Archive for circulation

Identifying the water mass composition of a sample has never been so easy!

Posted by mmaheigan 
· Thursday, August 31st, 2023 

When we collect seawater in any point of the ocean, we are collecting a mix of water masses from different origin that traveled until there keeping their salinity and temperature properties. The Atlantic Ocean is likely the most complex basin in term of water masses containing more than 15 in its depths. Some of them were “born” in the North Atlantic Ocean, others in the Southern Ocean, even in the Mediterranean Sea! And when we collect a seawater sample we can know which water masses are there, where they come from, what happened to each of them during their journey to us, what story can they tell us.

The variation of any non-conservative property (such as dissolved organic carbon or nutrients) in the deep open ocean depends on the mixing of those water masses and on the biogeochemical processes affecting it (such as heterotrophic respiration). But the effect of the water mass mixing is usually very high, so in order to study the biogeochemical processes, it is necessary to remove that effect.

On the other hand, estimating the contribution of the water masses composing a sample is useful to trace the distribution of each water mass identifying the depth of maximum water mass contribution or the depth-range where the water mass is dominant contributing > 50%. Ocean biogeochemists and microbiologists can get more out of their data estimating the impact of water mass mixing on the variability of any chemical (e.g. inorganic nutrients and dissolved organic carbon) or biological (e.g. prokaryotic heterotrophic abundance and production) property.

Knowing the contribution of each water mass to each sample was not an easy task and required expertise on the origin, circulation and mixing patterns of the water masses present in the study area. This could be even harder in very complex oceanic basin such as the deep Atlantic Ocean. The most commonly used methodology is the Optimum Multi-Parameter (OMP) analysis that was first applied by Tomczak in 1981. However, this methodology is time consuming and requires availability of a large set of quality-controlled chemical variables (e.g. nutrients, oxygen,..) together with a deep knowledge of the oceanography of the studied area. Those chemical variables are not always available or do not have the required quality by contrast to potential temperature and salinity that are high standard core variables in any cruise or database. In a recent research article, we applied multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). The model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Potential temperature and salinity are the most commonly collected and curated variables in oceanography both from oceanographic cruises and autonomous vehicles (e.g. ARGO) avoiding the use of less commonly measured chemical variables which also require longer and time-consuming analyses of both the water samples and the data.

Figure 1. A16 section for the contribution of the water masses (A) AAIW5, (B) ENACW12, (C) AAIW3, (D) MW, (E) LSW, (F) ISOW, (G) CDW and (H) WSDW obtained with the Extremely Randomized Trees algorithm. Ocean Data View software (Schlitzer, 2015).

We also provide the code with instructions where any user can easily introduce the required variables (latitude, longitude, depth, temperature and salinity) of the chosen Atlantic samples and obtain the water mass proportion of each one in a fast and easy way. Actually, it would allow the user to obtain this information in real time during a cruise.

New research using other methods like OMP and its variants can be incorporated to the existing model increasing its accuracy and prediction capacity. Help us to improve the model and increase its spatial resolution!

Ocean biogeochemists and microbiologists can benefit from this tool even if they do not have a deep knowledge of the oceanography of the studied area. Identifying the water masses composition of a sample has never been so easy!

Author
Cristina Romera-Castillo (Instituto de Ciencias del Mar-CSIC, Barcelona, Spain)

Twitter: @crisrcas

A close-up view of biomass controls in Southern Ocean eddies

Posted by mmaheigan 
· Thursday, August 20th, 2020 

Southern Ocean biological productivity is instrumental in regulating the global carbon cycle. Previous correlative studies associated widespread mesoscale activity with anomalous chlorophyll levels. However, eddies simultaneously modify both the physical and biogeochemical environments via several competing pathways, making it difficult to discern which mechanisms are responsible for the observed biological anomalies within them. Two recently published papers track Southern Ocean eddies in a global, eddy-resolving, 3-D ocean simulation. By closely examining eddy-induced perturbations to phytoplankton populations, the authors are able to explicitly link eddies to co-located biological anomalies through an underlying mechanistic framework.

Figure caption: Simulated Southern Ocean eddies modify phytoplankton division rates in different directions of depending on the polarity of the eddy and background seasonal conditions. During summer anticyclones (top right panel) deliver extra iron from depth via eddy-induced Ekman pumping and fuel faster phytoplankton division rates. During winter (bottom right panel) the extra iron supply is eclipsed by deeper mixed layer depths and elevated light limitation resulting in slower division rates. The opposite occurs in cyclones.

In the first paper, the authors observe that eddies primarily affect phytoplankton division rates by modifying the supply of iron via eddy-induced Ekman pumping. This results in elevated iron and faster phytoplankton division rates in anticyclones throughout most of the year. However, during deep mixing winter periods, exacerbated light stress driven by anomalously deep mixing in anticyclones can dominate elevated iron and drive division rates down. The opposite response occurs in cyclones.

The second paper tracks how eddy-modified division rates combine with eddy-modified loss rates and physical transport to produce anomalous biomass accumulation. The biomass anomaly is highly variable, but can exhibit an intense seasonal cycle, in which cyclones and anticyclones consistently modify biomass in different directions. This cycle is most apparent in the South Pacific sector of the Antarctic Circumpolar Current, a deep mixing region where the largest biomass anomalies are driven by biological mechanisms rather than lateral transport mechanisms such as eddy stirring or propagation.

