Monsoon-driven biogeochemical processes in the Arabian Sea

Although it is nominally a tropical locale, the semiannual wind reversals associated with the Monsoon system of the Arabian Sea result annually in two distinct periods of elevated biological activity. While in both cases monsoonal forcing drives surface layer nutrient enrichment that supports increased rates of primary productivity, fundamentally different entrainment mechanisms are operating in summer (Southwest) and winter (Northeast) Monsoons. Moreover, the intervening intermonsoon periods, during which the region relaxes toward oligotrophic conditions more typical of tropical environments, provide a stark contrast to the dynamic biogeochemical activity of the monsoons. The resulting spatial and temporal variability is great and provides a significant challenge for ship-based surveys attempting to characterize the physical and biogeochemical environments of the region. This was especially true for expeditions in the pre-satellite era. Here, we present an overview of the dynamical response to seasonal monsoonal forcing and the characteristics of the physical environment that fundamentally drive regional biogeochemical variability. We then review past observations of the biological distributions that provided our initial insights into the pelagic system of the Arabian Sea. These evolved through the 1980s as additional methodologies, in particular the first synoptic ocean color distributions gathered by the Coastal Zone Color Scanner, became available. Through analyses of these observations and the first largescale physical–biogeochemical modeling attempts, a pre-JGOFS understanding of the Arabian Sea emerged. During the 1990s, the in situ and remotely sensed observational databases were significantly extended by regional JGOFS activities and the onset of Sea-viewing Wide Field-of-View Sensor ocean color measurements. Analyses of these new data and coupled physical–biogeochemical models have already advanced our understanding and have led to either an amplification or revision of the pre-JGOFS paradigms. Our understanding of this complex and variable ocean region is still evolving. Nonetheless, we have a much better understanding of time–space variability of biogeochemical properties in the Arabian Sea and much deeper insights about the physical and biological factors that drive them, as well as a number of challenging new directions to pursue

The estuarine hypoxia component of the Coastal Ocean Modeling Testbed (COMT)

Due to increased nutrient loads that are delivered to our coastal ecosystems, hypoxic events are becoming increasingly prevalent. In response, NOAA is working to monitor, understand and predict hypoxia in U.S. waters in order to develop strategies for forecasting these events and minimizing their detrimental effects. The Chesapeake Bay and its associated tidal tributaries, which together form one of the world’s largest and most important estuaries, is one of the coastal systems where degraded water quality and hypoxia are a major concern. Partially as a result of the number of public livelihoods affected by hypoxic events, the Chesapeake Bay is highlighted as a region of specific interest in terms of developing a NOAA operational system. Fully understanding and being able to hindcast and forecast these complex interactions is of significant ecological and economic importance, and thus the overall goal of this COMT project is to assess the readiness and maturity of a suite of existing coastal ecological community models for determining past, present and future hypoxia events within the Chesapeake Bay, in an effort to accelerate the transition of hypoxia model formulations from research to Federal operational facilities. Achieving this overall goal is feasible because of the multiple existing hydrodynamic models and ecological/dissolved oxygen models currently being successfully implemented in the Chesapeake Bay region. To date, management decisions related to Bay hypoxia have been based on the complex coupled hydrodynamic+water quality models developed under the auspices of the EPA Chesapeake Bay Program; however, because of its complexity, the EPA Chesapeake Bay Program model cannot be run in an operational fashion. Therefore, as part of the COMT, we are implementing a suite of simpler hydrodynamic and hypoxia models and assessing their relative skill in terms of reproducing dissolved oxygen observations; this will help us determine which combination of model formulations will be ideally suited for operational hypoxia modeling in the Chesapeake region. Efforts are currently underway to incorporate the most reliable model formulations into a pre-operational physical code at the NOAA/NOS/Coast Survey Development Laboratory, thus positioning NOAA so that it can consider adopting this new model with minimal effort during the next upgrade to the currently operational Chesapeake Bay Operational Forecast System (CBOFS). This will provide a seamless scheduled transition that will establish a fully operational oxygen and hypoxic volume nowcast/forecast capability within NOAA for the Chesapeake Bay, thus addressing one of the High Priority Focus areas identified in the NOAA Ecological Forecasting Roadmap

