monitoring sites including those in an emerging national network of environmental monitoring sites known as the National Ecological Observatory Network (NEON).
We analyzed data on the uptake of carbon dioxide by forests in the US in order to better understand how these ecosystems contribute to the carbon budget of the United States and the effect of climate warming on that contribution. We explored a novel way to incorporate multiple years of data into a computer model and use that model to inform us about which components of the forest ecosystems were most sensitive to climate variation. We discovered that in contrast to previous studies in which the stable isotope ratio of carbon dioxide was used as a means to estimate carbon uptake rates of ecosystems, our data could not be used reliably in this regard. Thus, we contradict some past studies which have relied on isotopes to accurately estimate carbon dioxide uptake rates. We also used an eleven year data set from a single forest ecosystem in the mountains of Colorado to estimate the parameters for a single ecosystem process model called SIPNET. We then used these parameters, along with the SIPNET model to predict how this forest would respond to future climate warming. we discovered that this forest is likely to increase in its capacity to take up carbon dioxide from the atmosphere due to warmer winters and a shift from snow to rain in the late winter months. These results, however, are oppostie to those obtained from previous studies in which data from only the current climate were used for prediction. In those studies, it was predicted that this forest would take up less carbon dioxide, not more, in a future warmer climate. There is a need to conduct follow-up analyses on these contrasting results to determine the exact response of this forest.
