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Matthew T. Boehm and Sukyoung Lee

Abstract

This study puts forward a mechanism for the observed upwelling in the tropical upper troposphere and lower stratosphere. In this hypothesis, the tropical upwelling is driven by momentum transport by Rossby waves that are generated by tropical convection. To test this hypothesis, model runs are conducted using an axisymmetric, global, primitive equation model. In these runs, the effect of Rossby waves is included by driving the model with observed fields of large-scale eddy momentum flux convergence. The resulting overturning circulation includes both meridional flow from the intertropical convergence zone (ITCZ) to the equator and rising motion in the tropical tropopause transition layer (TTL). This circulation therefore helps to explain the transport of moisture from the lower portion of the TTL in the ITCZ to the equatorial cold-point tropopause, where tropopause cirrus layers frequently occur.

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Matthew T. Boehm, Donald E. Aylor, and Elson J. Shields

Abstract

The widespread adoption of genetically modified (GM) crops has led to a need to better understand the atmospheric transport of pollen because of concerns over potential cross-pollination between GM and non-GM crops. Maize pollen concentrations were modeled by a modified Lagrangian stochastic (LS) model of the convective boundary layer (CBL) and were compared with concentrations measured by airborne remotely piloted vehicles (RPVs) flown from directly above to 2 km from source fields. The turbulence parameterization in an existing CBL LS model was modified to incorporate the effects of shear-driven turbulence, which has an especially large impact near the surface, where maize pollen is released. The modified model was used to calculate concentrations corresponding to the RPV flight tracks. For the most convective cases, when at least 95% of the pollen came from sources near the RPV flight track for which source strength measurements are available and the results are less sensitive to uncertainty in wind direction since most of the pollen came from directly beneath the flight track, the geometric mean of the ratio between the modeled and measured concentrations was 0.94. When cases with larger contributions from more distant fields were included, the overall geometric mean decreased to 0.43. The scatter of the measured concentrations about the modeled values followed a lognormal distribution. These results indicate that the modified model presented herein can substantially improve the description of the near-source dispersion of heavy particles released near the surface during convective conditions.

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Donald E. Aylor, Matthew T. Boehm, and Elson J. Shields

Abstract

The extensive adoption of genetically modified crops has led to a need to understand better the dispersal of pollen in the atmosphere because of the potential for unwanted movement of genetic traits via pollen flow in the environment. The aerial dispersal of maize pollen was studied by comparing the results of a Lagrangian stochastic (LS) model with pollen concentration measurements made over cornfields using a combination of tower-based rotorod samplers and airborne radio-controlled remote-piloted vehicles (RPVs) outfitted with remotely operated pollen samplers. The comparison between model and measurements was conducted in two steps. In the first step, the LS model was used in combination with the rotorod samplers to estimate the pollen release rate Q for each sampling period. In the second step, a modeled value for the concentration C model, corresponding to each RPV measured value C measure, was calculated by simulating the RPV flight path through the LS model pollen plume corresponding to the atmospheric conditions, field geometry, wind direction, and source strength. The geometric mean and geometric standard deviation of the ratio C model/C measure over all of the sampling periods, except those determined to be upwind of the field, were 1.42 and 4.53, respectively, and the lognormal distribution corresponding to these values was found to fit closely the PDF of C model/C measure. Model output was sensitive to the turbulence parameters, with a factor-of-100 difference in the average value of C model over the range of values encountered during the experiment. In comparison with this large potential variability, it is concluded that the average factor of 1.4 between C model and C measure found here indicates that the LS model is capable of accurately predicting, on average, concentrations over a range of atmospheric conditions.

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