Misra, V., Selman, C., Waite, A. J., Bastola, S., & Mishra, A. (2017). Terrestrial and Ocean Climate of the 20th Century. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 485–509). Gainesville, FL: Florida Climate Institute.
Xu, X., & Chassignet, E. P., Wang, F. (2018). On the variability of the Atlantic meridional overturning circulation transports in coupled CMIP5 simulations. Clim Dyn. , 51 (11), 6511–6531.
Abstract: The Atlantic meridional overturning circulation (AMOC) plays a fundamental role in the climate system, and long-term climate simulations are used to understand the AMOC variability and to assess its impact. This study examines the basic characteristics of the AMOC variability in 44 CMIP5 (Phase 5 of the Coupled Model Inter-comparison Project) simulations, using the 18 atmospherically-forced CORE-II (Phase 2 of the Coordinated Ocean-ice Reference Experiment) simulations as a reference. The analysis shows that on interannual and decadal timescales, the AMOC variability in the CMIP5 exhibits a similar magnitude and meridional coherence as in the CORE-II simulations, indicating that the modeled atmospheric variability responsible for AMOC variability in the CMIP5 is in reasonable agreement with the CORE-II forcing. On multidecadal timescales, however, the AMOC variability is weaker by a factor of more than 2 and meridionally less coherent in the CMIP5 than in the CORE-II simulations. The CMIP5 simulations also exhibit a weaker long-term atmospheric variability in the North Atlantic Oscillation (NAO). However, one cannot fully attribute the weaker AMOC variability to the weaker variability in NAO because, unlike the CORE-II simulations, the CMIP5 simulations do not exhibit a robust NAO-AMOC linkage. While the variability of the wintertime heat flux and mixed layer depth in the western subpolar North Atlantic is strongly linked to the AMOC variability, the NAO variability is not.
Shinoda, T., Han, W., Zamudio, L., Lien, R. - C., & Katsumata, M. (2017). Remote Ocean Response to the Madden-Julian Oscillation during the DYNAMO Field Campaign: Impact on Somali Current System and the Seychelles-Chagos Thermocline Ridge. Atmosphere , 8 (9), 171.
May, J. (2010). Quantifying Variance Due to Temporal and Spatial Difference Between Ship and Satellite Winds . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Ocean vector winds measured by the SeaWinds scatterometer onboard the QuikSCAT satellite can be validated with in situ data. Ideally the comparison in situ data would be collocated in both time and space to the satellite overpass; however, this is rarely the case because of the time sampling interval of the in situ data and the sparseness of data. To compensate for the lack of ideal collocations, in situ data that are within a certain time and space range of the satellite overpass are used for comparisons. To determine the total amount of random observational error, additional uncertainty from the temporal and spatial difference must be considered along with the uncertainty associated with the data sets. The purpose of this study is to quantify the amount of error associated with the two data sets, as well as the amount of error associated with the temporal and/or spatial difference between two observations. The variance associated with a temporal difference between two observations is initially examined in an idealized case that includes only Shipboard Automated Meteorological and Oceanographic System (SAMOS) one-minute data. Temporal differences can be translated into spatial differences by using Taylor's hypothesis. The results show that as the time difference increases, the amount of variance increases. Higher wind speeds are also associated with a larger amount of variance. Collocated SeaWinds and SAMOS observations are used to determine the total variance associated with a temporal (equivalent) difference from 0 to 60 minutes. If the combined temporal and spatial difference is less than 25 minutes (equivalent), the variance associated with the temporal and spatial difference is offset by the observational errors, which are approximately 1.0 m2s-2 for wind speeds between 4 and 7 ms-1 and approximately 1.5 m2s-2 for wind speeds between 7 and 12 ms-1. If the combined temporal and spatial difference is greater than 25 minutes (equivalent), then the variance associated with the temporal and spatial difference is no longer offset by the variance associated with observational error in the data sets; therefore, the total variance gradually increases as the time difference increases.
Morey, S., Koch, M., Liu, Y., & Lee, S. - K. (2017). Florida's oceans and marine habitats in a changing climate. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 391–425). Gainesville, FL: Florida Climate Institute.
Misra, V., & DiNapoli, S. M. (2013). Understanding the wet season variations over Florida. Clim Dyn , 40 (5-6), 1361–1372.
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.
Jardak, M., Navon, I. M., & Zupanski, M. (2009). Comparison of sequential data assimilation methods for the Kuramoto-Sivashinsky equation. Int. J. Numer. Meth. Fluids , .
Hu, A., Meehl, G. A., Han, W., Lu, J., & Strand, W. G. (2013). Energy balance in a warm world without the ocean conveyor belt and sea ice. Geophys. Res. Lett. , 40 (23), 6242–6246.
