Conceptual model diagram of the Millennial (top) and Century models (bottom). The black boxes are carbon pools, and the colored boxes are fluxes. Solid arrows indicate the direction of each flux. The color legend indicates edaphic, biological, and climatic factors that may modify the rate of a given flux. Dash lines indicate controls (i.e., microbial biomass regulates the depolymerization rate).
There may be a more direct way to estimate the amount of carbon that exists within soil due to expected changes in climate, according to new research. Scientists use mathematical models that evaluate the carbon content of soils to predict how global change affects the amount of carbon released from soil into the atmosphere as carbon dioxide. An ecologist working in the Climate Sciences Department within EESA’s Climate and Ecosystem Sciences Division led a team of researchers in developing the first model to extensively take into account the measurable chemical and physical qualities of soil.
Rose Abramoff says her team’s goal in creating their new Millennial Model was to build a tool that would reflect what scientists now know about chemical and physical changes within soils. The researchers, who represent Berkeley Lab, Oak Ridge and Argonne National Laboratories, and Colorado State University published the results of their work in the journal, Biogeochemistry.
This is the first model to simulate all of the following measurable soil components: microbial biomass, particulate organic matter, aggregation, low molecular weight carbon, and mineral-associated organic matter. Because all components of the model are measurable, considering them together can help produce more verifiable predictions about how factors like temperature, drought, or rainfall affect soil carbon composition.
The traditional Century Model relies on conceptual soil pools representing different rates of decomposition: active, passive, and slow. The model wasdeveloped in the 1970s for grasslands research by measuring the amount of decomposition in grasslands and assigning turnover rates to total soil organic carbon. It is now used widely in the study of the rate of soil decomposition in all types of ecosystems across the world.
“Both models are trying to predict soil decomposition – the amount of carbon that is released from soil as carbon dioxide – over time,” says Abramoff. “We think that it’s difficult to relate to – or derive accurate study results from – things that you can’t hold in a beaker. With the Millennial Model, we wanted to redefine the soil components used for these predictions with physical measurements.”
Abramoff’s team wanted to offer an alternative that enables researchers who study global change to quantify the amount of carbon in each of those pools – by experiment site, over a period of time, or both.
“Both models are trying to predict soil decomposition – the amount of carbon that is released from soil as carbon dioxide – over time,” says Abramoff. “We think that it’s difficult to relate to – or derive accurate study results from – things that you can’t hold in a beaker. With the Millennial Model, we wanted to redefine the soil components used for these predictions with physical measurements.”
The team compared their new model to the traditional model to evaluate differences in predictions when factors such as temperature, soil moisture, and clay content were considered. When just one factor – such as half the amount of water – was taken into account the models generated similar predictions. However, once the team imposed a combination of factors – for example, doubled plant inputs and an increase in temperature – the Millennial Model showed a loss in soil organic carbon where the Century Model showed a gain.
Time series of the Millennial (a) and Century (b) model predictions of the change in total soil carbon (C) relative to the control following a sustained change to temperature, moisture, plant inputs, or a combination for 2000 years. W 5 C warming, I double C input, D half water, WI warming and double C input, WD warming and half water
Abramoff says this could be the result of interactions among the different factors having a distinguishable effect on soil carbon makeup. One benefit of the Millennial Model is the possibility it creates to verify the accuracy of predictions with newly obtained field measurements.
The research team believes these results indicate that additional research into the potential for mathematical models like the Millennial Model to generate more-reliable climate predictions is warranted. “We have so many more tools at our disposal today that can help us make better predictions about the effects of global change. With technological advances come more measurements,” Abramoff says. “As modelers, we need to be ready to make use of them.”