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  • Author or Editor: T. A. O’Brien x
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T. M. Hard, L. A. George, and R. J. O'Brien


FAGE (fluorescence assay with gas expansion) was developed as a sensitive technique for the detection of low-concentration free radicals in the atmosphere. The application of FAGE to tropospheric hydroxyl (H0) and hydroperoxyl (H02) radicals has yielded calibrated measurements of both species in both clean air and highly polluted urban air. For HO calibration, a continuously stirred tank reactor provides a uniform external HO concentration, which can be measured by gas chromatography of an HO-reactive hydrocarbon. The aerodynamics of the air-sampling process has been modeled computationally, with results that agree with empirical observations of the effects of nozzle diameter on HO loss during sampling. The authors have also modeled airborne fluid dynamics of a FAGE probe. They have recently obtained FAGE sensitivity as high as ± 1 × 106 cm−3 for a 6-minute averaging period, during field studies in highly polluted Los Angeles air, yielding a 7:1 signal-to-noise ratio near midday. Multipass excitation can further improve this sensitivity. The authors summarize their recent field studies of HO and HO2, current work on improved calibration methods, other improvements, and future plans.

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D. W. Shin, G. A. Baigorria, Y-K. Lim, S. Cocke, T. E. LaRow, James J. O’Brien, and James W. Jones


A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Niño–Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO-based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield simulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

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W. J. Gutowski Jr., P. A. Ullrich, A. Hall, L. R. Leung, T. A. O’Brien, C. M. Patricola, R. W. Arritt, M. S. Bukovsky, K. V. Calvin, Z. Feng, A. D. Jones, G. J. Kooperman, E. Monier, M. S. Pritchard, S. C. Pryor, Y. Qian, A. M. Rhoades, A. F. Roberts, K. Sakaguchi, N. Urban, and C. Zarzycki
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W. J. Gutowski Jr, P. A. Ullrich, A. Hall, L. R. Leung, T. A. O’Brien, C. M. Patricola, R. W. Arritt, M. S. Bukovsky, K. V. Calvin, Z. Feng, A. D. Jones, G. J. Kooperman, E. Monier, M. S. Pritchard, S. C. Pryor, Y. Qian, A. M. Rhoades, A. F. Roberts, K. Sakaguchi, N. Urban, and C. Zarzycki


Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

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