Weather

Subterms

    More stories

    • 125 Shares109 Views

      in Environment

      Climate modeling confirms historical records showing rise in hurricane activity

      by

      When forecasting how storms may change in the future, it helps to know something about their past. Judging from historical records dating back to the 1850s, hurricanes in the North Atlantic have become more frequent over the last 150 years.

      However, scientists have questioned whether this upward trend is a reflection of reality, or simply an artifact of lopsided record-keeping. If 19th-century storm trackers had access to 21st-century technology, would they have recorded more storms? This inherent uncertainty has kept scientists from relying on storm records, and the patterns within them, for clues to how climate influences storms.

      A new MIT study published today in Nature Communications has used climate modeling, rather than storm records, to reconstruct the history of hurricanes and tropical cyclones around the world. The study finds that North Atlantic hurricanes have indeed increased in frequency over the last 150 years, similar to what historical records have shown.

      In particular, major hurricanes, and hurricanes in general, are more frequent today than in the past. And those that make landfall appear to have grown more powerful, carrying more destructive potential.

      Curiously, while the North Atlantic has seen an overall increase in storm activity, the same trend was not observed in the rest of the world. The study found that the frequency of tropical cyclones globally has not changed significantly in the last 150 years.

      “The evidence does point, as the original historical record did, to long-term increases in North Atlantic hurricane activity, but no significant changes in global hurricane activity,” says study author Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in MIT’s Department of Earth, Atmospheric, and Planetary Sciences. “It certainly will change the interpretation of climate’s effects on hurricanes — that it’s really the regionality of the climate, and that something happened to the North Atlantic that’s different from the rest of the globe. It may have been caused by global warming, which is not necessarily globally uniform.”

      Chance encounters

      The most comprehensive record of tropical cyclones is compiled in a database known as the International Best Track Archive for Climate Stewardship (IBTrACS). This historical record includes modern measurements from satellites and aircraft that date back to the 1940s. The database’s older records are based on reports from ships and islands that happened to be in a storm’s path. These earlier records date back to 1851, and overall the database shows an increase in North Atlantic storm activity over the last 150 years.

      “Nobody disagrees that that’s what the historical record shows,” Emanuel says. “On the other hand, most sensible people don’t really trust the historical record that far back in time.”

      Recently, scientists have used a statistical approach to identify storms that the historical record may have missed. To do so, they consulted all the digitally reconstructed shipping routes in the Atlantic over the last 150 years and mapped these routes over modern-day hurricane tracks. They then estimated the chance that a ship would encounter or entirely miss a hurricane’s presence. This analysis found a significant number of early storms were likely missed in the historical record. Accounting for these missed storms, they concluded that there was a chance that storm activity had not changed over the last 150 years.

      But Emanuel points out that hurricane paths in the 19th century may have looked different from today’s tracks. What’s more, the scientists may have missed key shipping routes in their analysis, as older routes have not yet been digitized.

      “All we know is, if there had been a change (in storm activity), it would not have been detectable, using digitized ship records,” Emanuel says “So I thought, there’s an opportunity to do better, by not using historical data at all.”

      Seeding storms

      Instead, he estimated past hurricane activity using dynamical downscaling — a technique that his group developed and has applied over the last 15 years to study climate’s effect on hurricanes. The technique starts with a coarse global climate simulation and embeds within this model a finer-resolution model that simulates features as small as hurricanes. The combined models are then fed with real-world measurements of atmospheric and ocean conditions. Emanuel then scatters the realistic simulation with hurricane “seeds” and runs the simulation forward in time to see which seeds bloom into full-blown storms.

      For the new study, Emanuel embedded a hurricane model into a climate “reanalysis” — a type of climate model that combines observations from the past with climate simulations to generate accurate reconstructions of past weather patterns and climate conditions. He used a particular subset of climate reanalyses that only accounts for observations collected from the surface — for instance from ships, which have recorded weather conditions and sea surface temperatures consistently since the 1850s, as opposed to from satellites, which only began systematic monitoring in the 1970s.

      “We chose to use this approach to avoid any artificial trends brought about by the introduction of progressively different observations,” Emanuel explains.

      He ran an embedded hurricane model on three different climate reanalyses, simulating tropical cyclones around the world over the past 150 years. Across all three models, he observed “unequivocal increases” in North Atlantic hurricane activity.

