What combinations of low- and high-frequency ocean-atmopshere variability generate droughts? Our work aims to understand the forcings of hydroclimatic variability using paleoclimate data, instrumental observations, and models. As part of this, we investigate and diagnose the drivers of historical hydroclimate changes and the consequences for terrestrial climate and hydrology — from low-frequency modes of internal variability to vegetation-atmosphere interactions. Current projects include examination of modes of variability and the land surface in shaping historical drought events and the drivers of persistent drought risks in forced versus unforced climates.
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How well do models simulate the real-world Earth system processes we care about and what are the model schemes responsible for structural uncertainties in such processes? There is a high degree of uncertainty in future climate changes, for example, due to the range of scales required to simulate things like precipitation and turbulent fluxes in models and the uncertainty inherent in a complex climate. Our work aims to understand the sources of model response uncertainty and constrain such uncertainty using data-model comparisons to improve model fidelity (and the prediction of the range of climate outcomes this coming century). Current projects include the role of land surface schemes and assumptions (pariticualry vegetation and snow) on hydroclimate and hydrology.
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Translating the range of outcomes in physical climate impacts is insufficient to understand what such impacts imply for people and the systems they value. For example, snow is projected to melt in a warmer world, but the human impacts of reduced snowpack depends on where and how people use snow. The aim of this work is to incorporate other sciences, both social and natural, to translate physical climate impacts into impacts on people and ecosystems. Current projects include an examination of the risks of declines in future water availability given human water demands and reconciling top-down and bottom-up approaches to identifing climate-driven impacts on agriculture.
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We know that model-simulated internal variability is sufficient to mask, amplify, or reverse the direction of anthropogenically-forced trends in temperature, circulation, and precipitation at large spatial and temporal scales, complicating adaptation decisions. Characterizing the most likely climate outcome is not sufficient for planning. Rather, quantifying the full extent of outcomes from internal variability under global warming is key to enable robust adaptation in the face of uncertain climate threats. Current projects include identification of the time of emergence and distribution of benefits of adaptations.
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Christopher Callahan is a PhD student in Dartmouth's Ecology, Evolution, Ecosystems, and Society (EEES) program. His research focuses on the possible impacts of climate change on human and natural systems and the uncertainties associated with projecting those impacts.
He's from the suburbs of Chicago, and he graduated from Northwestern University in 2018 with a BA in Environmental Science, where he also led Northwestern's award-winning debate team and collaborated with researchers at the University of Chicago.
Ethan Coffel is a Neukom Postdoctoral Fellow in the Climate Modeling & Impacts Group. Ethan studies how climate change is affecting extreme weather and how to quantify the impacts of these changes on human societies and natural ecosystems. He strives to understand the physical mechanisms driving changes in the climate and to clearly communicate climate change projections and their uncertainty.
Alex Gottlieb is a PhD student in Dartmouth's Ecology, Evolution, Ecosystems, and Society (EEES) program. He is interested in researching feedback loops between climate and food systems, as well as how individual actors and broader societies adapt to ensure their food security in the face of climate variability.
He's from Pittsburgh, Pennsylvania and graduated from Princeton University in 2018 with a BA in Politics and certificates in Computer Science, Statistics and Machine Learning.
Yaqian He is a Postdoctoral Research Fellow in the Climate Modeling & Impacts Group. She uses remote sensing, statistics, and climate science to explore the effects of human-induced land use and land cover changes on monsoon climates, including the West Africa monsoon and the East Asia monsoon. In particular, she applies remote sensing to classify the land surface and statistical and climate models to examine the underlying physical mechanisms.
Yaqian holds a Bachelors in Survey Engineering from China University of Mining and Technology, an M.S. in Cartography and GIS from the School of Geography at Beijing Normal University, China, and a PhD from the Climatology Lab in the Department of Geography at West Virginia University.
Justin Mankin is a climate scientist, assistant professor in the Department of Geography at Dartmouth College, and PI of the Dartmouth Climate Modeling & Impacts Group. He also holds courtesy appointments in the Department of Earth Sciences (EARS) and the Ecology, Evolution, Environment, & Society (EEES) graduate program, and is an Adjunct Research Scientist in the Division of Ocean & Climate Physics at Lamont-Doherty Earth Observatory of Columbia University. His interdisciplinary research constrains the uncertainty essential to understanding and responding to climate change’s impacts on people and ecosystems.
His previous career was as an intelligence officer working in South Asia and the Middle East. He's from Vermont, did his postdoctoral training at Lamont-Doherty Earth Observatory of Columbia University and NASA's Goddard Institute for Space Studies, and holds degrees from Columbia (BA, MPA), the London School of Economics (MSc), and Stanford (PhD).
If you are a Dartmouth undergraduate student, a geography department honors thesis student, or interested in working with the group on climate research send Justin an email briefly outlining your research interests and we can set up a time to discuss research projects beginning in the fall of 2018.