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    Using deep learning to image the Earth’s planetary boundary layer

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    Although the troposphere is often thought of as the closest layer of the atmosphere to the Earth’s surface, the planetary boundary layer (PBL) — the lowest layer of the troposphere — is actually the part that most significantly influences weather near the surface. In the 2018 planetary science decadal survey, the PBL was raised as an important scientific issue that has the potential to enhance storm forecasting and improve climate projections.  

    “The PBL is where the surface interacts with the atmosphere, including exchanges of moisture and heat that help lead to severe weather and a changing climate,” says Adam Milstein, a technical staff member in Lincoln Laboratory’s Applied Space Systems Group. “The PBL is also where humans live, and the turbulent movement of aerosols throughout the PBL is important for air quality that influences human health.” 

    Although vital for studying weather and climate, important features of the PBL, such as its height, are difficult to resolve with current technology. In the past four years, Lincoln Laboratory staff have been studying the PBL, focusing on two different tasks: using machine learning to make 3D-scanned profiles of the atmosphere, and resolving the vertical structure of the atmosphere more clearly in order to better predict droughts.  

    This PBL-focused research effort builds on more than a decade of related work on fast, operational neural network algorithms developed by Lincoln Laboratory for NASA missions. These missions include the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission as well as Aqua, a satellite that collects data about Earth’s water cycle and observes variables such as ocean temperature, precipitation, and water vapor in the atmosphere. These algorithms retrieve temperature and humidity from the satellite instrument data and have been shown to significantly improve the accuracy and usable global coverage of the observations over previous approaches. For TROPICS, the algorithms help retrieve data that are used to characterize a storm’s rapidly evolving structures in near-real time, and for Aqua, it has helped increase forecasting models, drought monitoring, and fire prediction. 

    These operational algorithms for TROPICS and Aqua are based on classic “shallow” neural networks to maximize speed and simplicity, creating a one-dimensional vertical profile for each spectral measurement collected by the instrument over each location. While this approach has improved observations of the atmosphere down to the surface overall, including the PBL, laboratory staff determined that newer “deep” learning techniques that treat the atmosphere over a region of interest as a three-dimensional image are needed to improve PBL details further.

    “We hypothesized that deep learning and artificial intelligence (AI) techniques could improve on current approaches by incorporating a better statistical representation of 3D temperature and humidity imagery of the atmosphere into the solutions,” Milstein says. “But it took a while to figure out how to create the best dataset — a mix of real and simulated data; we needed to prepare to train these techniques.”

    The team collaborated with Joseph Santanello of the NASA Goddard Space Flight Center and William Blackwell, also of the Applied Space Systems Group, in a recent NASA-funded effort showing that these retrieval algorithms can improve PBL detail, including more accurate determination of the PBL height than the previous state of the art. 

    While improved knowledge of the PBL is broadly useful for increasing understanding of climate and weather, one key application is prediction of droughts. According to a Global Drought Snapshot report released last year, droughts are a pressing planetary issue that the global community needs to address. Lack of humidity near the surface, specifically at the level of the PBL, is the leading indicator of drought. While previous studies using remote-sensing techniques have examined the humidity of soil to determine drought risk, studying the atmosphere can help predict when droughts will happen.  

    In an effort funded by Lincoln Laboratory’s Climate Change Initiative, Milstein, along with laboratory staff member Michael Pieper, are working with scientists at NASA’s Jet Propulsion Laboratory (JPL) to use neural network techniques to improve drought prediction over the continental United States. While the work builds off of existing operational work JPL has done incorporating (in part) the laboratory’s operational “shallow” neural network approach for Aqua, the team believes that this work and the PBL-focused deep learning research work can be combined to further improve the accuracy of drought prediction. 

    “Lincoln Laboratory has been working with NASA for more than a decade on neural network algorithms for estimating temperature and humidity in the atmosphere from space-borne infrared and microwave instruments, including those on the Aqua spacecraft,” Milstein says. “Over that time, we have learned a lot about this problem by working with the science community, including learning about what scientific challenges remain. Our long experience working on this type of remote sensing with NASA scientists, as well as our experience with using neural network techniques, gave us a unique perspective.”

    According to Milstein, the next step for this project is to compare the deep learning results to datasets from the National Oceanic and Atmospheric Administration, NASA, and the Department of Energy collected directly in the PBL using radiosondes, a type of instrument flown on a weather balloon. “These direct measurements can be considered a kind of ‘ground truth’ to quantify the accuracy of the techniques we have developed,” Milstein says.

    This improved neural network approach holds promise to demonstrate drought prediction that can exceed the capabilities of existing indicators, Milstein says, and to be a tool that scientists can rely on for decades to come. More

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      in Environment

      MIT-derived algorithm helps forecast the frequency of extreme weather

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      To assess a community’s risk of extreme weather, policymakers rely first on global climate models that can be run decades, and even centuries, forward in time, but only at a coarse resolution. These models might be used to gauge, for instance, future climate conditions for the northeastern U.S., but not specifically for Boston.

      To estimate Boston’s future risk of extreme weather such as flooding, policymakers can combine a coarse model’s large-scale predictions with a finer-resolution model, tuned to estimate how often Boston is likely to experience damaging floods as the climate warms. But this risk analysis is only as accurate as the predictions from that first, coarser climate model.

      “If you get those wrong for large-scale environments, then you miss everything in terms of what extreme events will look like at smaller scales, such as over individual cities,” says Themistoklis Sapsis, the William I. Koch Professor and director of the Center for Ocean Engineering in MIT’s Department of Mechanical Engineering.

      Sapsis and his colleagues have now developed a method to “correct” the predictions from coarse climate models. By combining machine learning with dynamical systems theory, the team’s approach “nudges” a climate model’s simulations into more realistic patterns over large scales. When paired with smaller-scale models to predict specific weather events such as tropical cyclones or floods, the team’s approach produced more accurate predictions for how often specific locations will experience those events over the next few decades, compared to predictions made without the correction scheme.

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      This animation shows the evolution of storms around the northern hemisphere, as a result of a high-resolution storm model, combined with the MIT team’s corrected global climate model. The simulation improves the modeling of extreme values for wind, temperature, and humidity, which typically have significant errors in coarse scale models. Credit: Courtesy of Ruby Leung and Shixuan Zhang, PNNL

      Sapsis says the new correction scheme is general in form and can be applied to any global climate model. Once corrected, the models can help to determine where and how often extreme weather will strike as global temperatures rise over the coming years. 

      “Climate change will have an effect on every aspect of human life, and every type of life on the planet, from biodiversity to food security to the economy,” Sapsis says. “If we have capabilities to know accurately how extreme weather will change, especially over specific locations, it can make a lot of difference in terms of preparation and doing the right engineering to come up with solutions. This is the method that can open the way to do that.”

      The team’s results appear today in the Journal of Advances in Modeling Earth Systems. The study’s MIT co-authors include postdoc Benedikt Barthel Sorensen and Alexis-Tzianni Charalampopoulos SM ’19, PhD ’23, with Shixuan Zhang, Bryce Harrop, and Ruby Leung of the Pacific Northwest National Laboratory in Washington state.

