Curriculum Vitae
Last updated: December 2025
Education
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Ph.D., Atmospheric and Oceanic SciencesPrinceton University — Princeton, NJAdvisor: Yi Ming
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B.S., Engineering PhysicsCornell University — Ithaca, NYHonors in Research; magna cum laudeThesis Advisor: Natalie Mahowald
Experience
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Senior Research ScientistAllen Institute for Artificial Intelligence — Princeton, NJ- Demonstrated that the Ai2 Climate Emulator can be coupled to a slab ocean model and trained to emulate the equilibrium climate response to changes in the concentration of carbon dioxide.
- Participated in the development of the Ai2 Climate Emulator (ACE) through the design and execution of custom reference simulations with physics-based models, data-processing pipelines, software contributions, and scientific experimentation.
- Led work to port the cloud-based end-to-end corrective ML workflow to run on HPC systems.
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Research ScientistAllen Institute for Artificial Intelligence — Princeton, NJ- Demonstrated that corrective ML could be used to improve the coarse resolution of land precipitation and surface temperature in multi-year simulations in multiple climates.
- Advanced corrective ML parameterization via coarse-graining by enabling learning and overriding the surface radiative fluxes and accurately applying corrections to the horizontal winds.
- Built an environment to run multi-node Python-wrapped FV3GFS simulations on HPC systems.
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Software Engineer for Climate Model DevelopmentVulcan Inc. — Princeton, NJ- Implemented the ability to compute and save online coarse-grained diagnostics and restart files in GFDL’s SHiELD model, facilitating global storm resolving model research, including Vulcan's.
- Ran multiple global 3-km resolution simulations with online-coarsened outputs for machine learning model training and testing.
- Contributed to the development of the infrastructure to run a Python-wrapped version of NOAA’s FV3GFS model for machine learning experiments, in which output from the 3-km runs was used to train ML models to improve coarse-resolution simulations.
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Research AssistantPrinceton University — Princeton, NJ- Characterized the role of water vapor in the ITCZ response to hemispherically asymmetric perturbations.
- Demonstrated that South Asian monsoon low pressure systems can be simulated in an idealized moist GCM, and that the storms exhibit some properties consistent with moisture vortex instability theory, as well as some properties that deviate from it.
- Studied the sensitivity of equatorial wave variability in an idealized moist GCM to various forms of heating perturbations.
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Research AssistantCornell University — Ithaca, NY- Characterized the episodicity of forest and grass fires and developed 7 new prescribed emissions cases to test the impact of fire episodicity on fire’s aerosol direct and indirect radiative forcings.
- Implemented 8 GCM lightning parameterizations in CAM5, compared their results to LIS/OTD observations, and studied their future projections.
Publications
Submitted Articles & Preprints
2025
- Duncan, J. P. C., Wu, E., Dheeshjith, S., Subel, A., Arcomano, T., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., Gregory, W., Fernandez-Granda, C., Busecke, J., Watt-Meyer, O., Hurlin, W. J., Adcroft, A., Zanna, L., & Bretherton, C. (2025). SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators. (arXiv:2509.12490).
- Perkins, W. A., Kwa, A., McGibbon, J., Arcomano, T., Clark, S. K., Watt-Meyer, O., Bretherton, C. S., & Harris, L. M. (2025). HiRO-ACE: Fast and Skillful AI Emulation and Downscaling Trained on a 3 Km Global Storm-Resolving Model. (arXiv:2512.18224).
Journal Articles
2025
- Clark, S. K., Watt-Meyer, O., Kwa, A., McGibbon, J., Henn, B., Perkins, W. A., Wu, E., Harris, L. M., & Bretherton, C. S. (2025). ACE2-SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO2. Journal of Geophysical Research: Machine Learning and Computation, 2(4), e2024JH000575.
- Guendelman, I., Merlis, T. M., Cheng, K. Y., Harris, L. M., Bretherton, C. S., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S. K., & Fueglistaler, S. (2025). Detecting Changes in Large-Scale Metrics of Climate in Short Integrations of a Global Storm-Resolving Model of the Atmosphere. Environmental Research: Climate, 4(2), 025010.
- Watt-Meyer, O., Henn, B., McGibbon, J., Clark, S. K., Kwa, A., Perkins, W. A., Wu, E., Harris, L., & Bretherton, C. S. (2025). ACE2: Accurately Learning Subseasonal to Decadal Atmospheric Variability and Forced Responses. npj Climate and Atmospheric Science, 8(1), 1–15.
