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    Empowering people to adapt on the frontlines of climate change

    On April 11, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the fifth in a five-part series highlighting the most promising concepts to emerge from the competition and the interdisciplinary research teams behind them.

    In the coastal south of Bangladesh, rice paddies that farmers could once harvest three times a year lie barren. Sea-level rise brings saltwater to the soil, ruining the staple crop. It’s one of many impacts, and inequities, of climate change. Despite producing less than 1 percent of global carbon emissions, Bangladesh is suffering more than most countries. Rising seas, heat waves, flooding, and cyclones threaten 90 million people.

    A platform being developed in a collaboration between MIT and BRAC, a Bangladesh-based global development organization, aims to inform and empower climate-threatened communities to proactively adapt to a changing future. Selected as one of five MIT Climate Grand Challenges flagship projects, the Climate Resilience Early Warning System (CREWSnet) will forecast the local impacts of climate change on people’s lives, homes, and livelihoods. These forecasts will guide BRAC’s development of climate-resiliency programs to help residents prepare for and adapt to life-altering conditions.

    “The communities that CREWSnet will focus on have done little to contribute to the problem of climate change in the first place. However, because of socioeconomic situations, they may be among the most vulnerable. We hope that by providing state-of-the-art projections and sharing them broadly with communities, and working through partners like BRAC, we can help improve the capacity of local communities to adapt to climate change, significantly,” says Elfatih Eltahir, the H.M. King Bhumibol Professor in the Department of Civil and Environmental Engineering.

    Eltahir leads the project with John Aldridge and Deborah Campbell in the Humanitarian Assistance and Disaster Relief Systems Group at Lincoln Laboratory. Additional partners across MIT include the Center for Global Change Science; the Department of Earth, Atmospheric and Planetary Sciences; the Joint Program on the Science and Policy of Global Change; and the Abdul Latif Jameel Poverty Action Lab. 

    Predicting local risks

    CREWSnet’s forecasts rely upon a sophisticated model, developed in Eltahir’s research group over the past 25 years, called the MIT Regional Climate Model. This model zooms in on climate processes at local scales, at a resolution as granular as 6 miles. In Bangladesh’s population-dense cities, a 6-mile area could encompass tens, or even hundreds, of thousands of people. The model takes into account the details of a region’s topography, land use, and coastline to predict changes in local conditions.

    When applying this model over Bangladesh, researchers found that heat waves will get more severe and more frequent over the next 30 years. In particular, wet-bulb temperatures, which indicate the ability for humans to cool down by sweating, will rise to dangerous levels rarely observed today, particularly in western, inland cities.

    Such hot spots exacerbate other challenges predicted to worsen near Bangladesh’s coast. Rising sea levels and powerful cyclones are eroding and flooding coastal communities, causing saltwater to surge into land and freshwater. This salinity intrusion is detrimental to human health, ruins drinking water supplies, and harms crops, livestock, and aquatic life that farmers and fishermen depend on for food and income.

    CREWSnet will fuse climate science with forecasting tools that predict the social and economic impacts to villages and cities. These forecasts — such as how often a crop season may fail, or how far floodwaters will reach — can steer decision-making.

    “What people need to know, whether they’re a governor or head of a household, is ‘What is going to happen in my area, and what decisions should I make for the people I’m responsible for?’ Our role is to integrate this science and technology together into a decision support system,” says Aldridge, whose group at Lincoln Laboratory specializes in this area. Most recently, they transitioned a hurricane-evacuation planning system to the U.S. government. “We know that making decisions based on climate change requires a deep level of trust. That’s why having a powerful partner like BRAC is so important,” he says.

    Testing interventions

    Established 50 years ago, just after Bangladesh’s independence, BRAC works in every district of the nation to provide social services that help people rise from extreme poverty. Today, it is one of the world’s largest nongovernmental organizations, serving 110 million people across 11 countries in Asia and Africa, but its success is cultivated locally.

    “BRAC is thrilled to partner with leading researchers at MIT to increase climate resilience in Bangladesh and provide a model that can be scaled around the globe,” says Donella Rapier, president and CEO of BRAC USA. “Locally led climate adaptation solutions that are developed in partnership with communities are urgently needed, particularly in the most vulnerable regions that are on the frontlines of climate change.”

    CREWSnet will help BRAC identify communities most vulnerable to forecasted impacts. In these areas, they will share knowledge and innovate or bolster programs to improve households’ capacity to adapt.