      Over the hood

      Today’s large-scale climate models simulate weather features such as the average temperature, humidity, and precipitation around the world, on a grid-by-grid basis. Running simulations of these models takes enormous computing power, and in order to simulate how weather features will interact and evolve over periods of decades or longer, models average out features every 100 kilometers or so.

      “It’s a very heavy computation requiring supercomputers,” Sapsis notes. “But these models still do not resolve very important processes like clouds or storms, which occur over smaller scales of a kilometer or less.”

      To improve the resolution of these coarse climate models, scientists typically have gone under the hood to try and fix a model’s underlying dynamical equations, which describe how phenomena in the atmosphere and oceans should physically interact.

      “People have tried to dissect into climate model codes that have been developed over the last 20 to 30 years, which is a nightmare, because you can lose a lot of stability in your simulation,” Sapsis explains. “What we’re doing is a completely different approach, in that we’re not trying to correct the equations but instead correct the model’s output.”

      The team’s new approach takes a model’s output, or simulation, and overlays an algorithm that nudges the simulation toward something that more closely represents real-world conditions. The algorithm is based on a machine-learning scheme that takes in data, such as past information for temperature and humidity around the world, and learns associations within the data that represent fundamental dynamics among weather features. The algorithm then uses these learned associations to correct a model’s predictions.

      “What we’re doing is trying to correct dynamics, as in how an extreme weather feature, such as the windspeeds during a Hurricane Sandy event, will look like in the coarse model, versus in reality,” Sapsis says. “The method learns dynamics, and dynamics are universal. Having the correct dynamics eventually leads to correct statistics, for example, frequency of rare extreme events.”

      Climate correction

      As a first test of their new approach, the team used the machine-learning scheme to correct simulations produced by the Energy Exascale Earth System Model (E3SM), a climate model run by the U.S. Department of Energy, that simulates climate patterns around the world at a resolution of 110 kilometers. The researchers used eight years of past data for temperature, humidity, and wind speed to train their new algorithm, which learned dynamical associations between the measured weather features and the E3SM model. They then ran the climate model forward in time for about 36 years and applied the trained algorithm to the model’s simulations. They found that the corrected version produced climate patterns that more closely matched real-world observations from the last 36 years, not used for training.

      “We’re not talking about huge differences in absolute terms,” Sapsis says. “An extreme event in the uncorrected simulation might be 105 degrees Fahrenheit, versus 115 degrees with our corrections. But for humans experiencing this, that is a big difference.”

      When the team then paired the corrected coarse model with a specific, finer-resolution model of tropical cyclones, they found the approach accurately reproduced the frequency of extreme storms in specific locations around the world.

      “We now have a coarse model that can get you the right frequency of events, for the present climate. It’s much more improved,” Sapsis says. “Once we correct the dynamics, this is a relevant correction, even when you have a different average global temperature, and it can be used for understanding how forest fires, flooding events, and heat waves will look in a future climate. Our ongoing work is focusing on analyzing future climate scenarios.”

      “The results are particularly impressive as the method shows promising results on E3SM, a state-of-the-art climate model,” says Pedram Hassanzadeh, an associate professor who leads the Climate Extremes Theory and Data group at the University of Chicago and was not involved with the study. “It would be interesting to see what climate change projections this framework yields once future greenhouse-gas emission scenarios are incorporated.”

      This work was supported, in part, by the U.S. Defense Advanced Research Projects Agency. More

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      in Energy

      Optimizing nuclear fuels for next-generation reactors

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      In 2010, when Ericmoore Jossou was attending college in northern Nigeria, the lights would flicker in and out all day, sometimes lasting only for a couple of hours at a time. The frustrating experience reaffirmed Jossou’s realization that the country’s sporadic energy supply was a problem. It was the beginning of his path toward nuclear engineering.

      Because of the energy crisis, “I told myself I was going to find myself in a career that allows me to develop energy technologies that can easily be scaled to meet the energy needs of the world, including my own country,” says Jossou, an assistant professor in a shared position between the departments of Nuclear Science and Engineering (NSE), where is the John Clark Hardwick (1986) Professor, and of Electrical Engineering and Computer Science.

      Today, Jossou uses computer simulations for rational materials design, AI-aided purposeful development of cladding materials and fuels for next-generation nuclear reactors. As one of the shared faculty hires between the MIT Schwarzman College of Computing and departments across MIT, his appointment recognizes his commitment to computing for climate and the environment.

      A well-rounded education in Nigeria

      Growing up in Lagos, Jossou knew education was about more than just bookish knowledge, so he was eager to travel and experience other cultures. He would start in his own backyard by traveling across the Niger river and enrolling in Ahmadu Bello University in northern Nigeria. Moving from the south was a cultural education with a different language and different foods. It was here that Jossou got to try and love tuwo shinkafa, a northern Nigerian rice-based specialty, for the first time.

      After his undergraduate studies, armed with a bachelor’s degree in chemistry, Jossou was among a small cohort selected for a specialty master’s training program funded by the World Bank Institute and African Development Bank. The program at the African University of Science and Technology in Abuja, Nigeria, is a pan-African venture dedicated to nurturing homegrown science talent on the continent. Visiting professors from around the world taught intensive three-week courses, an experience which felt like drinking from a fire hose. The program widened Jossou’s views and he set his sights on a doctoral program with an emphasis on clean energy systems.

      A pivot to nuclear science

      While in Nigeria, Jossou learned of Professor Jerzy Szpunar at the University of Saskatchewan in Canada, who was looking for a student researcher to explore fuels and alloys for nuclear reactors. Before then, Jossou was lukewarm on nuclear energy, but the research sounded fascinating. The Fukushima, Japan, incident was recently in the rearview mirror and Jossou remembered his early determination to address his own country’s energy crisis. He was sold on the idea and graduated with a doctoral degree from the University of Saskatchewan on an international dean’s scholarship.

      Jossou’s postdoctoral work registered a brief stint at Brookhaven National Laboratory as staff scientist. He leaped at the opportunity to join MIT NSE as a way of realizing his research interest and teaching future engineers. “I would really like to conduct cutting-edge research in nuclear materials design and to pass on my knowledge to the next generation of scientists and engineers and there’s no better place to do that than at MIT,” Jossou says.

      Merging material science and computational modeling

      Jossou’s doctoral work on designing nuclear fuels for next-generation reactors forms the basis of research his lab is pursuing at MIT NSE. Nuclear reactors that were built in the 1950s and ’60s are getting a makeover in terms of improved accident tolerance. Reactors are not confined to one kind, either: We have micro reactors and are now considering ones using metallic nuclear fuels, Jossou points out. The diversity of options is enough to keep researchers busy testing materials fit for cladding, the lining that prevents corrosion of the fuel and release of radioactive fission products into the surrounding reactor coolant.

      The team is also investigating fuels that improve burn-up efficiencies, so they can last longer in the reactor. An intriguing approach has been to immobilize the gas bubbles that arise from the fission process, so they don’t grow and degrade the fuel.