It is important to remember that the correlation between chlorophyll and eddy activity observable from space can result from a variety of physical and biological mechanisms. Understanding the nuances of how these mechanisms change regionally and seasonally is integral in both scaling up local observations and parameterizing coarser, non-eddy resolving general circulation models with embedded biogeochemistry.

Authors:
Tyler Rohr (Australian Antarctic Partnership Program, previously at MIT/WHOI)
Cheryl Harrison (University of Texas Rio Grande Valley)
Matthew Long (National Center for Atmospheric Research)
Peter Gaube (University of Washington)
Scott Doney (University of Virginia)

The causes of the 90-ppm glacial atmospheric CO2 drawdown still strongly debated

Posted by mmaheigan 
· Tuesday, July 9th, 2019 

Joint feature with GEOTRACES

Figure: Illustration of the two main mechanisms identified by this study to explain lower atmospheric CO2 during glacial periods. Left: present-day conditions; right: conditions around 19,000 years ago during the Last Glacial Maximum. The obvious explanation for lower CO2 during glacial periods – cooler ocean temperatures (darker blue shade) making CO2 more soluble, much as a glass of sparkling wine will remain fizzier for longer when it is colder – has long been dismissed as not being a significant factor. However, previous calculations assumed that the ocean cooled uniformly and was saturated in dissolved CO2. The model, consistent with reconstructions of sea surface temperature, predicts more cooling at mid latitudes compared with polar regions and also accounts for undersaturation. This nearly doubles the effect of temperature change and accounts for almost half the 90 ppm glacial-interglacial atmospheric CO2 difference. Another quarter is explained in this model by increased growth of marine algae (green blobs and inset) in the waters off Antarctica. Algae absorb CO2 from the atmosphere during photosynthesis and “pump” it into the deep ocean when they die and sink. But their growth in the present-day ocean, especially the waters off Antarctica, is limited by the availability of iron, an essential micronutrient primarily supplied by wind-borne dust. In our model an increased supply of iron to the Southern Ocean, likely originating from Patagonia, Australia and New Zealand, enhances their growth and sucks CO2 out of the atmosphere. This “fertilization” effect was greatly underestimated by previous studies. The study also finds that, contrary to the current consensus, a large expansion of sea ice off Antarctica and reconfiguration of ocean circulation may have played only a minor role in glacial-interglacial CO2 changes. Credit: Illustration by Andrew Orkney, University of Oxford.

Using an observationally constrained earth system model, S. Khatiwala and co-workers compare different processes that could lead to the 90-ppm glacial atmospheric CO2 drawdown, with an important improvement on the deep carbon storage quantification (i.e. Biological Carbon Pump efficiency). They demonstrate that circulation and sea ice changes had only a modest net effect on glacial ocean carbon storage and atmospheric CO2, whereas temperature and iron input effects were more important than previously thought due to their effects on disequilibrium carbon storage.

Authors:
Samar Khatiwala (University of Oxford, UK)
Andreas Schmittner and Juan Muglia (Oregon State University)

Constraints on glacial overturning circulation and export production lead to answers about the carbon cycle

Posted by mmaheigan 
· Friday, January 4th, 2019 

One of the biggest unsolved mysteries in climate science concerns the dynamics and feedbacks of the ice age carbon dioxide (CO2) cycle.

At the height of the Pleistocene ice ages, the atmospheric CO2 concentration was about 1/3 lower than during the warm interglacial periods. Most scientists think that the CO2 that was missing from the atmosphere was in the deep ocean, but how and why remains unclear. In a study published in Earth and Planetary Science Letters, we compared different computer simulations of the ice age ocean with δ13C, radiocarbon (14C), and δ15N data from sea floor sediments.

We find that a weak and shallow Atlantic Meridional Overturning Circulation (6-9 Sv, or approximately half of today’s overturning rate) best reproduces the glacial sediment isotope data. Increasing the atmospheric soluble iron flux in the model’s Southern Ocean intensifies export production, carbon storage, and further improves agreement with glacial δ13C and δ15N reconstructions.

Figure Caption: Depth profiles of global mean δ13C, calculated using only grid boxes for which there exists Last Glacial Maximum data. Blue: Weak Atlantic circulation; Red: Strong Atlantic circulation; Green: Collapsed Atlantic circulation; Dashed: Extra iron in the Southern Ocean; Orange: Last Glacial Maximum Data.

Our best-fitting simulation (blue, dashed line in the figure) is a significant improvement over previous studies and suggests that both circulation and export production changes were necessary to maximize carbon storage in the glacial ocean. These findings provide an equilibrium glacial state, consistent with a combination of proxies, that can be used as a basis for simulations covering the last deglaciation time period. Understanding the different states that the global climate system can transit, and the characteristics of the transitions, is crucial to project possible outcomes of current climate change processes.

 

Authors:
Juan Muglia (Oregon State University)
Luke C. Skinner (Godwin Laboratory for Palaeoclimate Research, University of Cambridge)
Andreas Schmittner (Oregon State University)

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