An underwater navigation system powered by sound

GPS isn’t waterproof. The navigation system depends on radio waves, which break down rapidly in liquids, including seawater. To track undersea objects like drones or whales, researchers rely on acoustic signaling. But devices that generate and send sound usually require batteries — bulky, short-lived batteries that need regular changing. Could we do without them?

MIT researchers think so. They’ve built a battery-free pinpointing system dubbed Underwater Backscatter Localization (UBL). Rather than emitting its own acoustic signals, UBL reflects modulated signals from its environment. That provides researchers with positioning information, at net-zero energy. Though the technology is still developing, UBL could someday become a key tool for marine conservationists, climate scientists, and the U.S. Navy.

These advances are described in a paper being presented this week at the Association for Computing Machinery’s Hot Topics in Networks workshop, by members of the Media Lab’s Signal Kinetics group. Research Scientist Reza Ghaffarivardavagh led the paper, along with co-authors Sayed Saad Afzal, Osvy Rodriguez, and Fadel Adib, who leads the group and is the Doherty Chair of Ocean Utilization as well as an associate professor in the MIT Media Lab and the MIT Department of Electrical Engineering and Computer Science.

“Power-hungry”

It’s nearly impossible to escape GPS’ grasp on modern life. The technology, which relies on satellite-transmitted radio signals, is used in shipping, navigation, targeted advertising, and more. Since its introduction in the 1970s and ’80s, GPS has changed the world. But it hasn’t changed the ocean. If you had to hide from GPS, your best bet would be underwater.

Because radio waves quickly deteriorate as they move through water, subsea communications often depend on acoustic signals instead. Sound waves travel faster and further underwater than through air, making them an efficient way to send data. But there’s a drawback.

“Sound is power-hungry,” says Adib. For tracking devices that produce acoustic signals, “their batteries can drain very quickly.” That makes it hard to precisely track objects or animals for a long time-span — changing a battery is no simple task when it’s attached to a migrating whale. So, the team sought a battery-free way to use sound.

Good vibrations

Adib’s group turned to a unique resource they’d previously used for low-power acoustic signaling: piezoelectric materials. These materials generate their own electric charge in response to mechanical stress, like getting pinged by vibrating soundwaves. Piezoelectric sensors can then use that charge to selectively reflect some soundwaves back into their environment. A receiver translates that sequence of reflections, called backscatter, into a pattern of 1s (for soundwaves reflected) and 0s (for soundwaves not reflected). The resulting binary code can carry information about ocean temperature or salinity.

In principle, the same technology could provide location information. An observation unit could emit a soundwave, then clock how long it takes that soundwave to reflect off the piezoelectric sensor and return to the observation unit. The elapsed time could be used to calculate the distance between the observer and the piezoelectric sensor. But in practice, timing such backscatter is complicated, because the ocean can be an echo chamber.

The sound waves don’t just travel directly between the observation unit and sensor. They also careen between the surface and seabed, returning to the unit at different times. “You start running into all of these reflections,” says Adib. “That makes it complicated to compute the location.” Accounting for reflections is an even greater challenge in shallow water — the short distance between seabed and surface means the confounding rebound signals are stronger.

The researchers overcame the reflection issue with “frequency hopping.” Rather than sending acoustic signals at a single frequency, the observation unit sends a sequence of signals across a range of frequencies. Each frequency has a different wavelength, so the reflected sound waves return to the observation unit at different phases. By combining information about timing and phase, the observer can pinpoint the distance to the tracking device. Frequency hopping was successful in the researchers’ deep-water simulations, but they needed an additional safeguard to cut through the reverberating noise of shallow water.

Where echoes run rampant between the surface and seabed, the researchers had to slow the flow of information. They reduced the bitrate, essentially waiting longer between each signal sent out by the observation unit. That allowed the echoes of each bit to die down before potentially interfering with the next bit. Whereas a bitrate of 2,000 bits/second sufficed in simulations of deep water, the researchers had to dial it down to 100 bits/second in shallow water to obtain a clear signal reflection from the tracker. But a slow bitrate didn’t solve everything.

To track moving objects, the researchers actually had to boost the bitrate. One thousand bits/second was too slow to pinpoint a simulated object moving through deep water at 30 centimeters/second. “By the time you get enough information to localize the object, it has already moved from its position,” explains Afzal. At a speedy 10,000 bits/second, they were able to track the object through deep water.

Efficient exploration

Adib’s team is working to improve the UBL technology, in part by solving challenges like the conflict between low bitrate required in shallow water and the high bitrate needed to track movement. They’re working out the kinks through tests in the Charles River. “We did most of the experiments last winter,” says Rodriguez. That included some days with ice on the river. “It was not very pleasant.”

