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    Coordinating climate and air-quality policies to improve public health

    As America’s largest investment to fight climate change, the Inflation Reduction Act positions the country to reduce its greenhouse gas emissions by an estimated 40 percent below 2005 levels by 2030. But as it edges the United States closer to achieving its international climate commitment, the legislation is also expected to yield significant — and more immediate — improvements in the nation’s health. If successful in accelerating the transition from fossil fuels to clean energy alternatives, the IRA will sharply reduce atmospheric concentrations of fine particulates known to exacerbate respiratory and cardiovascular disease and cause premature deaths, along with other air pollutants that degrade human health. One recent study shows that eliminating air pollution from fossil fuels in the contiguous United States would prevent more than 50,000 premature deaths and avoid more than $600 billion in health costs each year.

    While national climate policies such as those advanced by the IRA can simultaneously help mitigate climate change and improve air quality, their results may vary widely when it comes to improving public health. That’s because the potential health benefits associated with air quality improvements are much greater in some regions and economic sectors than in others. Those benefits can be maximized, however, through a prudent combination of climate and air-quality policies.

    Several past studies have evaluated the likely health impacts of various policy combinations, but their usefulness has been limited due to a reliance on a small set of standard policy scenarios. More versatile tools are needed to model a wide range of climate and air-quality policy combinations and assess their collective effects on air quality and human health. Now researchers at the MIT Joint Program on the Science and Policy of Global Change and MIT Institute for Data, Systems and Society (IDSS) have developed a publicly available, flexible scenario tool that does just that.

    In a study published in the journal Geoscientific Model Development, the MIT team introduces its Tool for Air Pollution Scenarios (TAPS), which can be used to estimate the likely air-quality and health outcomes of a wide range of climate and air-quality policies at the regional, sectoral, and fuel-based level. 

    “This tool can help integrate the siloed sustainability issues of air pollution and climate action,” says the study’s lead author William Atkinson, who recently served as a Biogen Graduate Fellow and research assistant at the IDSS Technology and Policy Program’s (TPP) Research to Policy Engagement Initiative. “Climate action does not guarantee a clean air future, and vice versa — but the issues have similar sources that imply shared solutions if done right.”

    The study’s initial application of TAPS shows that with current air-quality policies and near-term Paris Agreement climate pledges alone, short-term pollution reductions give way to long-term increases — given the expected growth of emissions-intensive industrial and agricultural processes in developing regions. More ambitious climate and air-quality policies could be complementary, each reducing different pollutants substantially to give tremendous near- and long-term health benefits worldwide.

    “The significance of this work is that we can more confidently identify the long-term emission reduction strategies that also support air quality improvements,” says MIT Joint Program Deputy Director C. Adam Schlosser, a co-author of the study. “This is a win-win for setting climate targets that are also healthy targets.”

    TAPS projects air quality and health outcomes based on three integrated components: a recent global inventory of detailed emissions resulting from human activities (e.g., fossil fuel combustion, land-use change, industrial processes); multiple scenarios of emissions-generating human activities between now and the year 2100, produced by the MIT Economic Projection and Policy Analysis model; and emissions intensity (emissions per unit of activity) scenarios based on recent data from the Greenhouse Gas and Air Pollution Interactions and Synergies model.

    “We see the climate crisis as a health crisis, and believe that evidence-based approaches are key to making the most of this historic investment in the future, particularly for vulnerable communities,” says Johanna Jobin, global head of corporate reputation and responsibility at Biogen. “The scientific community has spoken with unanimity and alarm that not all climate-related actions deliver equal health benefits. We’re proud of our collaboration with the MIT Joint Program to develop this tool that can be used to bridge research-to-policy gaps, support policy decisions to promote health among vulnerable communities, and train the next generation of scientists and leaders for far-reaching impact.”

    The tool can inform decision-makers about a wide range of climate and air-quality policies. Policy scenarios can be applied to specific regions, sectors, or fuels to investigate policy combinations at a more granular level, or to target short-term actions with high-impact benefits.

    TAPS could be further developed to account for additional emissions sources and trends.

    “Our new tool could be used to examine a large range of both climate and air quality scenarios. As the framework is expanded, we can add detail for specific regions, as well as additional pollutants such as air toxics,” says study supervising co-author Noelle Selin, professor at IDSS and the MIT Department of Earth, Atmospheric and Planetary Sciences, and director of TPP.    

