Abstract. Ground-level ozone (O3) is a major air pollutant that adversely affects human health and ecosystem productivity. Removal of troposphericO3 by plant stomatal uptake can in turn cause damage to plant tissues with ramifications for ecosystem and crop health. In manyatmospheric and land surface models, the functionality of stomata opening is represented by a bulk stomatal conductance, which is oftensemi-empirically parameterized and highly fitted to historical observations. A lack of mechanistic linkage to ecophysiological processes such asphotosynthesis may render models inadequate to represent plant-mediated responses of atmospheric chemistry to long-term changes in CO2,climate, and short-lived air pollutant concentrations. A new ecophysiology module was thus developed to mechanistically simulate land−atmosphereexchange of important gas species in GEOS-Chem, a chemical transport model widely used in atmospheric chemistry studies. The implementation not onlyallows for dry deposition to be coupled with plant ecophysiology but also enables plant and crop productivity and functions to respond dynamically toatmospheric chemical changes. We conduct simulations to evaluate the effects of the ecophysiology module on simulated dry deposition velocity andconcentration of surface O3 against an observation-derived dataset known as SynFlux. Our estimated stomatal conductance and dry depositionvelocity of O3 are close to SynFlux with root-mean-squared errors (RMSEs) below 0.3 cm s−1 across different plant functionaltypes (PFTs), despite an overall positive bias in surface O3 concentration (by up to 16 ppbv). Representing ecophysiology wasfound to reduce the simulated biases in deposition fluxes from the prior model but worsen the positive biases in simulated O3concentrations. The increase in positive concentration biases is mostly attributable to the ecophysiology-based stomatal conductance being generallysmaller (and closer to SynFlux values) than that estimated by the prior semi-empirical formulation, calling for further improvements in non-stomataldepositional and non-depositional processes relevant for O3 simulations. The estimated global O3 deposition flux is864 Tg O3 yr−1 with GEOS-Chem, and the new module decreases this estimate by 92 Tg O3 yr−1. Estimated global grossprimary production (GPP) without O3 damage is 119 Pg C yr−1. O3-induced reduction in GPP is 4.2 Pg C yr−1(3.5 %). An elevated CO2 scenario (580 ppm) yields higher global GPP (+16.8 %) and lower global O3depositional sink (−3.3 %). Global isoprene emission simulated with a photosynthesis-based scheme is 317.9 Tg C yr−1, which is31.2 Tg C yr−1 (−8.9 %) less than that calculated using the MEGAN(Model of Emissions of Gases and Aerosols from Nature) emission algorithm. This new model development dynamicallyrepresents the two-way interactions between vegetation and air pollutants and thus provides a unique capability in evaluating vegetation-mediatedprocesses and feedbacks that can shape atmospheric chemistry and air quality, as well as pollutant impacts on vegetation health, especially for anytimescales shorter than the multidecadal timescale.
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Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder
Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R2value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health.
more » « less- NSF-PAR ID:
- 10477094
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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