      Since joining MIT in July 2023, Jossou is setting up a lab that optimizes the composition of accident-tolerant nuclear fuels. He is leaning on his materials science background and looping computer simulations and artificial intelligence in the mix.

      Computer simulations allow the researchers to narrow down the potential field of candidates, optimized for specific parameters, so they can synthesize only the most promising candidates in the lab. And AI’s predictive capabilities guide researchers on which materials composition to consider next. “We no longer depend on serendipity to choose our materials, our lab is based on rational materials design,” Jossou says, “we can rapidly design advanced nuclear fuels.”

      Advancing energy causes in Africa

      Now that he is at MIT, Jossou admits the view from the outside is different. He now harbors a different perspective on what Africa needs to address some of its challenges. “The starting point to solve our problems is not money; it needs to start with ideas,” he says, “we need to find highly skilled people who can actually solve problems.” That job involves adding economic value to the rich arrays of raw materials that the continent is blessed with. It frustrates Jossou that Niger, a country rich in raw material for uranium, has no nuclear reactors of its own. It ships most of its ore to France. “The path forward is to find a way to refine these materials in Africa and to be able to power the industries on that continent as well,” Jossou says.

      Jossou is determined to do his part to eliminate these roadblocks.

      Anchored in mentorship, Jossou’s solution aims to train talent from Africa in his own lab. He has applied for a MIT Global Experiences MISTI grant to facilitate travel and research studies for Ghanaian scientists. “The goal is to conduct research in our facility and perhaps add value to indigenous materials,” Jossou says.

      Adding value has been a consistent theme of Jossou’s career. He remembers wanting to become a neurosurgeon after reading “Gifted Hands,” moved by the personal story of the author, Ben Carson. As Jossou grew older, however, he realized that becoming a doctor wasn’t necessarily what he wanted. Instead, he was looking to add value. “What I wanted was really to take on a career that allows me to solve a societal problem.” The societal problem of clean and safe energy for all is precisely what Jossou is working on today. More

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      in Energy

      Cutting carbon emissions on the US power grid

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      To help curb climate change, the United States is working to reduce carbon emissions from all sectors of the energy economy. Much of the current effort involves electrification — switching to electric cars for transportation, electric heat pumps for home heating, and so on. But in the United States, the electric power sector already generates about a quarter of all carbon emissions. “Unless we decarbonize our electric power grids, we’ll just be shifting carbon emissions from one source to another,” says Amanda Farnsworth, a PhD candidate in chemical engineering and research assistant at the MIT Energy Initiative (MITEI).

      But decarbonizing the nation’s electric power grids will be challenging. The availability of renewable energy resources such as solar and wind varies in different regions of the country. Likewise, patterns of energy demand differ from region to region. As a result, the least-cost pathway to a decarbonized grid will differ from one region to another.

      Over the past two years, Farnsworth and Emre Gençer, a principal research scientist at MITEI, developed a power system model that would allow them to investigate the importance of regional differences — and would enable experts and laypeople alike to explore their own regions and make informed decisions about the best way to decarbonize. “With this modeling capability you can really understand regional resources and patterns of demand, and use them to do a ‘bespoke’ analysis of the least-cost approach to decarbonizing the grid in your particular region,” says Gençer.

      To demonstrate the model’s capabilities, Gençer and Farnsworth performed a series of case studies. Their analyses confirmed that strategies must be designed for specific regions and that all the costs and carbon emissions associated with manufacturing and installing solar and wind generators must be included for accurate accounting. But the analyses also yielded some unexpected insights, including a correlation between a region’s wind energy and the ease of decarbonizing, and the important role of nuclear power in decarbonizing the California grid.

      A novel model

      For many decades, researchers have been developing “capacity expansion models” to help electric utility planners tackle the problem of designing power grids that are efficient, reliable, and low-cost. More recently, many of those models also factor in the goal of reducing or eliminating carbon emissions. While those models can provide interesting insights relating to decarbonization, Gençer and Farnsworth believe they leave some gaps that need to be addressed.

      For example, most focus on conditions and needs in a single U.S. region without highlighting the unique peculiarities of their chosen area of focus. Hardly any consider the carbon emitted in fabricating and installing such “zero-carbon” technologies as wind turbines and solar panels. And finally, most of the models are challenging to use. Even experts in the field must search out and assemble various complex datasets in order to perform a study of interest.

      Gençer and Farnsworth’s capacity expansion model — called Ideal Grid, or IG — addresses those and other shortcomings. IG is built within the framework of MITEI’s Sustainable Energy System Analysis Modeling Environment (SESAME), an energy system modeling platform that Gençer and his colleagues at MITEI have been developing since 2017. SESAME models the levels of greenhouse gas emissions from multiple, interacting energy sectors in future scenarios.

      Importantly, SESAME includes both techno-economic analyses and life-cycle assessments of various electricity generation and storage technologies. It thus considers costs and emissions incurred at each stage of the life cycle (manufacture, installation, operation, and retirement) for all generators. Most capacity expansion models only account for emissions from operation of fossil fuel-powered generators. As Farnsworth notes, “While this is a good approximation for our current grid, emissions from the full life cycle of all generating technologies become non-negligible as we transition to a highly renewable grid.”

      Through its connection with SESAME, the IG model has access to data on costs and emissions associated with many technologies critical to power grid operation. To explore regional differences in the cost-optimized decarbonization strategies, the IG model also includes conditions within each region, notably details on demand profiles and resource availability.

      In one recent study, Gençer and Farnsworth selected nine of the standard North American Electric Reliability Corporation (NERC) regions. For each region, they incorporated hourly electricity demand into the IG model. Farnsworth also gathered meteorological data for the nine U.S. regions for seven years — 2007 to 2013 — and calculated hourly power output profiles for the renewable energy sources, including solar and wind, taking into account the geography-limited maximum capacity of each technology.

      The availability of wind and solar resources differs widely from region to region. To permit a quick comparison, the researchers use a measure called “annual capacity factor,” which is the ratio between the electricity produced by a generating unit in a year and the electricity that could have been produced if that unit operated continuously at full power for that year. Values for the capacity factors in the nine U.S. regions vary between 20 percent and 30 percent for solar power and for between 25 percent and 45 percent for wind.

      Calculating optimized grids for different regions

      For their first case study, Gençer and Farnsworth used the IG model to calculate cost-optimized regional grids to meet defined caps on carbon dioxide (CO2) emissions. The analyses were based on cost and emissions data for 10 technologies: nuclear, wind, solar, three types of natural gas, three types of coal, and energy storage using lithium-ion batteries. Hydroelectric was not considered in this study because there was no comprehensive study outlining potential expansion sites with their respective costs and expected power output levels.

      To make region-to-region comparisons easy, the researchers used several simplifying assumptions. Their focus was on electricity generation, so the model calculations assume the same transmission and distribution costs and efficiencies for all regions. Also, the calculations did not consider the generator fleet currently in place. The goal was to investigate what happens if each region were to start from scratch and generate an “ideal” grid.