    This research was supported by the U.S. Environmental Protection Agency and its Science to Achieve Results (STAR) program; Biogen; TPP’s Leading Technology and Policy Initiative; and TPP’s Research to Policy Engagement Initiative. More

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    Using seismology for groundwater management

    As climate change increases the number of extreme weather events, such as megadroughts, groundwater management is key for sustaining water supply. But current groundwater monitoring tools are either costly or insufficient for deeper aquifers, limiting our ability to monitor and practice sustainable management in populated areas.

    Now, a new paper published in Nature Communications bridges seismology and hydrology with a pilot application that uses seismometers as a cost-effective way to monitor and map groundwater fluctuations.

    “Our measurements are independent from and complementary to traditional observations,” says Shujuan Mao PhD ’21, lead author on the paper. “It provides a new way to dictate groundwater management and evaluate the impact of human activity on shaping underground hydrologic systems.”

    Mao, currently a Thompson Postdoctoral Fellow in the Geophysics department at Stanford University, conducted most of the research during her PhD in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). Other contributors to the paper include EAPS department chair and Schlumberger Professor of Earth and Planetary Sciences Robert van der Hilst, as well as Michel Campillo and Albanne Lecointre from the Institut des Sciences de la Terre in France.

    While there are a few different methods currently used for measuring groundwater, they all come with notable drawbacks. Hydraulic heads, which drill through the ground and into the aquifers, are expensive and can only give limited information at the specific location they’re placed. Noninvasive techniques based on satellite- or airborne-sensing lack the sensitivity and resolution needed to observe deeper depths.

    Mao proposes using seismometers, which are instruments used to measure ground vibrations such as the waves produced by earthquakes. They can measure seismic velocity, which is the propagation speed of seismic waves. Seismic velocity measurements are unique to the mechanical state of rocks, or the ways rocks respond to their physical environment, and can tell us a lot about them.

    The idea of using seismic velocity to characterize property changes in rocks has long been used in laboratory-scale analysis, but only recently have scientists been able to measure it continuously in realistic-scale geological settings. For aquifer monitoring, Mao and her team associate the seismic velocity with the hydraulic property, or the water content, in the rocks.

    Seismic velocity measurements make use of ambient seismic fields, or background noise, recorded by seismometers. “The Earth’s surface is always vibrating, whether due to ocean waves, winds, or human activities,” she explains. “Most of the time those vibrations are really small and are considered ‘noise’ by traditional seismologists. But in recent years scientists have shown that the continuous noise records in fact contain a wealth of information about the properties and structures of the Earth’s interior.”

    To extract useful information from the noise records, Mao and her team used a technique called seismic interferometry, which analyzes wave interference to calculate the seismic velocity of the medium the waves pass through. For their pilot application, Mao and her team applied this analysis to basins in the Metropolitan Los Angeles region, an area suffering from worsening drought and a growing population.

    By doing this, Mao and her team were able to see how the aquifers changed physically over time at a high resolution. Their seismic velocity measurements verified measurements taken by hydraulic heads over the last 20 years, and the images matched very well with satellite data. They could also see differences in how the storage areas changed between counties in the area that used different water pumping practices, which is important for developing water management protocol.

    Mao also calls using the seismometers a “buy-one get-one free” deal, since seismometers are already in use for earthquake and tectonic studies not just across California, but worldwide, and could help “avoid the expensive cost of drilling and maintaining dedicated groundwater monitoring wells,” she says.

    Mao emphasizes that this study is just the beginning of exploring possible applications of seismic noise interferometry in this way. It can be used to monitor other near-surface systems, such as geothermal or volcanic systems, and Mao is currently applying it to oil and gas fields. But in places like California currently experiencing megadroughts, and who rely on groundwater for a large portion of their water needs, this kind of information is key for sustainable water management.

    “It’s really important, especially now, to characterize these changes in groundwater storage so that we can promote data-informed policymaking to help them thrive under increasing water stress,” she says.

