Climate+

Climate+ is a new vertical offered within Duke University’s Data+ program, a full-time, ten-week summer research experience that welcomes Duke undergraduate and masters students interested in exploring new data-driven approaches to interdisciplinary challenges.

Climate+ is aligned with Duke University’s commitment to advancing interdisciplinary understanding of the causes and societal impacts of climate change as well as potential solutions for long-term sustainability, including climate change mitigation and adaptation strategies.

In addition to experiencing all the general educational benefits of the Data+ program, Climate+ students will form a subcohort that engages regularly with climate, environment, and energy researchers and practitioners. Like the broader Data+ program, each Climate+ project team will consist of at most three undergraduates and one graduate student, who will work in a communal environment to learn how to marshal, analyze, and visualize data. Graduate students (including master’s and PhD students) typically serve as project managers, helping their teams stay on track with deliverables and timeline; their compensation may vary.

Climate + is offered by the Rhodes Information Initiative at Duke in partnership with the newly merged Nicholas Institute for Environmental Policy Solutions and Duke University Energy Initiative.

Applications Now Open! 

Browse the current Climate+ projects to find opportunities.

Projects

A team of students led by researchers at the Duke Marine Lab will explore the changing distribution of krill around the Antarctic Peninsula. Krill are a key prey species in this ecosystem, supporting a number of animals including whales, seals, and penguins, but they are dependent on winter sea ice and may be in trouble as climate change progresses. Using data from acoustic zooplankton surveys, students will create maps and other products to visualize the spatial distribution of krill over the past 20 summers, then create metrics that allow us to quantify the way that krill distribution around the Antarctic Peninsula is changing as the climate shifts and ice melts. These results will be key to our understanding of the impacts of climate change on this polar ecosystem.

 

Project Lead: Douglas Nowacek

Project Manager: Amanda Lohmann

 

This project is also part of Duke’s first Climate+ cohort

A team of students led by researchers at Duke and abroad will develop and evaluate machine learning solutions to model behavioral patterns of electric use, emphasizing data privacy. Data collected in different parts of the world will be analyzed to understand the electric patterns that characterize various appliances and how that information can model users' consumption profiles and prevent fraud. 

 

Project Lead: J. Matias Di Martino

This project is also part of Duke’s first Climate+ cohort

A student team working with the Energy Data Analytics Lab will work to democratize access to data relevant to climate change mitigation and adaptation planning as well as the underlying models to acquire those data. This project will work towards building the first “foundation model” specifically for remote sensing imagery for the purpose of extracting climate change relevant content at scale to enable near real-time tracking of climate causes and impacts.  A foundation model is a model (usually a deep neural network) that has been trained on a large and diverse set of data, after which it can be adapted to a variety of different (but related) inference tasks with a small fraction additional training data and computation. Leveraging recent developments in self-supervised learning, we will develop the dataset for creating this foundation model and begin training it on real-world data. A model developed using such a dataset will enhance climate change mitigation/adaptation monitoring and planning through developing robust features that can be used to monitor a broad range of climate change contributing factors (e.g. energy infrastructure and use, agricultural activity) and impacts (e.g. economic impacts and human migration) for informing climate mitigation and adaptation strategies.

 

Project Lead: Kyle Bradbury

This project is also part of Duke’s first Climate+ cohort

A team of students led by researchers in the Hydroclimatological Lab will comprehensively quantify the wetland carbon emissions in the entire Southeast (SE) US using machine learning techniques and various climate datasets—including in situ measurements, remote sensing data, climate observations, and hydrological model (PIHM-Wetland) outputs. Students will first apply machine learning techniques to establish the relationship between hydroclimatological variables and wetland carbon emissions at observational sites. Spatial distributed carbon emissions from the entire SE US wetland ecosystems will be created afterwards. Based on the current climate analysis, future wetland carbon emissions will be predicted in a warming climate. This research will better assess wetland carbon emissions over the entire Southeast and provide critical information on future carbon budgets on regional scales.

Project Lead: Wenhong Li

This project is also part of Duke’s first Climate+ cohort

Duke Data+ students, in collaboration with Dr. Emily Bernhardt (faculty advisor) and Audrey Thellman (graduate student) will evaluate how changing ice and snow conditions are impacting river ecosystems through classified ice imagery. Currently, our team has data from 7 field cameras that have been taking photos of the stream channel each day since 2018. We have created training data and code for a machine learning classifier to transform these photos into ecologically relevant indices, such as percent snow coverage. The Data+ team will modularize and visualize this classification pipeline to increase accessibility of our data product. Students will have the opportunity to work with a team of scientists at the New Hampshire site, U.S. Geological Survey partners with vested interest in the data product, and data scientists working in the Bernhardt Lab who have completed and are currently working on similar projects that increase availability and usability of environmental data (see  https://cuahsi.shinyapps.io/macrosheds/).

 

Project Lead: Audrey Thellman, Emily Bernhardt