Assimilating high-resolution salinity data into a model of a partially mixed estuary

[1] A three-dimensional circulation model of the Chesapeake Bay is used to validate a simple data assimilation scheme, using high-resolution salinity data acquired from a ship-towed undulating vehicle (a Scanfish). The simulation period spans the entire year of 1995 during which the high-resolution Scanfish data were available in July and October, lasting a few days each. Since Scanfish data were irregularly distributed in time and space, only salinity fields are nudged in the model for simplicity. Model improvements through data assimilation are evaluated from a pair of experiments: one with data assimilation and one without. Data from scattered Chesapeake Bay Program monitoring stations and a few stations maintained by the National Ocean Service inside the bay are used independently to check the model performance. In general, the simple assimilation scheme leads to visibly improved density structures in the upper and middle reaches of the bay. The improvement in the lower bay is equally pronounced after data assimilation but diminishes in a shorter timescale because of faster flushing from the adjacent coastal ocean. Moderate to weak nudging normally enhances the gravitational circulation. Strong nudging may produce transient overshooting, during which the gravitational circulation is renewed vigorously

Report of the special session of "Chesapeake Bay Ecological Forecasting: Moving Ecosystem Modeling from Research to Operation" of the 2010 Chesapeake Modeling Symposium

Moving ecosystem modeling from research to applications and operations has direct management relevance and will be integral to achieving the water quality and living resource goals of the 2010 Chesapeake Bay Executive Order. Yet despite decades of ecosystem modeling efforts of linking climate to water quality, plankton and fish, ecological models are rarely taken to the operational phase. In an effort to promote operational ecosystem modeling and ecological forecasting in Chesapeake Bay, a meeting was convened on this topic at the 2010 Chesapeake Modeling Symposium (May, 10-11). These presentations show that tremendous progress has been made over the last five years toward the development of operational ecological forecasting models, and that efforts in Chesapeake Bay are leading the way nationally. Ecological forecasts predict the impacts of chemical, biological, and physical changes on ecosystems, ecosystem components, and people. They have great potential to educate and inform not only ecosystem management, but also the outlook and opinion of the general public, for whom we manage coastal ecosystems. In the context of the Chesapeake Bay Executive Order, ecological forecasting can be used to identify favorable restoration sites, predict which sites and species will be viable under various climate scenarios, and predict the impact of a restoration project on water quality

Chesapeake Bay nitrogen fluxes derived from a land-estuarine ocean biogeochemical modeling system: Model description, evaluation, and nitrogen budgets

The Chesapeake Bay plays an important role in transforming riverine nutrients before they are exported to the adjacent continental shelf. Although the mean nitrogen budget of the Chesapeake Bay has been previously estimated from observations, uncertainties associated with interannually varying hydrological conditions remain. In this study, a land-estuarine-ocean biogeochemical modeling system is developed to quantify Chesapeake riverine nitrogen inputs, within-estuary nitrogen transformation processes and the ultimate export of nitrogen to the coastal ocean. Model skill was evaluated using extensive in situ and satellite-derived data, and a simulation using environmental conditions for 2001-2005 was conducted to quantify the Chesapeake Bay nitrogen budget. The 5 year simulation was characterized by large riverine inputs of nitrogen (154 x 10(9) g N yr(-1)) split roughly 60: 40 between inorganic: organic components. Much of this was denitrified (34 x 10(9) g N yr(-1)) and buried (46 x 10(9) g N yr(-1)) within the estuarine system. A positive net annual ecosystem production for the bay further contributed to a large advective export of organic nitrogen to the shelf (91 x 10(9) g N yr(-1)) and negligible inorganic nitrogen export. Interannual variability was strong, particularly for the riverine nitrogen fluxes. In years with higher than average riverine nitrogen inputs, most of this excess nitrogen (50-60%) was exported from the bay as organic nitrogen, with the remaining split between burial, denitrification, and inorganic export to the coastal ocean. In comparison to previous simulations using generic shelf biogeochemical model formulations inside the estuary, the estuarine biogeochemical model described here produced more realistic and significantly greater exports of organic nitrogen and lower exports of inorganic nitrogen to the shelf

Chesapeake Bay Nitrogen Fluxes Derived From a Land-Estuarine Ocean Biogeochemical Modeling System: Model Description, Evaluation, and Nitrogen Budgets