      “There’s been this quite large increase in activity in the Atlantic since the mid-19th century, which I didn’t expect to see,” Emanuel says.

      Within this overall rise in storm activity, he also observed a “hurricane drought” — a period during the 1970s and 80s when the number of yearly hurricanes momentarily dropped. This pause in storm activity can also be seen in historical records, and Emanuel’s group proposes a cause: sulfate aerosols, which were byproducts of fossil fuel combustion, likely set off a cascade of climate effects that cooled the North Atlantic and temporarily suppressed hurricane formation.

      “The general trend over the last 150 years was increasing storm activity, interrupted by this hurricane drought,” Emanuel notes. “And at this point, we’re more confident of why there was a hurricane drought than why there is an ongoing, long-term increase in activity that began in the 19th century. That is still a mystery, and it bears on the question of how global warming might affect future Atlantic hurricanes.”

      This research was supported, in part, by the National Science Foundation. More

    • 125 Shares129 Views

      in Environment

      New “risk triage” platform pinpoints compounding threats to US infrastructure

      by

      Over a 36-hour period in August, Hurricane Henri delivered record rainfall in New York City, where an aging storm-sewer system was not built to handle the deluge, resulting in street flooding. Meanwhile, an ongoing drought in California continued to overburden aquifers and extend statewide water restrictions. As climate change amplifies the frequency and intensity of extreme events in the United States and around the world, and the populations and economies they threaten grow and change, there is a critical need to make infrastructure more resilient. But how can this be done in a timely, cost-effective way?

      An emerging discipline called multi-sector dynamics (MSD) offers a promising solution. MSD homes in on compounding risks and potential tipping points across interconnected natural and human systems. Tipping points occur when these systems can no longer sustain multiple, co-evolving stresses, such as extreme events, population growth, land degradation, drinkable water shortages, air pollution, aging infrastructure, and increased human demands. MSD researchers use observations and computer models to identify key precursory indicators of such tipping points, providing decision-makers with critical information that can be applied to mitigate risks and boost resilience in infrastructure and managed resources.

      At MIT, the Joint Program on the Science and Policy of Global Change has since 2018 been developing MSD expertise and modeling tools and using them to explore compounding risks and potential tipping points in selected regions of the United States. In a two-hour webinar on Sept. 15, MIT Joint Program researchers presented an overview of the program’s MSD research tool set and its applications.  

      MSD and the risk triage platform

      “Multi-sector dynamics explores interactions and interdependencies among human and natural systems, and how these systems may adapt, interact, and co-evolve in response to short-term shocks and long-term influences and stresses,” says MIT Joint Program Deputy Director C. Adam Schlosser, noting that such analysis can reveal and quantify potential risks that would likely evade detection in siloed investigations. “These systems can experience cascading effects or failures after crossing tipping points. The real question is not just where these tipping points are in each system, but how they manifest and interact across all systems.”

      To address that question, the program’s MSD researchers have developed the MIT Socio-Environmental Triage (MST) platform, now publicly available for the first time. Focused on the continental United States, the first version of the platform analyzes present-day risks related to water, land, climate, the economy, energy, demographics, health, and infrastructure, and where these compound to create risk hot spots. It’s essentially a screening-level visualization tool that allows users to examine risks, identify hot spots when combining risks, and make decisions about how to deploy more in-depth analysis to solve complex problems at regional and local levels. For example, MST can identify hot spots for combined flood and poverty risks in the lower Mississippi River basin, and thereby alert decision-makers as to where more concentrated flood-control resources are needed.

      Successive versions of the platform will incorporate projections based on the MIT Joint Program’s Integrated Global System Modeling (IGSM) framework of how different systems and stressors may co-evolve into the future and thereby change the risk landscape. This enhanced capability could help uncover cost-effective pathways for mitigating and adapting to a wide range of environmental and economic risks.  

      MSD applications

      Five webinar presentations explored how MIT Joint Program researchers are applying the program’s risk triage platform and other MSD modeling tools to identify potential tipping points and risks in five key domains: water quality, land use, economics and energy, health, and infrastructure. 

