Adams, D. K., McGillicuddy, D. J. J., Zamudio, L., Thurnherr, A. M., Liang, X., Rouxel, O., et al. (2011). Surface-generated mesoscale eddies transport deep-sea products from hydrothermal vents.
Science, 332(6029), 580–583.
Abstract: Atmospheric forcing, which is known to have a strong influence on surface ocean dynamics and production, is typically not considered in studies of the deep sea. Our observations and models demonstrate an unexpected influence of surface-generated mesoscale eddies in the transport of hydrothermal vent efflux and of vent larvae away from the northern East Pacific Rise. Transport by these deep-reaching eddies provides a mechanism for spreading the hydrothermal chemical and heat flux into the deep-ocean interior and for dispersing propagules hundreds of kilometers between isolated and ephemeral communities. Because the eddies interacting with the East Pacific Rise are formed seasonally and are sensitive to phenomena such as El Nino, they have the potential to introduce seasonal to interannual atmospheric variations into the deep sea.
Ahern, K. K. (2015).
Analysis of Polar Mesocyclonic Surface Turbulent Fluxes in the Arctic System Reanalysis (ASRv1) Dataset. Master's thesis, Florida State University, Tallahassee, FL.
Ahlberg, C. M., Allwardt, K., Broocks, A., Bruno, K., McPhillips, L., Taylor, A., et al. (2018). Test duration for water intake, ADG, and DMI in beef cattle.
J Anim Sci, 96(8), 3043–3054.
Abstract: Water is an essential nutrient, but the effect it has on performance generally receives little attention. There are few systems and guidelines for collection of water intake (WI) phenotypes in beef cattle, which makes large-scale research on WI a challenge. The Beef Improvement Federation has established guidelines for feed intake (FI) and ADG tests, but no guidelines exist for WI. The goal of this study was to determine the test duration necessary for collection of accurate WI phenotypes. To facilitate this goal, individual daily WI and FI records were collected on 578 crossbred steers for a total of 70 d using an Insentec system at the Oklahoma State University Willard Sparks Beef Research Unit. Steers were fed in five groups and were individually weighed every 14 d. Within each group, steers were blocked by BW (low and high) and randomly assigned to one of four pens containing approximately 30 steers per pen. Each pen provided 103.0 m2 of shade and included an Insentec system containing six feed bunks and one water bunk. Steers were fed a constant diet across groups and DMI was calculated using the average of weekly percent DM within group. Average FI and WI for each animal were computed for increasingly large test durations (7, 14, 21, 28, 35, 42, 49, 56, 63, and 70 d), and ADG was calculated using a regression formed from BW taken every 14 d (0, 14, 28, 42, 56, and 70 d). Intervals for all traits were computed starting from both the beginning (day 0) and the end of the testing period (day 70). Pearson and Spearman correlations were computed for phenotypes from each shortened test period and for the full 70-d test. Minimum test duration was determined when the Pearson correlations were greater than 0.95 for each trait. Our results indicated that minimum test duration for WI, DMI, and ADG were 35, 42, and 70 d, respectively. No comparable studies exist for WI; however, our results for FI and ADG are consistent with those in the literature. Although further testing in other populations of cattle and areas of the country should take place, our results suggest that WI phenotypes can be collected concurrently with DMI, without extending test duration, even if following procedures for decoupled intake and gain tests.
Ahmed, A. (2013).
Visualization of geo spatial data in real time. Master's thesis, Florida State University, Tallahassee, FL.
AjayaMohan, R. S., Jagtap, S., LaRow, T. E., Cocke, S., O'Brien, J. J., Jones, J., et al. (2004). Using climate models to generate crop yield forecasts in southeast USA. In
Research Activities in Atmospheric and Ocean Modeling, CAS/JSC Working Group on Numerical Experimentation.
Ali, M., Singh, N., Kumar, M., Zheng, Y., Bourassa, M., Kishtawal, C., et al. (2018). Dominant Modes of Upper Ocean Heat Content in the North Indian Ocean.
Climate, 6(3), 71.
Abstract: The thermal energy needed for the development of hurricanes and monsoons as well as any prolonged marine weather event comes from layers in the upper oceans, not just from the thin layer represented by sea surface temperature alone. Ocean layers have different modes of thermal energy variability because of the different time scales of ocean–atmosphere interaction. Although many previous studies have focused on the influence of upper ocean heat content (OHC) on tropical cyclones and monsoons, no study thus far—particularly in the North Indian Ocean (NIO)—has specifically concluded the types of dominant modes in different layers of the ocean. In this study, we examined the dominant modes of variability of OHC of seven layers in the NIO during 1998–2014. We conclude that the thermal variability in the top 50 m of the ocean had statistically significant semiannual and annual modes of variability, while the deeper layers had the annual mode alone. Time series of OHC for the top four layers were analyzed separately for the NIO, Arabian Sea, and Bay of Bengal. For the surface to 50 m layer, the lowest and the highest values of OHC were present in January and May every year, respectively, which was mainly caused by the solar radiation cycle.
Ali, M. M., Bhat, G. S., Long, D. G., Bharadwaj, S., & Bourassa, M. A. (2013). Estimating Wind Stress at the Ocean Surface From Scatterometer Observations.
IEEE Geosci. Remote Sensing Lett., 10(5), 1129–1132.
Ali, M. M., Bhowmick, S. A., Sharma, R., Chaudhury, A., Pezzullo, J. C., Bourassa, M. A., et al. (2015). An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds.
IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 8(11), 5317–5323.
Ali, M. M., Bourassa, M. A., Bhowmick, S. A., Sharma, R., & Niharika, K. (2016). Retrieval of Wind Stress at the Ocean Surface From AltiKa Measurements.
IEEE Geosci. Remote Sensing Lett., 13(6), 821–825.
Ali, M. M., Nagamani, P. V., Sharma, N., Venu Gopal, R. T., Rajeevan, M., Goni, G. J., et al. (2015). Relationship between ocean mean temperatures and Indian summer monsoon rainfall.
Atmos. Sci. Lett., 16(3), 408–413.