Conditions aside, the tests provided a proof-of-concept in a challenging shallow-water environment. UBL estimated the distance between a transmitter and backscatter node at various distances up to nearly half a meter. The team is working to increase UBL’s range in the field, and they hope to test the system with their collaborators at the Wood Hole Oceanographic Institution on Cape Cod.

They hope UBL can help fuel a boom in ocean exploration. Ghaffarivardavagh notes that scientists have better maps of the moon’s surface than of the ocean floor. “Why can’t we send out unmanned underwater vehicles on a mission to explore the ocean? The answer is: We will lose them,” he says.

UBL could one day help autonomous vehicles stay found underwater, without spending precious battery power. The technology could also help subsea robots work more precisely, and provide information about climate change impacts in the ocean. “There are so many applications,” says Adib. “We’re hoping to understand the ocean at scale. It’s a long-term vision, but that’s what we’re working toward and what we’re excited about.”

This work was supported, in part, by the Office of Naval Research.

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National Science Foundation Convergence Accelerator awards two grants to MIT

Two grants have been awarded to MIT researchers on the themes of socio-resilient infrastructure, and on the future of oceans. The grants are part of the U.S. National Science Foundation Convergence Accelerator program, designed to foster global cross-disciplinary and cross-sector workshops on emerging areas of critical societal importance. The NSF Convergence Accelerator program further aims to accelerate use-inspired, convergence research via partnerships between academic and non-academic stakeholders.

Socio-resilient infrastructures

The Socioresilient Infrastructure: Precision Materials, Assemblages, and Systems project is co-led by Christine Ortiz, the Morris Cohen Professor of Materials Science and Engineering, and Ellan Spero, a historian of science and technology and instructor in the Department of Materials Science and Engineering. This project will engage leading researchers from around the world to advance an intellectual framework for socio-resilient infrastructure, where social resilience is considered to be the ability of human communities to cope with and adapt to stresses and shocks such as social, political, environmental, or economic change.

This workshop will integrate exciting advances across length scales from materials (i.e., materials science and engineering, chemistry, and mechanical engineering) to assemblages (i.e., civil, structural, and environmental engineering; architecture; art and design) to systems (i.e., engineering systems, computer science and engineering, urban studies and planning including civic design and engagement). It will incorporate and center cross-cutting humanistic and socially-focused research in material culture, social justice, equity-based, community and participatory co-design, environmental and social life cycle assessment, socio-technical and sociological analysis (i.e., social sciences, STS, history).

Broad topic areas will include emerging approaches to socio-resilient and circular materials design, structural engineering, and intelligent infrastructure systems. The merging of ideas, new computational and manufacturing technologies, and research methods across disparate disciplines is expected to lead to the development of equitable, inclusive, and sustainable innovation and commercialization of socio-resilient infrastructure.

The future of oceans

In addition, the Signal Kinetics Group at the MIT Media Lab has joined forces with the Woods Hole Oceanographic Institution (WHOI) to spearhead Smart Oceans 2020, a series of cross-cutting, multidisciplinary virtual plenaries and workshops to be held the week of Oct. 5-9. Under the leadership of Media Lab associate professor and Doherty Chair Fadel Adib and WHOI’s assistant scientist Seth Zippel, this conference will “blue sky” the future of the ocean, aiming to accelerate the field of ocean science.

The conference will feature a mix of invited plenary speakers, lightning talks, and brainstorming sessions, all with the purpose of accelerating use-inspired convergence research. The goal of the conference organizers is to foster a wealth of synergy, connections, and cooperation, which will lead to partnerships between academia, industry, non-governmental organizations, government, philanthropy, and venture capital across sectors such as climate and environmental sustainability, computing, marine biology and ecology, geopolitics, and defense.

“This workshop is a tremendously exciting opportunity to bring together communities in the ocean sciences who don’t necessarily cross paths,” comments Lara Campbell, program director for the NSF Convergence Accelerator Program in the Office of Integrative Activities at the U.S. National Science Foundation. “More valuable still, it’s a chance to think together about what the greatest opportunities are for accelerating research into near-term impacts that allow us to sustainably engage with the tremendous resources of the oceans.”

The organizers of Smart Oceans 2020 are hopeful that by identifying convergent approaches for ocean innovation, exploration, and utilization, this series of workshops will have a direct impact on issues of sustainability, national security, and national industries. “The series of workshops will cover a variety of exciting topics, ranging from designing and building an ocean internet-of-things to identifying convergent approaches to address climate challenges,” notes Adib. “The NSF convergence accelerator is uniquely positioned to propel this field forward, as it has done for other topics through investments of over $50 million.” 