The Chesapeake Bay plays an important role in transforming riverine nutrients before they are exported to the adjacent continental shelf. Although the mean nitrogen budget of the Chesapeake Bay has been previously estimated from observations, uncertainties associated with interannually varying hydrological conditions remain. In this study, a land-estuarine-ocean biogeochemical modeling system is developed to quantify Chesapeake riverine nitrogen inputs, within-estuary nitrogen transformation processes and the ultimate export of nitrogen to the coastal ocean. Model skill was evaluated using extensive in situ and satellite-derived data, and a simulation using environmental conditions for 2001–2005 was conducted to quantify the Chesapeake Bay nitrogen budget. The 5 year simulation was characterized by large riverine inputs of nitrogen (154 × 109 g N yr−1) split roughly 60:40 between inorganic:organic components. Much of this was denitrified (34 × 109 g N yr−1) and buried (46 × 109 g N yr−1) within the estuarine system. A positive net annual ecosystem production for the bay further contributed to a large advective export of organic nitrogen to the shelf (91 × 109 g N yr−1) and negligible inorganic nitrogen export. Interannual variability was strong, particularly for the riverine nitrogen fluxes. In years with higher than average riverine nitrogen inputs, most of this excess nitrogen (50–60%) was exported from the bay as organic nitrogen, with the remaining split between burial, denitrification, and inorganic export to the coastal ocean. In comparison to previous simulations using generic shelf biogeochemical model formulations inside the estuary, the estuarine biogeochemical model described here produced more realistic and significantly greater exports of organic nitrogen and lower exports of inorganic nitrogen to the shelf

Predicting the distribution of Vibrio vulnificus in Chesapeake Bay

Vibrio vulnificus is a gram-negative pathogenic bacterium endemic to coastal waters worldwide, and a leading cause of seafood related mortality. Because of human health concerns, understanding the ecology of the species and potentially predicting its distribution is of great importance. We evaluated and applied a previously published qPCR assay to water samples (n = 235) collected from the main-stem of the Chesapeake Bay (2007 – 2008) by Maryland and Virginia State water quality monitoring programs. Results confirmed strong relationships between the likelihood of Vibrio vulnificus presence and both temperature and salinity that were used to develop a logistic regression model. The habitat model demonstrated a high degree of concordance (93%), and robustness as subsequent bootstrapping (n=1000) did not change model output (P > 0.05). We forced this empirical habitat model with temperature and salinity predictions generated by a regional hydrodynamic modeling system to demonstrate its utility in future pathogen forecasting efforts in the Chesapeake Bay

Forecasting Prorocentrum minimum blooms in the Chesapeake Bay using empirical habitat models

Aquaculturists, local beach managers, and other stakeholders require forecasts of harmful biotic events, so they can assess and respond to health threats when harmful algal blooms (HABs) are present. Based on this need, we are developing empirical habitat suitability models for a variety of Chesapeake Bay HABs to forecast their occurrence based on a set of physical-biogeochemical environmental conditions, and start with the dinoflagellate Prorocentrum minimum (also known as P. cordatum).To identify an optimal set of environmental variables to forecast P. minimum blooms, we first assumed a linear relationship between the environmental variables and the inverse of the logistic function used to forecast the likelihood of bloom presence, and repeated the method using more than 16,000 combinations of variables. By comparing goodness-of-fit, we found water temperature, salinity, pH, solar irradiance, and total organic nitrogen represented the most suitable set of variables. The resulting algorithm forecasted P. minimum blooms with an overall accuracy of 78%, though with a significant variability ~ 30-90% depending on region and season. To understand this variability and improve model performance, we incorporated nonlinear effects into the model by implementing a generalized additive model. Even without considering interactions between the five variables used to train the model, this yielded an increase in overall model accuracy (~ 81%) due to the model’s ability to refine the regions in which P. minimum blooms occurred. Including nonlinear interactions increased the overall model accuracy even further (~ 85%) by accounting for seasonality in the interaction between solar irradiance and water temperature. Our findings suggest that the influence of predictors of these blooms change in time and space, and that model complexity impacts the model performance and our interpretation of the driving factors causing P. minimum blooms. Apart from their forecasting potential, our results may be particularly useful when constructing explicit relationships between environmental conditions and P. minimum presence in mechanistic models