      To begin, Gençer and Farnsworth calculated the most economic combination of technologies for each region if it limits its total carbon emissions to 100, 50, and 25 grams of CO2 per kilowatt-hour (kWh) generated. For context, the current U.S. average emissions intensity is 386 grams of CO2 emissions per kWh.

      Given the wide variation in regional demand, the researchers needed to use a new metric to normalize their results and permit a one-to-one comparison between regions. Accordingly, the model calculates the required generating capacity divided by the average demand for each region. The required capacity accounts for both the variation in demand and the inability of generating systems — particularly solar and wind — to operate at full capacity all of the time.

      The analysis was based on regional demand data for 2021 — the most recent data available. And for each region, the model calculated the cost-optimized power grid seven times, using weather data from seven years. This discussion focuses on mean values for cost and total capacity installed and also total values for coal and for natural gas, although the analysis considered three separate technologies for each fuel.

      The results of the analyses confirm that there’s a wide variation in the cost-optimized system from one region to another. Most notable is that some regions require a lot of energy storage while others don’t require any at all. The availability of wind resources turns out to play an important role, while the use of nuclear is limited: the carbon intensity of nuclear (including uranium mining and transportation) is lower than that of either solar or wind, but nuclear is the most expensive technology option, so it’s added only when necessary. Finally, the change in the CO2 emissions cap brings some interesting responses.

      Under the most lenient limit on emissions — 100 grams of CO2 per kWh — there’s no coal in the mix anywhere. It’s the first to go, in general being replaced by the lower-carbon-emitting natural gas. Texas, Central, and North Central — the regions with the most wind — don’t need energy storage, while the other six regions do. The regions with the least wind — California and the Southwest — have the highest energy storage requirements. Unlike the other regions modeled, California begins installing nuclear, even at the most lenient limit.

      As the model plays out, under the moderate cap — 50 grams of CO2 per kWh — most regions bring in nuclear power. California and the Southeast — regions with low wind capacity factors — rely on nuclear the most. In contrast, wind-rich Texas, Central, and North Central don’t incorporate nuclear yet but instead add energy storage — a less-expensive option — to their mix. There’s still a bit of natural gas everywhere, in spite of its CO2 emissions.

      Under the most restrictive cap — 25 grams of CO2 per kWh — nuclear is in the mix everywhere. The highest use of nuclear is again correlated with low wind capacity factor. Central and North Central depend on nuclear the least. All regions continue to rely on a little natural gas to keep prices from skyrocketing due to the necessary but costly nuclear component. With nuclear in the mix, the need for storage declines in most regions.

      Results of the cost analysis are also interesting. Texas, Central, and North Central all have abundant wind resources, and they can delay incorporating the costly nuclear option, so the cost of their optimized system tends to be lower than costs for the other regions. In addition, their total capacity deployment — including all sources — tends to be lower than for the other regions. California and the Southwest both rely heavily on solar, and in both regions, costs and total deployment are relatively high.

      Lessons learned

      One unexpected result is the benefit of combining solar and wind resources. The problem with relying on solar alone is obvious: “Solar energy is available only five or six hours a day, so you need to build a lot of other generating sources and abundant storage capacity,” says Gençer. But an analysis of unit-by-unit operations at an hourly resolution yielded a less-intuitive trend: While solar installations only produce power in the midday hours, wind turbines generate the most power in the nighttime hours. As a result, solar and wind power are complementary. Having both resources available is far more valuable than having either one or the other. And having both impacts the need for storage, says Gençer: “Storage really plays a role either when you’re targeting a very low carbon intensity or where your resources are mostly solar and they’re not complemented by wind.”

      Gençer notes that the target for the U.S. electricity grid is to reach net zero by 2035. But the analysis showed that reaching just 100 grams of CO2 per kWh would require at least 50 percent of system capacity to be wind and solar. “And we’re nowhere near that yet,” he says.

      Indeed, Gençer and Farnsworth’s analysis doesn’t even include a zero emissions case. Why not? As Gençer says, “We cannot reach zero.” Wind and solar are usually considered to be net zero, but that’s not true. Wind, solar, and even storage have embedded carbon emissions due to materials, manufacturing, and so on. “To go to true net zero, you’d need negative emission technologies,” explains Gençer, referring to techniques that remove carbon from the air or ocean. That observation confirms the importance of performing life-cycle assessments.

      Farnsworth voices another concern: Coal quickly disappears in all regions because natural gas is an easy substitute for coal and has lower carbon emissions. “People say they’ve decreased their carbon emissions by a lot, but most have done it by transitioning from coal to natural gas power plants,” says Farnsworth. “But with that pathway for decarbonization, you hit a wall. Once you’ve transitioned from coal to natural gas, you’ve got to do something else. You need a new strategy — a new trajectory to actually reach your decarbonization target, which most likely will involve replacing the newly installed natural gas plants.”

      Gençer makes one final point: The availability of cheap nuclear — whether fission or fusion — would completely change the picture. When the tighter caps require the use of nuclear, the cost of electricity goes up. “The impact is quite significant,” says Gençer. “When we go from 100 grams down to 25 grams of CO2 per kWh, we see a 20 percent to 30 percent increase in the cost of electricity.” If it were available, a less-expensive nuclear option would likely be included in the technology mix under more lenient caps, significantly reducing the cost of decarbonizing power grids in all regions.

      The special case of California

      In another analysis, Gençer and Farnsworth took a closer look at California. In California, about 10 percent of total demand is now met with nuclear power. Yet current power plants are scheduled for retirement very soon, and a 1976 law forbids the construction of new nuclear plants. (The state recently extended the lifetime of one nuclear plant to prevent the grid from becoming unstable.) “California is very motivated to decarbonize their grid,” says Farnsworth. “So how difficult will that be without nuclear power?”

      To find out, the researchers performed a series of analyses to investigate the challenge of decarbonizing in California with nuclear power versus without it. At 200 grams of CO2 per kWh — about a 50 percent reduction — the optimized mix and cost look the same with and without nuclear. Nuclear doesn’t appear due to its high cost. At 100 grams of CO2 per kWh — about a 75 percent reduction — nuclear does appear in the cost-optimized system, reducing the total system capacity while having little impact on the cost.

      But at 50 grams of CO2 per kWh, the ban on nuclear makes a significant difference. “Without nuclear, there’s about a 45 percent increase in total system size, which is really quite substantial,” says Farnsworth. “It’s a vastly different system, and it’s more expensive.” Indeed, the cost of electricity would increase by 7 percent.

      Going one step further, the researchers performed an analysis to determine the most decarbonized system possible in California. Without nuclear, the state could reach 40 grams of CO2 per kWh. “But when you allow for nuclear, you can get all the way down to 16 grams of CO2 per kWh,” says Farnsworth. “We found that California needs nuclear more than any other region due to its poor wind resources.”

      Impacts of a carbon tax

      One more case study examined a policy approach to incentivizing decarbonization. Instead of imposing a ceiling on carbon emissions, this strategy would tax every ton of carbon that’s emitted. Proposed taxes range from zero to $100 per ton.