2024
- Duncan, J. P. C., Wu, E., Golaz, J. C., Caldwell, P. M., Watt-Meyer, O., Clark, S. K., McGibbon, J., Dresdner, G., Kashinath, K., Bonev, B., Pritchard, M. S., & Bretherton, C. S. (2024). Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity. Journal of Geophysical Research: Machine Learning and Computation, 1(3), e2024JH000136.
- Guendelman, I., Merlis, T. M., Cheng, K. Y., Harris, L. M., Bretherton, C. S., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S. K., & Fueglistaler, S. (2024). The Precipitation Response to Warming and CO2 Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models. Geophysical Research Letters, 51(7), e2023GL107008.
- Henn, B., Jauregui, Y. R., Clark, S. K., Brenowitz, N. D., McGibbon, J., Watt-Meyer, O., Pauling, A. G., & Bretherton, C. S. (2024). A Machine Learning Parameterization of Clouds in a Coarse-Resolution Climate Model for Unbiased Radiation. Journal of Advances in Modeling Earth Systems, 16(3), e2023MS003949.
- McGibbon, J., Clark, S. K., Henn, B., Kwa, A., Watt-Meyer, O., Perkins, W. A., & Bretherton, C. S. (2024). Global Precipitation Correction Across a Range of Climates Using CycleGAN. Geophysical Research Letters, 51(4), e2023GL105131.
- Merlis, T. M., Cheng, K. Y., Guendelman, I., Harris, L., Bretherton, C. S., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S. K., Vecchi, G. A., & Fueglistaler, S. (2024). Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change. Science Advances, 10(26), eadn5217.
- Merlis, T. M., Guendelman, I., Cheng, K. Y., Harris, L., Chen, Y. T., Bretherton, C. S., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S. K., & Fueglistaler, S. (2024). The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change. Geophysical Research Letters, 51(23), e2024GL111549.
- Watt-Meyer, O., Brenowitz, N. D., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., Harris, L., & Bretherton, C. S. (2024). Neural Network Parameterization of Subgrid-Scale Physics From a Realistic Geography Global Storm-Resolving Simulation. Journal of Advances in Modeling Earth Systems, 16(2), e2023MS003668.
2023
- Bolot, M., Harris, L. M., Cheng, K. Y., Merlis, T. M., Blossey, P. N., Bretherton, C. S., Clark, S. K., Kaltenbaugh, A., Zhou, L., & Fueglistaler, S. (2023). Kilometer-Scale Global Warming Simulations and Active Sensors Reveal Changes in Tropical Deep Convection. npj Climate and Atmospheric Science, 6(1), 1–8.
- Harris, L., Zhou, L., Kaltenbaugh, A., Clark, S., Cheng, K. Y., & Bretherton, C. (2023). A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model. Journal of Geophysical Research: Atmospheres, 128(10), e2022JD037823.
- Kwa, A., Clark, S. K., Henn, B., Brenowitz, N. D., McGibbon, J., Watt-Meyer, O., Perkins, W. A., Harris, L., & Bretherton, C. S. (2023). Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle. Journal of Advances in Modeling Earth Systems, 15(5), e2022MS003400.
- Sanford, C., Kwa, A., Watt-Meyer, O., Clark, S. K., Brenowitz, N., McGibbon, J., & Bretherton, C. (2023). Improving the Reliability of ML-Corrected Climate Models With Novelty Detection. Journal of Advances in Modeling Earth Systems, 15(11), e2023MS003809.
2022
- Bretherton, C. S., Henn, B., Kwa, A., Brenowitz, N. D., Watt-Meyer, O., McGibbon, J., Perkins, W. A., Clark, S. K., & Harris, L. (2022). Correcting Coarse-Grid Weather and Climate Models by Machine Learning From Global Storm-Resolving Simulations. Journal of Advances in Modeling Earth Systems, 14(2), e2021MS002794.
- Cheng, K. Y., Harris, L., Bretherton, C., Merlis, T. M., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S., & Fueglistaler, S. (2022). Impact of Warmer Sea Surface Temperature on the Global Pattern of Intense Convection: Insights From a Global Storm Resolving Model. Geophysical Research Letters, 49(16), e2022GL099796.
- Clark, S. K., Brenowitz, N. D., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., Bretherton, C. S., & Harris, L. M. (2022). Correcting a 200 Km Resolution Climate Model in Multiple Climates by Machine Learning From 25 Km Resolution Simulations. Journal of Advances in Modeling Earth Systems, 14(9), e2022MS003219.
