Particle aggregation at the edges of anticyclonic eddies and implications for distribution of biomass

Acoustic measurements show that the biomass of zooplankton and mesopelagic fish is redistributed by mesoscale variability and that the signal extends over several hundred meters depth. The mechanisms governing this distribution are not well understood, but influences from both physical (i.e. redistribution) and biological processes (i.e. nutrient transport, primary production, active swimming, etc.) are likely. This study examines how hydrodynamic conditions and basic vertical swimming behavior act to distribute biomass in an anticyclonic eddy. Using an eddy-resolving 2.3 km-resolution physical ocean model as forcing for a particle-tracking module, particles representing passively floating organisms and organisms with vertical swimming behavior are released within an eddy and monitored for 20 to 30 days. The role of hydrodynamic conditions on the distribution of biomass is discussed in relation to the acoustic measurements. Particles released close to the surface tend, in agreement with the observations, to accumulate around the edge of the eddy, whereas particles released at depth gradually become distributed along the isopycnals. After a month they are displaced several hundreds meters in the vertical with the deepest particles found close to the eddy center and the shallowest close to the edge. There is no evidence of aggregation of particles along the eddy rim in the last simulation. The model results points towards a physical mechanism for aggregation at the surface, however biological processes cannot be ruled out using the current modeling tool.publishedVersio

Environmental Change at Deep-Sea Sponge Habitats Over the Last Half Century: A Model Hindcast Study for the Age of Anthropogenic Climate Change

Deep-sea sponges inhabit multiple areas of the deep North Atlantic at depths below 250 m. Living in the deep ocean, where environmental properties below the permanent thermocline generally change slowly, they may not easily acclimatize to abrupt changes in the environment. Until now consistent monitoring timeseries of the environment at deep sea sponge habitats are missing. Therefore, long-term simulation with coupled bio-physical models can shed light on the changes in environmental conditions sponges are exposed to. To investigate the variability of North Atlantic sponge habitats for the past half century, the deep-sea conditions have been simulated with a 67-year model hindcast from 1948 to 2014. The hindcast was generated using the ocean general circulation model HYCOM, coupled to the biogeochemical model ECOSMO. The model was validated at known sponge habitats with available observations of hydrography and nutrients from the deep ocean to evaluate the biases, errors, and drift in the model. Knowing the biases and uncertainties we proceed to study the longer-term (monthly to multi-decadal) environmental variability at selected sponge habitats in the North Atlantic and Arctic Ocean. On these timescales, these deep sponge habitats generally exhibit small variability in the water-mass properties. Three of the sponge habitats, the Flemish Cap, East Greenland Shelf and North Norwegian Shelf, had fluctuations of temperature and salinity in 4–6 year periods that indicate the dominance of different water masses during these periods. The fourth sponge habitat, the Reykjanes Ridge, showed a gradual warming of about 0.4°C over the simulation period. The flux of organic matter to the sea floor had a large interannual variability, that, compared to the 67-year mean, was larger than the variability of primary production in the surface waters. Lateral circulation is therefore likely an important control mechanism for the influx of organic material to the sponge habitats. Simulated oxygen varies interannually by less than 1.5 ml/l and none of the sponge habitats studied had oxygen concentrations below hypoxic levels. The present study establishes a baseline for the recent past deep conditions that future changes in deep sea conditions from observations and climate models can be evaluated against.publishedVersio

Structural insights into TMB-1 and the role of residues 119 and 228 in substrate and inhibitor binding