      To investigate the effectiveness of different levels of carbon tax, Farnsworth and Gençer used the IG model to calculate the minimum-cost system for each region, assuming a certain cost for emitting each ton of carbon. The analyses show that a low carbon tax — just $10 per ton — significantly reduces emissions in all regions by phasing out all coal generation. In the Northwest region, for example, a carbon tax of $10 per ton decreases system emissions by 65 percent while increasing system cost by just 2.8 percent (relative to an untaxed system).

      After coal has been phased out of all regions, every increase in the carbon tax brings a slow but steady linear decrease in emissions and a linear increase in cost. But the rates of those changes vary from region to region. For example, the rate of decrease in emissions for each added tax dollar is far lower in the Central region than in the Northwest, largely due to the Central region’s already low emissions intensity without a carbon tax. Indeed, the Central region without a carbon tax has a lower emissions intensity than the Northwest region with a tax of $100 per ton.

      As Farnsworth summarizes, “A low carbon tax — just $10 per ton — is very effective in quickly incentivizing the replacement of coal with natural gas. After that, it really just incentivizes the replacement of natural gas technologies with more renewables and more energy storage.” She concludes, “If you’re looking to get rid of coal, I would recommend a carbon tax.”

      Future extensions of IG

      The researchers have already added hydroelectric to the generating options in the IG model, and they are now planning further extensions. For example, they will include additional regions for analysis, add other long-term energy storage options, and make changes that allow analyses to take into account the generating infrastructure that already exists. Also, they will use the model to examine the cost and value of interregional transmission to take advantage of the diversity of available renewable resources.

      Farnsworth emphasizes that the analyses reported here are just samples of what’s possible using the IG model. The model is a web-based tool that includes embedded data covering the whole United States, and the output from an analysis includes an easy-to-understand display of the required installations, hourly operation, and overall techno-economic analysis and life-cycle assessment results. “The user is able to go in and explore a vast number of scenarios with no data collection or pre-processing,” she says. “There’s no barrier to begin using the tool. You can just hop on and start exploring your options so you can make an informed decision about the best path forward.”

      This work was supported by the International Energy Agency Gas and Oil Technology Collaboration Program and the MIT Energy Initiative Low-Carbon Energy Centers.

      This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Energy

      Moving past the Iron Age

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      MIT graduate student Sydney Rose Johnson has never seen the steel mills in central India. She’s never toured the American Midwest’s hulking steel plants or the mini mills dotting the Mississippi River. But in the past year, she’s become more familiar with steel production than she ever imagined.

      A fourth-year dual degree MBA and PhD candidate in chemical engineering and a graduate research assistant with the MIT Energy Initiative (MITEI) as well as a 2022-23 Shell Energy Fellow, Johnson looks at ways to reduce carbon dioxide (CO2) emissions generated by industrial processes in hard-to-abate industries. Those include steel.

      Almost every aspect of infrastructure and transportation — buildings, bridges, cars, trains, mass transit — contains steel. The manufacture of steel hasn’t changed much since the Iron Age, with some steel plants in the United States and India operating almost continually for more than a century, their massive blast furnaces re-lined periodically with carbon and graphite to keep them going.

      According to the World Economic Forum, steel demand is projected to increase 30 percent by 2050, spurred in part by population growth and economic development in China, India, Africa, and Southeast Asia.

      The steel industry is among the three biggest producers of CO2 worldwide. Every ton of steel produced in 2020 emitted, on average, 1.89 tons of CO2 into the atmosphere — around 8 percent of global CO2 emissions, according to the World Steel Association.

      A combination of technical strategies and financial investments, Johnson notes, will be needed to wrestle that 8 percent figure down to something more planet-friendly.

      Johnson’s thesis focuses on modeling and analyzing ways to decarbonize steel. Using data mined from academic and industry sources, she builds models to calculate emissions, costs, and energy consumption for plant-level production.

      “I optimize steel production pathways using emission goals, industry commitments, and cost,” she says. Based on the projected growth of India’s steel industry, she applies this approach to case studies that predict outcomes for some of the country’s thousand-plus factories, which together have a production capacity of 154 million metric tons of steel. For the United States, she looks at the effect of Inflation Reduction Act (IRA) credits. The 2022 IRA provides incentives that could accelerate the steel industry’s efforts to minimize its carbon emissions.

      Johnson compares emissions and costs across different production pathways, asking questions such as: “If we start today, what would a cost-optimal production scenario look like years from now? How would it change if we added in credits? What would have to happen to cut 2005 levels of emissions in half by 2030?”

      “My goal is to gain an understanding of how current and emerging decarbonization strategies will be integrated into the industry,” Johnson says.

      Grappling with industrial problems

      Growing up in Marietta, Georgia, outside Atlanta, the closest she ever came to a plant of any kind was through her father, a chemical engineer working in logistics and procuring steel for an aerospace company, and during high school, when she spent a semester working alongside chemical engineers tweaking the pH of an anti-foaming agent.

      At Kennesaw Mountain High School, a STEM magnet program in Cobb County, students devote an entire semester of their senior year to an internship and research project.

      Johnson chose to work at Kemira Chemicals, which develops chemical solutions for water-intensive industries with a focus on pulp and paper, water treatment, and energy systems.

      “My goal was to understand why a polymer product was falling out of suspension — essentially, why it was less stable,” she recalls. She learned how to formulate a lab-scale version of the product and conduct tests to measure its viscosity and acidity. Comparing the lab-scale and regular product results revealed that acidity was an important factor. “Through conversations with my mentor, I learned this was connected with the holding conditions, which led to the product being oxidized,” she says. With the anti-foaming agent’s problem identified, steps could be taken to fix it.

      “I learned how to apply problem-solving. I got to learn more about working in an industrial environment by connecting with the team in quality control as well as with R&D and chemical engineers at the plant site,” Johnson says. “This experience confirmed I wanted to pursue engineering in college.”

      As an undergraduate at Stanford University, she learned about the different fields — biotechnology, environmental science, electrochemistry, and energy, among others — open to chemical engineers. “It seemed like a very diverse field and application range,” she says. “I was just so intrigued by the different things I saw people doing and all these different sets of issues.”

      Turning up the heat

      At MIT, she turned her attention to how certain industries can offset their detrimental effects on climate.

      “I’m interested in the impact of technology on global communities, the environment, and policy. Energy applications affect every field. My goal as a chemical engineer is to have a broad perspective on problem-solving and to find solutions that benefit as many people, especially those under-resourced, as possible,” says Johnson, who has served on the MIT Chemical Engineering Graduate Student Advisory Board, the MIT Energy and Climate Club, and is involved with diversity and inclusion initiatives.

      The steel industry, Johnson acknowledges, is not what she first imagined when she saw herself working toward mitigating climate change.

      “But now, understanding the role the material has in infrastructure development, combined with its heavy use of coal, has illuminated how the sector, along with other hard-to-abate industries, is important in the climate change conversation,” Johnson says.

