A Caltech Library Service

Southeast Atmosphere Studies: learning from model-observation syntheses

Mao, Jingqiu and Carlton, Annmarie and Cohen, Ronald C. and Brune, William H. and Brown, Steven S. and Wolfe, Glenn M. and Jimenez, Jose L. and Pye, Havala O. T. and Lee Ng, Nga and Xu, Lu and McNeill, V. Faye and Tsigaridis, Kostas and McDonald, Brian C. and Warneke, Carsten and Guenther, Alex and Alvarado, Matthew J. and de Gouw, Joost and Mickley, Loretta J. and Leibensperger, Eric M. and Mathur, Rohit and Nolte, Christopher G. and Portmann, Robert W. and Unger, Nadine and Tosca, Mika and Horowitz, Larry W. (2018) Southeast Atmosphere Studies: learning from model-observation syntheses. Atmospheric Chemistry and Physics, 18 (4). pp. 2615-2651. ISSN 1680-7324.

[img] PDF - Published Version
Creative Commons Attribution.


Use this Persistent URL to link to this item:


Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts.

Item Type:Article
Related URLs:
URLURL TypeDescription
Cohen, Ronald C.0000-0001-6617-7691
Brune, William H.0000-0002-1609-4051
Wolfe, Glenn M.0000-0001-6586-4043
Jimenez, Jose L.0000-0001-6203-1847
Pye, Havala O. T.0000-0002-2014-2140
Xu, Lu0000-0002-0021-9876
Guenther, Alex0000-0001-6283-8288
de Gouw, Joost0000-0002-0385-1826
Mickley, Loretta J.0000-0002-7859-3470
Additional Information:© Author(s) 2018. This work is distributed under the Creative Commons Attribution 3.0 License. Received: 29 Nov 2016. Discussion started: 23 Dec 2016. Revised: 23 Dec 2017. Accepted: 07 Jan 2018. Published: 22 Feb 2018. This work is based on a workshop held in GFDL in 2015, funded by the National Science Foundation Atmospheric Chemistry Program (AGS-1505306). Jose L. Jimenez was supported by EPA STAR 83587701-0 and NASA NNX15AT96G. We acknowledge Haofei Yu (University of Central Florida), Vaishali Naik (NOAA GFDL), Tom Knutson (NOAA GFDL), John Crounse (Caltech), Paul Wennberg (Caltech), Daniel Jacob (Harvard), Jen Kaiser (Harvard), Luke Valin (EPA), Petros Vasilakos (Georgia Tech), Arlene Fiore (Columbia), Nora Mascioli (Columbia), Yiqi Zheng (Yale), Tzung-May Fu (PKU), Michael Trainer (NOAA ESRL), Siwan Kim (NOAA ESRL), Ravan Ahmadov (NOAA ESRL), Nick Wagner (NOAA ESRL) and Eladio Knipping (EPRI) for their contributions. We also acknowledge travel supports from US Environmental Protection Agency (EPA) NOAA Climate Program Office and the Cooperative Institute for Climate Science (CICS) at Princeton University. In particular, we would like to thank the Princeton and GFDL staff for support on logistics. We would also like to thank Ann Marie Carlton’s group (Thien Khoi Nguyen, Caroline Farkas, Neha Sareen) and Luke Valin for additional support on meeting logistics.
Funding AgencyGrant Number
Environmental Protection Agency (EPA)83587701-0
National Oceanic and Atmospheric Administration (NOAA)UNSPECIFIED
Cooperative Institute for Climate ScienceUNSPECIFIED
Issue or Number:4
Record Number:CaltechAUTHORS:20180307-102640884
Persistent URL:
Official Citation:Mao, J., Carlton, A., Cohen, R. C., Brune, W. H., Brown, S. S., Wolfe, G. M., Jimenez, J. L., Pye, H. O. T., Lee Ng, N., Xu, L., McNeill, V. F., Tsigaridis, K., McDonald, B. C., Warneke, C., Guenther, A., Alvarado, M. J., de Gouw, J., Mickley, L. J., Leibensperger, E. M., Mathur, R., Nolte, C. G., Portmann, R. W., Unger, N., Tosca, M., and Horowitz, L. W.: Southeast Atmosphere Studies: learning from model-observation syntheses, Atmos. Chem. Phys., 18, 2615-2651,, 2018.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85177
Deposited By: Ruth Sustaita
Deposited On:07 Mar 2018 18:57
Last Modified:09 Mar 2020 13:19

Repository Staff Only: item control page