- Xiang, B., Harris, L., Delworth, T. L., Wang, B., Chen, G., Chen, J. H., Clark, S. K., Cooke, W. F., Gao, K., Huff, J. J., Jia, L., Johnson, N. C., Kapnick, S. B., Lu, F., McHugh, C., Sun, Y., Tong, M., Yang, X., Zeng, F., Zhao, M., Zhou, L., & Zhou, X. (2022). S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections. Bulletin of the American Meteorological Society, 103(2), E463–E484.
2021
- McGibbon, J., Brenowitz, N. D., Cheeseman, M., Clark, S. K., Dahm, J. P. S., Davis, E. C., Elbert, O. D., George, R. C., Harris, L. M., Henn, B., Kwa, A., Perkins, W. A., Watt-Meyer, O., Wicky, T. F., Bretherton, C. S., & Fuhrer, O. (2021). Fv3gfs-Wrapper: A Python Wrapper of the FV3GFS Atmospheric Model. Geoscientific Model Development, 14(7), 4401–4409.
- Watt-Meyer, O., Brenowitz, N. D., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., & Bretherton, C. S. (2021). Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations. Geophysical Research Letters, 48(15), e2021GL092555.
2020
- Clark, S. K., Ming, Y., & Adames, Á. F. (2020). Monsoon Low Pressure System--Like Variability in an Idealized Moist Model. Journal of Climate, 33(6), 2051–2074.
- Harris, L., Zhou, L., Lin, S. J., Chen, J. H., Chen, X., Gao, K., Morin, M., Rees, S., Sun, Y., Tong, M., Xiang, B., Bender, M., Benson, R., Cheng, K. Y., Clark, S., Elbert, O. D., Hazelton, A., Huff, J. J., Kaltenbaugh, A., Liang, Z., Marchok, T., Shin, H. H., & Stern, W. (2020). GFDL SHiELD: A Unified System for Weather-to-Seasonal Prediction. Journal of Advances in Modeling Earth Systems, 12(10), e2020MS002223.
- Narinesingh, V., Booth, J. F., Clark, S. K., & Ming, Y. (2020). Atmospheric Blocking in an Aquaplanet and the Impact of Orography. Weather and Climate Dynamics, 1(2), 293–311.
2019
- Adames, Á. F., Kim, D., Clark, S. K., Ming, Y., & Inoue, K. (2019). Scale Analysis of Moist Thermodynamics in a Simple Model and the Relationship between Moisture Modes and Gravity Waves. Journal of the Atmospheric Sciences, 76(12), 3863–3881.
2018
- Clark, S. K., Ming, Y., Held, I. M., & Phillipps, P. J. (2018). The Role of the Water Vapor Feedback in the ITCZ Response to Hemispherically Asymmetric Forcings. Journal of Climate, 31(9), 3659–3678.
2017
- Clark, S. K., Ward, D. S., & Mahowald, N. M. (2017). Parameterization-Based Uncertainty in Future Lightning Flash Density. Geophysical Research Letters, 44(6), 2017GL073017.
2015
- Clark, S. K., Ward, D. S., & Mahowald, N. M. (2015). The Sensitivity of Global Climate to the Episodicity of Fire Aerosol Emissions. Journal of Geophysical Research: Atmospheres, 120(22), 2015JD024068.
Conference Proceedings
2025
- McGibbon, J., Arcomano, T., Clark, S. K., Duncan, J., Henn, B., Kwa, A., Perkins, W. A., Watt-Meyer, O., Wu, E., & Bretherton, C. (2025). Emulating Climate Across Scales with Conditional Spherical Fourier Neural Operators. NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning.
- Wu, E., Duncan, J., Arcomano, T., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, A., Watt-Meyer, O., Bretherton, C., Dheeshjith, S., Subel, A., Zanna, L., Hurlin, W., Gregory, W., & Adcroft, A. (2025). Coupled Climate Simulations with ACE and Samudra. NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning.
2023
- Watt-Meyer, O., Dresdner, G., McGibbon, J., Clark, S. K., Duncan, J., Henn, B., Peters, M., Brenowitz, N. D., Kashinath, K., Pritchard, M., Bonev, B., & Bretherton, C. (2023). ACE: A fast, skillful learned global atmospheric model for climate prediction. NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning.
2022
- Brenowitz, N. D., Perkins, W. A., Nugent, J. M., Watt-Meyer, O., Clark, S. K., Kwa, A., Henn, B., McGibbon, J., & Bretherton, C. S. (2022). Emulating Fast Processes in Climate Models. NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences.
