Research
Climate intervention
Climate intervention is the study of potential methods to intervene in the Earth system to reduce the risks of climate change alongside actions to reduce greenhouse gas emissions. I study stratospheric aerosol injection: a proposal to place reflective particles in the upper atmosphere, cooling the planet by blocking a small portion of incoming sunlight. I use output from Earth system modeling experiments to identify potential risks and benefits of this approach.
I led a project to create a set of four short animated videos describing climate intervention for a general audience, focused on the work done by the Barnes, Hurrell, and Keys groups at CSU and our collaborators. Check them out at the Barnes Group website! [link]
PUBLICATIONS
Hueholt, Daniel M., Elizabeth A. Barnes, James W. Hurrell, and Ariel L. Morrison, 2024: Speed of environmental change frames relative ecological risk in climate change and climate intervention scenarios. Nature Communications, 15(3332) doi.org/10.1038/s41467-024-47656-z *Featured as Editors' Highlight at Nature Communications April 2024
Hueholt, Daniel M., Elizabeth A. Barnes, James W. Hurrell, Jadwiga H. Richter, Lantao Sun, 2023: Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment. Earth’s Future, 11(5). doi.org/10.1029/2023EF003488
SELECTED CONFERENCE ABSTRACTS
Hueholt, Daniel M., E.A. Barnes, J.W. Hurrell, A.L. Morrison. “Climate speeds help frame relative ecological risk in future climate change and stratospheric aerosol injection scenarios.” Oral Presentation January 2024 at the American Meteorological Society 37th Conference on Climate Variability and Change at 104th Annual Meeting
Hueholt, Daniel M., E.A. Barnes, J.W. Hurrell, A.L. Morrison. “Ecological risks from rapid cooling with stratospheric aerosol injection.” Oral Presentation June 2023 at the Community Earth System Model Workshop 2023
Hueholt, Daniel M., E.A. Barnes, J.W. Hurrell, J.H. Richter, L. Sun, 2022: Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment. Oral presentation January 2023 at the American Meteorological Society 36th Conference on Climate Variability and Change at 103rd Annual Meeting
Atmospheric ice growth
Ice habit diagrams display the shapes of ice as a function of thermodynamic conditions. Many diagrams in the scientific literature and in educational materials do not reflect the most recent science. In my undergraduate research, I created a new ice diagram based on up-to-date research. This diagram was specifically designed to be useful for students and non-ice microphysics experts, and to facilitate meteorological data visualization.
PUBLICATIONS
Hueholt, Daniel M., Sandra E. Yuter, Matthew A. Miller, 2022: Revisiting Diagrams of Ice Growth Environments. Bulletin of the American Meteorological Society. doi.org/10.1175/BAMS-D-21-0271.1
Open Science Foundation archive: osf.io/g9vzj/
SELECTED CONFERENCE ABSTRACTS
Hueholt, Daniel M., Sandra E. Yuter, Matthew A. Miller. Diagrams of Ice Growth Environments Designed for Educational Use. Poster February 2024 at the American Meteorological Society First Conference on Cloud Physics at 104th Annual Meeting [View poster]
Data-driven methods
Data-driven methods such as machine learning can yield valuable insights on large datasets and nonlinear systems. Since these methods encode biases in data into their future performance and are often difficult to analyze, understanding the datasets we use is critical to ensure trustworthy and reproducible performance. In co-led work with Charlotte Connolly, we adapt best practices from software engineering to transparently document biases and technical aspects of Earth science datasets through an iterative process based on community feedback. Improved dataset curation supports the scientific enterprise in general and lower barriers to entry for the field by making public information that may otherwise be confined to internal networks.
PUBLICATIONS
Connolly, Charlotte J. & Hueholt, Daniel M. (co-led), Melissa A. Burt: Datasheets for Earth Science Datasets. In review at Bulletin of the American Meteorological Society. Preprint at doi.org/10.31219/osf.io/hwa3p
In addition, I design educational materials to help practitioners use data-driven methods and understand their limitations.
AI Model Summary (last updated October 2024): docs.google.com/document/d/1hZ19xsh5K76oYRsTd3Fk8Uy866e7DXbBf7DI35sguWE/edit?usp=sharing. Compiles examples of fully machine learning-based weather and climate "emulators." Maintained in support of ATS780A8 "Data-driven Forecasting" at Colorado State University.
palmerpenguins-classifiers (last updated September 2024): github.com/dmhuehol/palmerpenguins-classifiers. These notebooks demonstrate classification tasks using the Palmer Penguins dataset. Used in graduate classes at Colorado State University.
Arcodia, Marybeth, Elizabeth Barnes, Charlotte Connolly, Frances Davenport, Zaibeth Carlo Frontera, Emily Gordon, Daniel Hueholt, Antonios Mamalakis and Elina Valkonen. (2022) “Applied Machine Learning Tutorial for Earth Scientists” doi.org/10.5281/zenodo.6686879 Derived from materials we used for a tutorial at the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory in May 2022. I was involved in the sessions "Decision Trees and Random Forests" and "Ethical Use of AI in Earth Science".
Other research interests: ethics and philosophy of science, science communication, mesoscale meteorology, atmospheric waves, software development, remote sensing, topology, data visualization
Broader interests: classical music, music history, birds, plants, reading, chess