    Many climate initiatives are already underway. One program equips homes to filter and store rainwater, as salinity intrusion makes safe drinking water hard to access. Another program is building resilient housing, able to withstand 120-mile-per-hour winds, that can double as local shelters during cyclones and flooding. Other services are helping farmers switch to different livestock or crops better suited for wetter or saltier conditions (e.g., ducks instead of chickens, or salt-tolerant rice), providing interest-free loans to enable this change.

    But adapting in place will not always be possible, for example in areas predicted to be submerged or unbearably hot by midcentury. “Bangladesh is working on identifying and developing climate-resilient cities and towns across the country, as closer-by alternative destinations as compared to moving to Dhaka, the overcrowded capital of Bangladesh,” says Campbell. “CREWSnet can help identify regions better suited for migration, and climate-resilient adaptation strategies for those regions.” At the same time, BRAC’s Climate Bridge Fund is helping to prepare cities for climate-induced migration, building up infrastructure and financial services for people who have been displaced.

    Evaluating impact

    While CREWSnet’s goal is to enable action, it can’t quite measure the impact of those actions. The Abdul Latif Jameel Poverty Action Lab (J-PAL), a development economics program in the MIT School of Humanities, Arts, and Social Sciences, will help evaluate the effectiveness of the climate-adaptation programs.

    “We conduct randomized controlled trials, similar to medical trials, that help us understand if a program improved people’s lives,” says Claire Walsh, the project director of the King Climate Action Initiative at J-PAL. “Once CREWSnet helps BRAC implement adaptation programs, we will generate scientific evidence on their impacts, so that BRAC and CREWSnet can make a case to funders and governments to expand effective programs.”

    The team aspires to bring CREWSnet to other nations disproportionately impacted by climate change. “Our vision is to have this be a globally extensible capability,” says Campbell. CREWSnet’s name evokes another early-warning decision-support system, FEWSnet, that helped organizations address famine in eastern Africa in the 1980s. Today it is a pillar of food-security planning around the world.

    CREWSnet hopes for a similar impact in climate change planning. Its selection as an MIT Climate Grand Challenges flagship project will inject the project with more funding and resources, momentum that will also help BRAC’s fundraising. The team plans to deploy CREWSnet to southwestern Bangladesh within five years.

    “The communities that we are aspiring to reach with CREWSnet are deeply aware that their lives are changing — they have been looking climate change in the eye for many years. They are incredibly resilient, creative, and talented,” says Ashley Toombs, the external affairs director for BRAC USA. “As a team, we are excited to bring this system to Bangladesh. And what we learn together, we will apply at potentially even larger scales.” More

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    Looking forward to forecast the risks of a changing climate

    On April 11, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the third in a five-part series highlighting the most promising concepts to emerge from the competition, and the interdisciplinary research teams behind them.

    Extreme weather events that were once considered rare have become noticeably less so, from intensifying hurricane activity in the North Atlantic to wildfires generating massive clouds of ozone-damaging smoke. But current climate models are unprepared when it comes to estimating the risk that these increasingly extreme events pose — and without adequate modeling, governments are left unable to take necessary precautions to protect their communities.

    MIT Department of Earth, Atmospheric and Planetary Science (EAPS) Professor Paul O’Gorman researches this trend by studying how climate affects the atmosphere and incorporating what he learns into climate models to improve their accuracy. One particular focus for O’Gorman has been changes in extreme precipitation and midlatitude storms that hit areas like New England.

    “These extreme events are having a lot of impact, but they’re also difficult to model or study,” he says. Seeing the pressing need for better climate models that can be used to develop preparedness plans and climate change mitigation strategies, O’Gorman and collaborators Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in EAPS, and Miho Mazereeuw, associate professor in MIT’s Department of Architecture, are leading an interdisciplinary group of scientists, engineers, and designers to tackle this problem with their MIT Climate Grand Challenges flagship project, “Preparing for a new world of weather and climate extremes.”

    “We know already from observations and from climate model predictions that weather and climate extremes are changing and will change more,” O’Gorman says. “The grand challenge is preparing for those changing extremes.”

    Their proposal is one of five flagship projects recently announced by the MIT Climate Grand Challenges initiative — an Institute-wide effort catalyzing novel research and engineering innovations to address the climate crisis. Selected from a field of almost 100 submissions, the team will receive additional funding and exposure to help accelerate and scale their project goals. Other MIT collaborators on the proposal include researchers from the School of Engineering, the School of Architecture and Planning, the Office of Sustainability, the Center for Global Change Science, and the Institute for Data, Systems and Society.

