||This research addresses an important DOE science area: the role of natural disturbances in carbon cycling. Our project includes both (a) modeling and field measurements and (b) strengthening ties between DOE national laboratories and universities. Our focus is on understanding the consequences of drought and emissions (fire and urban) to atmospheric trace gas composition (carbon dioxide, carbon monoxide, and methane), including both concentration and stable isotope composition. Measurements of these atmospheric gases are used to infer the spatial and temporal patterns of both sources and sinks in the carbon cycle. As part of this effort, we will maintain two long-term monitoring networks along a geographic gradient in Utah and Colorado that spans montane forests, urban regions, and oil/gas fields. In this research, we expand on a well-accepted atmospheric model (STILT-WRF) as a tool to evaluate fluxes in CLM/CESM-related models through (a) existing long-term data sets, (b) ongoing monitoring networks, and (c) field campaigns using a mobile observatory. We will use the STILT model to provide a strong linkage between point atmospheric measurements and the surface parameterizations/emissions that are part of the CLM/CESM models. Since trace gases associated with source and sink fluxes differ in their stable isotope composition, our isotopic analyses of trace gases will allow us to partition changes in carbon dioxide, carbon monoxide, and methane fluxes in CESM into natural (drought and fire related) versus anthropogenic components. Overall, this project benefits and supports the DOE Long Term Mission and Goals in four distinct ways: (a) testing of carbon cycle models, (b) testing and evaluating model mechanisms whereby factors in CLM influence trace gas composition in CESM, (c) acquiring high-quality, long-term data on concentrations and isotope ratios of carbon dioxide, carbon monoxide, and methane in western USA ecosystems, and (d) by reducing uncertainties associated with the representation of climate-carbon feedbacks in Earth System models through the development of new methods for evaluating model performance.