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.
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.
Lu, J., Chassignet, E. P., Yin, J., Misra, V., & Michael, J. - P. (2013).
Comparison of HYCOM and POP models in the CCSM3.0 Framework. Part I: Modes of climate variability beyond ENSO.
Michael, J. - P., Misra, V., Chassignet, E. P., & Lu, J. (2013).
Comparison of HYCOM and POP models in the CCSM3.0 Framework. Part II: ENSO fidelity.
Shropshire, T., Morey, S. L., Chassignet, E. P., Bozec, A., Coles, V. J., Landry, M. R., et al. (2019). Quantifying spatiotemporal variability in zooplankton dynamics in the Gulf of Mexico with a physical-biogeochemical model.
Abstract: Zooplankton play an important role in global biogeochemistry and their secondary production supports valuable fisheries of the world's oceans. Currently, zooplankton abundances cannot be estimated using remote sensing techniques. Hence, coupled physical-biogeochemical models (PBMs) provide an important tool for studying zooplankton on regional and global scales. However, evaluating the accuracy of zooplankton abundance estimates from PBMs has been a major challenge as a result of sparse observations. In this study, we configure a PBM for the Gulf of Mexico (GoM) from 1993�2012 and validate the model against an extensive combination of in situ biomass and rate measurements including total mesozooplankton biomass, size-fractionated mesozooplankton biomass and grazing rates, microzooplankton specific grazing rates, surface chlorophyll, deep chlorophyll maximum depth, phytoplankton specific growth rates, and net primary production. Spatial variability in mesozooplankton biomass climatology observed in a multi-decadal database for the northern GoM is well resolved by the model with a statistically significant (p < 0.01) correlation of 0.90. Mesozooplankton secondary production for the region averaged 66 + 8 mt C yr−1 equivalent to approximately 10 % of NPP and ranged from 51 to 82 mt C yr−1. In terms of diet, model results from the shelf regions suggest that herbivory is the dominant feeding mode for small mesozooplankton (< 1-mm) whereas larger mesozooplankton are primarily carnivorous. However, in open-ocean, oligotrophic regions, both groups of mesozooplankton have proportionally greater reliance on heterotrophic protists as a food source. This highlights the important role of microbial and protistan food webs in sustaining mesozooplankton biomass in the GoM which serves as the primary food source for early life stages of many commercially-important fish species, including tuna.
Vinayachandran, P. N., Davidson, F., & Chassignet, E. P. (2020). Towards joint assessments, modern capabilities and new links for ocean prediction systems.
Bull. Amer. Meteor. Soc., 101(4).
Abstract: Approximately 260 individuals from forecasting centers, research laboratories, academia, and industry representing 40 countries met to discuss recent developments in operational oceanography and brainstorm about the future directions of ocean prediction services.