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MIT research on seawater surface tension becomes international guideline

The property of water that enables a bug to skim the surface of a pond or keeps a carefully placed paperclip floating on the top of a cup of water is known as surface tension. Understanding the surface tension of water is important in a wide range of applications including heat transfer, desalination, and oceanography. Although much is known about the surface tension of fresh water, very little has been known about the surface tension of seawater — until recently.

In 2012, John Lienhard, the Abdul Latif Jameel Professor of Water and Mechanical Engineering, and then-graduate student Kishor Nayar SM ’14, PhD ’19 embarked on a research project to understand how the surface tension of seawater changes with temperature and salinity. Two years later, they published their findings in the Journal of Physical and Chemical Reference Data. This spring, the International Association for the Properties of Water and Steam (IAPWS) announced that they had deemed Lienhard and Nayar’s work an international guideline.

According to the IAPWS, Lienhard and Nayar’s research “presents a correlation for the surface tension of seawater as a function of temperature and salinity.” The announcement of the guideline marked the completion of eight years of work with dozens of collaborators from MIT and across the globe.

“This project grew out of my work in desalination. In desalination, you need to know about the surface tension of water because that affects how water travels through pores in a membrane,” explains Lienhard, a world leading expert in desalination — the process by which salt water is treated to become potable freshwater.

Lienhard suggested Nayar take measurements of seawater’s surface tension and compare the results to the surface tension of pure water. As they would soon find out, getting reliable data from salt water would prove to be incredibly difficult. 

“We had thought originally that these experiments would be pretty simple to do, that we'd be done in a month or two. But as we started looking into it, we realized it was a much harder problem to tackle,” says Lienhard.

From the outset, Nayar hoped to get enough accurate data to inform a property standard. Doing so would require the uncertainty in the measurements to be less than 1 percent.

“When you talk about property measurements, you need to be as accurate as possible,” explains Nayar. The first hurdle he had to surmount to achieve this level of accuracy was finding the appropriate instrumentation to make reliable measurements — something that turned out to be no easy feat.

Measuring surface tension

To measure the surface tension of water, Lienhard and Nayar teamed up with Gareth McKinley, professor of mechanical engineering, and then-graduate student Divya Panchanathan SM '15, PhD '18. They began with a device known as a Wilhelmy plate, which finds the surface tension by lowering a small platinum plate into a beaker of water then measuring the force the water exerts as the plate is raised.

Nayar and Panchanathan struggled to measure the surface tension of salt water at higher temperatures. “The issue we kept finding was once the temperature was above 50 degrees Celsius, the water on the beaker evaporated faster than we could take the measurements,” Nayar says. 

No instrument would allow them to get the data they needed — so Nayar turned to the MIT Hobby Shop. Using a lathe, he built a special lid for the beaker to keep vapor in.

“The little lid Kishor built had accurately cut doors that allowed him to put a surface tension probe through the lid without letting water vapor get out,” explains Lienhard.

After making progress on obtaining data, the team suffered a massive setback. They found that barely visible salt scales, which formed on their test beaker over time, had introduced errors to their measurements. To get the most accurate values, they decided to use fresh new beakers for every single test. As a result, Nayar had to repeat nine months of work just prior to his master’s thesis being due. Fortunately, since the main problem was identified and solved, experiments could be repeated much faster.

Nayar was able to redo the experiments on time. The team measured surface tension in seawater ranging from room temperature to 90 degrees Celsius and salinity levels ranging from pure water to four times the salinity of ocean water. They found that surface tension decreases by roughly 20 percent as water goes from room temperature toward boiling. Meanwhile, as salinity increases, surface tension increases as well. The team had unlocked the mystery of seawater surface tension.

“It was literally the most technically challenging thing I had ever done,” Nayar recalls.

Their data had an average deviation of 0.19 percent, with a maximum deviation of just 0.6 percent — well within the 1 percent bound needed for a guideline.

From master’s thesis to international guideline

Three years after completing his master’s thesis, Nayar, by then a PhD student, attended an IAPWS meeting in Kyoto, Japan. The IAPWS is a nonprofit international organization responsible for releasing standards on the properties of water and steam. There, Nayar met with leaders in the field of water surface tension who had been struggling with the same issues Nayar had faced. These contacts introduced him to the long, rigorous process of declaring something an international guideline.

