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BER Research Highlights


Maximizing Ozone Signals
Published: May 15, 2018
Posted: November 19, 2019

Technique enables efficient and precise estimates of trends in ozone and other atmospheric constituents within selected geographical regions and timeframes.

The Science
Ozone is an atmospheric constituent that affects not only human health but also vegetation, especially annual crops. Thus, ozone can impact land and water use. Detecting increases and declines in ozone and other constituents within a certain area or timeframe is hard. Why? The ozone signal is swamped by natural variations or cycles. Researchers have developed a method to optimize the air quality signal detection capability over much of the continental United States. Their method uses spatial and temporal averaging scales.

The Impact
The new air quality signal detection method could improve researchers' understanding of and ability to track air quality trends. It may be applied not only to surface ozone data but also to a wide range of modeled or observational data.

Summary
Working with simulated and observed surface ozone data within the United States covering a 25-year period, the researchers analyzed how the magnitude of the data variability due to meteorology depended on the spatial (kilometers) or temporal (years) scale over which the data were averaged. As they homed in on the extent of the region and timeframe needed to obtain a clear signal of air quality change within the dataset, they effectively determined the risk of getting an insufficiently representative sample when averaging the data over too small a region or timeframe. As expected, they found that averaging over a greater area and timeframe, which reduces the "noise" from natural variability, will boost signal detection accuracy. The researchers' most salient finding was that over much of the continental United States, they could achieve the most sensitive signal detection capability by strategically combining specific spatial and temporal averaging scales. In other words, they developed a way to systematically identify a data set's "sweet spot"—the number of kilometers and years over which to average the data so as to detect the signal most efficiently. For the hardest-to-detect signals, they recommended averaging the data over 10 to 15 years and over an area extending up to several hundred kilometers. 

Program Manager
Bob Vallario
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research

Climate and Environmental Sciences Division (SC-23.1)
Multisector Dynamics
bob.vallario@science.doe.gov

Principal Investigators
Noelle Selin
Massachusetts Institute of Technology
selin@mit.edu

Ronald Prinn
Massachusetts Institute of Technology
rprinn@mit.edu

Funding
The study was funded by the U.S. Department of Energy, Office of Science and other government, industry and foundation sponsors of the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change. Additional computing resources were provided by the National Center for Atmospheric Research's Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies.

Publications
Brown-Steiner, B., N. E. Selin, R. G. Prinn, E. Monier, S. Tilmes, L. Emmons, and F. Garcia-Menendez, "Maximizing ozone signals among chemical, meteorological, and climatological variability." Atmospheric Chemistry and Physics 18, 8373 (2018). [DOI:10.5194/acp-18-8373-2018]

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Multisector Dynamics (formerly Integrated Assessment)
  • Research Area: Atmospheric System Research

Division: SC-33.1 Earth and Environmental Sciences Division, BER

 

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