scholarly journals Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

2021 ◽  
Author(s):  
Bin Wang ◽  
Katja Fennel ◽  
Liuqian Yu
Keyword(s):  
Gulf Of Mexico ◽  
A Priori ◽  
Biological Properties ◽  
Satellite Observations ◽  
Sea Surface ◽  
Biogeochemical Model ◽  
Biological Variables ◽  
Biogeochemical Models ◽  
Optical Module ◽  
Argo Profiles

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the satellite observations in combination with sparse BGC Argo profiles can improve subsurface biogeochemical properties. The multivariate Deterministic Ensemble Kalman Filter (DEnKF) has been implemented to assimilate physical and biological observations into a biogeochemical model of the Gulf of Mexico. First, the biogeochemical model component was tuned using BGC-Argo observations. Then, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated, but used in the a priori tuning. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model’s optical module. Repeating the assimilation run after adjusting light attenuation parameterization through further a priori tuning greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits to biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere is insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

scholarly journals Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

Ocean Science ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 1141-1156
Author(s):  
Bin Wang ◽  
Katja Fennel ◽  
Liuqian Yu
Keyword(s):  
Data Assimilation ◽  
Gulf Of Mexico ◽  
A Priori ◽  
Three Dimensional ◽  
Biological Properties ◽  
Satellite Observations ◽  
Sea Surface ◽  
Biogeochemical Models ◽  
Optical Module ◽  
Argo Profiles

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the assimilation of satellite observations in combination with a priori model calibration by sparse BGC-Argo profiles can improve subsurface biogeochemical properties. The multivariate deterministic ensemble Kalman filter (DEnKF) has been implemented to assimilate physical and biological observations into a three-dimensional coupled physical–biogeochemical model, the biogeochemical component of which has been calibrated by BGC-Argo float data for the Gulf of Mexico. Specifically, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated. An alternative light parameterization that was tuned a priori using BGC-Argo observations was also applied to test the sensitivity of data assimilation impact on subsurface biological properties. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model's optical module. Repeating the assimilation run by using the alternative light parameterization greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits for biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere has been insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

scholarly journals Seasonal patterns in phytoplankton biomass across the northern and deep Gulf of Mexico: a numerical model study

2018 ◽  
Vol 15 (11) ◽  
pp. 3561-3576 ◽  
Author(s):  
Fabian A. Gomez ◽  
Sang-Ki Lee ◽  
Yanyun Liu ◽  
Frank J. Hernandez Jr. ◽  
Frank E. Muller-Karger ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Primary Production ◽  
Phytoplankton Biomass ◽  
Mississippi Delta ◽  
Three Dimensional ◽  
Rate Of Change ◽  
Integrated Analysis ◽  
Biogeochemical Model ◽  
Seasonal Patterns ◽  
Biogeochemical Models

Abstract. Biogeochemical models that simulate realistic lower-trophic-level dynamics, including the representation of main phytoplankton and zooplankton functional groups, are valuable tools for improving our understanding of natural and anthropogenic disturbances in marine ecosystems. Previous three-dimensional biogeochemical modeling studies in the northern and deep Gulf of Mexico (GoM) have used only one phytoplankton and one zooplankton type. To advance our modeling capability of the GoM ecosystem and to investigate the dominant spatial and seasonal patterns of phytoplankton biomass, we configured a 13-component biogeochemical model that explicitly represents nanophytoplankton, diatoms, micro-, and mesozooplankton. Our model outputs compare reasonably well with observed patterns in chlorophyll, primary production, and nutrients over the Louisiana–Texas shelf and deep GoM region. Our model suggests silica limitation of diatom growth in the deep GoM during winter and near the Mississippi delta during spring. Model nanophytoplankton growth is weakly nutrient limited in the Mississippi delta year-round and strongly nutrient limited in the deep GoM during summer. Our examination of primary production and net phytoplankton growth from the model indicates that the biomass losses, mainly due to zooplankton grazing, play an important role in modulating the simulated seasonal biomass patterns of nanophytoplankton and diatoms. Our analysis further shows that the dominant physical process influencing the local rate of change of model phytoplankton is horizontal advection in the northern shelf and vertical mixing in the deep GoM. This study highlights the need for an integrated analysis of biologically and physically driven biomass fluxes to better understand phytoplankton biomass phenologies in the GoM.

scholarly journals Evolution of primary production and its drivers on the Lebanese coast between 1986 and 2013