The IAPWS had previously published standards on the properties of steam developed by the late Joseph Henry Keenan, professor and one-time department head of mechanical engineering at MIT. To join Keenan as authors of an IAPWS standard, the team’s data needed to be verified by measurements conducted by other researchers. After three years of working with the IAPWS, the team’s work was finally adopted as an international guideline.

For Nayar, who graduated with his PhD last year and is now a senior industrial water/wastewater engineer at engineering consulting firm GHD, the guideline announcement made the long months collecting data well worth it. “It felt like something getting completed,” he recalls. 

The findings that Nayar, Panchanathan, McKinley, and Lienhard reported back in 2014 are broadly applicable to a number of industries, according to Lienhard. “It’s certainly relevant for desalination work, but also for oceanographic problems such as capillary wave dynamics,” he explains.

It also helps explain how small things — like a bug or a paperclip — can float on seawater.

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Machine learning helps map global ocean communities

On land, it’s fairly obvious where one ecological region ends and another begins, for instance at the boundary between a desert and savanna. In the ocean, much of life is microscopic and far more mobile, making it challenging for scientists to map the boundaries between ecologically distinct marine regions.

One way scientists delineate marine communities is through satellite images of chlorophyll, the green pigment produced by phytoplankton. Chlorophyll concentrations can indicate how rich or productive the underlying ecosystem might be in one region versus another. But chlorophyll maps can only give an idea of the total amount of life that might be present in a given region. Two regions with the same concentration of chlorophyll may in fact host very different combinations of plant and animal life.

“It’s like if you were to look at all the regions on land that don’t have a lot of biomass, that would include Antarctica and the Sahara, even though they have completely different ecological assemblages,” says Maike Sonnewald, a former postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences.

Now Sonnewald and her colleagues at MIT have developed an unsupervised machine-learning technique that automatically combs through a highly complicated set of global ocean data to find commonalities between marine locations, based on their ratios and interactions between multiple phytoplankton species. With their technique, the researchers found that the ocean can be split into over 100 types of “provinces” that are distinct in their ecological makeup. Any given location in the ocean would conceivably fit into one of these 100 ecological  provinces.

The researchers then looked for similarities between these 100 provinces, ultimately grouping them into 12 more general categories. From these “megaprovinces,” they were able to see that, while some had the same total amount of life within a region, they had very different community structures, or balances of animal and plant species. Sonnewald says capturing these ecological subtleties is essential to tracking the ocean’s health and productivity.

“Ecosystems are changing with climate change, and the community structure needs to be monitored to understand knock on effects on fisheries and the ocean’s capacity to draw down carbon dioxide,” Sonnewald says. “We can't fully understand these vital dynamics with conventional methods, that to date don’t include the ecology that’s there. But our method, combined with satellite data and other tools, could offer important progress.”

Sonnewald, who is now an associate research scholar at Princeton University and a visitor at the University of Washington, has reported the results today in the journal Science Advances. Her coauthors at MIT are Senior Research Scientist Stephanie Dutkiewitz, Principal Research Engineer Christopher Hill, and Research Scientist Gael Forget.

Rolling out a data ball

The team’s new machine learning technique, which they’ve named SAGE, for the Systematic AGgregated Eco-province method, is designed to take large, complicated datasets, and probabilistically project that data down to a simpler, lower-dimensional dataset.

“It’s like making cookies,” Sonnewald says. “You take this horrifically complicated ball of data and roll it out to reveal its elements.”

In particular, the researchers used a clustering algorithm that Sonnewald says is designed to “crawl along a dataset” and hone in on regions with a large density of points — a sign that these points share something in common. 

Sonnewald and her colleagues set this algorithm loose on ocean data from MIT’s Darwin Project, a three-dimensional model of the global ocean that combines a model of the ocean’s climate, including wind, current, and temperature patterns, with an ocean ecology model. That model includes 51 species of phytoplankton and the ways in which each species grows and interacts with each other as well as with the surrounding climate and available nutrients.

If one were to try and look through this very complicated, 51-layered space of data, for every available point in the ocean, to see which points share common traits, Sonnewald says the task would be “humanly intractable.” With the team’s unsupervised machine learning algorithm, such commonalities “begin to crystallize out a bit.”

This first “data cleaning” step in the team’s SAGE method was able to parse the global ocean into about 100 different ecological provinces, each with a distinct balance of species.

The researchers assigned each available location in the ocean model to one of the 100 provinces, and assigned a color to each province. They then generated a map of the global ocean, colorized by province type.  

