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Selman, C., & Misra, V. (2017). The impact of an extreme case of irrigation on the southeastern United States climate. Clim Dyn, 48(3-4), 1309–1327.
Keywords: Regional climate modeling; Irrigation; Diurnal climatology; Diurnal; Southeast United States; Southeast US; Regional model; Agriculture; Anthropogenic influences; Anthropogenic; Climate; Climate change; Regional; Impact; Southeast; Model; Parametrization
|Selman, C., Misra, V., Stefanova, L., Dinapoli, S., & Smith III, T. J. (2013). On the twenty-first-century wet season projections over the Southeastern United States. Reg Environ Change, 13(S1), 153–164.|
|Selman, C. M. (2015). Simulating the Impacts and Sensitivity of the Southeastern United States Climatology to Irrigation. Ph.D. thesis, Florida State University, Tallahassee, FL.|
Smith, R. A. (2007). Trends in Maximum and Minimum Temperature Deciles in Select Regions of the United States. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Daily maximum and minimum temperature data from 758 COOP stations in nineteen states are used to create temperature decile maps. All stations used contain records from 1948 through 2004 and could not be missing more than 5 consecutive years of data. Missing data are replaced using a multiple linear regression technique from surrounding stations. For each station, the maximum and minimum temperatures are first sorted in ascending order for every two years (to reduce annual variability) and divided into ten equal parts (or deciles). The first decile represents the coldest temperatures, and the last decile contains the warmest temperatures. Patterns and trends in these deciles can be examined for the 57-year period. A linear least-squares regression method is used to calculate best-fit lines for each decile to determine the long-term trends at each station. Significant warming or cooling is determined using the Student's t-test, and bootstrapping the decile data will further examine the validity of significance. Two stations are closely examined. Apalachicola, Florida shows significant warming in its maximum deciles and significant cooling in its minimum deciles. The maximum deciles seem to be affected by some localized change. The minimum deciles are discontinuous, and the trends are a result of a minor station move. Columbus, Georgia has experienced significant warming in its minimum deciles, and this appears to be the result of an urban heat-island effect. The discontinuities seen in the Apalachicola case study illustrate the need for a quality control method. This method will eliminate stations from the regional analysis that experience large changes in the ten-year standard deviations within their time series. The regional analysis shows that most of the region is dominated by significant cooling in the maximum deciles and significant warming in the minimum deciles, with more variability in the lower deciles. Field significance testing is performed on subregions (based on USGS 2000 land cover data) and supports the findings from the regional analysis; it also isolates regions, such as the Florida peninsula and the Maryland/Delaware region, that appear to be affected by more local forcings.
|Solís, D., & Letson, D. (2013). Assessing the value of climate information and forecasts for the agricultural sector in the Southeastern United States: multi-output stochastic frontier approach. Reg Environ Change, 13(S1), 5–14.|
|Stefanova, L., Misra, V., Chan, S., Griffin, M., O'Brien, J. J., & Smith III, T. J. (2012). A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses. Clim Dyn, 38(11-12), 2449–2466.|
|Strazzo, S. (2011). Low-Frequency Minimum Temperature Variability Throughout the Southeastern United States during the 1970s: Regime Shift or Phase Coincidence? Master's thesis, Florida State University, Tallahassee, FL.|
|Strazzo, S. E., Elsner, J. B., LaRow, T. E., Murakami, H., Wehner, M., & Zhao, M. (2016). The influence of model resolution on the simulated sensitivity of North Atlantic tropical cyclone maximum intensity to sea surface temperature. J. Adv. Model. Earth Syst., 8(3), 1037–1054.|
|Sura, P. (2011). A general perspective of extreme events in weather and climate. Atmospheric Research, 101(1-2), 1–21.|
|Sura, P., & Hannachi, A. (2015). Perspectives of Non-Gaussianity in Atmospheric Synoptic and Low-Frequency Variability. J. Climate, 28(13), 5091–5114.|