    This study was funded, in part, by the European Research Council, with additional support from the Thompson Fellowship at Stanford University. 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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    MIT announces five flagship projects in first-ever Climate Grand Challenges competition

    MIT today announced the five flagship projects selected in its first-ever Climate Grand Challenges competition. These multiyear projects will define a dynamic research agenda focused on unraveling some of the toughest unsolved climate problems and bringing high-impact, science-based solutions to the world on an accelerated basis.

    Representing the most promising concepts to emerge from the two-year competition, the five flagship projects will receive additional funding and resources from MIT and others to develop their ideas and swiftly transform them into practical solutions at scale.

    “Climate Grand Challenges represents a whole-of-MIT drive to develop game-changing advances to confront the escalating climate crisis, in time to make a difference,” says MIT President L. Rafael Reif. “We are inspired by the creativity and boldness of the flagship ideas and by their potential to make a significant contribution to the global climate response. But given the planet-wide scale of the challenge, success depends on partnership. We are eager to work with visionary leaders in every sector to accelerate this impact-oriented research, implement serious solutions at scale, and inspire others to join us in confronting this urgent challenge for humankind.”

    Brief descriptions of the five Climate Grand Challenges flagship projects are provided below.

    Bringing Computation to the Climate Challenge

    This project leverages advances in artificial intelligence, machine learning, and data sciences to improve the accuracy of climate models and make them more useful to a variety of stakeholders — from communities to industry. The team is developing a digital twin of the Earth that harnesses more data than ever before to reduce and quantify uncertainties in climate projections.

    Research leads: Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in the Department of Earth, Atmospheric and Planetary Sciences, and director of the Program in Atmospheres, Oceans, and Climate; and Noelle Eckley Selin, director of the Technology and Policy Program and professor with a joint appointment in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences

    Center for Electrification and Decarbonization of Industry

    This project seeks to reinvent and electrify the processes and materials behind hard-to-decarbonize industries like steel, cement, ammonia, and ethylene production. A new innovation hub will perform targeted fundamental research and engineering with urgency, pushing the technological envelope on electricity-driven chemical transformations.

    Research leads: Yet-Ming Chiang, the Kyocera Professor of Materials Science and Engineering, and Bilge Yıldız, the Breene M. Kerr Professor in the Department of Nuclear Science and Engineering and professor in the Department of Materials Science and Engineering

    Preparing for a new world of weather and climate extremes

    This project addresses key gaps in knowledge about intensifying extreme events such as floods, hurricanes, and heat waves, and quantifies their long-term risk in a changing climate. The team is developing a scalable climate-change adaptation toolkit to help vulnerable communities and low-carbon energy providers prepare for these extreme weather events.

    Research leads: Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in the Department of Earth, Atmospheric and Planetary Sciences and co-director of the MIT Lorenz Center; Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab; and Paul O’Gorman, professor in the Program in Atmospheres, Oceans, and Climate in the Department of Earth, Atmospheric and Planetary Sciences

    The Climate Resilience Early Warning System

    The CREWSnet project seeks to reinvent climate change adaptation with a novel forecasting system that empowers underserved communities to interpret local climate risk, proactively plan for their futures incorporating resilience strategies, and minimize losses. CREWSnet will initially be demonstrated in southwestern Bangladesh, serving as a model for similarly threatened regions around the world.

    Research leads: John Aldridge, assistant leader of the Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory, and Elfatih Eltahir, the H.M. King Bhumibol Professor of Hydrology and Climate in the Department of Civil and Environmental Engineering

    Revolutionizing agriculture with low-emissions, resilient crops

    This project works to revolutionize the agricultural sector with climate-resilient crops and fertilizers that have the ability to dramatically reduce greenhouse gas emissions from food production.

    Research lead: Christopher Voigt, the Daniel I.C. Wang Professor in the Department of Biological Engineering

    “As one of the world’s leading institutions of research and innovation, it is incumbent upon MIT to draw on our depth of knowledge, ingenuity, and ambition to tackle the hard climate problems now confronting the world,” says Richard Lester, MIT associate provost for international activities. “Together with collaborators across industry, finance, community, and government, the Climate Grand Challenges teams are looking to develop and implement high-impact, path-breaking climate solutions rapidly and at a grand scale.”