      Joint Program Principal Research Scientist Xiang Gao described her efforts to apply a high-resolution U.S. water-quality model to calculate a location-specific, water-quality index over more than 2,000 river basins in the country. By accounting for interactions among climate, agriculture, and socioeconomic systems, various water-quality measures can be obtained ranging from nitrate and phosphate levels to phytoplankton concentrations. This modeling approach advances a unique capability to identify potential water-quality risk hot spots for freshwater resources.

      Joint Program Research Scientist Angelo Gurgel discussed his MSD-based analysis of how climate change, population growth, changing diets, crop-yield improvements and other forces that drive land-use change at the global level may ultimately impact how land is used in the United States. Drawing upon national observational data and the IGSM framework, the analysis shows that while current U.S. land-use trends are projected to persist or intensify between now and 2050, there is no evidence of any concerning tipping points arising throughout this period.  

      MIT Joint Program Research Scientist Jennifer Morris presented several examples of how the risk triage platform can be used to combine existing U.S. datasets and the IGSM framework to assess energy and economic risks at the regional level. For example, by aggregating separate data streams on fossil-fuel employment and poverty, one can target selected counties for clean energy job training programs as the nation moves toward a low-carbon future. 

      “Our modeling and risk triage frameworks can provide pictures of current and projected future economic and energy landscapes,” says Morris. “They can also highlight interactions among different human, built, and natural systems, including compounding risks that occur in the same location.”  

      MIT Joint Program research affiliate Sebastian Eastham, a research scientist at the MIT Laboratory for Aviation and the Environment, described an MSD approach to the study of air pollution and public health. Linking the IGSM with an atmospheric chemistry model, Eastham ultimately aims to better understand where the greatest health risks are in the United States and how they may compound throughout this century under different policy scenarios. Using the risk triage tool to combine current risk metrics for air quality and poverty in a selected county based on current population and air-quality data, he showed how one can rapidly identify cardiovascular and other air-pollution-induced disease risk hot spots.

      Finally, MIT Joint Program research affiliate Alyssa McCluskey, a lecturer at the University of Colorado at Boulder, showed how the risk triage tool can be used to pinpoint potential risks to roadways, waterways, and power distribution lines from flooding, extreme temperatures, population growth, and other stressors. In addition, McCluskey described how transportation and energy infrastructure development and expansion can threaten critical wildlife habitats.

      Enabling comprehensive, location-specific analyses of risks and hot spots within and among multiple domains, the Joint Program’s MSD modeling tools can be used to inform policymaking and investment from the municipal to the global level.

      “MSD takes on the challenge of linking human, natural, and infrastructure systems in order to inform risk analysis and decision-making,” says Schlosser. “Through our risk triage platform and other MSD models, we plan to assess important interactions and tipping points, and to provide foresight that supports action toward a sustainable, resilient, and prosperous world.”

      This research is funded by the U.S. Department of Energy’s Office of Science as an ongoing project. More

    • 88 Shares179 Views

      in Environment

      Research collaboration puts climate-resilient crops in sight

      by

      Any houseplant owner knows that changes in the amount of water or sunlight a plant receives can put it under immense stress. A dying plant brings certain disappointment to anyone with a green thumb. 

      But for farmers who make their living by successfully growing plants, and whose crops may nourish hundreds or thousands of people, the devastation of failing flora is that much greater. As climate change is poised to cause increasingly unpredictable weather patterns globally, crops may be subject to more extreme environmental conditions like droughts, fluctuating temperatures, floods, and wildfire. 

      Climate scientists and food systems researchers worry about the stress climate change may put on crops, and on global food security. In an ambitious interdisciplinary project funded by the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), David Des Marais, the Gale Assistant Professor in the Department of Civil and Environmental Engineering at MIT, and Caroline Uhler, an associate professor in the MIT Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, are investigating how plant genes communicate with one another under stress. Their research results can be used to breed plants more resilient to climate change.

      Crops in trouble

      Governing plants’ responses to environmental stress are gene regulatory networks, or GRNs, which guide the development and behaviors of living things. A GRN may be comprised of thousands of genes and proteins that all communicate with one another. GRNs help a particular cell, tissue, or organism respond to environmental changes by signaling certain genes to turn their expression on or off.