- Kwa, A., Clark, S. K., Henn, B., Brenowitz, N. D., McGibbon, J., Perkins, W. A., Watt-Meyer, O., Harris, L., & Bretherton, C. S. (2022). Machine-Learned Climate Model Corrections from a Global Storm-Resolving Model. NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences.
- Sanford, C. H., Kwa, A., Watt-Meyer, O., Clark, S. K., Brenowitz, N., McGibbon, J., & Bretherton, C. (2022). Improving the predictions of ML-corrected climate models with novelty detection. NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning.
2020
- Brenowitz, N. D., Henn, B., Clark, S. K., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., & Bretherton, C. S. (2020). Machine Learning Climate Model Dynamics: Offline versus Online Performance. NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning.
Presentations
2025
- Clark, S. K., Arcomano, T., Duncan, James P. C., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., Wu, E., Watt-Meyer, O., Bretherton, C. S., Harris, L. M. (September 2025). The Ai2 Climate Emulator: a flexible platform for climate simulation, weather forecasting, and downscaling. NOAA Artificial Intelligence Workshop, Virtual.Talk
- Clark, S. K., Arcomano, T., Duncan, James P. C., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., Wu, E., Watt-Meyer, O., Bretherton, C. S. (June 2025). Learning the Response to Abrupt CO2 Changes in the Ai2 Climate Emulator. Gordon Research Conference on Actionable Machine Learning for Climate Science, Smithfield, RI.Talk
2024
- Bretherton, C. S., Watt-Meyer, O., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., Dresdner, G., Duncan, J. P. C., Rebassoo, F., Bonev, B., Harris, L. M., Caldwell, P. M. (December 2024). The AI2 Climate Emulator (ACE): Capabilities, Challenges, and Opportunities. AGU Annual Meeting, Washington D.C..Talk
- Clark, S. K., Watt-Meyer, O., Kwa, A., McGibbon, J., Henn, B., Perkins, W. A., Wu, E., Bretherton, C. S., Harris, L. M. (December 2024). ACE2-SOM: Coupling to a slab ocean and learning the sensitivity of climate to changes in CO2. AGU Annual Meeting, Washington D.C..eLightning
- Clark, S. K., Watt-Meyer, O., Kwa, A., McGibbon, J., Henn, B., Dresdner G., Perkins, W. A., Harris, L. M., Bretherton, C. S. (August 2024). Coupling the Ai2 Climate Emulator to a slab ocean and learning the climate sensitivity to changes in CO2. Workshop on Large-Scale Deep Learning for the Earth System, Bonn, Germany.Talk
2023
- Clark, S. K., Brenowitz, N., Henn, B. M., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., Bretherton, C.S., and Harris, Lucas M. (January 2023). Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations. AMS Annual Meeting, Denver, CO.Talk
2022
- Clark, S. K., Brenowitz, N., Henn, B. M., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., Bretherton, C.S., and Harris, Lucas M. (June 2022). Correcting Coarse-Grid Weather and Climate Models by Machine Learning From Global Storm-Resolving Simulations. Physics Dynamics Coupling Workshop, Princeton, NJ.Talk
2021
- Clark, S. K., Brenowitz, N., Henn, B. M., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., and Bretherton, C.S. (December 2021). Applying machine learning parameterization through coarse graining to improve the skill in simulating multiple climates in a full complexity GCM. AGU Fall Meeting, Virtual.Poster
- Clark, S. K., Brenowitz, N., Henn, B. M., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., and Bretherton, C.S. (September 2021). Applying machine learning parameterization through coarse graining to improve the skill in simulating multiple climates in a full complexity GCM. NOAA Artificial Intelligence Workshop, Virtual.Talk
2020
- Clark, S. K., Brenowitz, N., Henn, B. M., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., Harris, L., Bretherton, C.S. (December 2020). Challenges associated with training a machine-learning based moist physics parameterization by coarse-graining in a model with topography. NOAA Artificial Intelligence Workshop, Virtual.Talk
- Clark, S. K., Brenowitz, N., Bretherton, C. S., Henn, B. M., Kwa, A., McGibbon, J., Perkins, W. A., Watt-Meyer, O., Chen, X., Harris, L., Zhou, L. (December 2020). Using nudging to investigate biases in a global 3 km resolution simulation with GFDL’s X-SHiELD model. AGU Fall Meeting, Virtual.Poster
2018
- Clark, S. K., Ming, Y., Adames, Á. F. (December 2018). An idealized framework for simulating monsoon low pressure systems and their potential sensitivity to the mean state. AGU Fall Meeting, Washington D.C..Poster
2017
- Clark, S. K., Ming, Y. (December 2017). Investigating synoptic-scale monsoonal disturbances in an idealized moist model. AGU Fall Meeting, New Orleans, LA.Poster
