Sharp, R. J., Bourassa, M. A., O'Brien, J. J., Katsaros, K. B., & Forde, E. B. (2003). Early Detection Tropical Cyclones using SeaWinds-Derived Vorticity (H. Ritchie, Ed.). CAS/JSC Working Group on Numerical Experimentation, Research Activities in Atmospheric and Oceanic Modeling. Geneva, Switzerland: World Meteorological Organization.
Shi, W. (2003). Estimation of heat and salt storage variability in the Indian Ocean from TOPEX/Poseidon altimetry. J. Geophys. Res. , 108 (C7).
Shin, D. W. (2003). Short- to medium-range superensemble precipitation forecasts using satellite products: 1. Deterministic forecasting. J. Geophys. Res. , 108 (D8).
Shin, D. W. (2003). Short- to medium-range superensemble precipitation forecasts using satellite products: 2. Probabilistic forecasting. J. Geophys. Res. , 108 (D8).
Shin, D. W., & O'Brien, J. J. (2003). A Note on Korean Monsoon Energetics. Korean Journal of Atmospheric Sciences , 6 (1), 55–61.
Shin, D. W., Bellow, J. G., Cocke, S., LaRow, T. E., & O'Brien, J. J. (2006). Seasonal dynamical downscaling for crop yield estimation . Research Activities in Atmospheric and Ocean Modeling, CAS/JSC Working Group on Numerical Experimentation.
Shin, D. W., Cocke, S., & LaRow, T. E. (2002). Superensemble precipitation forecasts using TRMM and DMSP satellite microwave imager products .
Shin, D. W., Cocke, S., LaRow, T. E., & O'Brien, J. J. (2004). Seasonal climate signatures in the FSU climate model coupled to the CLM2. In Research Activities in Atmospheric and Ocean Modeling, CAS/JSC Working Group on Numerical Experimentation .
Shin, D. W., LaRow, T. E., Cocke, S., & O'Brien, J. J. (2005). The role of the CLM2 in the surface air temperature and precipitation of the FSU climate model . Research Activities in Atmospheric and Ocean Modeling, CAS/JSC Working Group on Numerical Experimentation.
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.