2020 ◽  
Vol 20 (4) ◽  
pp. 207-217
Author(s):  
Ali Fadel ◽  
Lama Salameh ◽  
Malak Kanj ◽  
Ahmad Kobaissi
Keyword(s):  
Primary Production ◽  
Phytoplankton Community ◽  
Controlling Factors ◽  
Sea Surface ◽  
Biogeochemical Model ◽  
Average Temperature ◽  
Biogeochemical Models ◽  
Increasing Trend ◽  
Production Dynamics ◽  
Lebanese Coast

AbstractPhysical-biogeochemical models help us to understand the dynamics and the controlling factors of primary production. In this study, the outputs of a validated hydrodynamic and biogeochemical model were used to elucidate the primary production dynamics between 1992 and 2012 for three studied sites on the Lebanese coast: Naqoura, Beirut, and Tripoli. The results showed that primary production presents a homogeneous spatial distribution along the Lebanese coastline. The phytoplankton community has a low optimal temperature. The thermocline develops in March, with maximum stratification in August and fades in October. Chlorophyll, dissolved oxygen and salinity were positively correlated throughout the water column. A significant increasing trend of sea surface temperature was found on the Lebanese coast over 27 years, between 1986 and 2013. Annual averages increased from 22°C in 1986 to 23.1°C in 2013 with the highest recorded average temperature of 23.7 °C in 2010.

scholarly journals Seasonal Patterns in Phytoplankton Biomass across the Northern and Deep Gulf of Mexico: A Numerical Model Study

2017 ◽  
Author(s):  
Fabian A. Gomez ◽  
Sang-Ki Lee ◽  
Yanyun Liu ◽  
Frank J. Hernandez Jr. ◽  
Frank E. Muller-Karger ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Phytoplankton Biomass ◽  
Three Dimensional ◽  
Trophic Levels ◽  
Biogeochemical Model ◽  
Seasonal Patterns ◽  
Biogeochemical Models ◽  
Model Study ◽  
Numerical Model Study

Abstract. Biogeochemical models that simulate realistic lower trophic levels dynamics, including the representation of main phytoplankton and zooplankton functional groups, are valuable tools for our understanding of natural and anthropogenic disturbances in marine ecosystems. However, previous three-dimensional biogeochemical modeling studies in the northern and deep Gulf of Mexico (GoM) have used only one phytoplankton and one zooplankton type. To advance our modeling capability of the GoM ecosystem and to investigate the dominant spatial and seasonal patterns phytoplankton biomass, we configured a 14-component biogeochemical model that explicitly represents nanophytoplankton, diatoms, micro-, and mesozooplankton. Our model outputs compare well with satellite and in situ observations, reproducing dominant seasonal patterns in chlorophyll and primary production. The model results show that diatom growth is strongly silica limited (> 95 %) in the deep GoM, and both nitrogen and silica limited (30–70 %) in the northern shelf. Nanophytoplankton growth is weakly nutrient limited in the Mississippi delta year-round (

scholarly journals Marine regime shifts in ocean biogeochemical models: a case study in the Gulf of Alaska

2016 ◽  
Vol 13 (15) ◽  
pp. 4533-4553 ◽  
Author(s):  
Claudie Beaulieu ◽  
Harriet Cole ◽  
Stephanie Henson ◽  
Andrew Yool ◽  
Thomas R. Anderson ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gulf Of Alaska ◽  
Regime Shifts ◽  
Trophic Levels ◽  
Sea Surface ◽  
Abrupt Increase ◽  
Abrupt Decrease ◽  
Biological Variables ◽  
Biogeochemical Models

Abstract. Regime shifts have been reported in many marine ecosystems, and are often expressed as an abrupt change occurring in multiple physical and biological components of the system. In the Gulf of Alaska, a regime shift in the late 1970s was observed, indicated by an abrupt increase in sea surface temperature and major shifts in the catch of many fish species. A thorough understanding of the extent and mechanisms leading to such regime shifts is challenged by data paucity in time and space. We investigate the ability of a suite of ocean biogeochemistry models of varying complexity to simulate regime shifts in the Gulf of Alaska by examining the presence of abrupt changes in time series of physical variables (sea surface temperature and mixed-layer depth), nutrients and biological variables (chlorophyll, primary productivity and plankton biomass) using change-point analysis. Our results show that some ocean biogeochemical models are capable of simulating the late 1970s shift, manifested as an abrupt increase in sea surface temperature followed by an abrupt decrease in nutrients and biological productivity. Models from low to intermediate complexity simulate an abrupt transition in the late 1970s (i.e. a significant shift from one year to the next) while the transition is smoother in higher complexity models. Our study demonstrates that ocean biogeochemical models can successfully simulate regime shifts in the Gulf of Alaska region. These models can therefore be considered useful tools to enhance our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.