“In the Southern Ocean around Antarctica, there’s burgundy and orange colors that are shaped how we expect them, in these zonal streaks that encircle Antarctica,” Sonnewald says. “Together with other features, this gives us a lot of confidence that our method works and makes sense, at least in the model.”

Ecologies unified

The team then looked for ways to further simplify the more than 100 provinces they identified, to see whether they could pick out commonalities even among these ecologically distinct regions.

“We started thinking about things like, how are groups of people distinguished from each other? How do we see how connected to each other we are? And we used this type of intuition to see if we could quantify how ecologically similar different provinces are,” Sonnewald says.

To do this, the team applied techniques from graph theory to represent all 100 provinces in a single graph, according to biomass — a measure that’s analogous to the amount of chlorophyll produced in a region. They chose to group the 100 provinces into 12 general categories, or “megaprovinces.” When they compared these megaprovinces, they found that those that had a similar biomass were composed of very different biological species.

“For instance, provinces D and K have almost the same amount of biomass, but when we look deeper, K has diatoms and hardly any prokaryotes, while D has hardly any diatoms, and a lot of prokaryotes. But from a satellite, they could look the same,” Sonnewald says. “So our method could start the process of adding the ecological information to bulk chlorophyll measures, and ultimately aid observations.”

The team has developed an online widget that researchers can use to find other similarities among the 100 provinces. In their paper, Sonnewald’s colleagues chose to group the provinces into 12 categories. But others may want to divide the provinces into more groups, and drill down into the data to see what traits are shared among these groups.

Sonnewald is sharing the tool with oceanographers who want to identify precisely where regions of a particular ecological makeup are located, so they could, for example, send ships to sample in those regions, and not in others where the balance of species might be slightly different.

“Instead of guiding sampling with tools based on bulk chlorophyll, and guessing where the interesting ecology could be found with this method, you can surgically go in and say, ‘this is what the model says you might find here,’” Sonnewald says. “Knowing what species assemblages are where, for things like ocean science and global fisheries, is really powerful.”

This research was funded, in part, by NASA and the Jet Propulsion Laboratory.

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Search-and-rescue algorithm identifies hidden “traps” in ocean waters

The ocean is a messy and turbulent space, where winds and weather kick up waves in all directions. When an object or person goes missing at sea, the complex, constantly changing conditions of the ocean can confound and delay critical search-and-rescue operations.

Now researchers at MIT, the Swiss Federal Institute of Technology (ETH), the Woods Hole Oceanographic Institution (WHOI), and Virginia Tech have developed a technique that they hope will help first responders quickly zero in on regions of the sea where missing objects or people are likely to be.

The technique is a new algorithm that analyzes ocean conditions such as the strength and direction of ocean currents, surface winds, and waves , and identifies in real-time the most attracting regions of the ocean where floating objects are likely to converge.

The team demonstrated the technique in several field experiments in which they deployed drifters and human-shaped manikins in various locations in the ocean. They found that over the course of a few hours, the objects migrated to the regions that the algorithm predicted would be strongly attracting, based on the present ocean conditions.

The algorithm can be applied to existing models of ocean conditions in a way that allows rescue teams to quickly uncover hidden “traps” where the ocean may be steering missing people at a given time.

“This new tool we’ve provided can be run on various models to see where these traps are predicted to be, and thus the most likely locations for a stranded vessel or missing person,” says Thomas Peacock, professor of mechanical engineering at MIT. “This method uses data in a way that it hasn’t been used before, so it provides first responders with a new perspective.”

Peacock and Pierre Lermusiaux, also a professor of mechanical engineering at MIT, who oversaw the project, and their colleagues report their results in a study published today in the journal Nature Communications. Their coauthors are lead author Mattia Serra and corresponding author George Haller of ETH Zurich, Irina Rypina and Anthony Kirincich of WHOI, Shane Ross of Virginia Tech, Arthur Allen of the U.S. Coast Guard, and Pratik Sathe of the University of California at Los Angeles.

Hidden traps

Today’s search-and-rescue operations combine weather forecasts with models of both ocean dynamics and the ways in which objects can drift through the ocean, to map out a search plan, or regions where teams should concentrate their search.

But the ocean is a complicated space of unsteady, ever-changing flow patterns. Coupled with the fact that a missing person has likely been continuously floating through this unsteady flow field for some time, Peacock and his colleagues say that significant errors can accumulate in predicting where to look first, when using a simple approach that directly predicts the trajectories of a few drifting objects.

Instead, the team developed a method to interpret the ocean’s complex flows using advanced, data-driven ocean modeling and prediction systems. They used a novel “Eulerian” approach, in contrast to more commonly used “Lagrangian” approaches — mathematical techniques that involve integrating snapshots of the ocean velocity due to waves and currents to slowly generate an uncertain trajectory for where a missing person or object may have been carried.

