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|Arguez, A., O'Brien, J. J., & Smith, S. R. (2009). Air temperature impacts over Eastern North America and Europe associated with low-frequency North Atlantic SST variability. Int. J. Climatol., 29(1), 1–10.|
|Nguyen, T. T. (2014). Variability of Cross-Slope Flow in the Desoto Canyon Region. Master's thesis, Florida State University, Tallahassee, FL.|
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
Venugopal, T., Ali, M. M., Bourassa, M. A., Zheng, Y., Goni, G. J., Foltz, G. R., et al. (2018). Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall. Sci Rep, 8(1), 12092.
Abstract: This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 degrees C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Nino Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Keywords: SEA-SURFACE TEMPERATURE; EL-NINO; EQUATORIAL PACIFIC; IMPACT; PREDICTION; ENSO; DIPOLE; REGION; SST
|Zheng, Y., Bourassa, M. A., & Hughes, P. (2013). Influences of Sea Surface Temperature Gradients and Surface Roughness Changes on the Motion of Surface Oil: A Simple Idealized Study. J. Appl. Meteor. Climatol., 52(7), 1561–1575.|