Collaborative Research: Evaluation of a Coupled Meteorology-Chemistry Model against Long-term Measurements of Elemental Carbon and Sulfate for Regional Climate Applications

Principal Investigator: Uma Shankar
Project Status: ACTIVE

This is a collaborative research proposal between the State University of New York (SUNY) and the Center for Environmental Modeling for Policy Development (CEMPD) at the University of North CarolinaInstitute for the Environment. Its primary objectives are to: (1) determine the elemental carbon concentrations, {EC}, in archived filters collected at Mayville, and ~530 km downwind at Whiteface Mt, NY, for a summer and a winter month in two different years; and (2) to use these data to evaluate a coupled meteorology-chemistry model developed by CEP researchers for the chemistry and transport of particulates, ozone and their precursors, and their radiative feedbacks to the atmospheric dynamics. The project is motivated by the fact that particulate black carbon (BC), which is treated equivalent to EC in the measurements, is a strong absorber of solar radiation, and thought to account for ~15-30% of global warming estimates, next only to greenhouse gases; however, the magnitude of this forcing on climate is highly uncertain. With a mean atmospheric residence time of about a week,, aerosol BC can travel thousands of kilometers before removal from the atmosphere; thus its measured concentrations are highly dependent on the distance from emission sources. Reliable estimation of the radiative impacts in the U.S. from nearby and remote sources of BC depends critically on obtaining region¬ally representative measurements. Atmospheric chemistry models driven by realistic emissions and meteorological inputs are often used to fill information gaps due to the spatial sparseness of such measurements; to improve the reliability of these models, they must in turn be evaluated against field observations. While intensive field campaigns provide temporally dense data for this purpose, they typically lack seasonality due to their short duration, and may not be regionally representative. Long-term, regionally representative observations are critical to the evaluation of such models under different meteorological conditions, to understand the seasonal and regional biases in their predictions, and reduce uncertainties in their climate forcing estimates for BC, and other radiatively important pollutants such as SO4 and O3. BC is particularly useful for evaluating predicted aerosol spatial distributions, and radiative impacts. Being chemically inert, it is a good tracer to examine the effects of meteorology on atmospheric loadings; and has strong radiative characteristics.

The project thus seeks to determine the {EC} in filters using the thermal-optical method collected every 6 h during summer and every 48 h during the winter of 1998 and 2002 at Whiteface Mt, and every 24 h at Mayville for the same years. In addition, {SO4} have already been determined, and real-time measurements of ozone are available for both the sites for the duration. Prior studies have shown EC and SO4 measurements from these two sites to be regionally representative and highly correlated with known import of upwind airmasses. The measurements will be used to evaluate METCHEM, a model that dynamically couples the mesoscale meteorology of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5) with algorithms for particulates and gas-phase species chemistry and transport simulated by the Multiscale Air Quality Simulation Platform (MAQSIP), to simulate their radiative feedbacks to the atmospheric dynamics. Evaluation of such models is critical to improving our understanding of the regional climate forcing exerted by radiatively important atmospheric pollutants. Nested continental- to-regional-to-local-scale simulations over the observational sites will allow us to examine the effects of long-range transport and grid refinement on the model predictions. The model sensitivity to emissions growth and control, and to successive improvements in the representation of sources such as wild fires and wind-blown dust will be examined using three different emission inventories in the input data.

The proposed project will build upon, and considerably augment current NSF-funded measurement and modeling projects being performed by both participating organizations, and will help evaluate a model used to predict the downwind burden and climate impacts of EC and other emissions from fossil fuel consumption. The evaluated model and the spatially and temporally rich datasets it generates will provide valuable tools in illuminating the impacts of these emissions on climate.