期刊
GLOBAL BIOGEOCHEMICAL CYCLES
卷 33, 期 10, 页码 1264-1288出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2018GB006157
关键词
ecosystem-atmosphere interactions; drought; ozone deposition; air quality; stomatal conductance
资金
- National Oceanic and Atmospheric Administration, U.S. Department of Commerce [NA14OAR4320106, NA18OAR4320123]
- NOAA [NA1OAR4310133]
The response of ozone (O-3) dry deposition to ecosystem-atmosphere interactions is poorly understood but is central to determining the potential for extreme pollution events under current and future climate conditions. Using observations and an interactive dry deposition scheme within two dynamic vegetation land models (Geophysical Fluid Dynamics Laboratory LM3.0/LM4.0) driven by observation-based meteorological forcings over 1948-2014, we investigate the factors controlling seasonal and interannual variability (IAV) in O-3 deposition velocities (V-d,V-O3). Stomatal activity in this scheme is determined mechanistically, depending on phenology, soil moisture, vapor pressure deficit, and CO2 concentration. Soil moisture plays a key role in modulating the observed and simulated V-d,V-O3 seasonal changes over evergreen forests in Mediterranean Europe, South Asia, and the Amazon. Analysis of multiyear observations at forest sites in Europe and North America reveals drought stress to reduce V-d,V-O3 by similar to 50%. Both LM3.0 and LM4.0 capture the observed V-d,V-O3 decreases due to drought; however, IAV is weaker by a factor of 2 in LM3.0 coupled to atmospheric models, particularly in regions with large precipitation biases. IAV in summertime V-d,V-O3 to forests, driven primarily by the stomatal pathway, is largest (15-35%) in semiarid regions of western Europe, eastern North America, and northeastern China. Monthly mean V-d,V-O3 for the highest year is 2 to 4 times that of the lowest, with significant implications for surface O-3 variability and extreme events. Using V-d,V-O3 from LM4.0 in an atmospheric chemistry model improves the simulation of surface O-3 abundance and spatial variability (reduces mean biases by similar to 10 ppb) relative to the widely used Wesely scheme.
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