    Weather risk modeling

    Fifteen years ago, Kerry Emanuel developed a simple hurricane model. It was based on physics equations, rather than statistics, and could run in real time, making it useful for modeling risk assessment. Emanuel wondered if similar models could be used for long-term risk assessment of other things, such as changes in extreme weather because of climate change.

    “I discovered, somewhat to my surprise and dismay, that almost all extant estimates of long-term weather risks in the United States are based not on physical models, but on historical statistics of the hazards,” says Emanuel. “The problem with relying on historical records is that they’re too short; while they can help estimate common events, they don’t contain enough information to make predictions for more rare events.”

    Another limitation of weather risk models which rely heavily on statistics: They have a built-in assumption that the climate is static.

    “Historical records rely on the climate at the time they were recorded; they can’t say anything about how hurricanes grow in a warmer climate,” says Emanuel. The models rely on fixed relationships between events; they assume that hurricane activity will stay the same, even while science is showing that warmer temperatures will most likely push typical hurricane activity beyond the tropics and into a much wider band of latitudes.

    As a flagship project, the goal is to eliminate this reliance on the historical record by emphasizing physical principles (e.g., the laws of thermodynamics and fluid mechanics) in next-generation models. The downside to this is that there are many variables that have to be included. Not only are there planetary-scale systems to consider, such as the global circulation of the atmosphere, but there are also small-scale, extremely localized events, like thunderstorms, that influence predictive outcomes.

    Trying to compute all of these at once is costly and time-consuming — and the results often can’t tell you the risk in a specific location. But there is a way to correct for this: “What’s done is to use a global model, and then use a method called downscaling, which tries to infer what would happen on very small scales that aren’t properly resolved by the global model,” explains O’Gorman. The team hopes to improve downscaling techniques so that they can be used to calculate the risk of very rare but impactful weather events.

    Global climate models, or general circulation models (GCMs), Emanuel explains, are constructed a bit like a jungle gym. Like the playground bars, the Earth is sectioned in an interconnected three-dimensional framework — only it’s divided 100 to 200 square kilometers at a time. Each node comprises a set of computations for characteristics like wind, rainfall, atmospheric pressure, and temperature within its bounds; the outputs of each node are connected to its neighbor. This framework is useful for creating a big picture idea of Earth’s climate system, but if you tried to zoom in on a specific location — like, say, to see what’s happening in Miami or Mumbai — the connecting nodes are too far apart to make predictions on anything specific to those areas.

    Scientists work around this problem by using downscaling. They use the same blueprint of the jungle gym, but within the nodes they weave a mesh of smaller features, incorporating equations for things like topography and vegetation or regional meteorological models to fill in the blanks. By creating a finer mesh over smaller areas they can predict local effects without needing to run the entire global model.

    Of course, even this finer-resolution solution has its trade-offs. While we might be able to gain a clearer picture of what’s happening in a specific region by nesting models within models, it can still make for a computing challenge to crunch all that data at once, with the trade-off being expense and time, or predictions that are limited to shorter windows of duration — where GCMs can be run considering decades or centuries, a particularly complex local model may be restricted to predictions on timescales of just a few years at a time.

    “I’m afraid that most of the downscaling at present is brute force, but I think there’s room to do it in better ways,” says Emanuel, who sees the problem of finding new and novel methods of achieving this goal as an intellectual challenge. “I hope that through the Grand Challenges project we might be able to get students, postdocs, and others interested in doing this in a very creative way.”

    Adapting to weather extremes for cities and renewable energy

    Improving climate modeling is more than a scientific exercise in creativity, however. There’s a very real application for models that can accurately forecast risk in localized regions.

    Another problem is that progress in climate modeling has not kept up with the need for climate mitigation plans, especially in some of the most vulnerable communities around the globe.

    “It is critical for stakeholders to have access to this data for their own decision-making process. Every community is composed of a diverse population with diverse needs, and each locality is affected by extreme weather events in unique ways,” says Mazereeuw, the director of the MIT Urban Risk Lab. 