      Despite the advanced age of many steel mills, some are quite energy-efficient, she notes. Yet these operations, which produce heat upwards of 3,000 degrees Fahrenheit, are still emission-intensive.

      Steel is made from iron ore, a mixture of iron, oxygen, and other minerals found on virtually every continent, with Brazil and Australia alone exporting millions of metric tons per year. Commonly based on a process dating back to the 19th century, iron is extracted from the ore through smelting — heating the ore with blast furnaces until the metal becomes spongy and its chemical components begin to break down.

      A reducing agent is needed to release the oxygen trapped in the ore, transforming it from its raw form to pure iron. That’s where most emissions come from, Johnson notes.

      “We want to reduce emissions, and we want to make a cleaner and safer environment for everyone,” she says. “It’s not just the CO2 emissions. It’s also sometimes NOx and SOx [nitrogen oxides and sulfur oxides] and air pollution particulate matter at some of these production facilities that can affect people as well.”

      In 2020, the International Energy Agency released a roadmap exploring potential technologies and strategies that would make the iron and steel sector more compatible with the agency’s vision of increased sustainability. Emission reductions can be accomplished with more modern technology, the agency suggests, or by substituting the fuels producing the immense heat needed to process ore. Traditionally, the fuels used for iron reduction have been coal and natural gas. Alternative fuels include clean hydrogen, electricity, and biomass.

      Using the MITEI Sustainable Energy System Analysis Modeling Environment (SESAME), Johnson analyzes various decarbonization strategies. She considers options such as switching fuel for furnaces to hydrogen with a little bit of natural gas or adding carbon-capture devices. The models demonstrate how effective these tactics are likely to be. The answers aren’t always encouraging.

      “Upstream emissions can determine how effective the strategies are,” Johnson says. Charcoal derived from forestry biomass seemed to be a promising alternative fuel, but her models showed that processing the charcoal for use in the blast furnace limited its effectiveness in negating emissions.

      Despite the challenges, “there are definitely ways of moving forward,” Johnson says. “It’s been an intriguing journey in terms of understanding where the industry is at. There’s still a long way to go, but it’s doable.”

      Johnson is heartened by the steel industry’s efforts to recycle scrap into new steel products and incorporate more emission-friendly technologies and practices, some of which result in significantly lower CO2 emissions than conventional production.

      A major issue is that low-carbon steel can be more than 50 percent more costly than conventionally produced steel. “There are costs associated with making the transition, but in the context of the environmental implications, I think it’s well worth it to adopt these technologies,” she says.

      After graduation, Johnson plans to continue to work in the energy field. “I definitely want to use a combination of engineering knowledge and business knowledge to work toward mitigating climate change, potentially in the startup space with clean technology or even in a policy context,” she says. “I’m interested in connecting the private and public sectors to implement measures for improving our environment and benefiting as many people as possible.” More

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      in Energy

      Generative AI for smart grid modeling

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      MIT’s Laboratory for Information and Decision Systems (LIDS) has been awarded $1,365,000 in funding from the Appalachian Regional Commission (ARC) to support its involvement with an innovative project, “Forming the Smart Grid Deployment Consortium (SGDC) and Expanding the HILLTOP+ Platform.”

      The grant was made available through ARC’s Appalachian Regional Initiative for Stronger Economies, which fosters regional economic transformation through multi-state collaboration.

      Led by Kalyan Veeramachaneni, research scientist and principal investigator at LIDS’ Data to AI Group, the project will focus on creating AI-driven generative models for customer load data. Veeramachaneni and colleagues will work alongside a team of universities and organizations led by Tennessee Tech University, including collaborators across Ohio, Pennsylvania, West Virginia, and Tennessee, to develop and deploy smart grid modeling services through the SGDC project.

      These generative models have far-reaching applications, including grid modeling and training algorithms for energy tech startups. When the models are trained on existing data, they create additional, realistic data that can augment limited datasets or stand in for sensitive ones. Stakeholders can then use these models to understand and plan for specific what-if scenarios far beyond what could be achieved with existing data alone. For example, generated data can predict the potential load on the grid if an additional 1,000 households were to adopt solar technologies, how that load might change throughout the day, and similar contingencies vital to future planning.

      The generative AI models developed by Veeramachaneni and his team will provide inputs to modeling services based on the HILLTOP+ microgrid simulation platform, originally prototyped by MIT Lincoln Laboratory. HILLTOP+ will be used to model and test new smart grid technologies in a virtual “safe space,” providing rural electric utilities with increased confidence in deploying smart grid technologies, including utility-scale battery storage. Energy tech startups will also benefit from HILLTOP+ grid modeling services, enabling them to develop and virtually test their smart grid hardware and software products for scalability and interoperability.

      The project aims to assist rural electric utilities and energy tech startups in mitigating the risks associated with deploying these new technologies. “This project is a powerful example of how generative AI can transform a sector — in this case, the energy sector,” says Veeramachaneni. “In order to be useful, generative AI technologies and their development have to be closely integrated with domain expertise. I am thrilled to be collaborating with experts in grid modeling, and working alongside them to integrate the latest and greatest from my research group and push the boundaries of these technologies.”

      “This project is testament to the power of collaboration and innovation, and we look forward to working with our collaborators to drive positive change in the energy sector,” says Satish Mahajan, principal investigator for the project at Tennessee Tech and a professor of electrical and computer engineering. Tennessee Tech’s Center for Rural Innovation director, Michael Aikens, adds, “Together, we are taking significant steps towards a more sustainable and resilient future for the Appalachian region.” More

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      in Environment

      MIT researchers remotely map crops, field by field

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      Crop maps help scientists and policymakers track global food supplies and estimate how they might shift with climate change and growing populations. But getting accurate maps of the types of crops that are grown from farm to farm often requires on-the-ground surveys that only a handful of countries have the resources to maintain.

      Now, MIT engineers have developed a method to quickly and accurately label and map crop types without requiring in-person assessments of every single farm. The team’s method uses a combination of Google Street View images, machine learning, and satellite data to automatically determine the crops grown throughout a region, from one fraction of an acre to the next. 

      The researchers used the technique to automatically generate the first nationwide crop map of Thailand — a smallholder country where small, independent farms make up the predominant form of agriculture. The team created a border-to-border map of Thailand’s four major crops — rice, cassava, sugarcane, and maize — and determined which of the four types was grown, at every 10 meters, and without gaps, across the entire country. The resulting map achieved an accuracy of 93 percent, which the researchers say is comparable to on-the-ground mapping efforts in high-income, big-farm countries.

      The team is applying their mapping technique to other countries such as India, where small farms sustain most of the population but the type of crops grown from farm to farm has historically been poorly recorded.

      “It’s a longstanding gap in knowledge about what is grown around the world,” says Sherrie Wang, the d’Arbeloff Career Development Assistant Professor in MIT’s Department of Mechanical Engineering, and the Institute for Data, Systems, and Society (IDSS). “The final goal is to understand agricultural outcomes like yield, and how to farm more sustainably. One of the key preliminary steps is to map what is even being grown — the more granularly you can map, the more questions you can answer.”

