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Multiscale Modeling of Multi-Decadal Trends in Air Pollutant Concentrations & Radiative Properties: The Role of Models in an Integrated Observing System
Citation:
Mathur, R., J. Xing, AND M. Gan. Multiscale Modeling of Multi-Decadal Trends in Air Pollutant Concentrations & Radiative Properties: The Role of Models in an Integrated Observing System. American Geophysical Union, San Francisco, CA, December 14 - 18, 2015.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
EPA’s coupled WRF-CMAQ modeling system is applied over a domain encompassing the northern hemisphere for the period spanning 1990-2010. This period has witnessed significant reductions in anthropogenic emissions in North America and Europe as a result of implementation of control measures and dramatic increases across Asia associated with economic and population growth, resulting in contrasting trends in air pollutant distributions and transport patterns across the northern hemisphere. Model results (trends in pollutant concentrations, optical and radiative characteristics) across the northern hemisphere are analyzed in conjunction with surface, aloft and remote sensing measurements to contrast the differing trends in air pollution and aerosol-radiation interactions in these regions over the past two decades. Given the future LEO (TropOMI) and GEO (Sentinel-4, GEMS, and TEMPO) atmospheric chemistry satellite observing capabilities, the results from these model applications will be discussed in the context of how the new satellite observations could help constrain and reduce uncertainties in the models.
