Meet graduate students geophysicist Ekaterina Bolotskaya, planetary scientist Zhuchang Zhan, geobiologist Jeemin Rhim, and climate modeler Tristan Abbot.
Throughout my life I found rocks fascinating. It’s no surprise that now, as a first-year PhD student in geophysics at MIT, I’m dealing with…rocks!
I have been working with Professor Bradford Hager on the theory of fracturing and friction, explaining the origin and the features of faults’ motion through finite element simulations of shear faulting. I have also been studying fracture mechanisms experimentally with Professor Brian Evans, exploring fracture transmissivity for fractures of different roughness using different fluids with Solnhofen limestone samples. Our first experiment tested argon gas. We cored and polished the sample, created an artificial fracture with a certain roughness, attached all the sensors (it was really exciting to recall my soldering skills) and operated the triaxial loading machine. I really enjoyed the process and now I’m moving to data processing and coming up with some initial results.
I also spent this past summer at Shell Technology Center in Bangalore, working on Groningen gas field subsidence data and associated induced seismicity, possibly occurring
as a result of gas production. The measurements were made using InSAR Persistent Scatterer technique for multiple points (~500,000). My responsibility was to filter the points based on the properties of the underlying surface—e.g. if it’s part of a building, or a road, or a forest using the data exported from Open Street Map—and assign the relative error to the respective measurement groups.
Image: In the lab, Ekaterina Bolotskaya solders sensors onto a rock sample to better understand the mechanisms behind fault fractures.
In just two decades, the study of exoplanets has blossomed into a prominent field in planetary science—and one prime goal is to answer whether life exists beyond the solar system. My advisor, Sara Seager, and her team are confident that the search for biosignature gases in future observations of exoplanetary atmospheres will be the key. But which gases should scientists be looking for?
While a few biosignature gases are prominent in Earth’s atmospheric spectrum (O₂, CH₄, N₂O as familiar examples), life on Earth is known to produce thousands of different gases. Scientists theorize that some may be able to accumulate at similar or higher levels (e.g., dimethyl sulfide and CH₃Cl) in exo-Earths’ atmospheres, depending on their ecology and surface and atmospheric chemistry. To maximize the chance of recognizing signs of life, we take the approach that all stable and potentially volatile molecules should initially be considered as viable biosignature gases.
Starting with a spectra database of a much wider range of volatile molecules than would typically be included, we then eliminate gases that are not detectable in the context of transit spectroscopy. Then, for the promising molecules, we construct atmospheric simulation models to explore their detectability under a variety of atmospheric scenarios and biochemistries. The complexity of the models and the multiplicity of atmospheric spectra we are producing have also led us to begin employing machine learning methods to explore the parameters in which a gas can be detectable.
Image: Using complex computer models, Zhuchang Zhan tries to simulate all atmospheric scenarios which might allow biosignature gases to be detected on faraway planets.
Methane is an important energy source, a potent global warming agent, and a potential biosignature in sub-surface and extraterrestrial environments. Accurately identifying the sources of methane is important in many different fields. One widely used geochemical tracer for this purpose is the bulk isotopic composition of methane (i.e. the ratios of heavy to light carbon and hydrogen atoms). We can make inferences about the processes that produced methane by quantifying how “heavy” or “light” the total populations of carbon and hydrogen atoms are in a sample. A recently-emerged tracer is clumped isotopologue abundance. Clumped isotopologues are rare types of molecules that have more than one heavy atom in one molecule (e.g. ¹³CH₃D for methane). By measuring how abundant these rare, “extra heavy” molecules are, we can track the temperature at which a sample of methane formed or last equilibrated with the environment.
Our interpretation of these geochemical tracers heavily depends on empirical observations; we can reproduce methane-forming processes in the laboratory and constrain the range of ensuing isotopic compositions. However, one major outstanding question is that methane produced via the same process in the laboratory often does not reproduce the isotopic signatures observed in nature. I am specifically focusing on unraveling the apparent discrepancy between microbial methane from laboratory cultures and from natural environments. By combining culturing and analytical techniques, I aim to better understand the correlation between microbial methanogenic processes and the resulting isotopic signatures under different and environmentally relevant conditions. This will help us better constrain the origin of methane, as microbial methanogenesis is the greatest natural source of methane. I greatly enjoy the aspect of applying principles that govern at atomic and microscopic levels to investigate larger-scale geochemical processes.
Image: Working in the Bosak and Ono Labs, Jeemin Rhim seeks to understand how the effects of biological and non-biological processes manifest in the geochemical signatures of methane.
Global climate models, our primary tool for predicting the effects of climate change, have a hard time predicting future changes in clouds and rainfall. Because we don’t have the computer power to simulate every cloud and every raindrop on the planet, climate models have to represent them in a simplified way. These simplifications—called parameterizations by climate scientists—lead to uncertainties in everything from how much warming greenhouse gas emissions will cause to how climate change will affect the distribution and intensity of precipitation.
Part of my research tries to address some of these uncertainties by using a different type of computational model. The models I use, which are called cloud-resolving models, are missing a lot of the components of a global climate model. They don’t, for example, represent global ocean circulations or changes in sea ice coverage, and they usually only simulate a limited part of Earth’s atmosphere. Although this might seem like a disadvantage, it allows them to spend a lot of computing power on the cloud and precipitation processes that global climate models struggle with.
Updrafts inside of individual clouds are one of the important processes that I can look at using cloud-resolving models. Because these updrafts lift moist air and allow the moisture to condense, their speed plays an important role in determining rainfall intensities. By running cloud-resolving model experiments that vary things like sea surface temperatures, atmospheric humidity, and the separation between clouds, I’m working on understanding how changes in Earth’s climate are likely to affect updraft speeds and, in turn, rainfall rates. Comparing these results with climate model runs will improve our best guess of how rainfall is likely to change in the future and might suggest better ways to parameterize clouds and precipitation in global climate models.
Image: Tristan Abbot is using a new approach to modeling cloud behavior in order to better understand how changes in climate might affect future rainfall—with implications for building better future global models.
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