COAPS Virtual Library (Publications)

Timko, P. G., Arbic, B. K., Richman, J. G., Scott, R. B., Metzger, E. J., & Wallcraft, A. J. (2012). Skill tests of three-dimensional tidal currents in a global ocean model: A look at the North Atlantic. J. Geophys. Res., 117(C8), n/a-n/a. https://www.coaps.fsu.edu/bibliography/show.php?record=242
Tiscareno-Lopez, M., N. J. Rosenberg, D. M. Legler, A. R. Corral, R. Shrinivasan, R. A. Brown, G. G. Medina, M. A. V. Valle, and R. C. Izaurralde. (1998). Algunos efectos del fenomeno climatico El Nino en la agricultura Mexicana. Ciencia y Desarrollo, 24, 4–14. https://www.coaps.fsu.edu/bibliography/show.php?record=742
Todd, A., D. Dukhovskoy, M. Griffin. (2009). Effectiveness of the Keetch-Byram Drought Index toward the estimation of fires in Florida. Agricultural and Forest Meteorology, , submitted. Keywords: KBDI, wildland fire, fire prediction, statistical models https://www.coaps.fsu.edu/bibliography/show.php?record=677
Todd, A. C., Morey, S. L., & Chassignet, E. P. (2014). Circulation and cross-shelf transport in the Florida Big Bend. J Mar Res, 72(6), 445–475. Keywords: Coastal circulation; ocean modeling; larval transport; gag grouper; Florida Big Bend https://www.coaps.fsu.edu/bibliography/show.php?record=148
Todd, A. (2013). Circulation Dynamics and Larval Transport Mechanisms in the Florida Big Bend. Ph.D. thesis, Florida State University, Tallahassee, FL. Keywords: Department of Earth, Ocean and Atmospheric Science https://www.coaps.fsu.edu/bibliography/show.php?record=228
Trasviña, A., Heywood, K. J., Renner, A. H. H., Thorpe, S. E., Thompson, A. F., & Zamudio, L. (2011). The impact of high-frequency current variability on dispersion off the eastern Antarctic Peninsula. J. Geophys. Res., 116(C11). https://www.coaps.fsu.edu/bibliography/show.php?record=285
Trossman, D. S., Arbic, B. K., Straub, D. N., Richman, J. G., Chassignet, E. P., Wallcraft, A. J., et al. (2017). The Role of Rough Topography in Mediating Impacts of Bottom Drag in Eddying Ocean Circulation Models. J. Phys. Oceanogr., 47(8), 1941–1959. https://www.coaps.fsu.edu/bibliography/show.php?record=230
Tseng, Y. -heng, Lin, H., Chen, H. -ching, Thompson, K., Bentsen, M., Böning, C. W., et al. (2016). North and equatorial Pacific Ocean circulation in the CORE-II hindcast simulations. Ocean Modelling, 104, 143–170. Keywords: CORE global ocean-ice simulations; Kuroshio; Mode water; Subtropical cell; North Pacific simulations https://www.coaps.fsu.edu/bibliography/show.php?record=55
van Sebille, E., Griffies, S. M., Abernathey, R., Adams, T. P., Berloff, P., Biastoch, A., et al. (2018). Lagrangian ocean analysis: Fundamentals and practices. Ocean Modelling, 121, 49–75. Keywords: Ocean circulation; Lagrangian analysis; Connectivity; Particle tracking; Future modelling https://www.coaps.fsu.edu/bibliography/show.php?record=466
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 https://www.coaps.fsu.edu/bibliography/show.php?record=972

Timko, P. G., Arbic, B. K., Richman, J. G., Scott, R. B., Metzger, E. J., & Wallcraft, A. J. (2012). Skill tests of three-dimensional tidal currents in a global ocean model: A look at the North Atlantic. J. Geophys. Res., 117(C8), n/a-n/a.

Tiscareno-Lopez, M., N. J. Rosenberg, D. M. Legler, A. R. Corral, R. Shrinivasan, R. A. Brown, G. G. Medina, M. A. V. Valle, and R. C. Izaurralde. (1998). Algunos efectos del fenomeno climatico El Nino en la agricultura Mexicana. Ciencia y Desarrollo, 24, 4–14.

Todd, A., D. Dukhovskoy, M. Griffin. (2009). Effectiveness of the Keetch-Byram Drought Index toward the estimation of fires in Florida. Agricultural and Forest Meteorology, , submitted.

Todd, A. C., Morey, S. L., & Chassignet, E. P. (2014). Circulation and cross-shelf transport in the Florida Big Bend. J Mar Res, 72(6), 445–475.

Todd, A. (2013). Circulation Dynamics and Larval Transport Mechanisms in the Florida Big Bend. Ph.D. thesis, Florida State University, Tallahassee, FL.

Trasviña, A., Heywood, K. J., Renner, A. H. H., Thorpe, S. E., Thompson, A. F., & Zamudio, L. (2011). The impact of high-frequency current variability on dispersion off the eastern Antarctic Peninsula. J. Geophys. Res., 116(C11).

Trossman, D. S., Arbic, B. K., Straub, D. N., Richman, J. G., Chassignet, E. P., Wallcraft, A. J., et al. (2017). The Role of Rough Topography in Mediating Impacts of Bottom Drag in Eddying Ocean Circulation Models. J. Phys. Oceanogr., 47(8), 1941–1959.

Tseng, Y. -heng, Lin, H., Chen, H. -ching, Thompson, K., Bentsen, M., Böning, C. W., et al. (2016). North and equatorial Pacific Ocean circulation in the CORE-II hindcast simulations. Ocean Modelling, 104, 143–170.

van Sebille, E., Griffies, S. M., Abernathey, R., Adams, T. P., Berloff, P., Biastoch, A., et al. (2018). Lagrangian ocean analysis: Fundamentals and practices. Ocean Modelling, 121, 49–75.

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.

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