COAPS Virtual Library (Publications)

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. https://www.coaps.fsu.edu/bibliography/show.php?record=931
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. https://www.coaps.fsu.edu/bibliography/show.php?record=880
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. https://www.coaps.fsu.edu/bibliography/show.php?record=904
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. https://www.coaps.fsu.edu/bibliography/show.php?record=1095
Smith, S. R. Automated marine weather observations on research vessels as part of an ocean observing system. (cdrom). Symposium on Forecasting the Weather and Climate of the Atmosphere and Ocean, American Meteorological Society, Seattle, WA, USA. https://www.coaps.fsu.edu/bibliography/show.php?record=891
Smith, S. R. (2004). Report from the 2nd workshop on high-resolution marine meteorology. COAPS Technical Report 04-01. Tallahassee, FL: Center for Ocean-Atmospheric Prediction Studies, Florida State University. https://www.coaps.fsu.edu/bibliography/show.php?record=883
Smith, S. R. (2004). Surface Meteorology from Research Vessels. GSOP Report on Ocean Observations, International CLIVAR Project Office, UK. https://www.coaps.fsu.edu/bibliography/show.php?record=888
Smith, S. R. (2004). The Shipboard Automated Meteorological and Oceanographic Systems (SAMOS) Initiative. 2004 meeting of the UNOLS Research Vessel Technical Enhancement Committee, UNOLS, St. Petersburg, Florida, USA. https://www.coaps.fsu.edu/bibliography/show.php?record=889
Smith, S. R. (2005). Shipboard Automated Meteorological and Oceanographic System (SAMOS) Initiative. 3rd Session of the JCOMM Ship Observation Team. World Meteorological Organization. https://www.coaps.fsu.edu/bibliography/show.php?record=898
Smith, S. R. (2005). Shipboard Automated Meteorological and Oceanographic Systems: A Key Component of an Ocean Observing System. In 9th Symposium on Integrated Observing and Assimilation Systems of Atmosphere, Oceans, and Land Surfaces, American Meteorological Society, San Diego, California, USA (cdrom). https://www.coaps.fsu.edu/bibliography/show.php?record=900

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.

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.

https://www.coaps.fsu.edu/bibliography/show.php?record=1095

Smith, S. R. (2004). Report from the 2nd workshop on high-resolution marine meteorology. COAPS Technical Report 04-01. Tallahassee, FL: Center for Ocean-Atmospheric Prediction Studies, Florida State University.

Smith, S. R. (2004). The Shipboard Automated Meteorological and Oceanographic Systems (SAMOS) Initiative. 2004 meeting of the UNOLS Research Vessel Technical Enhancement Committee, UNOLS, St. Petersburg, Florida, USA.

Smith, S. R. (2005). Shipboard Automated Meteorological and Oceanographic Systems: A Key Component of an Ocean Observing System. In 9th Symposium on Integrated Observing and Assimilation Systems of Atmosphere, Oceans, and Land Surfaces, American Meteorological Society, San Diego, California, USA (cdrom).

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