    The initial call for ideas in 2020 yielded nearly 100 letters of interest from almost 400 faculty members and senior researchers, representing 90 percent of MIT departments. After an extensive evaluation, 27 finalist teams received a total of $2.7 million to develop comprehensive research and innovation plans. The projects address four broad research themes:

    To select the winning projects, research plans were reviewed by panels of international experts representing relevant scientific and technical domains as well as experts in processes and policies for innovation and scalability.

    “In response to climate change, the world really needs to do two things quickly: deploy the solutions we already have much more widely, and develop new solutions that are urgently needed to tackle this intensifying threat,” says Maria Zuber, MIT vice president for research. “These five flagship projects exemplify MIT’s strong determination to bring its knowledge and expertise to bear in generating new ideas and solutions that will help solve the climate problem.”

    “The Climate Grand Challenges flagship projects set a new standard for inclusive climate solutions that can be adapted and implemented across the globe,” says MIT Chancellor Melissa Nobles. “This competition propels the entire MIT research community — faculty, students, postdocs, and staff — to act with urgency around a worsening climate crisis, and I look forward to seeing the difference these projects can make.”

    “MIT’s efforts on climate research amid the climate crisis was a primary reason that I chose to attend MIT, and remains a reason that I view the Institute favorably. MIT has a clear opportunity to be a thought leader in the climate space in our own MIT way, which is why CGC fits in so well,” says senior Megan Xu, who served on the Climate Grand Challenges student committee and is studying ways to make the food system more sustainable.

    The Climate Grand Challenges competition is a key initiative of “Fast Forward: MIT’s Climate Action Plan for the Decade,” which the Institute published in May 2021. Fast Forward outlines MIT’s comprehensive plan for helping the world address the climate crisis. It consists of five broad areas of action: sparking innovation, educating future generations, informing and leveraging government action, reducing MIT’s own climate impact, and uniting and coordinating all of MIT’s climate efforts. More

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    Ocean vital signs

    Without the ocean, the climate crisis would be even worse than it is. Each year, the ocean absorbs billions of tons of carbon from the atmosphere, preventing warming that greenhouse gas would otherwise cause. Scientists estimate about 25 to 30 percent of all carbon released into the atmosphere by both human and natural sources is absorbed by the ocean.

    “But there’s a lot of uncertainty in that number,” says Ryan Woosley, a marine chemist and a principal research scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at MIT. Different parts of the ocean take in different amounts of carbon depending on many factors, such as the season and the amount of mixing from storms. Current models of the carbon cycle don’t adequately capture this variation.

    To close the gap, Woosley and a team of other MIT scientists developed a research proposal for the MIT Climate Grand Challenges competition — an Institute-wide campaign to catalyze and fund innovative research addressing the climate crisis. The team’s proposal, “Ocean Vital Signs,” involves sending a fleet of sailing drones to cruise the oceans taking detailed measurements of how much carbon the ocean is really absorbing. Those data would be used to improve the precision of global carbon cycle models and improve researchers’ ability to verify emissions reductions claimed by countries.

    “If we start to enact mitigation strategies—either through removing CO2 from the atmosphere or reducing emissions — we need to know where CO2 is going in order to know how effective they are,” says Woosley. Without more precise models there’s no way to confirm whether observed carbon reductions were thanks to policy and people, or thanks to the ocean.

    “So that’s the trillion-dollar question,” says Woosley. “If countries are spending all this money to reduce emissions, is it enough to matter?”

    In February, the team’s Climate Grand Challenges proposal was named one of 27 finalists out of the almost 100 entries submitted. From among this list of finalists, MIT will announce in April the selection of five flagship projects to receive further funding and support.

    Woosley is leading the team along with Christopher Hill, a principal research engineer in EAPS. The team includes physical and chemical oceanographers, marine microbiologists, biogeochemists, and experts in computational modeling from across the department, in addition to collaborators from the Media Lab and the departments of Mathematics, Aeronautics and Astronautics, and Electrical Engineering and Computer Science.

    Today, data on the flux of carbon dioxide between the air and the oceans are collected in a piecemeal way. Research ships intermittently cruise out to gather data. Some commercial ships are also fitted with sensors. But these present a limited view of the entire ocean, and include biases. For instance, commercial ships usually avoid storms, which can increase the turnover of water exposed to the atmosphere and cause a substantial increase in the amount of carbon absorbed by the ocean.