Cardiovascular implications and physical activity in middle-aged and older adults with a history of COVID-19 (CV COVID)::a protocol for a randomised controlled trial

Background: The clinical manifestation of COVID-19 is associated with infection and inflammation of the lungs, but there is evidence to suggest that COVID-19 may also affect the structure and function of the cardiovascular system. At present, it is not fully understood to what extent COVID-19 impacts cardiovascular function in the short- and long-term following infection. The aim of the present study is twofold: (i) to define the effect of COVID-19 on cardiovascular function (i.e. arterial stiffness, cardiac systolic and diastolic function) in otherwise healthy individuals and (ii) to evaluate the effect of a home-based physical activity intervention on cardiovascular function in people with a history of COVID-19. Methods: This prospective, single-centre, observational study will recruit 120 COVID-19-vaccinated adult participants aged between 50 and 85 years, i.e. 80 with a history of COVID-19 and 40 healthy controls without a history of COVID-19. All participants will undergo baseline assessments including 12-lead electrocardiography, heart rate variability, arterial stiffness, rest and stress echocardiography with speckle tracking imaging, spirometry, maximal cardiopulmonary exercise testing, 7-day physical activity and sleep measures and quality of life questionnaires. Blood samples will be collected to assess the microRNA expression profiles, cardiac and inflammatory biomarkers, i.e. cardiac troponin T; N-terminal pro B-type natriuretic peptide; tumour necrosis factor alpha; interleukins 1, 6 and 10; C-reactive protein; d-dimer; and vascular endothelial growth factors. Following baseline assessments, COVID-19 participants will be randomised 1:1 into a 12-week home-based physical activity intervention aiming to increase their daily number of steps by 2000 from baseline. The primary outcome is change in left ventricular global longitudinal strain. Secondary outcomes are arterial stiffness, systolic and diastolic function of the heart, functional capacity, lung function, sleep measures, quality of life and well-being (depression, anxiety, stress and sleep efficiency). Discussion: The study will provide insights into the cardiovascular implications of COVID-19 and their malleability with a home-based physical activity intervention. Trial registration: ClinicalTrials.gov NCT05492552. Registered on 7 April 2022

Pelagic Functional Group Modeling: Progress, Challenges and Prospects

In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochernical cycles in the ocean will respond to global warming. We define the term biogeochemical functional group to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, functional groups have no phylogenetic meaning-these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. One obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our tendency to model the organisms for which we have the most validation data (e.g., E huxleyi and Trichodesmium) even when they may represent only a fraction of the biogeochemical functional group we are trying to represent. When we step back and look at the paleo-oceanographic record, it suggests that oxygen concentrations have played a central role in the evolution and emergence of many of the key functional groups that influence biogeochemical cycles in the present-day ocean. However, more subtle effects are likely to be important over the next century like changes in silicate supply or turbulence that can influence the relative success of diatoms versus dinoflagellates, coccolithophorids and diazotrophs. In general, inferences drawn from the paleo-oceanographic record and theoretical work suggest that global warming will tend to favor the latter because it will give rise to increased stratification. However, decreases in pH and Fe supply could adversely impact coccolithophorids and diazotrophs in the future. It may be necessary to include explicit dynamic representations of nitrogen fixation, denitrification, silicification and calcification in our models if our goal is predicting the oceanic carbon cycle in the future, because these processes appear to play a very significant role in the carbon cycle of the present-day ocean and they are sensitive to climate change. Observations and models suggest that it may also be necessary to include the DMS cycle to predict future climate, though the effects are still highly uncertain. We have learned a tremendous amount about the distributions and biogeochemical impact of bacteria in the ocean in recent years, yet this improved understanding has not yet been incorporated into many of our models. All of these considerations lead us toward the development of increasingly complex models. However, recent quantitative model intercomparison studies suggest that continuing to add complexity and more functional groups to our ecosystem models may lead to decreases in predictive ability if the models are not properly constrained with available data. We also caution that capturing the present-day variability tells us little about how well a particular model can predict the future. If our goal is to develop models that can be used to predict how the oceans will respond to global warming, then we need to make more rigorous assessments of predictive skill using the available data