The Quantitative Analysis of Water Mass during Winter on the East China Sea Shelf Using an Extended OMP Analysis

The distribution and quantification of water masses on the East China Sea (ECS) shelf is important for identifying and understanding historical climate-driven changes in ocean properties and circulation in the region. We applied an extended Optimum Multiparameter (eOMP) analysis to quantify the relative contribution of water masses using wintertime temperature, salinity, nitrate (NO3−), phosphate (PO43−), and silicate (SiO32−) measurements from a five-cruises dataset spanning from 2013 to 2018. Average ratios (NO3−:PO43−:SiO32− = 47:1:35) derived from field observations were used to correct the equations referring to the chemical parameters. Our analysis indicated that wintertime seawater on the ECS shelf consisted mainly of Changjiang Dilute Water (CDW), Yellow Sea Coastal Water (YSCW), Taiwan Warm Current Water (TWCW), and East China Sea Shelf Water (ECSSW). The results from the eOMP analysis demonstrated the natural boundaries of four water masses during winter. The interannual variability of water masses showed that the CDW distribution was relatively stable in winter, and there was strong anticorrelation between the YSCW and TWCW extents, suggesting that these two water masses mostly displace each other in the north-south direction.publishedVersio

The sensitivity of primary productivity in Disko Bay, a coastal Arctic ecosystem, to changes in freshwater discharge and sea ice cover

The Greenland ice sheet is melting, and the rate of ice loss has increased 6-fold since the 1980s. At the same time, the Arctic sea ice extent is decreasing. Meltwater runoff and sea ice reduction both influence light and nutrient availability in the coastal ocean, with implications for the timing, distribution, and magnitude of phytoplankton production. However, the integrated effect of both glacial and sea ice melt is highly variable in time and space, making it challenging to quantify. In this study, we evaluate the relative importance of these processes for the primary productivity of Disko Bay, west Greenland, one of the most important areas for biodiversity and fisheries around Greenland. We use a high-resolution 3D coupled hydrodynamic–biogeochemical model for 2004–2018 validated against in situ observations and remote sensing products. The model-estimated net primary production (NPP) varied between 90–147 gC m−2 yr−1 during 2004–2018, a period with variable freshwater discharges and sea ice cover. NPP correlated negatively with sea ice cover and positively with freshwater discharge. Freshwater discharge had a strong local effect within ∼ 25 km of the source-sustaining productive hot spots during summer. When considering the annual NPP at bay scale, sea ice cover was the most important controlling factor. In scenarios with no sea ice in spring, the model predicted a ∼ 30 % increase in annual production compared to a situation with high sea ice cover. Our study indicates that decreasing ice cover and more freshwater discharge can work synergistically and will likely increase primary productivity of the coastal ocean around Greenland.publishedVersio

Accounting for unpredictable spatial variability in plankton ecosystem models

Limitations on our ability to predict fine-scale spatial variability in plankton ecosystems canseriously compromise our ability to predict coarse-scale behaviour. Spatial variability whichis deterministically unpredictable may distort parameter estimates when the ecosystem modelis fitted to (or assimilates) ocean data, may compromise model validation, and may producemean-field ecosystem behaviour discrepant with that predicted by the model. New statisticalmethods are investigated to mitigate these effects and thus improve understanding and predictionof coarse-scale behaviour e.g. in response to climate change. First, the standard modelfitting technique is generalised to allow model-data ‘phase errors’ in the form of time lags,as has been observed to approximate mesoscale plankton variability in the open ocean. Theresulting ‘variable lag fit’ is shown to enable ‘Lagrangian’ parameter recovery with artificialecosystem data. A second approach employs spatiotemporal averaging, fitting a ‘weak prior’box model to suitably-averaged data from Georges Bank (as an example), allowing liberalbiological parameter adjustments to account for mean effects of unresolved variability. Anovel skill assessment technique is used to show that the extrapolative skill of the box modelfails to improve on a strictly empirical model. Third, plankton models where horizontal variabilityis resolved ‘implicitly’ are investigated as an alternative to coarse or higher explicitresolution. A simple simulation study suggests that the mean effects of fine-scale variabilityon coarse-scale plankton dynamics can be serious, and that ‘spatial moment closure’ andsimilar statistical modelling techniques may be profitably applied to account for them

Potential effects of reduced riverine inorganic particle loading on water quality in the Oslofjord region

Project Manager Phil WallheadA model investigation was carried out of the potential impacts of reduced riverine inorganic particle loading in the Oslofjord region. Some significant improvements in water clarity, particularly in the Glomma estuary, were projected by the model for a 100% reduction in inorganic particle loading. This was partly offset by an intensification of phytoplankton blooms, due to more light availability, although this did not strongly impact bottom water dissolved oxygen levels. More focused modelling and observational work is recommended before any large-scale implementation of particle reduction measures, in view of the risks of exacerbating eutrophication and potential harmful algal blooms, and a potential need for concurrent reductions in nutrient loading to minimize these risks.publishedVersio

Potential effects of reduced riverine inorganic particle loading on water quality in the Oslofjord region

Project Manager Phil WallheadA model investigation was carried out of the potential impacts of reduced riverine inorganic particle loading in the Oslofjord region. Some significant improvements in water clarity, particularly in the Glomma estuary, were projected by the model for a 100% reduction in inorganic particle loading. This was partly offset by an intensification of phytoplankton blooms, due to more light availability, although this did not strongly impact bottom water dissolved oxygen levels. More focused modelling and observational work is recommended before any large-scale implementation of particle reduction measures, in view of the risks of exacerbating eutrophication and potential harmful algal blooms, and a potential need for concurrent reductions in nutrient loading to minimize these risks.publishedVersio

