Evaluating Modeled Impact Metrics for Human Health, Agriculture Growth, and Near‐Term Climate. Issue 24 (26th December 2017)

• Record Type: Journal Article
• Authors: Seltzer, K. M.; Shindell, D. T.; Faluvegi, G.; Murray, L. T.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 24(2017)
• Page Start: 13, 506
• Page End: 13, 524
• Publication Date: 2017-12-26
• Subjects: global modeling -- atmospheric composition -- air quality -- near‐term climate -- impact metrics; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD026780
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Simulated metrics that assess impacts on human health, agriculture growth, and near‐term climate were evaluated using ground‐based and satellite observations. The NASA GISS ModelE2 and GEOS‐Chem models were used to simulate the near‐present chemistry of the atmosphere. A suite of simulations that varied by model, meteorology, horizontal resolution, emissions inventory, and emissions year were performed, enabling an analysis of metric sensitivities to various model components. All simulations utilized consistent anthropogenic global emissions inventories (ECLIPSE V5a or CEDS), and an evaluation of simulated results were carried out for 2004–2006 and 2009–2011 over the United States and 2014–2015 over China. Results for O3 ‐ and PM2.5 ‐based metrics featured minor differences due to the model resolutions considered here (2.0° × 2.5° and 0.5° × 0.666°) and model, meteorology, and emissions inventory each played larger roles in variances. Surface metrics related to O3 were consistently high biased, though to varying degrees, demonstrating the need to evaluate particular modeling frameworks before O3 impacts are quantified. Surface metrics related to PM2.5 were diverse, indicating that a multimodel mean with robust results are valuable tools in predicting PM2.5 ‐related impacts. Oftentimes, the configuration that captured the change of a metric best over time differed from the configuration that captured the magnitude of the same metric best, demonstrating the challenge in skillfully simulating impacts. These results highlight the strengths and weaknesses of these models in simulating impact metrics related to air quality and near‐term climate. With such information, the reliability of historical and future simulations can be better understood. Key Points: Predictions related to surface impact metrics of O3 were consistently high biased and strongly influenced by meteorological drivers Predictions related to surface impact metrics of PM2.5 were diverse and strongly benefit from scaling to a national emission inventory Model resolution provided minor differences in simulating changes to metrics over time, with meteorology and model playing a stronger role