      Wang, along with MIT graduate student Jordi Laguarta Soler and Thomas Friedel of the agtech company PEAT GmbH, will present a paper detailing their mapping method later this month at the AAAI Conference on Artificial Intelligence.

      Ground truth

      Smallholder farms are often run by a single family or farmer, who subsist on the crops and livestock that they raise. It’s estimated that smallholder farms support two-thirds of the world’s rural population and produce 80 percent of the world’s food. Keeping tabs on what is grown and where is essential to tracking and forecasting food supplies around the world. But the majority of these small farms are in low to middle-income countries, where few resources are devoted to keeping track of individual farms’ crop types and yields.

      Crop mapping efforts are mainly carried out in high-income regions such as the United States and Europe, where government agricultural agencies oversee crop surveys and send assessors to farms to label crops from field to field. These “ground truth” labels are then fed into machine-learning models that make connections between the ground labels of actual crops and satellite signals of the same fields. They then label and map wider swaths of farmland that assessors don’t cover but that satellites automatically do.

      “What’s lacking in low- and middle-income countries is this ground label that we can associate with satellite signals,” Laguarta Soler says. “Getting these ground truths to train a model in the first place has been limited in most of the world.”

      The team realized that, while many developing countries do not have the resources to maintain crop surveys, they could potentially use another source of ground data: roadside imagery, captured by services such as Google Street View and Mapillary, which send cars throughout a region to take continuous 360-degree images with dashcams and rooftop cameras.

      In recent years, such services have been able to access low- and middle-income countries. While the goal of these services is not specifically to capture images of crops, the MIT team saw that they could search the roadside images to identify crops.

      Cropped image

      In their new study, the researchers worked with Google Street View (GSV) images taken throughout Thailand — a country that the service has recently imaged fairly thoroughly, and which consists predominantly of smallholder farms.

      Starting with over 200,000 GSV images randomly sampled across Thailand, the team filtered out images that depicted buildings, trees, and general vegetation. About 81,000 images were crop-related. They set aside 2,000 of these, which they sent to an agronomist, who determined and labeled each crop type by eye. They then trained a convolutional neural network to automatically generate crop labels for the other 79,000 images, using various training methods, including iNaturalist — a web-based crowdsourced  biodiversity database, and GPT-4V, a “multimodal large language model” that enables a user to input an image and ask the model to identify what the image is depicting. For each of the 81,000 images, the model generated a label of one of four crops that the image was likely depicting — rice, maize, sugarcane, or cassava.

      The researchers then paired each labeled image with the corresponding satellite data taken of the same location throughout a single growing season. These satellite data include measurements across multiple wavelengths, such as a location’s greenness and its reflectivity (which can be a sign of water). 

      “Each type of crop has a certain signature across these different bands, which changes throughout a growing season,” Laguarta Soler notes.

      The team trained a second model to make associations between a location’s satellite data and its corresponding crop label. They then used this model to process satellite data taken of the rest of the country, where crop labels were not generated or available. From the associations that the model learned, it then assigned crop labels across Thailand, generating a country-wide map of crop types, at a resolution of 10 square meters.

      This first-of-its-kind crop map included locations corresponding to the 2,000 GSV images that the researchers originally set aside, that were labeled by arborists. These human-labeled images were used to validate the map’s labels, and when the team looked to see whether the map’s labels matched the expert, “gold standard” labels, it did so 93 percent of the time.

      “In the U.S., we’re also looking at over 90 percent accuracy, whereas with previous work in India, we’ve only seen 75 percent because ground labels are limited,” Wang says. “Now we can create these labels in a cheap and automated way.”

      The researchers are moving to map crops across India, where roadside images via Google Street View and other services have recently become available.

      “There are over 150 million smallholder farmers in India,” Wang says. “India is covered in agriculture, almost wall-to-wall farms, but very small farms, and historically it’s been very difficult to create maps of India because there are very sparse ground labels.”

      The team is working to generate crop maps in India, which could be used to inform policies having to do with assessing and bolstering yields, as global temperatures and populations rise.

      “What would be interesting would be to create these maps over time,” Wang says. “Then you could start to see trends, and we can try to relate those things to anything like changes in climate and policies.” More

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      in Environment

      Researchers release open-source space debris model

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      MIT’s Astrodynamics, Space Robotics, and Controls Laboratory (ARCLab) announced the public beta release of the MIT Orbital Capacity Assessment Tool (MOCAT) during the 2023 Organization for Economic Cooperation and Development (OECD) Space Forum Workshop on Dec. 14. MOCAT enables users to model the long-term future space environment to understand growth in space debris and assess the effectiveness of debris-prevention mechanisms.

      With the escalating congestion in low Earth orbit, driven by a surge in satellite deployments, the risk of collisions and space debris proliferation is a pressing concern. Conducting thorough space environment studies is critical for developing effective strategies for fostering responsible and sustainable use of space resources. 

      MOCAT stands out among orbital modeling tools for its capability to model individual objects, diverse parameters, orbital characteristics, fragmentation scenarios, and collision probabilities. With the ability to differentiate between object categories, generalize parameters, and offer multi-fidelity computations, MOCAT emerges as a versatile and powerful tool for comprehensive space environment analysis and management.

      MOCAT is intended to provide an open-source tool to empower stakeholders including satellite operators, regulators, and members of the public to make data-driven decisions. The ARCLab team has been developing these models for the last several years, recognizing that the lack of open-source implementation of evolutionary modeling tools limits stakeholders’ ability to develop consensus on actions to help improve space sustainability. This beta release is intended to allow users to experiment with the tool and provide feedback to help guide further development.

      Richard Linares, the principal investigator for MOCAT and an MIT associate professor of aeronautics and astronautics, expresses excitement about the tool’s potential impact: “MOCAT represents a significant leap forward in orbital capacity assessment. By making it open-source and publicly available, we hope to engage the global community in advancing our understanding of satellite orbits and contributing to the sustainable use of space.”

      MOCAT consists of two main components. MOCAT-MC evaluates space environment evolution with individual trajectory simulation and Monte Carlo parameter analysis, providing both a high-level overall view for the environment and a fidelity analysis into the individual space objects evolution. MOCAT Source Sink Evolutionary Model (MOCAT-SSEM), meanwhile, uses a lower-fidelity modeling approach that can run on personal computers within seconds to minutes. MOCAT-MC and MOCAT-SSEM can be accessed separately via GitHub.

      MOCAT’s initial development has been supported by the Defense Advanced Research Projects Agency (DARPA) and NASA’s Office of Technology and Strategy.

      “We are thrilled to support this groundbreaking orbital debris modeling work and the new knowledge it created,” says Charity Weeden, associate administrator for the Office of Technology, Policy, and Strategy at NASA headquarters in Washington. “This open-source modeling tool is a public good that will advance space sustainability, improve evidence-based policy analysis, and help all users of space make better decisions.” More

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      in Environment

      Co-creating climate futures with real-time data and spatial storytelling

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      Virtual story worlds and game engines aren’t just for video games anymore. They are now tools for scientists and storytellers to digitally twin existing physical spaces and then turn them into vessels to dream up speculative climate stories and build collective designs of the future. That’s the theory and practice behind the MIT WORLDING initiative.