The new Eulerian approach uses the most reliable velocity forecast snapshots, close to the point where a missing person or object was last seen, and quickly uncovers the most attracting regions of the ocean at a given time. These Eulerian predictions are then continuously updated when the next batch of updated velocity information becomes available.

The team has named their approach TRAPS, for its goal of identifying TRansient Attracting Profiles, or short-lived regions where water may converge and be likely to pull objects or people. The method is based on a recent mathematical theory,

developed by Serra and Haller at ETH Zurich, to uncover hidden attracting structures in highly unsteady flow data.

“We were a bit skeptical whether a mathematical theory like this would work out on a ship, in real time,” Haller says. “We were all pleasantly surprised to see how well it repeatedly did.”

“We can think of these ‘traps’ as moving magnets, attracting a set of coins thrown on a table. The Lagrangian trajectories of coins are very uncertain, yet the strongest Eulerian magnets predict the coin positions over short times,” Serra says.

“The key thing is, the traps may not have any signature in the ocean current field,” Peacock adds. “If you do this processing for the traps, they might pop up in very different places from where you’re seeing the ocean current projecting where you might go. So you have to do this other level of processing to pull out these structures. They’re not immediately visible.”

Out at sea

Led by WHOI sea-going experts, the researchers tested the TRAPS approach in several experiments out at sea. “As with any new theoretical technique, it is important to test how well it works in the real ocean,” Rypina says.

In 2017 and 2018, the team sailed a small research vessel several hours out off the coast of Martha’s Vineyard, where they deployed at various locations, an array of small round buoys, and manikins.

“These objects tend to travel differently relative to the ocean because different shapes feel the wind and currents differently,” Peacock says. “Even so, the traps are so strongly attracting and robust to uncertainties that they should overcome these differences and pull everything onto them.”

The team ran their modeling and prediction systems, forecasting the ocean’s behavior and currents, and used the TRAPS algorithm to map out strongly attracting regions over the course of the experiment. The researchers let the objects drift freely with the currents for a few hours, and recorded their positions via GPS trackers, before retrieving the objects at the end of the day.

“With the GPS trackers, we could see where everything was going, in real-time,” Peacock says. “So we laid out this initial, widespread pattern of the drifters, and saw that, in the end, they converged on these traps.”

The researchers are planning to share the TRAPS method with first responders such as the U.S. Coast Guard, as a way to speed up search-and-rescue algorithms, and potentially save many more people lost at sea.

“People like Coast Guard are constantly running simulations and models of what the ocean currents are doing at any particular time and they’re updating them with the best data that inform that model,” Peacock says. “Using this method, they can have knowledge right now of where the traps currently are, with the data they have available. So if there’s an accident in the last hour, they can immediately look and see where the sea traps are. That’s important for when there’s a limited time window in which they have to respond, in hopes of a successful outcome.”

This research was primarily funded by the National Science Foundation’s Hazards SEES program, with additional support from the Office of Naval Research and the German National Science Foundation.

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Towable sensor free-falls to measure vertical slices of ocean conditions

The motion of the ocean is often thought of in horizontal terms, for instance in the powerful currents that sweep around the planet, or the waves that ride in and out along a coastline. But there is also plenty of vertical motion, particularly in the open seas, where water from the deep can rise up, bringing nutrients to the upper ocean, while surface waters sink, sending dead organisms, along with oxygen and carbon, to the deep interior.

Oceanographers use instruments to characterize the vertical mixing of the ocean’s waters and the biological communities that live there. But these tools are limited in their ability to capture small-scale features, such as the up- and down-welling of water and organisms over a small, kilometer-wide ocean region. Such features are essential for understanding the makeup of marine life that exists in a given volume of the ocean (such as in a fishery), as well as the amount of carbon that the ocean can absorb and sequester away.

Now researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) have engineered a lightweight instrument that measures both physical and biological features of the vertical ocean over small, kilometer-wide patches. The “ocean profiler,” named EcoCTD, is about the size of a waist-high model rocket and can be dropped off the back of a moving ship. As it free-falls through the water, its sensors measure physical features, such as temperature and salinity, as well as biological properties, such as the optical scattering of chlorophyll, the green pigment of phytoplankton.