- Clark, S. K., Ming, Y., Held, I. M., and Phillipps, P. J. (November 2017). The role of water vapor in the ITCZ response to hemispherically asymmetric forcings. Dynamics seminar series, Princeton, NJ.Talk
2016
- Clark, S. K., Ming, Y., and Held, I. M. (December 2016). The role of water vapor in the ITCZ response to hemispherically asymmetric forcings. AGU Fall Meeting, San Francisco, CA.Poster
- Clark, S. K., Ming, Y., and Held, I. M. (November 2016). The role of water vapor in the ITCZ response to hemispherically asymmetric forcings. WCRP Model Hierarchies Workshop, Princeton, NJ.Poster
- Hill, S. A., Clark, S. K. (November 2016). The other ’aospy’: automated climate data analysis and management. AOSPy Workshop at Columbia University, New York, NY.Talk
- Clark, S. K., Ming, Y., and Held, I. M. (June 2016). The role of water vapor in the ITCZ response to hemispherically asymmetric forcings. Dynamical Core Model Intercomparison Project, Boulder, CO.Poster
2015
- Clark, S. K., Ming, Y., and Held, I. M. (December 2015). Climate Impacts of Inter-hemispherically Asymmetric Radiativen Forcing. AGU Fall Meeting, San Francisco, CA.Poster
- Clark, S. K., Ming, Y., and Held, I. M. (July 2015). Climate Impacts of Inter-hemispherically Asymmetric Radiativen Forcing. Gordon Research Conference, Lewiston, ME.Poster
2012
- Clark, S. K., Ward, D. S., and Mahowald, N. M. (June 2012). Climate Model Responses to Increased Episodicity in Prescribed Fire Aerosol Emissions. CESM Workshop, Breckenridge, CO.Poster
Select open source software contributions
xarray
Core developerA widely-used Python library providing N-dimensional labeled array data structures.
- Led an effort to fully support of non-standard calendar types frequently used in climate science, i.e. serialization, standard and partial-datetime-string indexing, constructing date ranges, groupby operations, resampling, interpolation, plotting, and more.
- Continue to take part in reviewing new contributions, fixing bugs, adding requested features, and answering user/developer questions.
cftime
ContributorA Python library providing datetime instances for non-standard calendars.
- Enabled exact numerical decoding and encoding of datetimes.
- Proposed strategy that led to speeding up fundamental operations, e.g. datetime construction and timedelta arithmetic, by 200 to 400x.
- Implemented an approach to speed up decoding arrays of datetimes by 200x in particularly problematic circumstances.
nc-time-axis
ContributorA Python library providing the ability to plot cftime datetimes in matplotlib.
- Enabled plotting
cftime.datetimeobjects directly in matplotlib, instead of requiring a wrapped version of acftime.datetimeobject. - Added infrastructure for documentation and basic documentation content.
xpartition
Primary authorA Python package for writing large xarray datasets to zarr stores with independent processes on HPC or in the cloud.
- Used regularly when processing the hundreds of terabytes of output from physics-based climate model simulations we use as ML reference data at Ai2.
- Supports writing both traditional and sharded zarr stores.
faceted
Primary authorA Python package that makes it easier to create matplotlib figures with precise control over the overall width, plot aspect ratio, between-plot spacing, and colorbar dimensions.
- Reduces boilerplate and automates algebra required to make clean publication-ready plots.
Teaching experience
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Princeton University — ENV 367: Modeling the Earth System- Assisted Professor Laure Resplandy in adapting an existing compact Earth System Model for use as a teaching tool. Wrote comprehensive web-based documentation for the model.
- Authored lab exercises to help students learn about the features and limitations of the model, and illustrate Earth system modeling concepts.
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Cornell University — AEP 4220: Mathematical Physics II- Held weekly office hours, wrote official solutions, and graded homework and exam problems for a course led by Professor Bruce Kusse.
Reviewing
Peer-reviewed manuscripts for:
Select honors and awards
- Arnold Guyot Teaching Award — Princeton University Department of Geosciences2018
- National Defense Science and Engineering Graduate Fellowship — American Society for Engineering Education2016-2019
- Dorothy and Fred Chau Award — Cornell University Department of Applied and Engineering Physics2014
- Rawlings Cornell Presidential Research Scholar — Cornell University2010-2014