    A key piece of the team’s project is building on partnerships the Urban Risk Lab has developed with several cities to test their models once they have a usable product up and running. The cities were selected based on their vulnerability to increasing extreme weather events, such as tropical cyclones in Broward County, Florida, and Toa Baja, Puerto Rico, and extratropical storms in Boston, Massachusetts, and Cape Town, South Africa.

    In their proposal, the team outlines a variety of deliverables that the cities can ultimately use in their climate change preparations, with ideas such as online interactive platforms and workshops with stakeholders — such as local governments, developers, nonprofits, and residents — to learn directly what specific tools they need for their local communities. By doing so, they can craft plans addressing different scenarios in their region, involving events such as sea-level rise or heat waves, while also providing information and means of developing adaptation strategies for infrastructure under these conditions that will be the most effective and efficient for them.

    “We are acutely aware of the inequity of resources both in mitigating impacts and recovering from disasters. Working with diverse communities through workshops allows us to engage a lot of people, listen, discuss, and collaboratively design solutions,” says Mazereeuw.

    By the end of five years, the team is hoping that they’ll have better risk assessment and preparedness tool kits, not just for the cities that they’re partnering with, but for others as well.

    “MIT is well-positioned to make progress in this area,” says O’Gorman, “and I think it’s an important problem where we can make a difference.” More

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    Computing our climate future

    On Monday, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the first in a five-part series highlighting the most promising concepts to emerge from the competition, and the interdisciplinary research teams behind them.

    With improvements to computer processing power and an increased understanding of the physical equations governing the Earth’s climate, scientists are continually working to refine climate models and improve their predictive power. But the tools they’re refining were originally conceived decades ago with only scientists in mind. When it comes to developing tangible climate action plans, these models remain inscrutable to the policymakers, public safety officials, civil engineers, and community organizers who need their predictive insight most.

    “What you end up having is a gap between what’s typically used in practice, and the real cutting-edge science,” says Noelle Selin, a professor in the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences (EAPS), and co-lead with Professor Raffaele Ferrari on the MIT Climate Grand Challenges flagship project “Bringing Computation to the Climate Crisis.” “How can we use new computational techniques, new understandings, new ways of thinking about modeling, to really bridge that gap between state-of-the-art scientific advances and modeling, and people who are actually needing to use these models?”

    Using this as a driving question, the team won’t just be trying to refine current climate models, they’re building a new one from the ground up.

    This kind of game-changing advancement is exactly what the MIT Climate Grand Challenges is looking for, which is why the proposal has been named one of the five flagship projects in the ambitious Institute-wide program aimed at tackling the climate crisis. The proposal, which was selected from 100 submissions and was among 27 finalists, will receive additional funding and support to further their goal of reimagining the climate modeling system. It also brings together contributors from across the Institute, including the MIT Schwarzman College of Computing, the School of Engineering, and the Sloan School of Management.

    When it comes to pursuing high-impact climate solutions that communities around the world can use, “it’s great to do it at MIT,” says Ferrari, EAPS Cecil and Ida Green Professor of Oceanography. “You’re not going to find many places in the world where you have the cutting-edge climate science, the cutting-edge computer science, and the cutting-edge policy science experts that we need to work together.”

    The climate model of the future

    The proposal builds on work that Ferrari began three years ago as part of a joint project with Caltech, the Naval Postgraduate School, and NASA’s Jet Propulsion Lab. Called the Climate Modeling Alliance (CliMA), the consortium of scientists, engineers, and applied mathematicians is constructing a climate model capable of more accurately projecting future changes in critical variables, such as clouds in the atmosphere and turbulence in the ocean, with uncertainties at least half the size of those in existing models.

    To do this, however, requires a new approach. For one thing, current models are too coarse in resolution — at the 100-to-200-kilometer scale — to resolve small-scale processes like cloud cover, rainfall, and sea ice extent. But also, explains Ferrari, part of this limitation in resolution is due to the fundamental architecture of the models themselves. The languages most global climate models are coded in were first created back in the 1960s and ’70s, largely by scientists for scientists. Since then, advances in computing driven by the corporate world and computer gaming have given rise to dynamic new computer languages, powerful graphics processing units, and machine learning.

    For climate models to take full advantage of these advancements, there’s only one option: starting over with a modern, more flexible language. Written in Julia, a part of Julialab’s Scientific Machine Learning technology, and spearheaded by Alan Edelman, a professor of applied mathematics in MIT’s Department of Mathematics, CliMA will be able to harness far more data than the current models can handle.

