Eleventh Year Annual Report: New Model Capabilities
The Sarmiento and Pacala Groups are improving model representations of processes important in carbon-cycle and ecosystem models.
Simulating food web dynamics in the Eastern Subarctic Pacific
The Sarmiento Group developed an end-to-end model architecture that can be used to investigate the ecosystem response to perturbations at both the top and bottom of the food chain. A prototype version of the model has been developed for the Eastern Subarctic Pacific ecosystem. The Eastern Subarctic Pacific has shown decadal variations in ecosystem state, sometimes characterized as regime shifts, that are correlated to the Pacific Decadal Oscillation and have been documented at several trophic levels, from primary producers to top predators. While correlative links between changes in physical properties and population changes at various levels of the food chain have been demonstrated, the underlying mechanisms of these population shifts remain unclear. The end-to-end model for the region fully couples a one-dimensional physical model, a biogeochemical model, and a predator-prey food web model, allowing two-way feedback between all trophic levels. The model has been shown to be capable of reproducing both seasonal and annual climatological conditions, and maintains robust and stable population dynamics when run over decadal to centennial timescales.
Incorporating the role of heterotrophic bacteria in ocean carbon sequestration
One of the main ways carbon is transported and stored in the deep ocean is by sinking of organic particles as part of the "biological pump." Most sinking particles are remineralized (transformed from organic carbon to carbon dioxide) in the mesopelagic zone (150 to 1000 m depth) due to the activity of heterotrophic bacteria. The Sarmiento Group has developed an ecosystem modeling approach to understand the mechanism connecting heterotrophic bacteria with sinking particles in the mesopelagic zone. The 1-dimensional idealized model of a sinking particle includes freeliving and attached heterotrophic bacteria, particulate and dissolved organic matter, extracellular enzyme, and hydrolysate. The model is currently being used to determine the predominant process connecting heterotrophic bacteria and sinking particles by manipulating attachment and detachment rates and observing the effect on the free-living bacteria abundance and the remineralization of the sinking particle. Preliminary results indicate that attachment and detachment rates are important for dispersing surface bacteria to the deep ocean.
One of the benefits of this model is that it can be integrated with the dynamics of existing models, i.e. the Martin Curve and the ballast model, currently used in IPCC-class models to predict particle attenuation in the deep ocean. This application will improve understanding of the effects of climate change on carbon sequestration in the ocean because it accounts for the non-linear effects of temperature on the microbial loop, including the rates of molecular diffusivity as well as the rates of bacterial growth and extracellular enzyme function. The processes in the bacterial enzyme model are temperature dependent, so future work will include ocean temperature profiles from the IPCC-class general circulation models to forecast the effects of climate change on sinking particle remineralization over the next century.
Improved representations of fire in vegetation models
Global models of vegetation fire are critical for understanding the carbon cycle, but so far none have considered human land uses or the types of fires that result from different land use. Graduate student Sam Rabin is using satellite observations of fire around the world, combined with cropland, pasture, and deforestation data, to identify the fingerprint of each of these land uses on burning. This will not only improve the Princeton model's projections of future burning, but also allow estimation of the fraction of observed fire attributable to each. Future applications of this work include use in global vegetation models as well as helping inform the development of policy related to local/regional fire management and international carbon management efforts such as REDD+.