scholarly journals Downscaling CMIP6 Global Solutions to Regional Ocean Carbon Model: Connecting the Mississippi, Gulf of Mexico, and Global Ocean

2021 ◽  
Author(s):  
Le Zhang ◽  
Z. George Xue
Keyword(s):  
Gulf Of Mexico ◽  
Carbon Sink ◽  
Biological Activities ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Spatial And Temporal Patterns ◽  
Saturation State ◽  
Biogeochemical Models ◽  
Ocean Carbon ◽  
Carbon System

Abstract. Coupled physical-biogeochemical models can significantly reduce uncertainties in estimating the spatial and temporal patterns of the ocean carbon system. Challenges of applying a coupled physical-biogeochemical model in the regional ocean include the reasonable prescription of carbon model boundary conditions, lack of in situ observations, and the oversimplification of certain biogeochemical processes. In this study, we applied a coupled physical-biogeochemical model (Regional Ocean Modelling System, ROMS) to the Gulf of Mexico (GoM) and achieved an unprecedented 20-year high-resolution (5 km, 1/22°) hindcast covering the period of 2000–2019. The model’s biogeochemical cycle is driven by the Coupled Model Intercomparison Project 6-Community Earth System Model 2 products (CMIP6-CESM2) and incorporates the dynamics of dissolved organic carbon (DOC) pools as well as the formation and dissolution of carbonate minerals. Model outputs include generally interested carbon system variables, such as pCO2, pH, aragonite saturation state (ΩArag), calcite saturation state (ΩCalc), CO2 air-sea flux, carbon burial rate, etc. The model’s robustness is evaluated via extensive model-data comparison against buoy, remote sensing-based Machine Learning (ML) predictions, and ship-based measurements. Model results reveal that the GoM water has been experiencing an ~ 0.0016 yr−1 decrease in surface pH over the past two decades, accompanied by a ~ 1.66 µatm yr−1 increase in sea surface pCO2. The air-sea CO2 exchange estimation confirms that the river-dominated northern GoM is a substantial carbon sink. The open water of GoM, affected mainly by the thermal effect, is a carbon source during summer and a carbon sink for the rest of the year. Sensitivity experiments are conducted to evaluate the impacts from river inputs and the global ocean via model boundaries. Our results show that the coastal ocean carbon cycle is dominated by enormous carbon inputs from the Mississippi River and nutrient-stimulated biological activities, and the carbon system condition of the open ocean is primarily driven by inputs from the Caribbean Sea via Yucatan Channel.

scholarly journals Quantifying spatiotemporal variability in zooplankton dynamics in the Gulf of Mexico with a physical–biogeochemical model

2020 ◽  
Vol 17 (13) ◽  
pp. 3385-3407 ◽  
Author(s):  
Taylor A. Shropshire ◽  
Steven L. Morey ◽  
Eric P. Chassignet ◽  
Alexandra Bozec ◽  
Victoria J. Coles ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Secondary Production ◽  
Food Source ◽  
Net Primary Production ◽  
Zooplankton Biomass ◽  
Spatiotemporal Variability ◽  
Biogeochemical Model ◽  
Feeding Mode ◽  
Mesozooplankton Biomass ◽  
Biogeochemical Models

Abstract. Zooplankton play an important role in global biogeochemistry, and their secondary production supports valuable fisheries of the world's oceans. Currently, zooplankton standing stocks 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 biomass estimates from PBMs has been a major challenge due to sparse observations. In this study, we configure a PBM for the Gulf of Mexico (GoM) from 1993 to 2012 and validate the model against an extensive combination of biomass and rate measurements. Spatial variability in a multidecadal database of mesozooplankton biomass 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±8×109 kg C yr−1, equivalent to ∼10 % of net primary production (NPP), and ranged from 51 to 82×109 kg C yr−1, with higher secondary production inside cyclonic eddies and substantially reduced secondary production in anticyclonic eddies. 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. In open-ocean oligotrophic waters, however, both mesozooplankton groups show proportionally greater reliance on heterotrophic protists as a food source. This highlights an 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.

scholarly journals Quantifying spatiotemporal variability in zooplankton dynamics in the Gulf of Mexico with a physical-biogeochemical model

2019 ◽  
Author(s):  
Taylor A. Shropshire ◽  
Steven L. Morey ◽  
Eric P. Chassignet ◽  
Alexandra Bozec ◽  
Victoria J. Coles ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Net Primary Production ◽  
Spatiotemporal Variability ◽  
Biogeochemical Model ◽  
Zooplankton Abundance ◽  
Mesozooplankton Biomass ◽  
Biogeochemical Models ◽  
Zooplankton Dynamics ◽  
Remote Sensing Techniques ◽  
Specific Growth Rates

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 

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