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Abstract Spatiotemporal variability in ozone dry deposition is often overlooked despite its implications for interpreting and modeling tropospheric ozone concentrations accurately. Understanding the influences of stomatal versus nonstomatal deposition processes on ozone deposition velocity is important for attributing observed changes in the ozone depositional sink and associated damage to ecosystems. Here, we aim to identify the stomatal versus nonstomatal deposition processes driving observed variability in ozone deposition velocity over the northeastern United States during June–September. We use ozone eddy covariance measurements from Harvard Forest in Massachusetts, which span a decade, and from Kane Experimental Forest in Pennsylvania and Sand Flats State Forest in New York, which span one growing season each, along with observation‐driven modeling. Using a cumulative precipitation indicator of soil wetness, we infer that high soil uptake during dry years and low soil uptake during wet years may contribute to the twofold interannual variability in ozone deposition velocity at Harvard Forest. We link stomatal deposition and humidity to variability in ozone deposition velocity on daily timescales. The humidity dependence may reflect higher uptake by leaf cuticles under humid conditions, noted in previous work. Previous work also suggests that uptake by leaf cuticles may be enhanced after rain, but we find that increases in ozone deposition velocity on rainy days are instead mostly associated with increases in stomatal conductance. Our analysis highlights a need for constraints on subseasonal variability in ozone dry deposition to soil and fast in‐canopy chemistry during ecosystem stress.
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A Deep Learning Parameterization for Ozone Dry Deposition Velocities
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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- Award ID(s):
- 1832210
- PAR ID:
- 10457210
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 46
- Issue:
- 2
- ISSN:
- 0094-8276
- Page Range / eLocation ID:
- p. 983-989
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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