Archive for boundary layer

What really controls deep-seafloor calcite dissolution?

Posted by mmaheigan 
· Monday, December 16th, 2019 

On time scales of tens to millions of years, seawater acidity is primarily controlled by biogenic calcite (CaCO3) dissolution on the seafloor. Our quantitative understanding of future oceanic pH and carbonate system chemistry requires knowledge of what controls this dissolution. Past experiments on the dissolution rate of suspended calcite grains have consistently suggested a high-order, nonlinear dependence on undersaturation that is independent of fluid flow rate. This form of kinetics has been extensively adopted in models of deep-sea calcite dissolution and pH of benthic sediments. However, stirred-chamber and rotating-disc dissolution experiments have consistently demonstrated linear kinetics of dissolution and a strong dependence on fluid flow velocity. This experimental discrepancy surrounding the kinetic control of seafloor calcite dissolution precludes robust predictions of oceanic response to anthropogenic acidification.

In a recent study published in Geochimica et Cosmochimica Acta, authors have reconciled these divergent experimental results through an equation for the mass balance of the carbonate ion at the sediment-water interface (SWI), which equates the rate of production of that ion via dissolution and its diffusion in sediment porewaters to the transport across the diffusive sublayer (DBL) at the SWI. If the rate constant derived from suspended-grain experiments is inserted into this balance equation, the rate of carbonate ion supply to the SWI from the sediment (sediment-side control) is much greater in the oceans than the rate of transfer across the DBL (water-side control). Thus, calcite dissolution at the seafloor, while technically under mixed control, is strongly water-side dominated. Consequently, a model that neglects boundary-layer transport (sediment-side control alone) invariably predicts CaCO3-versus-depth profiles that are too shallow compared to available data (Figure 1). These new findings will inform future attempts to model the ocean’s response to acidification.

Figure 1: Plots of the calcite (CaCO3) content of deep-sea sediments as a function of oceanic depth. Left panel: data from the Northwestern Atlantic Ocean. Right panel: data from the Southwest Pacific Ocean. The blue line represents predicted CaCO3 content assuming no boundary-layer effects (pure sediment-side control). The red line is the prediction that includes both sediment and water effects (mixed control), and the green line is the prediction with pure water-side control. The agreement between the red and green lines signifies that calcite dissolution is essentially water-side controlled at the seafloor. These results are duplicated for all tested regions of the oceans.

Authors:
Bernard P. Boudreau (Dalhousie University)
Olivier Sulpis (University of Utrecht)
Alfonso Mucci (McGill University)

Improved method to identify and reduce uncertainties in marine carbon cycle predictions

Posted by mmaheigan 
· Wednesday, September 26th, 2018 

Improved method to identify and reduce uncertainties in marine carbon cycle predictions

How well do contemporary Earth System Models (ESMs) represent the dynamics of the modern day ocean? Often we question the fidelity of biological and chemical processes represented in these ESMs. The fact is representations of biogeochemical processes in models are plagued with some degree of uncertainties; therefore, identifying and reducing such deficiencies could advance ESM development and improve model predictions.

An overview of several models with respect to each of the variables, using absolute (left) and relative (right) scores to determine the degree of uncertainty in relation to referenced datasets.

 

A recent publication in Atmosphere described the ongoing efforts to develop the International Ocean Model Benchmarking (IOMB) package to evaluate ESM skill sets in simulating marine biogeochemical variables and processes. Model performances were scored based on how well they captured the distribution and variability contained in high-quality observational datasets. The authors highlighted systematic model–data benchmarking as a technique to identify ocean model deficiencies, which could provide a pathway to improving representations of sub-grid-scale parameterizations. They have scaled the absolute score from zero to unity, where the red color tends toward zero to quantify weaknesses in the skill set of a particular model in capturing values from the observational datasets. On the other side of the spectrum, the green color signifies considerable temporal and spatial overlap between the predicted and the observational values. The authors also present the standard score to show the relative scores within two standard deviations from the model mean. The benchmarking package was employed in the published study to assess marine biogeochemical process representations, with a focus on surface ocean concentrations and sea–air fluxes of dimethylsulfide (DMS). The production and emission of natural aerosols remain one of the major limitations in estimating global radiative forcing. Appropriate representation of aerosols in the marine boundary layer (MBL) is essential to reduce uncertainty and provide reliable information on offsets to global warming. Results show that model–data biases increased as DMS enters the MBL, with models over-predicting sea surface concentrations in the productive region of the eastern tropical Pacific by almost a factor of two and the sea–air fluxes by a factor of three. The associated uncertainties with oceanic carbon cycle processes may be additive or antagonistic; in any case, a constructive effort to disentangle the subtleties begins with an objective benchmarking effort, which is focused specifically on marine biogeochemical processes. The tool in development will ensure we satisfy some of the Model Intercomparison Project (MIP) benchmarking needs for the sixth phase of Coupled Model Intercomparison Project (CMIP6).

 

Authors:
Oluwaseun Ogunro (ORNL)
Scott Elliott (LANL)
Oliver Wingenter (New Mexico Tech)
Clara Deal (University of Alaska)
Weiwei Fu (UC Irvine)
Nathan Collier (ORNL)
Forrest M. Hoffman (ORNL)

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