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The Earth’s atmosphere contains a multitude of emitted (primary) and chemically formed (secondary) gases and particles that degrade air quality and modulate the climate. Reactive organic carbon (ROC) species are the fuel of the chemistry of the atmosphere, dominating short-lived emissions, reactivity, and the secondary production of key species such as ozone, particulate matter, and carbon dioxide. Despite the central importance of ROC, the diversity and complexity of this class of species has been a longstanding obstacle to developing a comprehensive understanding of how the composition of our atmosphere, and the associated environmental implications, will evolve. Here, we characterize the role of ROC in atmospheric chemistry and the challenges inherent in measuring and modeling ROC, and highlight recent progress toward achieving mass closure for the complete description of atmospheric ROC.more » « less
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Ridley, D. A. ; Heald, C. L. ; Ridley, K. J. ; Kroll, J. H. ( , Proceedings of the National Academy of Sciences)
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Silva, S. J. ; Heald, C. L. ; Ravela, S. ; Mammarella, I. ; Munger, J. W. ( , Geophysical Research Letters)
Abstract The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiälä, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus ~0.1). The same DNN model, when driven by assimilated meteorology at 2° × 2.5° spatial resolution, outperforms the Wesely scheme as implemented in the GEOS‐Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models.
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Ford, B. ; Val Martin, M. ; Zelasky, S. E. ; Fischer, E. V. ; Anenberg, S. C. ; Heald, C. L. ; Pierce, J. R. ( , GeoHealth)