    “It’s been real fun finally working with people in computer science here at MIT,” Ferrari says. “Before it was impossible, because traditional climate models are in a language their students can’t even read.”

    The result is what’s being called the “Earth digital twin,” a climate model that can simulate global conditions on a large scale. This on its own is an impressive feat, but the team wants to take this a step further with their proposal.

    “We want to take this large-scale model and create what we call an ‘emulator’ that is only predicting a set of variables of interest, but it’s been trained on the large-scale model,” Ferrari explains. Emulators are not new technology, but what is new is that these emulators, being referred to as the “Earth digital cousins,” will take advantage of machine learning.

    “Now we know how to train a model if we have enough data to train them on,” says Ferrari. Machine learning for projects like this has only become possible in recent years as more observational data become available, along with improved computer processing power. The goal is to create smaller, more localized models by training them using the Earth digital twin. Doing so will save time and money, which is key if the digital cousins are going to be usable for stakeholders, like local governments and private-sector developers.

    Adaptable predictions for average stakeholders

    When it comes to setting climate-informed policy, stakeholders need to understand the probability of an outcome within their own regions — in the same way that you would prepare for a hike differently if there’s a 10 percent chance of rain versus a 90 percent chance. The smaller Earth digital cousin models will be able to do things the larger model can’t do, like simulate local regions in real time and provide a wider range of probabilistic scenarios.

    “Right now, if you wanted to use output from a global climate model, you usually would have to use output that’s designed for general use,” says Selin, who is also the director of the MIT Technology and Policy Program. With the project, the team can take end-user needs into account from the very beginning while also incorporating their feedback and suggestions into the models, helping to “democratize the idea of running these climate models,” as she puts it. Doing so means building an interactive interface that eventually will give users the ability to change input values and run the new simulations in real time. The team hopes that, eventually, the Earth digital cousins could run on something as ubiquitous as a smartphone, although developments like that are currently beyond the scope of the project.

    The next thing the team will work on is building connections with stakeholders. Through participation of other MIT groups, such as the Joint Program on the Science and Policy of Global Change and the Climate and Sustainability Consortium, they hope to work closely with policymakers, public safety officials, and urban planners to give them predictive tools tailored to their needs that can provide actionable outputs important for planning. Faced with rising sea levels, for example, coastal cities could better visualize the threat and make informed decisions about infrastructure development and disaster preparedness; communities in drought-prone regions could develop long-term civil planning with an emphasis on water conservation and wildfire resistance.

    “We want to make the modeling and analysis process faster so people can get more direct and useful feedback for near-term decisions,” she says.

    The final piece of the challenge is to incentivize students now so that they can join the project and make a difference. Ferrari has already had luck garnering student interest after co-teaching a class with Edelman and seeing the enthusiasm students have about computer science and climate solutions.

    “We’re intending in this project to build a climate model of the future,” says Selin. “So it seems really appropriate that we would also train the builders of that climate model.” More

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    Improving predictions of sea level rise for the next century

    When we think of climate change, one of the most dramatic images that comes to mind is the loss of glacial ice. As the Earth warms, these enormous rivers of ice become a casualty of the rising temperatures. But, as ice sheets retreat, they also become an important contributor to one the more dangerous outcomes of climate change: sea-level rise. At MIT, an interdisciplinary team of scientists is determined to improve sea level rise predictions for the next century, in part by taking a closer look at the physics of ice sheets.

    Last month, two research proposals on the topic, led by Brent Minchew, the Cecil and Ida Green Career Development Professor in the Department of Earth, Atmospheric and Planetary Sciences (EAPS), were announced as finalists in the MIT Climate Grand Challenges initiative. Launched in July 2020, Climate Grand Challenges fielded almost 100 project proposals from collaborators across the Institute who heeded the bold charge: to develop research and innovations that will deliver game-changing advances in the world’s efforts to address the climate challenge.

    As finalists, Minchew and his collaborators from the departments of Urban Studies and Planning, Economics, Civil and Environmental Engineering, the Haystack Observatory, and external partners, received $100,000 to develop their research plans. A subset of the 27 proposals tapped as finalists will be announced next month, making up a portfolio of multiyear “flagship” projects receiving additional funding and support.

    One goal of both Minchew proposals is to more fully understand the most fundamental processes that govern rapid changes in glacial ice, and to use that understanding to build next-generation models that are more predictive of ice sheet behavior as they respond to, and influence, climate change.