“With EcoCTD, we can see small-scale areas of fast vertical motion, where nutrients could be supplied to the surface, and where chlorophyll is carried downward, which tells you this could also be a carbon pathway. That’s something you would otherwise miss with existing technology,” says Mara Freilich, a graduate student in MIT’s Department of Earth, Atmospheric, and Planetary Sciences and the MIT-WHOI Joint Program in Oceanography/Applied Ocean Sciences and Engineering.

Freilich and her colleagues have published their results today in the Journal of Atmospheric and Oceanic Technology. The paper’s co-authors are J. Thomas Farrar, Benjamin Hodges, Tom Lanagan, and Amala Mahadevan of WHOI, and Andrew Baron of Dynamic System Analysis, in Nova Scotia. The lead author is Mathieu Dever of WHOI and RBR, a developer of ocean sensors based in Ottawa.

Ocean synergy

Oceanographers use a number of methods to measure the physical properties of the ocean. Some of the more powerful, high-resolution instruments used are known as CTDs, for their ability to measure the ocean’s conductivity, temperature, and depth. CTDs are typically bulky, as they contain multiple sensors as well as components that collect water and biological samples. Conventional CTDs require a ship to stop as scientists lower the instrument into the water, sometimes via a crane system. The ship has to stay put as the instrument collects measurements and water samples, and can only get back underway after the instrument is hauled back onboard.

Physical oceanographers who do not study ocean biology, and therefore do not need to collect water samples, can sometimes use “UCTDs” — underway versions of CTDs, without the bulky water sampling components, that can be towed as a ship is underway. These instruments can sample quickly since they do not require a crane or a ship to stop as they are dropped.

Freilich and her team looked to design a version of a UCTD that could also incorporate biological sensors, all in a small, lightweight, towable package, that would also keep the ship moving on course as it gathered its vertical measurements.

“It seemed there could be straightforward synergy between these existing instruments, to design an instrument that captures physical and biological information, and could do this underway as well,” Freilich says.

“Reaching the dark ocean”

The core of the EcoCTD is the RBR Concerto Logger, a sensor that measures the temperature of the water, as well as the conductivity, which is a proxy for the ocean’s salinity. The profiler also includes a lead collar that provides enough weight to enable the instrument to free-fall through the water at about 3 meters per second — a rate that takes the instrument down to about 500 meters below the surface in about two minutes.

“At 500 meters, we’re reaching the upper twilight zone,” Freilich says. “The euphotic zone is where there’s enough light in the ocean for photosynthesis, and that’s at about 100 to 200 meters in most places. So we’re reaching the dark ocean.”

Another sensor, the EcoPuck, is unique to other UCTDs in that it measures the ocean’s biological properties. Specifically, it is a small, puck-shaped bio-optical sensor that emits two wavelengths of light — red and blue. The sensor captures any change in these lights as they scatter back and as chlorophyll-containing phytoplankton fluoresce in response to the light. If the red light received resembles a certain wavelength characteristic of chlorophyll, scientists can deduce the presence of phytoplankton at a given depth. Variations in red and blue light scattered back to the sensor can indicate other matter in the water, such as sediments or dead cells — a measure of the amount of carbon at various depths.

The EcoCTD includes another sensor unique to UCTDs — the Rinko III Do, which measures the oxygen concentration in water, which can give scientists an estimate of how much oxygen is being taken up by any microbial communities living at a given depth and parcel of water.

Finally, the entire instrument is encased in a tube of aluminum and designed to attach via a long line to a winch at the back of a ship. As the ship is moving, a team can drop the instrument overboard and use the winch to pay the line out at a rate that the instrument drops straight down, even as the ship moves away. After about two minutes, once it has reached a depth of about 500 meters, the team cranks the winch to pull the instrument back up, at a rate that the  instrument catches up to the ship within 12 minutes. The crew can then drop the instrument again, this time at some distance from their last dropoff point.

“The nice thing is, by the time we go to the next cast, we’re 500 meters away from where we were the first time, so we’re exactly where we want to sample next,” Freilich says.

They tested the EcoCTD on two cruises in 2018 and 2019, one to the Mediterranean and the other in the Atlantic, and in both cases were able to collect both physical and biological data at a higher resolution than existing CTDs.

“The ecoCTD is capturing these ocean characteristics at a gold-standard quality with much more convenience and versatility,” Freilich says.

The team will further refine their design, and hopes that their high-resolution, easily-deployable, and more efficient alternative may be adapted by both scientists to monitor the ocean’s small-scale responses to climate change, as well as fisheries that want to keep track of a certain region’s biological productivity.  