Source at https://doi.org/10.1128/AAC.02602-16.Metallo-β-lactamases (MBLs) threaten the effectiveness of β-lactam antibiotics, including carbapenems, and are a concern for global public health. β-Lactam/β-lactamase inhibitor combinations active against class A and class D carbapenemases are used, but no clinically useful MBL inhibitor is currently available. Tripoli metallo-β-lactamase-1 (TMB-1) and TMB-2 are members of MBL subclass B1a, where TMB-2 is an S228P variant of TMB-1. The role of S228P was studied by comparisons of TMB-1 and TMB-2, and E119 was investigated through the construction of site-directed mutants of TMB-1, E119Q, E119S, and E119A (E119Q/S/A). All TMB variants were characterized through enzyme kinetic studies. Thermostability and crystallization analyses of TMB-1 were performed. Thiol-based inhibitors were investigated by determining the 50% inhibitory concentrations (IC50) and binding using surface plasmon resonance (SPR) for analysis of TMB-1. Thermostability measurements found TMB-1 to be stabilized by high NaCl concentrations. Steady-state enzyme kinetics analyses found substitutions of E119, in particular, substitutions associated with the penicillins, to affect hydrolysis to some extent. TMB-2 with S228P showed slightly reduced catalytic efficiency compared to TMB-1. The IC50 levels of the new thiol-based inhibitors were 0.66 μM (inhibitor 2a) and 0.62 μM (inhibitor 2b), and the equilibrium dissociation constant (KD) of inhibitor 2a was 1.6 μM; thus, both were more potent inhibitors than L-captopril (IC50 = 47 μM; KD = 25 μM). The crystal structure of TMB-1 was resolved to 1.75 Å. Modeling of inhibitor 2b in the TMB-1 active site suggested that the presence of the W64 residue results in T-shaped π-π stacking and R224 cation-π interactions with the phenyl ring of the inhibitor. In sum, the results suggest that residues 119 and 228 affect the catalytic efficiency of TMB-1 and that inhibitors 2a and 2b are more potent inhibitors for TMB-1 than L-captopril

Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas

Phytoplankton blooms provide biomass to the marine trophic web, contribute to the carbon removal from the atmosphere and can be deadly when associated with harmful species. This points to the need to understand the phenology of the blooms in the Barents, Norwegian, and North seas. We use satellite chlorophyll-a from 2000 to 2020 to assess robust climatological and the interannual trends of spring and summer blooms onset, peak day, duration and intensity. Further, we also correlate the interannual variability of the blooms with mixed layer depth (MLD), sea surface temperature (SST), wind speed and suspended particulate matter (SPM) retrieved from models and remote sensing. The climatological spring blooms start on March 10th and end on June 19th. The climatological summer blooms begin on July 13th and end on September 17th. In the Barents Sea, years of shallower mixed layer (ML) driven by both calm waters and higher freshwaters input keeps the phytoplankton in the euphotic zone, causing the spring bloom to start earlier and reach higher biomass but end sooner due to the lack of nutrients upwelling from the deep. In the Norwegian Sea, a correlation between SST and the spring blooms is found. Here, warmer waters are correlated to earlier and stronger blooms in most regions but with later and weaker blooms in the eastern Norwegian Sea. In the North Sea, years of shallower ML reduces the phytoplankton sinking below the euphotic zone and limits the SPM increase from the bed shear stress, creating an ideal environment of stratified and clear waters to develop stronger spring blooms. Last, the summer blooms onset, peak day and duration have been rapidly delaying at a rate of 1.25-day year–1, but with inconclusive causes based on the parameters assessed in this study.publishedVersio

Phytoplankton abundance in the Barents Sea is predictable up to five years in advance

The Barents Sea is a highly biologically productive Arctic shelf sea with several commercially important fish stocks. Interannual-to-decadal predictions of its ecosystem would therefore be valuable for marine resource management. Here, we demonstrate that the abundance of phytoplankton, the base of the marine food web, can be predicted up to five years in advance in the Barents Sea with the Norwegian Climate Prediction Model. We identify two different mechanisms giving rise to this predictability; 1) in the southern ice-free Atlantic Domain, skillful prediction is a result of the advection of waters with anomalous nitrate concentrations from the Subpolar North Atlantic; 2) in the northern Polar Domain, phytoplankton predictability is a result of the skillful prediction of the summer ice concentration, which influences the light availability. The skillful prediction of the phytoplankton abundance is an important step forward in the development of numerical ecosystem predictions of the Barents Sea.publishedVersio

Can environmental conditions at North Atlantic deep-sea habitats be predicted several years ahead? - Taking sponge habitats as an example

