Hurlburt, H. E., Chassignet, E. P., Cummings, J. A., Kara, A. B., Metzger, E. J., Shriver, J. F., et al. (2008). Eddy-Resolving Global Ocean Prediction. In M. W. Hecht, & H. Hasumi (Eds.), Ocean Modeling in an Eddying Regime. Washington, DC: Ocean Modeling in an Eddying Regime.
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Chassignet, E. P., & Marshall, D. P. (2008). Gulf Stream Separation in Numerical Ocean Models. In M. W. Hecht, & H. Hasumi (Eds.), Ocean Modeling in an Eddying Regime. Washington, DC: American Geophysical Union.
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Deng, J., Wu, Z., Zhang, M., Huang, N. E., Wang, S., & Qiao, F. (2019). Data concerning statistical relation between obliquity and Dansgaard-Oeschger events. Data Brief, 23.
Abstract: Data presented are related to the research article entitled “Using Holo-Hilbert spectral analysis to quantify the modulation of Dansgaard-Oeschger events by obliquity” (J. Deng et al., 2018). The datasets in Deng et al. (2018) are analyzed on the foundation of ensemble empirical mode decomposition (EEMD) (Z.H. Wu and N.E. Huang, 2009), and reveal more occurrences of Dansgaard-Oeschger (DO) events in the decreasing phase of obliquity. Here, we report the number of significant high Shannon entropy (SE) (C.E. Shannon and W. Weaver, 1949) of 95% significance level of DO events in the increasing and decreasing phases of obliquity, respectively. First, the proxy time series are filtered by EEMD to obtain DO events. Then, the time-varying SE of DO modes are calculated on the basis of principle of histogram. The 95% significance level is evaluated through surrogate data (T. Schreiber and A. Schmitz, 1996). Finally, a comparison between the numbers of SE values that are larger than 95% significance level in the increasing and decreasing phases of obliquity, respectively, is reported.
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Hoogenboom, G., C.W. Fraisse, J.W. Jones, K.T. Ingram, J.J. O'Brien, J.G. Bellow, D. Zierden, D.E. Stooksbury, J.O. Paz, A. Garcia y Garcia, L.C. Guerra, D. Letson, N.E. Breuer, V.C. Cabrera, L.U. Hatch and C. Roncoli. (2007). Climate-Based Agricultural Risk Management Tools for Florida, Georgia and Alabama, USA. In Sivakumar M.V.K., & Hansen J. (Eds.), Climate Prediction and Agriculture (pp. 273–278). Berlin, Heidelberg: Springer.
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Bourassa, M. A., Smith, S. R., & O'Brien, J. J. (2002). Assimilation of scatterometer and in situ winds for regularly gridded products. In 6th Symposium on Integrated Observing Systems (pp. 161–165).
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Rettig, J. T., M. A. Bourassa, J. Hu, E. M. McDonald, J. J. Rolph, and S. R. Smith. (2009). Data management system to collect, quality control, distribute, and archive near real-time marine data. E-journal of Data Integration and Management on the Gulf of Mexico, , 4.
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Bourassa, M. A. (2009). The future of wind measurements from space. Space News, (Nov. 23), 2.
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Bourassa, M. A., & Hughes, P. J. (2009). Impacts of High Resolution SST Fields on Objective Analyses of Wind Fields, and Practical Constraints Related to Sampling. In International GHRSST User Symposium, GHRSST (2).
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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.
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Bozec, A., Kageyama, M., Ramstein, G., Creepon, M. (2008). Impact of a Last Glacial Maximum sea-level drop on the Mediterranean Sea. Earth and Planetary Science Letters, , submitted.
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