A 1-Dimensional Sympagic–Pelagic–Benthic Transport Model (SPBM): Coupled Simulation of Ice, Water Column, and Sediment Biogeochemistry, Suitable for Arctic Applications

Marine biogeochemical processes can strongly interact with processes occurring in adjacent ice and sediments. This is especially likely in areas with shallow water and frequent ice cover, both of which are common in the Arctic. Modeling tools are therefore required to simulate coupled biogeochemical systems in ice, water, and sediment domains. We developed a 1D sympagic–pelagic–benthic transport model (SPBM) which uses input from physical model simulations to describe hydrodynamics and ice growth and modules from the Framework for Aquatic Biogeochemical Models (FABM) to construct a user-defined biogeochemical model. SPBM coupled with a biogeochemical model simulates the processes of vertical diffusion, sinking/burial, and biogeochemical transformations within and between the three domains. The potential utility of SPBM is demonstrated herein with two test runs using modules from the European regional seas ecosystem model (ERSEM) and the bottom-redox model biogeochemistry (BROM-biogeochemistry). The first run simulates multiple phytoplankton functional groups inhabiting the ice and water domains, while the second simulates detailed redox biogeochemistry in the ice, water, and sediments. SPBM is a flexible tool for integrated simulation of ice, water, and sediment biogeochemistry, and as such may help in producing well-parameterized biogeochemical models for regions with strong sympagic–pelagic–benthic interactions.publishedVersio

Retrieving monthly and interannual total-scale pH (pHT) on the East China Sea shelf using an artificial neural network: ANN-pHT-v1

While our understanding of pH dynamics has strongly progressed for open-ocean regions, for marginal seas such as the East China Sea (ECS) shelf progress has been constrained by limited observations and complex interactions between biological, physical and chemical processes. Seawater pH is a very valuable oceanographic variable but not always measured using high-quality instrumentation and according to standard practices. In order to predict total-scale pH (pHT) and enhance our understanding of the seasonal variability of pHT on the ECS shelf, an artificial neural network (ANN) model was developed using 11 cruise datasets from 2013 to 2017 with coincident observations of pHT, temperature (T), salinity (S), dissolved oxygen (DO), nitrate (N), phosphate (P) and silicate (Si) together with sampling position and time. The reliability of the ANN model was evaluated using independent observations from three cruises in 2018, and it showed a root mean square error accuracy of 0.04. The ANN model responded to T and DO errors in a positive way and S errors in a negative way, and the ANN model was most sensitive to S errors, followed by DO and T errors. Monthly water column pHT for the period 2000–2016 was retrieved using T, S, DO, N, P and Si from the Changjiang biology Finite-Volume Coastal Ocean Model (FVCOM). The agreement is good here in winter, while the reduced performance in summer can be attributed in large part to limitations of the Changjiang biology FVCOM in simulating summertime input variables.publishedVersio

Spatially implicit plankton population models: transient spatial variability

Ocean plankton models are useful tools for understanding and predicting the behaviour of planktonic ecosystems. However, when the regions represented by the model grid cells are not well mixed, the population dynamics of grid cell averages may differ from those of smaller scales (such as the laboratory scale). Here, the ‘mean field approximation’ fails due to ‘biological Reynolds fluxes’ arising from nonlinearity in the fine-scale biological interactions and unresolved spatial variability. We investigate the domain-scale behaviour of two-component, 2D reaction–diffusion plankton models producing transient dynamics, with spatial variability resulting only from the initial conditions. Failure of the mean field approximation can be quite significant for sub grid-scale mixing rates applicable to practical ocean models. To improve the approximation of domain-scale dynamics, we investigate implicit spatial resolution methods such as spatial moment closure. For weak and moderate strengths of biological nonlinearity, spatial moment closure models generally yield significant improvements on the mean field approximation, especially at low mixing rates. However, they are less accurate given weaker transience and stronger nonlinearity. In the latter case, an alternative ‘two-spike’ approximation is accurate at low mixing rates. We argue that, after suitable extension, these methods may be useful for understanding and skillfully predicting the large-scale behaviour of marine ecosystems.<br/

Why do regional biogeochemical models produce contrasting future projections of primary production in the Barents Sea?

Projected future changes in primary production in the Barents Sea vary among different regional biogeochemical models, with some showing an increase, some a decrease, and some no change. This variability has been attributed to differences in the underlying physics, but little effort has been spent to understand the primary causal processes. In this study, we compare two extreme projections: one model (NORWECOM.E2E) projects a 36% increase and another model (SINMOD) projects a 9% decrease in primary production in a future warmer Barents Sea. Using structural equation modeling, we identify the direct and indirect effects of the major environmental variables on primary production. The results show that the two biogeochemical models agree on the directions of impacts, and that differences in the physical environment, specifically the factors controlling nutrient availability, are the main cause of the disparities. Both models agree that decreasing ice-coverage leads to increased primary production. However, the projection with a decrease in primary production was characterized by a decrease in winter nitrate concentrations and stronger temperature-induced stratification. By contrast, the projection with an increase in primary production was characterized by an increase in winter nitrate concentrations and weaker stratification due to a relatively smaller temperature increase which was offset by increasing wind stress. The results emphasize the need for accurate descriptions of the physical environments and inform discussions about the future of the Barents Sea ecosystem and the potential for Arctic blue growth.publishedVersio