This research was funded in part by the U.S. Office of Naval Research.

from MIT News - Oceanography and ocean engineering https://ift.tt/2LJGFBp

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Melting glaciers cool the Southern Ocean

Tucked away at the very bottom of the globe surrounding Antarctica, the Southern Ocean has never been easy to study. Its challenging conditions have placed it out of reach to all but the most intrepid explorers. For climate modelers, however, the surface waters of the Southern Ocean provide a different kind of challenge: It doesn’t behave the way they predict it would. “It is colder and fresher than the models expected,” says Craig Rye, a postdoc in the group of Cecil and Ida Green Professor of Oceanography John Marshall within MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS).

In recent decades, as the world warms, the Southern Ocean’s surface temperature has cooled, allowing the amount of ice that crystallizes on the surface each winter to grow. This is not what climate models anticipated, and a recent study accepted in Geophysical Research Letters attempts to disentangle that discrepancy. “This paper is motivated by a disagreement between what should be happening according to simulations and what we observe,” says Rye, the lead author of the paper who is currently working remotely from NASA’s Goddard Institute for Space Studies, or GISS, in New York City.

“This is a big conundrum in the climate community,” says Marshall, a co-author on the paper along with Maxwell Kelley, Gary Russell, Gavin A. Schmidt, and Larissa S. Nazarenko of GISS; James Hansen of Columbia University’s Earth Institute; and Yavor Kostov of the University of Exeter. There are 30 or so climate models used to foresee what the world might look like as the climate changes. According to Marshall, models don’t match the recent observations of surface temperature in the Southern Ocean, leaving scientists with a question that Rye, Marshall, and their colleagues intend to answer: how can the Southern Ocean cool when the rest of the Earth is warming?

This isn’t the first time Marshall has investigated the Southern Ocean and its climate trends. In 2016, Marshall and Yavor Kostov PhD ’16 published a paper exploring two possible influences driving the observed ocean trends: greenhouse gas emissions, and westerly winds — strengthened by expansion of the Antarctic ozone hole — blowing cold water northward from the continent. Both explained some of the cooling in the Southern Ocean, but not all of it. “We ended that paper saying there must be something else,” says Marshall.

That something else could be meltwater released from thawing glaciers. Rye has probed the influence of glacial melt in the Southern Ocean before, looking at its effect on sea surface height during his PhD at the University of Southampton in the UK. “Since then, I’ve been interested in the potential for glacial melt playing a role in Southern Ocean climate trends,” says Rye.

The group’s recent paper uses a series of “perturbation” experiments carried out with the GISS global climate model where they abruptly introduce a fixed increase in melt water around Antarctica and then record how the model responds. The researchers then apply the model’s response to a previous climate state to estimate how the climate should react to the observed forcing. The results are then compared to the observational record, to see if a factor is missing. This method is called hindcasting.

Marshall likens perturbation experiments to walking into a room and being confronted with an object you don’t recognize. “You might give it a gentle whack to see what it’s made of,” says Marshall. Perturbation experiments, he explains, are like whacking the model with inputs, such as glacial melt, greenhouse gas emissions, and wind, to uncover the relative importance of these factors on observed climate trends.

In their hindcasting, they estimate what would have happened to a pre-industrial Southern Ocean (before anthropogenic climate change) if up to 750 gigatons of meltwater were added each year. That quantity of 750 gigatons of meltwater is estimated from observations of both floating ice shelves and the ice sheet that lies over land above sea level. A single gigaton of water is very large — it can fill 400,000 Olympic swimming pools, meaning 750 gigatons of meltwater is equivalent to pouring water from 300 million Olympic swimming pools into the ocean every year.

When this increase in glacial melt was added to the model, it led to sea surface cooling, decreases in salinity, and expansion of sea ice coverage that are consistent with observed trends in the Southern Ocean during the last few decades. Their model results suggest that meltwater may account for the majority of previously misunderstood Southern Ocean cooling.

The model shows that a warming climate may be driving, in a counterintuitive way, more sea ice by increasing the rate of melting of Antarctica’s glaciers. According to Marshall, the paper may solve the disconnect between what was expected and what was observed in the Southern Ocean, and answers the conundrum he and Kostov pointed to in 2016. “The missing process could be glacial melt.”

Research like Rye’s and Marshall’s help project the future state of Earth’s climate and guide society’s decisions on how to prepare for that future. By hindcasting the Southern Ocean’s climate trends, they and their colleagues have identified another process, which must be incorporated into climate models. “What we’ve tried to do is ground this model in the historical record,” says Marshall. Now the group can probe the GISS model response with further “what if?” glacial melt scenarios to explore what might be in store for the Southern Ocean.

from MIT News - Oceanography and ocean engineering https://ift.tt/2zGkycy

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