Last spring, MIT research scientist C. Adam Schlosser, who serves as deputy director of the MIT Joint Program on the Science and Policy of Global Change, and colleagues published a paper in the journal PLOS One that projected a “high risk of severe water stress” in much of Asia by midcentury. Attributing the projection to rising demands driven by population and economic growth and exacerbated by climate change, they estimated that within 35 years, 1 billion more people in the area would be affected. The region in question is home to about half of the global population, so this finding matters. News outlets from the Christian Science Monitor to TIME picked up the story, disseminating it to millions of potential readers.
“The response to this study illustrates the kind of scientific finding that makes people — including decision-makers and other stakeholders — listen and react,” says Schlosser. “We presented not only the science but also its potential impact on people’s lives. That’s a hallmark of the Joint Program.”
Assessing risk
So, too, is a methodology that underlies not only the Asia water-stress study but much of Schlosser’s research: the practice of running a computer model multiple times under varying assumptions (e.g., about the climate, population growth, or economic growth) to produce an exhaustive range of plausible future scenarios for a particular aspect of global change — such as water availability — and qualify each scenario with a level of uncertainty. In the vernacular, this is known as the application of Monte Carlo methods. By “rolling the dice” hundreds to thousands of times under different assumptions about Earth and human systems, Schlosser and colleagues can determine the odds of outcomes that policymakers are either targeting or trying to prevent. This information can then help guide decision-makers on how best to “weight the dice” to minimize risk to lives and infrastructure.
“The challenge with addressing and quantifying risks is to identify the bounds of your knowledge and everything in between, and then to simulate that environment with computer models,” notes Schlosser. “That demands that we not only use models in creative ways but also bring to bear observations that can help us isolate meaningful signals in the results we obtain from those models.”
Applying Monte Carlo methods to the Joint Program’s Integrated Global System Modeling (IGSM) framework to simulate the response of Earth and human systems to global change and assess risks that may lie ahead in the coming decades, Schlosser is now working to identify potential threats to regional water supplies and ecosystems, optimal locations for renewable energy generation around the globe, and trends in extreme events and their potential impact on the built environment.
Charting the future of water supplies, renewable energy, and the grid
Having recently upgraded the Water Resource Systems (WRS) model used in the Asia water-stress study — an extension of the IGSM framework — to more precisely represent water-demand sectors (regional watersheds) and the quality of water within them, Schlosser aims to simulate a large number of plausible futures for the U.S. water supply. The goal of his research team is to pinpoint any significant threats to the water system and project when water availability may become severely stressed by changes in the agricultural, energy, industrial, and other sectors of the economy.
Over the next two years, he plans to explore the range of risks that different climate pathways pose for the U.S. water system, and how those risks may be avoided through mitigation or adaptation measures, such as efficiency improvements in water use (e.g. irrigation) and transport. He also aims to account for the uncertainty in runoff changes that occur under climate change, and their impact on risks to water demand sectors.
Another key research objective of Schlosser’s is to determine how regional patterns of precipitation and temperature will impact the deployment of renewable energy technologies such as wind turbines and photovoltaics. As the world shifts away from fossil fuels and toward lower-carbon energy sources, it will become increasingly important to identify the prime locations where wind and solar power can thrive. By enhancing the IGSM framework to generate multiple simulations of wind and clouds on a regional basis, Schlosser aims to provide policymakers with more precise estimates of the times and locations at which wind and solar energy resources will be plentiful and reliable.
“In a world where wind and solar farm installations are ubiquitous, it would be very beneficial if the science of climate predictability could tell when and where those fundamentally intermittent resources are the most reliable without constantly relying on backup technologies which are the very same greenhouse gas-emitting technologies we’re trying to avoid in the first place,” he says.
Schlosser is also applying Monte Carlo methods to assess the risk to infrastructure posed by extreme weather events that range from storms to heatwaves. He and colleagues first developed a technique that draws upon the Joint Program’s climate model and those used by the institutions that have participated in the Intergovernmental Panel on Climate Change (IPCC) to explore how precipitation extremes shift under various climate policies — and which policies are likely to minimize the likelihood of shifts in extreme precipitation events that threaten infrastructure and livelihoods.
In a pilot project conducted in collaboration with the MIT Lincoln Laboratory, they next looked at how human-induced changes in climate affect the occurrence of heatwaves that could damage expensive transformers that are critical to the functioning of the electric power grid in the U.S. Northeast. The next step is to expand this analysis and evaluate the grid more comprehensively, so as to provide actionable information for how to make the grid more stable, reliable, and environmentally responsible.
“Our approach shrinks down the range of possible outcomes,” says Schlosser. “We’ll never be able to completely eliminate all uncertainty, but there are opportunities to constrain the uncertainty and give people an outlook of the future that we can act upon.”
Yearning for winter
Schlosser came to this work out of a love for snow. Growing up in Rhode Island, he lived for snow days, when he could trade reading, writing, and arithmetic for sledding, skating, and skiing. Over the years, as climate change emerged as a global threat, his affinity for winter storms and activities fueled a growing concern about how winter would change on a warmer planet. That led to an interest in hydrology: Studies of hydrology in graduate school at the University of Maryland, where he received a PhD in meteorology, deepened his focus on winter processes and raised his awareness about the challenges in representing hydrology in climate or earth system models.
After completing postgraduate work in climate predictability at NOAA’s Geophysical Fluid Dynamics Laboratory and further research at the Center for Ocean Land Atmosphere Studies, he served as a research scientist at the NASA Goddard Spaceflight Center, where he developed an ongoing program, the NASA Energy and Water Cycle Study, that uses multiple observations to generate a comprehensive picture of the global water and energy cycle. While Schlosser’s work at Goddard nurtured his scientific curiosity, there was something missing that he would find in his next position at the Joint Program, and keep him here for 12 years and counting.
“Throughout my career, my research has been personally compelling from a scientific discovery standpoint, but there’s nothing like advancing science that can make a substantive contribution to decision-making, strategic planning, and policy formation concerning critical global challenges,” he says. “I never had an appreciation for that until I came here.”
A version of this article originally appeared in the Fall 2016 issue of Global Changes, a triennial publication of the MIT Joint Program on the Science and Policy of Global Change.
