Croplands encompass roughly one fifth of the total land area of the continental USA, and make significant contributions to regional scale exchanges of carbon, water, and energy between the surface and atmosphere. We propose to use a coupled land-atmosphere modeling system (SiBcrop-RAMS) in analyzing these exchanges across most of North America, considering the interannual variability of weather, management, and changes in climate across the continent.
SiBcrop (Lokupitiya et al., 2009), a model specifically designed for land-atmosphere exchanges from croplands, was developed in the first phase of the project. SiBcrop was then tested for the Mid Continental Intensive (MCI) region under the North American Carbon Program (NACP), by evaluating its performance against the observed carbon exchanges at a variety of temporal scales at Ameriflux Eddy Covariance Flux tower sites with maize, soybean, and wheat. The model was subsequently coupled with RAMS, a mesoscale meterological model, and regional scale analyses have been performed using this coupled modeling system (Corbin, 2008).
Our objective is to compare, diagnose and reconcile estimates of CO2 exchange between atmospheric inversions and inventories at a regional scale, based on a quantitative synthesis of results from studies that have been funded through the North American Carbon Program (NACP). This study will rely on remote sensing products to evaluate and reconcile the two approaches, along with supplementary data on other surface features such as edaphic characteristics. The result will be a more complete accounting of CO2 fluxes with reduced uncertainties, while also identifying source-sinks that are not well understand given the studies included in the synthesis.
The methodology developed in this study will also likely have a direct application for US emission reporting and subsequent climate change policy development relying on this information. Currently, reporting of emissions is based solely on information from inventories (EPA 2007), which subdivide the flux into various sources and sinks, such as the role of energy production and consumption, forestlands, cropland soils, and land use change. Understanding of source-sink patterns is needed to target specific activities for reducing greenhouse gas emissions. The proposed method retains the source-sink attribution provided by the inventories, but also incorporates a top-down constraint based on inverse modeling of measured CO2 concentration change in the atmosphere.