Predicting the ambient environmental conditions in the coming several years to one decade is of key relevance for elucidating how deep-sea habitats, like for example sponge habitats, in the North Atlantic will evolve under near-future climate change. However, it is still not well known to what extent the deep-sea environmental properties can be predicted in advance. A regional downscaling prediction system is developed to assess the potential predictability of the North Atlantic deep-sea environmental factors. The large-scale climate variability predicted with the coupled Max Planck Institute Earth System Model with low-resolution configuration (MPI-ESM-LR) is dynamically downscaled to the North Atlantic by providing surface and lateral boundary conditions to the regional coupled physical-ecosystem model HYCOM-ECOSMO. Model results of two physical fields (temperature and salinity) and two biogeochemical fields (concentrations of silicate and oxygen) over 21 sponge habitats are taken as an example to assess the ability of the downscaling system to predict the interannual to decadal variations of the environmental properties based on ensembles of retrospective predictions over the period from 1985 to 2014. The ensemble simulations reveal skillful predictions of the environmental conditions several years in advance with distinct regional differences. In areas closely tied to large-scale climate variability and ice dynamics, both the physical and biogeochemical fields can be skillfully predicted more than 4 years ahead, while in areas under strong influence of upper oceans or open boundaries, the predictive skill for both fields is limited to a maximum of 2 years. The simulations suggest higher predictability for the biogeochemical fields than for the physical fields, which can be partly attributed to the longer persistence of the former fields. Predictability is improved by initialization in areas away from the influence of Mediterranean outflow and areas with weak coupling between the upper and deep oceans. Our study highlights the ability of the downscaling regional system to predict the environmental variations at deep-sea benthic habitats on time scales of management relevance. The downscaling system therefore will be an important part of an integrated approach towards the preservation and sustainable exploitation of the North Atlantic benthic habitats

Key physical processes and their model representation for projecting climate impacts on subarctic Atlantic net primary production: A synthesis

Oceanic net primary production forms the foundation of marine ecosystems. Understanding the impact of climate change on primary production is therefore critical and we rely on Earth System Models to project future changes. Stemming from their use of different physical dynamics and biogeochemical processes, these models yield a large spread in long-term projections of change on both the global and regional scale. Here we review the key physical processes and biogeochemical parameterizations that influence the estimation of primary production in Earth System Models and synthesize the available projections of productivity in the subarctic regions of the North Atlantic. The key processes and modelling issues we focus on are mixed layer depth dynamics, model resolution and the complexity and parameterization of biogeochemistry. From the model mean of five CMIP6 models, we found a large increase in PP in areas where the sea ice retreats throughout the 21st century. Stronger stratification and declining MLD in the Nordic Seas, caused by sea ice loss and regional freshening, reduce the vertical flux of nutrients into the photic zone. Following the synthesis of the primary production among the CMIP6 models, we recommend a number of measures: constraining model hindcasts through the assimilation of high-quality long-term observational records to improve physical and biogeochemical parameterizations in models, developing better parameterizations for the sub-grid scale processes, enhancing the model resolution, downscaling and multi-model comparison exercises for improved regional projections of primary production.publishedVersio

Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system

The Los Alamos Sea Ice Model (CICE) is used by several Earth system models where sea ice boundary conditions are not necessary, given their global scope. However, regional and local implementations of sea ice models require boundary conditions describing the time changes of the sea ice and snow being exchanged across the boundaries of the model domain. The physical detail of these boundary conditions regarding, for example, the usage of different sea ice thickness categories or the vertical resolution of thermodynamic properties, must be considered when matching them with the requirements of the sea ice model. Available satellite products do not include all required data. Therefore, the most straightforward way of getting sea ice boundary conditions is from a larger-scale model. The main goal of our study is to describe and evaluate the implementation of time-varying sea ice boundaries in the CICE model using two regional coupled ocean–sea ice models, both covering a large part of the Barents Sea and areas around Svalbard: the Barents-2.5 km, implemented at the Norwegian Meteorological Institute (MET), and the Svalbard 4 km (S4K) model, implemented at the Norwegian Polar Institute (NPI). We use the TOPAZ4 model and a Pan-Arctic 4 km resolution model (A4) to generate the boundary conditions for the sea ice and the ocean. The Barents-2.5 km model is MET’s main forecasting model for ocean state and sea ice in the Barents Sea. The S4K model covers a similar domain but it is used mainly for research purposes. Obtained results show significant improvements in the performance of the Barents-2.5 km model after the implementation of the time-varying boundary conditions. The performance of the S4K model in terms of sea ice and snow thickness is comparable to that of the TOPAZ4 system but with more accurate results regarding the oceanic component because of using ocean boundary conditions from the A4 model. The implementation of time-varying boundary conditions described in this study is similar regardless of the CICE versions used in different models. The main challenge remains the handling of data from larger models before its usage as boundary conditions for regional/local sea ice models, since mismatches between available model products from the former and specific requirements of the latter are expected, implying case-specific approaches and different assumptions. Ideally, model setups should be as similar as possible to allow a smoother transition from larger to smaller domains.Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling systempublishedVersio