      Twice this year, WORLDING matched world-class climate story teams working in XR (extended reality) with relevant labs and researchers across MIT. One global group returned for a virtual gathering online in partnership with Unity for Humanity, while another met for one weekend in person, hosted at the MIT Media Lab.

      “We are witnessing the birth of an emergent field that fuses climate science, urban planning, real-time 3D engines, nonfiction storytelling, and speculative fiction, and it is all fueled by the urgency of the climate crises,” says Katerina Cizek, lead designer of the WORLDING initiative at the Co-Creation Studio of MIT Open Documentary Lab. “Interdisciplinary teams are forming and blossoming around the planet to collectively imagine and tell stories of healthy, livable worlds in virtual 3D spaces and then finding direct ways to translate that back to earth, literally.”

      At this year’s virtual version of WORLDING, five multidisciplinary teams were selected from an open call. In a week-long series of research and development gatherings, the teams met with MIT scientists, staff, fellows, students, and graduates, as well as other leading figures in the field. Guests ranged from curators at film festivals such as Sundance and Venice, climate policy specialists, and award-winning media creators to software engineers and renowned Earth and atmosphere scientists. The teams heard from MIT scholars in diverse domains, including geomorphology, urban planning as acts of democracy, and climate researchers at MIT Media Lab.

      Mapping climate data

      “We are measuring the Earth’s environment in increasingly data-driven ways. Hundreds of terabytes of data are taken every day about our planet in order to study the Earth as a holistic system, so we can address key questions about global climate change,” explains Rachel Connolly, an MIT Media Lab research scientist focused in the “Future Worlds” research theme, in a talk to the group. “Why is this important for your work and storytelling in general? Having the capacity to understand and leverage this data is critical for those who wish to design for and successfully operate in the dynamic Earth environment.”

      Making sense of billions of data points was a key theme during this year’s sessions. In another talk, Taylor Perron, an MIT professor of Earth, atmospheric and planetary sciences, shared how his team uses computational modeling combined with many other scientific processes to better understand how geology, climate, and life intertwine to shape the surfaces of Earth and other planets. His work resonated with one WORLDING team in particular, one aiming to digitally reconstruct the pre-Hispanic Lake Texcoco — where current day Mexico City is now situated — as a way to contrast and examine the region’s current water crisis.

      Democratizing the future

      While WORLDING approaches rely on rigorous science and the interrogation of large datasets, they are also founded on democratizing community-led approaches.

      MIT Department of Urban Studies and Planning graduate Lafayette Cruise MCP ’19 met with the teams to discuss how he moved his own practice as a trained urban planner to include a futurist component involving participatory methods. “I felt we were asking the same limited questions in regards to the future we were wanting to produce. We’re very limited, very constrained, as to whose values and comforts are being centered. There are so many possibilities for how the future could be.”

      Scaling to reach billions

      This work scales from the very local to massive global populations. Climate policymakers are concerned with reaching billions of people in the line of fire. “We have a goal to reach 1 billion people with climate resilience solutions,” says Nidhi Upadhyaya, deputy director at Atlantic Council’s Adrienne Arsht-Rockefeller Foundation Resilience Center. To get that reach, Upadhyaya is turning to games. “There are 3.3 billion-plus people playing video games across the world. Half of these players are women. This industry is worth $300 billion. Africa is currently among the fastest-growing gaming markets in the world, and 55 percent of the global players are in the Asia Pacific region.” She reminded the group that this conversation is about policy and how formats of mass communication can be used for policymaking, bringing about change, changing behavior, and creating empathy within audiences.

      Socially engaged game development is also connected to education at Unity Technologies, a game engine company. “We brought together our education and social impact work because we really see it as a critical flywheel for our business,” said Jessica Lindl, vice president and global head of social impact/education at Unity Technologies, in the opening talk of WORLDING. “We upscale about 900,000 students, in university and high school programs around the world, and about 800,000 adults who are actively learning and reskilling and upskilling in Unity. Ultimately resulting in our mission of the ‘world is a better place with more creators in it,’ millions of creators who reach billions of consumers — telling the world stories, and fostering a more inclusive, sustainable, and equitable world.”

      Access to these technologies is key, especially the hardware. “Accessibility has been missing in XR,” explains Reginé Gilbert, who studies and teaches accessibility and disability in user experience design at New York University. “XR is being used in artificial intelligence, assistive technology, business, retail, communications, education, empathy, entertainment, recreation, events, gaming, health, rehabilitation meetings, navigation, therapy, training, video programming, virtual assistance wayfinding, and so many other uses. This is a fun fact for folks: 97.8 percent of the world hasn’t tried VR [virtual reality] yet, actually.”

      Meanwhile, new hardware is on its way. The WORLDING group got early insights into the highly anticipated Apple Vision Pro headset, which promises to integrate many forms of XR and personal computing in one device. “They’re really pushing this kind of pass-through or mixed reality,” said Dan Miller, a Unity engineer on the poly spatial team, collaborating with Apple, who described the experience of the device as “You are viewing the real world. You’re pulling up windows, you’re interacting with content. It’s a kind of spatial computing device where you have multiple apps open, whether it’s your email client next to your messaging client with a 3D game in the middle. You’re interacting with all these things in the same space and at different times.”

      “WORLDING combines our passion for social-impact storytelling and incredible innovative storytelling,” said Paisley Smith of the Unity for Humanity Program at Unity Technologies. She added, “This is an opportunity for creators to incubate their game-changing projects and connect with experts across climate, story, and technology.”

      Meeting at MIT

      In a new in-person iteration of WORLDING this year, organizers collaborated closely with Connolly at the MIT Media Lab to co-design an in-person weekend conference Oct. 25 – Nov. 7 with 45 scholars and professionals who visualize climate data at NASA, the National Oceanic and Atmospheric Administration, planetariums, and museums across the United States.

      A participant said of the event, “An incredible workshop that had had a profound effect on my understanding of climate data storytelling and how to combine different components together for a more [holistic] solution.”

      “With this gathering under our new Future Worlds banner,” says Dava Newman, director of the MIT Media Lab and Apollo Program Professor of Astronautics chair, “the Media Lab seeks to affect human behavior and help societies everywhere to improve life here on Earth and in worlds beyond, so that all — the sentient, natural, and cosmic — worlds may flourish.” 

      “WORLDING’s virtual-only component has been our biggest strength because it has enabled a true, international cohort to gather, build, and create together. But this year, an in-person version showed broader opportunities that spatial interactivity generates — informal Q&As, physical worksheets, and larger-scale ideation, all leading to deeper trust-building,” says WORLDING producer Srushti Kamat SM ’23.

      The future and potential of WORLDING lies in the ongoing dialogue between the virtual and physical, both in the work itself and in the format of the workshops. More