    “We need to develop more accurate and computationally efficient models that provide testable projections of sea-level rise over the coming decades. To do so quickly, we want to make better and more frequent observations and learn the physics of ice sheets from these data,” says Minchew. “For example, how much stress do you have to apply to ice before it breaks?”

    Currently, Minchew’s Glacier Dynamics and Remote Sensing group uses satellites to observe the ice sheets on Greenland and Antarctica primarily with interferometric synthetic aperture radar (InSAR). But the data are often collected over long intervals of time, which only gives them “before and after” snapshots of big events. By taking more frequent measurements on shorter time scales, such as hours or days, they can get a more detailed picture of what is happening in the ice.

    “Many of the key unknowns in our projections of what ice sheets are going to look like in the future, and how they’re going to evolve, involve the dynamics of glaciers, or our understanding of how the flow speed and the resistances to flow are related,” says Minchew.

    At the heart of the two proposals is the creation of SACOS, the Stratospheric Airborne Climate Observatory System. The group envisions developing solar-powered drones that can fly in the stratosphere for months at a time, taking more frequent measurements using a new lightweight, low-power radar and other high-resolution instrumentation. They also propose air-dropping sensors directly onto the ice, equipped with seismometers and GPS trackers to measure high-frequency vibrations in the ice and pinpoint the motions of its flow.

    How glaciers contribute to sea level rise

    Current climate models predict an increase in sea levels over the next century, but by just how much is still unclear. Estimates are anywhere from 20 centimeters to two meters, which is a large difference when it comes to enacting policy or mitigation. Minchew points out that response measures will be different, depending on which end of the scale it falls toward. If it’s closer to 20 centimeters, coastal barriers can be built to protect low-level areas. But with higher surges, such measures become too expensive and inefficient to be viable, as entire portions of cities and millions of people would have to be relocated.

    “If we’re looking at a future where we could get more than a meter of sea level rise by the end of the century, then we need to know about that sooner rather than later so that we can start to plan and to do our best to prepare for that scenario,” he says.

    There are two ways glaciers and ice sheets contribute to rising sea levels: direct melting of the ice and accelerated transport of ice to the oceans. In Antarctica, warming waters melt the margins of the ice sheets, which tends to reduce the resistive stresses and allow ice to flow more quickly to the ocean. This thinning can also cause the ice shelves to be more prone to fracture, facilitating the calving of icebergs — events which sometimes cause even further acceleration of ice flow.

    Using data collected by SACOS, Minchew and his group can better understand what material properties in the ice allow for fracturing and calving of icebergs, and build a more complete picture of how ice sheets respond to climate forces. 

    “What I want is to reduce and quantify the uncertainties in projections of sea level rise out to the year 2100,” he says.

    From that more complete picture, the team — which also includes economists, engineers, and urban planning specialists — can work on developing predictive models and methods to help communities and governments estimate the costs associated with sea level rise, develop sound infrastructure strategies, and spur engineering innovation.

    Understanding glacier dynamics

    More frequent radar measurements and the collection of higher-resolution seismic and GPS data will allow Minchew and the team to develop a better understanding of the broad category of glacier dynamics — including calving, an important process in setting the rate of sea level rise which is currently not well understood.  

    “Some of what we’re doing is quite similar to what seismologists do,” he says. “They measure seismic waves following an earthquake, or a volcanic eruption, or things of this nature and use those observations to better understand the mechanisms that govern these phenomena.”

    Air-droppable sensors will help them collect information about ice sheet movement, but this method comes with drawbacks — like installation and maintenance, which is difficult to do out on a massive ice sheet that is moving and melting. Also, the instruments can each only take measurements at a single location. Minchew equates it to a bobber in water: All it can tell you is how the bobber moves as the waves disturb it.

    But by also taking continuous radar measurements from the air, Minchew’s team can collect observations both in space and in time. Instead of just watching the bobber in the water, they can effectively make a movie of the waves propagating out, as well as visualize processes like iceberg calving happening in multiple dimensions.

    Once the bobbers are in place and the movies recorded, the next step is developing machine learning algorithms to help analyze all the new data being collected. While this data-driven kind of discovery has been a hot topic in other fields, this is the first time it has been applied to glacier research.

    “We’ve developed this new methodology to ingest this huge amount of data,” he says, “and from that create an entirely new way of analyzing the system to answer these fundamental and critically important questions.”  More