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Abstract Atmospheric conditions affect the release of anemophilous pollen, and the timing and magnitude will be altered by climate change. As simulated with a pollen emission model and future climate data, warmer end-of-century temperatures (4–6 K) shift the start of spring emissions 10–40 days earlier and summer/fall weeds and grasses 5–15 days later and lengthen the season duration. Phenological shifts depend on the temperature response of individual taxa, with convergence in some regions and divergence in others. Temperature and precipitation alter daily pollen emission maxima by −35 to 40% and increase the annual total pollen emission by 16–40% due to changes in phenology and temperature-driven pollen production. Increasing atmospheric CO₂may increase pollen production, and doubling production in conjunction with climate increases end-of-century emissions up to 200%. Land cover change modifies the distribution of pollen emitters, yet the effects are relatively small (<10%) compared to climate or CO₂. These simulations indicate that increasing pollen and longer seasons will increase the likelihood of seasonal allergies. 

We develop a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C₃, C₄), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in a regional climate model (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model: (1) using a taxa-specific land cover database, phenology and emission potential, and (2) a PFT-based land cover, phenology and emission potential. The resulting surface concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model, however we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States. 

Abstract We use TROPOMI (TROPOspheric Monitoring Instrument) tropospheric nitrogen dioxide (NO₂) measurements to identify cropland soil nitrogen oxide (NO_x = NO + NO₂) emissions at daily to seasonal scales in the U.S. Southern Mississippi River Valley. Evaluating 1.5 years of TROPOMI observations with a box model, we observe seasonality in local NO_xenhancements and estimate maximum cropland soil NO_xemissions (15–34 ng N m⁻² s⁻¹) early in growing season (May–June). We observe soil NO_xpulsing in response to daily decreases in volumetric soil moisture (VSM) as measured by the Soil Moisture Active Passive (SMAP) satellite. Daily NO₂enhancements reach up to 0.8 × 10¹⁵ molecules cm⁻²4–8 days after precipitation when VSM decreases to ~30%, reflecting emissions behavior distinct from previously defined soil NO_xpulse events. This demonstrates that TROPOMI NO₂observations, combined with observations of underlying process controls (e.g., soil moisture), can constrain soil NO_xprocesses from space. 

ABSTRACT To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.