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Particles matter, especially when airborne. Whether emitted from artificial sources such as power plants and internal combustion engines, or natural sources such as volcanic eruptions, airborne particulates or aerosols not only impact human health but also the global climate. The more particles emitted into the atmosphere, the more water droplets are likely to form around those particles inside clouds. Clouds with more droplets are thicker and brighter, so they reflect more solar radiation, thereby cooling the climate system in a process called the aerosol indirect effect. While much is known about the physics of how aerosols impact cloud formation, it’s hard to measure just how big a role the indirect effect plays in offsetting global warming. Today’s estimates of the magnitude of the indirect effect are highly uncertain; it may well have masked as much as 80 percent of warming during the 20th century due to carbon dioxide (CO2) emissions alone.

Lowering that uncertainty will become critical throughout this century if more and more countries significantly reduce their greenhouse gas (GHG) emissions in pursuit of the Paris Agreement’s goal of keeping the rise in global mean surface temperature since preindustrial times to well below 2 degrees Celsius. Any major cut in airborne particulates will reduce the indirect cooling effect considerably, resulting in additional warming that climate models will need to estimate as precisely as possible.

To help meet this challenge, Daniel Rothenberg, who until recently served as a research assistant at the MIT Joint Program on the Science and Policy of Global Change and a PhD student and postdoctoral fellow in the Department of Earth, Atmospheric and Planetary Sciences, has developed concepts and software aimed at reducing the uncertainty in the magnitude of the indirect effect.

Key to achieving this goal is to more precisely represent aerosol activation, the process by which aerosols form water droplets in clouds, in climate models. During this process, individual aerosol particles becomes cloud droplets, the building blocks out of which clouds are formed. Just as dew condenses on grass and leaves on a cold morning, water vapor in the atmosphere condenses onto airborne particulates. Now, in a new study in the journal Atmospheric Chemistry and Physics, Rothenberg—in collaboration with his PhD advisor, Joint Program Senior Research Scientist Chien Wang, and Alexander Avramov of Emory University (a former postdoctoral associate at the MIT Center for Global Change Science)—advances a method that represents aerosol activation with far greater accuracy and computational efficiency than existing approaches.  

The ability to separate out a distinct signal from ambient noise in reams of scientific data is critical to detecting a meaningful trend or turning point in the data. That’s especially true when it comes to identifying signals of improving or declining air quality trends, whose magnitude can be smaller than that of underlying natural variations or cycles in chemical, meteorological and climatological conditions. This discrepancy makes it challenging to track how concentrations of surface air pollutants such as ozone change as a result of policies or other causes within a particular geographical region or timeframe.

A case in point is any attempt to estimate whether there has been any change in summertime mean ozone concentration over the Northeastern U.S., which can vary from place to place as well as from year to year. To obtain a reliable estimate in the most computationally efficient manner, one would need to know the minimum geographical area required to capture the full range of localized ozone concentrations, as well as the minimum number of years to sample to rule out short-term natural variability of atmospheric conditions—such as an abnormally hot summer or El Niño year—that may skew the numbers.

Now a team of researchers from the MIT Joint Program on the Science and Policy of Global Change and collaborating institutions has developed a method to optimize air quality signal detection capability over much of the continental U.S. by applying a strategic combination of spatial and temporal averaging scales. Presented in a study in the journal Atmospheric Chemistry and Physics, the method could improve researchers’ and policymakers’ understanding of air quality trends and their ability to evaluate the efficacy of existing and proposed emissions-reduction policies.

Even “modest” action to limit climate change could help prevent the most extreme water-shortage scenarios facing Asia by the year 2050, according to a new study led by MIT researchers.

The study takes an inventive approach to modeling the effects of both climate change and economic growth on the world’s most heavily populated continent. Roughly 60 percent of the global population lives in Asia, often with limited access to water: There is less than half the amount of freshwater available per inhabitant in Asia, compared to the global average.

The ability to separate out a distinct signal from ambient noise in reams of scientific data is critical to detecting a meaningful trend or turning point in the data. That’s especially true when it comes to identifying signals of improving or declining air quality trends, whose magnitude can be smaller than that of underlying natural variations or cycles in chemical, meteorological and climatological conditions.

Peter Dizikes | MIT News Office 
June 18, 2018

Air pollution has smothered China’s cities in recent decades. In response, the Chinese government has implemented measures to clean up its skies. But are those policies effective? Now an innovative study co-authored by an MIT scholar shows that one of China’s key antipollution laws is indeed working — but unevenly, with one particular set of polluters most readily adapting to it.

When the Paris Agreement was launched in 2015, nearly 200 nations pledged to enact and continually strengthen policies aimed at keeping the rise in global average surface temperature since pre-industrial times to well below two degrees Celsius. Meeting that ambitious goal will require a dramatic decarbonization of the world’s energy system over the course of the 21st century. Critical to this collective effort will be the deployment of low-carbon energy sources at a very large scale.

Particles matter, especially when airborne. Whether emitted from artificial sources such as power plants and internal combustion engines, or natural sources such as volcanic eruptions, airborne particulates or aerosols not only impact human health but also the global climate. The more particles emitted into the atmosphere, the more water droplets are likely to form around those particles inside clouds. Clouds with more droplets are thicker and brighter, so they reflect more solar radiation, thereby cooling the climate system in a process called the aerosol indirect effect.

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