    “It’s very difficult for us to get to it and measure that,” says Woosley. “But these drones can.”

    If funded, the team’s project would begin by deploying a few drones in a small area to test the technology. The wind-powered drones — made by a California-based company called Saildrone — would autonomously navigate through an area, collecting data on air-sea carbon dioxide flux continuously with solar-powered sensors. This would then scale up to more than 5,000 drone-days’ worth of observations, spread over five years, and in all five ocean basins.

    Those data would be used to feed neural networks to create more precise maps of how much carbon is absorbed by the oceans, shrinking the uncertainties involved in the models. These models would continue to be verified and improved by new data. “The better the models are, the more we can rely on them,” says Woosley. “But we will always need measurements to verify the models.”

    Improved carbon cycle models are relevant beyond climate warming as well. “CO2 is involved in so much of how the world works,” says Woosley. “We’re made of carbon, and all the other organisms and ecosystems are as well. What does the perturbation to the carbon cycle do to these ecosystems?”

    One of the best understood impacts is ocean acidification. Carbon absorbed by the ocean reacts to form an acid. A more acidic ocean can have dire impacts on marine organisms like coral and oysters, whose calcium carbonate shells and skeletons can dissolve in the lower pH. Since the Industrial Revolution, the ocean has become about 30 percent more acidic on average.

    “So while it’s great for us that the oceans have been taking up the CO2, it’s not great for the oceans,” says Woosley. “Knowing how this uptake affects the health of the ocean is important as well.” 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

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    Understanding air pollution from space

    Climate change and air pollution are interlocking crises that threaten human health. Reducing emissions of some air pollutants can help achieve climate goals, and some climate mitigation efforts can in turn improve air quality.

    One part of MIT Professor Arlene Fiore’s research program is to investigate the fundamental science in understanding air pollutants — how long they persist and move through our environment to affect air quality.

    “We need to understand the conditions under which pollutants, such as ozone, form. How much ozone is formed locally and how much is transported long distances?” says Fiore, who notes that Asian air pollution can be transported across the Pacific Ocean to North America. “We need to think about processes spanning local to global dimensions.”

    Fiore, the Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences, analyzes data from on-the-ground readings and from satellites, along with models, to better understand the chemistry and behavior of air pollutants — which ultimately can inform mitigation strategies and policy setting.

    A global concern

    At the United Nations’ most recent climate change conference, COP26, air quality management was a topic discussed over two days of presentations.

    “Breathing is vital. It’s life. But for the vast majority of people on this planet right now, the air that they breathe is not giving life, but cutting it short,” said Sarah Vogel, senior vice president for health at the Environmental Defense Fund, at the COP26 session.

    “We need to confront this twin challenge now through both a climate and clean air lens, of targeting those pollutants that warm both the air and harm our health.”

    Earlier this year, the World Health Organization (WHO) updated its global air quality guidelines it had issued 15 years earlier for six key pollutants including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The new guidelines are more stringent based on what the WHO stated is the “quality and quantity of evidence” of how these pollutants affect human health. WHO estimates that roughly 7 million premature deaths are attributable to the joint effects of air pollution.

    “We’ve had all these health-motivated reductions of aerosol and ozone precursor emissions. What are the implications for the climate system, both locally but also around the globe? How does air quality respond to climate change? We study these two-way interactions between air pollution and the climate system,” says Fiore.

    But fundamental science is still required to understand how gases, such as ozone and nitrogen dioxide, linger and move throughout the troposphere — the lowermost layer of our atmosphere, containing the air we breathe.

    “We care about ozone in the air we’re breathing where we live at the Earth’s surface,” says Fiore. “Ozone reacts with biological tissue, and can be damaging to plants and human lungs. Even if you’re a healthy adult, if you’re out running hard during an ozone smog event, you might feel an extra weight on your lungs.”

    Telltale signs from space

    Ozone is not emitted directly, but instead forms through chemical reactions catalyzed by radiation from the sun interacting with nitrogen oxides — pollutants released in large part from burning fossil fuels—and volatile organic compounds. However, current satellite instruments cannot sense ground-level ozone.

