Modeling mercury cycling in the marine environment

2020 ◽  
Author(s):  
Johannes Bieser ◽  
Ute Daewel ◽  
Corinna Schrum
Keyword(s):  
Numerical Models ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Trophic Levels ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Large Variability ◽  
Marine Ecosystem Model ◽  
Set Up ◽  
Fully Coupled

<p>Five decades of Hg science have shown the <strong>tremendous complexity of the global Hg cycle</strong>. Yet, the pathways that lead from anthropogenic Hg emissions to MeHg exposure through sea food are not fully comprehended. Moreover, the observed amount of MeHg in fish exhibits a large temporal and spatial variability that we cannot predict yet. A key issue is that fully speciated Hg measurements in the ocean are difficult to perform and thus we will never be able to achieve a comprehensive spatial and temporal coverage.</p><p>Therefore, we need complex modeling tools that allow us to fill the gaps in the observations and to predict future changes in the system under changing external drivers (emissions, climate change, ecosystem changes). Numerical models have a long history in Hg research, but so far have virtually only addressed inorganic Hg cycling in atmosphere and oceans.</p><p>Here we present a novel 3d-hydrodynamic mercury modeling framework based on fully coupled compartmental models including atmosphere, ocean, and ecosystem. The generalized high resolution model has been set up for European shelf seas and was used to model the transition zone from estuaries to the open ocean. Based on this model we present our findings on intra- and inter-annual dynamics and variability of mercury speciation and distribution in a coastal ocean. Moreover, we present the first results on the dynamics of mercury bio-accumulation from a fully coupled marine ecosystem model. Most importantly, the model is able to reproduce the large variability in methylmercury accumulation in higher trophic levels.</p>

scholarly journals Wavelet-based spatial comparison technique for analysing and evaluating two-dimensional geophysical model fields

2011 ◽  
Vol 4 (4) ◽  
pp. 3161-3183 ◽  
Author(s):  
S. Saux Picart ◽  
M. Butenschön ◽  
J. D. Shutler
Keyword(s):  
Satellite Data ◽  
Fishery Management ◽  
Numerical Models ◽  
Spatial Scales ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Original Method ◽  
Spatial And Temporal Scales ◽  
Geographical Patterns ◽  
Marine Ecosystem Model

Abstract. Complex numerical models of the Earth's environment, based around 3-D or 4-D time and space domains are routinely used for applications including climate predictions, weather forecasts, fishery management and environmental impact assessments. Quantitatively assessing the ability of these models to accurately reproduce geographical patterns at a range of spatial and temporal scales has always been a difficult problem to address. However, this is crucial if we are to rely on these models for decision making. Satellite data are potentially the only observational dataset able to cover the large spatial domains analysed by many types of geophysical models. Consequently optical wavelength satellite data is beginning to be used to evaluate model hindcast fields of terrestrial and marine environments. However, these satellite data invariably contain regions of occluded or missing data due to clouds, further complicating or impacting on any comparisons with the model. A methodology has recently been developed to evaluate precipitation forecasts using radar observations. It allows model skill to be evaluated at a range of spatial scales and rain intensities. Here we extend the original method to allow its generic application to a range of continuous and discontinuous geophysical data fields, and therefore allowing its use with optical satellite data. This is achieved through two major improvements to the original method: (i) all thresholds are determined based on the statistical distribution of the input data, so no a priori knowledge about the model fields being analysed is required and (ii) occluded data can be analysed without impacting on the metric results. The method can be used to assess a model's ability to simulate geographical patterns over a range of spatial scales. We illustrate how the method provides a compact and concise way of visualising the degree of agreement between spatial features in two datasets. The application of the new method, its handling of bias and occlusion and the advantages of the novel method are demonstrated through analyzing model fields from a marine ecosystem model.

scholarly journals Wavelet-based spatial comparison technique for analysing and evaluating two-dimensional geophysical model fields

2012 ◽  
Vol 5 (1) ◽  
pp. 223-230 ◽  
Author(s):  
S. Saux Picart ◽  
M. Butenschön ◽  
J. D. Shutler
Keyword(s):  
Satellite Data ◽  
Fishery Management ◽  
Numerical Models ◽  
Spatial Scales ◽  
Marine Ecosystem ◽  
Original Method ◽  
Precipitation Forecast ◽  
Spatial And Temporal Scales ◽  
Geographical Patterns ◽  
Marine Ecosystem Model

