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  • Author or Editor: V. Rao Kotamarthi x
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Lucas Harris and V. Rao Kotamarthi

Abstract

The lake-breeze circulation that forms over Lake Michigan during the summer influences the Chicago, Illinois, metropolitan area’s weather in several ways. Of particular significance is the circulation’s effect on the dispersion of pollutants such as ozone and aerosols produced in and around the city. To investigate these effects, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was used to perform numerical simulations for two lake-breeze events—one in July 1999 and another in July 2002. The model runs were verified with data from several locations around the Chicago area. The simulated breeze circulation decreased the rate of increase in air temperature while penetrating roughly 12 km inland and lasting about 8 h, in reasonable agreement with observations. Furthermore, the inland penetration distance was related to the strength of the maximum vertical velocity within the front. Calculations of trajectories and transport of particles showed that the breeze tended to transport particles trapped within it to the north when release occurred before the circulation came ashore, whereas particles released at the time of the breeze’s landfall or afterward moved more northeasterly, in the direction of the prevailing wind. Thirty-four percent of all released particles were trapped by the circulation and raised to a height of at least 300 m, and 20% of the particles remained in the lowest 100 m above the surface. In addition, sensitivity tests showed little change in the modeled breeze when measured surface temperatures for Lake Michigan were used as initial conditions and boundary conditions in the place of surface skin temperature (as derived by the National Centers for Environmental Prediction). Raising the lake temperatures significantly in the simulation yielded a more elongated vertical circulation and a briefer lake-breeze event that did not reach as far inland.

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Ryan C. Sullivan, V. Rao Kotamarthi, and Yan Feng

Abstract

Future projections of evapotranspiration (ET) are of critical importance for agricultural and freshwater management and for predicting land–atmosphere feedbacks on the climate system. However, ET from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations exhibits substantial biases, bolstering little confidence in future ET projections. Despite poor predictive skill and large bias of ET from the global climate models, the information content necessary to calculate ET offline is available in the models’ archived outputs: temperature T, water vapor pressure e, atmospheric pressure P, and surface net radiation R. A relatively simple three-source energy balance model [Penman–Monteith (PM)], along with the mean annual cycle of remotely sensed vegetation properties, can then be used to reconstruct ET with a substantially reduced bias relative to in situ turbulent heat flux measurements. This methodology is used here to reconstruct ET projections from 2006 through 2100 over North America using output from selected CMIP5 models and to attribute projected ET trends to specific atmospheric controls. CMIP5 ET exhibits substantial bias in annual ET relative to in situ flux measurements across North America (38%–73%; 2006–15), but ET reconstructed from the CMIP5 meteorology with the PM method greatly reduces this bias (−8% to +14%). Present-day North American ET is more sensitive to changes in atmospheric demand for ET (temperature and water vapor pressure) than energy limitation (net radiation), and to a lesser extent vegetation properties (leaf area index). Accordingly, ET is projected to increase 0.26–0.87 mm yr−1 yr−1 over North America through 2100 driven primarily by trends in temperature.

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Won Chang, Michael L. Stein, Jiali Wang, V. Rao Kotamarthi, and Elisabeth J. Moyer

Abstract

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%–7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%–2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics—including starting location, intensity, spatial extent, duration, and trajectory—that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model–observation biases, implying that the best means of incorporating them into impact assessments is via “data-driven simulations” that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

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