    “We can’t retrieve surface- or even near-surface ozone from space,” says Fiore of the satellite data, “although the anticipated launch of a new instrument looks promising for new advances in retrieving lower-tropospheric ozone”. Instead, scientists can look at signatures from other gas emissions to get a sense of ozone formation. “Nitrogen dioxide and formaldehyde are a heavy focus of our research because they serve as proxies for two of the key ingredients that go on to form ozone in the atmosphere.”

    To understand ozone formation via these precursor pollutants, scientists have gathered data for more than two decades using spectrometer instruments aboard satellites that measure sunlight in ultraviolet and visible wavelengths that interact with these pollutants in the Earth’s atmosphere — known as solar backscatter radiation.

    Satellites, such as NASA’s Aura, carry instruments like the Ozone Monitoring Instrument (OMI). OMI, along with European-launched satellites such as the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), and the newest generation TROPOspheric Monitoring instrument (TROPOMI), all orbit the Earth, collecting data during daylight hours when sunlight is interacting with the atmosphere over a particular location.

    In a recent paper from Fiore’s group, former graduate student Xiaomeng Jin (now a postdoc at the University of California at Berkeley), demonstrated that she could bring together and “beat down the noise in the data,” as Fiore says, to identify trends in ozone formation chemistry over several U.S. metropolitan areas that “are consistent with our on-the-ground understanding from in situ ozone measurements.”

    “This finding implies that we can use these records to learn about changes in surface ozone chemistry in places where we lack on-the-ground monitoring,” says Fiore. Extracting these signals by stringing together satellite data — OMI, GOME, and SCIAMACHY — to produce a two-decade record required reconciling the instruments’ differing orbit days, times, and fields of view on the ground, or spatial resolutions. 

    Currently, spectrometer instruments aboard satellites are retrieving data once per day. However, newer instruments, such as the Geostationary Environment Monitoring Spectrometer launched in February 2020 by the National Institute of Environmental Research in the Ministry of Environment of South Korea, will monitor a particular region continuously, providing much more data in real time.

    Over North America, the Tropospheric Emissions: Monitoring of Pollution Search (TEMPO) collaboration between NASA and the Smithsonian Astrophysical Observatory, led by Kelly Chance of Harvard University, will provide not only a stationary view of the atmospheric chemistry over the continent, but also a finer-resolution view — with the instrument recording pollution data from only a few square miles per pixel (with an anticipated launch in 2022).

    “What we’re very excited about is the opportunity to have continuous coverage where we get hourly measurements that allow us to follow pollution from morning rush hour through the course of the day and see how plumes of pollution are evolving in real time,” says Fiore.

    Data for the people

    Providing Earth-observing data to people in addition to scientists — namely environmental managers, city planners, and other government officials — is the goal for the NASA Health and Air Quality Applied Sciences Team (HAQAST).

    Since 2016, Fiore has been part of HAQAST, including collaborative “tiger teams” — projects that bring together scientists, nongovernment entities, and government officials — to bring data to bear on real issues.

    For example, in 2017, Fiore led a tiger team that provided guidance to state air management agencies on how satellite data can be incorporated into state implementation plans (SIPs). “Submission of a SIP is required for any state with a region in non-attainment of U.S. National Ambient Air Quality Standards to demonstrate their approach to achieving compliance with the standard,” says Fiore. “What we found is that small tweaks in, for example, the metrics we use to convey the science findings, can go a long way to making the science more usable, especially when there are detailed policy frameworks in place that must be followed.”

    Now, in 2021, Fiore is part of two tiger teams announced by HAQAST in late September. One team is looking at data to address environmental justice issues, by providing data to assess communities disproportionately affected by environmental health risks. Such information can be used to estimate the benefits of governmental investments in environmental improvements for disproportionately burdened communities. The other team is looking at urban emissions of nitrogen oxides to try to better quantify and communicate uncertainties in the estimates of anthropogenic sources of pollution.

    “For our HAQAST work, we’re looking at not just the estimate of the exposure to air pollutants, or in other words their concentrations,” says Fiore, “but how confident are we in our exposure estimates, which in turn affect our understanding of the public health burden due to exposure. We have stakeholder partners at the New York Department of Health who will pair exposure datasets with health data to help prioritize decisions around public health.

    “I enjoy working with stakeholders who have questions that require science to answer and can make a difference in their decisions.” Fiore says. More