Abstract. Complex numerical models of the Earth's environment, based around 3-D or 4-D time and space domains are routinely used for applications including climate predictions, weather forecasts, fishery management and environmental impact assessments. Quantitatively assessing the ability of these models to accurately reproduce geographical patterns at a range of spatial and temporal scales has always been a difficult problem to address. However, this is crucial if we are to rely on these models for decision making. Satellite data are potentially the only observational dataset able to cover the large spatial domains analysed by many types of geophysical models. Consequently optical wavelength satellite data is beginning to be used to evaluate model hindcast fields of terrestrial and marine environments. However, these satellite data invariably contain regions of occluded or missing data due to clouds, further complicating or impacting on any comparisons with the model. This work builds on a published methodology, that evaluates precipitation forecast using radar observations based on predefined absolute thresholds. It allows model skill to be evaluated at a range of spatial scales and rain intensities. Here we extend the original method to allow its generic application to a range of continuous and discontinuous geophysical data fields, and therefore allowing its use with optical satellite data. This is achieved through two major improvements to the original method: (i) all thresholds are determined based on the statistical distribution of the input data, so no a priori knowledge about the model fields being analysed is required and (ii) occluded data can be analysed without impacting on the metric results. The method can be used to assess a model's ability to simulate geographical patterns over a range of spatial scales. We illustrate how the method provides a compact and concise way of visualising the degree of agreement between spatial features in two datasets. The application of the new method, its handling of bias and occlusion and the advantages of the novel method are demonstrated through the analysis of model fields from a marine ecosystem model.

scholarly journals Skillful prediction of tropical Pacific fisheries provided by Atlantic Niños

2021 ◽  
Author(s):  
Iñigo Gómara ◽  
Belén Rodríguez-Fonseca ◽  
Elsa Mohino ◽  
Teresa Losada ◽  
Irene Polo ◽  
...  
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Tropical Pacific ◽  
Equatorial Atlantic ◽  
Climate Conditions ◽  
Marine Food Web ◽  
Marine Ecosystem Model ◽  
The Tropical Pacific

AbstractTropical Pacific upwelling-dependent ecosystems are the most productive and variable worldwide, mainly due to the influence of El Niño Southern Oscillation (ENSO). ENSO can be forecasted seasons ahead thanks to assorted climate precursors (local-Pacific processes, pantropical interactions). However, owing to observational data scarcity and bias-related issues in earth system models, little is known about the importance of these precursors for marine ecosystem prediction. With recently released reanalysis-nudged global marine ecosystem simulations, these constraints can be sidestepped, allowing full examination of tropical Pacific ecosystem predictability. By complementing historical fishing records with marine ecosystem model data, we show herein that equatorial Atlantic Sea Surface Temperatures (SSTs) constitute a superlative predictability source for tropical Pacific marine yields, which can be forecasted over large-scale areas up to 2 years in advance. A detailed physical-biological mechanism is proposed whereby Atlantic SSTs modulate upwelling of nutrient-rich waters in the tropical Pacific, leading to a bottom-up propagation of the climate-related signal across the marine food web. Our results represent historical and near-future climate conditions and provide a useful springboard for implementing a marine ecosystem prediction system in the tropical Pacific.

Estimation of the Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation

2013 ◽  
Vol 321-324 ◽  
pp. 2419-2423
Author(s):  
Xiao Yan Li ◽  
Chun Hui Wang ◽  
Xian Qing Lv
Keyword(s):  
Marine Ecosystem ◽  
Absolute Error ◽  
Ecosystem Model ◽  
Global Scale ◽  
Time Step ◽  
Marine Ecosystem Model ◽  
Maximum Uptake Rate ◽  
Influence Radius ◽  
Adjoint Assimilation ◽  
Spatially Varying

By utilizing spatial biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem model (NPZD-type) and its adjoint model which were built on global scale based on climatological environment and data. When the spatially varying Vm (maximum uptake rate of nutrient by phytoplankton) was estimated alone, we discussed how would the distribution schemes of spatial parameterization and influence radius affected the results. The reduced cost function (RCF), the mean absolute error (MAE) of phytoplankton in the surface layer, and the relative error (RE) of Vm between given and simulated values decreased obviously. The influence of time step was studied then and we found that the assimilation recovery would not be more successful with a smaller time step of 3 hours compared with 6 hours.

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