      Even seemingly minor or short-term changes in weather patterns can have large effects on crop yield and food security. An environmental trigger, like a lack of water during a crucial phase of plant development, can turn a gene on or off, and is likely to affect many others in the GRN. For example, without water, a gene enabling photosynthesis may switch off. This can create a domino effect, where the genes that rely on those regulating photosynthesis are silenced, and the cycle continues. As a result, when photosynthesis is halted, the plant may experience other detrimental side effects, like no longer being able to reproduce or defend against pathogens. The chain reaction could even kill a plant before it has the chance to be revived by a big rain.

      Des Marais says he wishes there was a way to stop those genes from completely shutting off in such a situation. To do that, scientists would need to better understand how exactly gene networks respond to different environmental triggers. Bringing light to this molecular process is exactly what he aims to do in this collaborative research effort.

      Solving complex problems across disciplines

      Despite their crucial importance, GRNs are difficult to study because of how complex and interconnected they are. Usually, to understand how a particular gene is affecting others, biologists must silence one gene and see how the others in the network respond. 

      For years, scientists have aspired to an algorithm that could synthesize the massive amount of information contained in GRNs to “identify correct regulatory relationships among genes,” according to a 2019 article in the Encyclopedia of Bioinformatics and Computational Biology. 

      “A GRN can be seen as a large causal network, and understanding the effects that silencing one gene has on all other genes requires understanding the causal relationships among the genes,” says Uhler. “These are exactly the kinds of algorithms my group develops.”

      Des Marais and Uhler’s project aims to unravel these complex communication networks and discover how to breed crops that are more resilient to the increased droughts, flooding, and erratic weather patterns that climate change is already causing globally.

      In addition to climate change, by 2050, the world will demand 70 percent more food to feed a booming population. “Food systems challenges cannot be addressed individually in disciplinary or topic area silos,” says Greg Sixt, J-WAFS’ research manager for climate and food systems. “They must be addressed in a systems context that reflects the interconnected nature of the food system.”

      Des Marais’ background is in biology, and Uhler’s in statistics. “Dave’s project with Caroline was essentially experimental,” says Renee J. Robins, J-WAFS’ executive director. “This kind of exploratory research is exactly what the J-WAFS seed grant program is for.”

      Getting inside gene regulatory networks

      Des Marais and Uhler’s work begins in a windowless basement on MIT’s campus, where 300 genetically identical Brachypodium distachyon plants grow in large, temperature-controlled chambers. The plant, which contains more than 30,000 genes, is a good model for studying important cereal crops like wheat, barley, maize, and millet. For three weeks, all plants receive the same temperature, humidity, light, and water. Then, half are slowly tapered off water, simulating drought-like conditions.

      Six days into the forced drought, the plants are clearly suffering. Des Marais’ PhD student Jie Yun takes tissues from 50 hydrated and 50 dry plants, freezes them in liquid nitrogen to immediately halt metabolic activity, grinds them up into a fine powder, and chemically separates the genetic material. The genes from all 100 samples are then sequenced at a lab across the street.

      The team is left with a spreadsheet listing the 30,000 genes found in each of the 100 plants at the moment they were frozen, and how many copies there were. Uhler’s PhD student Anastasiya Belyaeva inputs the massive spreadsheet into the computer program she developed and runs her novel algorithm. Within a few hours, the group can see which genes were most active in one condition over another, how the genes were communicating, and which were causing changes in others. 

      The methodology captures important subtleties that could allow researchers to eventually alter gene pathways and breed more resilient crops. “When you expose a plant to drought stress, it’s not like there’s some canonical response,” Des Marais says. “There’s lots of things going on. It’s turning this physiologic process up, this one down, this one didn’t exist before, and now suddenly is turned on.” 

      In addition to Des Marais and Uhler’s research, J-WAFS has funded projects in food and water from researchers in 29 departments across all five MIT schools as well as the MIT Schwarzman College of Computing. J-WAFS seed grants typically fund seven to eight new projects every year.

      “The grants are really aimed at catalyzing new ideas, providing the sort of support [for MIT researchers] to be pushing boundaries, and also bringing in faculty who may have some interesting ideas that they haven’t yet applied to water or food concerns,” Robins says. “It’s an avenue for researchers all over the Institute to apply their ideas to water and food.”

      Alison Gold is a student in MIT’s Graduate Program in Science Writing. More