Climate Dynamics & Data Science
We are a research group in the Department of Climate, Meteorology, and Atmospheric Sciences and the Department of Earth Sciences and Environmental Change at the University of Illinois Urbana-Champaign, led by Cristi Proistosescu.
Our group studies the dynamics of Earth's climate system and how it responds to natural and anthropogenic forcing. We combine physical theory, numerical model simulations, and observational data using modern data science methods to understand climate variability and change across timescales — from the deep paleoclimate record to future projections of warming and extreme events.
Contact
Cristian Proistosescu
Department of Atmospheric Sciences & Department of Geology
University of Illinois Urbana-Champaign
cristi [at] illinois.edu
Research
Our group works on understanding how Earth's climate responds to forcing — and why that response is so hard to pin down. We combine theory, numerical model experiments, modern observations, and paleoclimate proxies to study radiative feedbacks, climate sensitivity, and the role of sea-surface temperature patterns in shaping both. A recurring theme is the interplay between forced and unforced variability, and how confounding the two leads to biased estimates of future warming.
We draw on both classic statistical methods and modern machine learning and AI to bridge analytical models, numerical experiments, and observations.
Current research spans coupled ocean-atmosphere dynamics, the physics of heat waves, paleoclimate variability, climate model evaluation, and the economics of climate risk. We are broadly interested in how uncertainty in the climate system propagates into uncertainty in impacts and policy.
People
Publications
Working Papers
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Land carbon response to positive, zero, and negative CO2 emissions across Earth system modelsEarth System Dynamics, discussion paper · link
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Optimizing Objective Model Calibration Approaches using Single Column Modelsin review at JAMES · link
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The forgotten role of wave dynamics in modulating the low cloud response to warm pool warmingin review at GRL · link
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Learning Climate Sensitivity from Future Observations, Fast and Slowin review at JClim · link
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Multidecadal preindustrial methane variability can be explained by noise in the source-sink imbalancein review at PNAS
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Closed-Form Statistical Moments of the Random Transport Equation with Newtonian Relaxation: its Application to Characterizing Midlatitude Temperature Variabilityin review at JGR-A
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ENSO Constrains the magnitude of the pattern effect and rules out low climate sensitivityin review at Nature Geosciences
2026
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Meteorological drivers of the low-cloud radiative feedback pattern effect and its uncertaintyAtmospheric Chemistry and Physics, 26(6), 4289-4311, 2026 · link
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Paleoclimate pattern effects help constrain climate sensitivity and 21st-century warmingProceedings of the National Academy of Sciences, 123(4), e2511370123, 2026 · link
2025
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An Analytical Model for the Influence of Soil Moisture on Temperature Extremes in the MidlatitudesJournal of Climate, 38(24), 7395-7413, 2025 · link
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Analytical model for the higher order moments of midlatitude atmospheric temperature distributionsGeophysical Research Letters, 52(3), e2024GL111626, 2025 · link
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Applying the ACE2 emulator to SST Green’s functions for the E3SMv3 global atmosphere modelJournal of Geophysical Research: Machine Learning and Computation, 2(3), e2025JH000774, 2025 · link
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Mid-pliocene climate forcing, sea surface temperature patterns, and implications for modern-day climate sensitivityJournal of Climate, 38(13), 3037-3053, 2025 · link
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Statistical fingerprints of forced and unforced variability reveal inconsistencies between marine proxies and climate models on multi‐decadal to millennial timescalesPaleoceanography and Paleoclimatology, 40(10), e2024PA004991, 2025 · link
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The relative importance of forced and unforced temperature patterns in driving the time variation of low-cloud feedbackJournal of Climate, 38(2), 513-529, 2025 · link
2024
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Carbon dioxide as a risky assetClimatic Change, 177(5), 72, 2024 · link
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Last Glacial Maximum pattern effects reduce climate sensitivity estimatesScience Advances, 10(16), eadk9461, 2024 · link
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Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivityProceedings of the National Academy of Sciences, 121(12), e2312093121, 2024 · link
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The green’s function model intercomparison project (GFMIP) protocolJournal of Advances in Modeling Earth Systems, 16(2), e2023MS003700, 2024 · link
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To what extent does discounting ‘hot’ climate models improve the predictive skill of climate model ensembles?Earth’s Future, 12(10), e2024EF004844, 2024 · link
2023
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Fingerprinting low-frequency Last Millennium temperature variability in forced and unforced climate modelsJournal of Climate, 36(20), 7005-7023, 2023 · link
2022
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Estimating the timing of geophysical commitment to 1.5 and 2.0 C of global warmingNature Climate Change, 12(6), 547-552, 2022 · link
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Influence of late Pleistocene sea-level variations on midocean ridge spacing in faulting simulations and a global analysis of bathymetryProceedings of the National Academy of Sciences, 119(28), e2204761119, 2022 · link
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Systematic climate model biases in the large‐scale patterns of recent sea‐surface temperature and sea‐level pressure changeGeophysical Research Letters, 49(17), e2022GL100011, 2022 · link
2021
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Biased estimates of equilibrium climate sensitivity and transient climate response derived from historical CMIP6 simulationsGeophysical Research Letters, 48(24), e2021GL095778, 2021 · link
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Origins of a relatively tight lower bound on anthropogenic aerosol radiative forcing from Bayesian analysis of historical observationsJournal of Climate, 34(21), 8777-8792, 2021 · link
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Slow modes of global temperature variability and their impact on climate sensitivity estimatesJournal of Climate, 34(21), 8717-8738, 2021 · link
2020
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An assessment of Earth’s climate sensitivity using multiple lines of evidenceReviews of Geophysics, 58(4), e2019RG000678, 2020 · link
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Intermodel spread in the pattern effect and its contribution to climate sensitivity in CMIP5 and CMIP6 modelsJournal of Climate, 33(18), 7755-7775, 2020 · link
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Magnitudes and spatial patterns of interdecadal temperature variability in CMIP6Geophysical Research Letters, 47(7), e2019GL086588, 2020 · link
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New generation of climate models track recent unprecedented changes in Earth’s radiation budget observed by CERESGeophysical Research Letters, 47(5), e2019GL086705, 2020 · link
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Strong remote control of future equatorial warming by off-equatorial forcingNature Climate Change, 10(2), 124-129, 2020 · link
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Uncertainties in climate and weather extremes increase the cost of carbonOne Earth, 2(6), 515-517, 2020 · link
2019
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Attributing historical and future evolution of radiative feedbacks to regional warming patterns using a Green’s function approach: The preeminence of the western PacificJournal of Climate, 32(17), 5471-5491, 2019 · link
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Natural variability has slowed the decline in western US snowpack since the 1980sGeophysical Research Letters, 46(1), 346-355, 2019 · link
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Ocean circulation signatures of North Pacific decadal variabilityGeophysical Research Letters, 46(3), 1690-1701, 2019 · link
2018
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Climate constraint reflects forced signalNature, 563(7729), E6-E9, 2018 · link
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Polar amplification dominated by local forcing and feedbacksNature Climate Change, 8(12), 1076-1081, 2018 · link
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Radiative feedbacks from stochastic variability in surface temperature and radiative imbalanceGeophysical Research Letters, 45(10), 5082-5094, 2018 · link
2017
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Slow climate mode reconciles historical and model-based estimates of climate sensitivityScience Advances, 3(7), e1602821, 2017 · link
2016
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Comment on "Sensitivity of seafloor bathymetry to climate-driven fluctuations in mid-ocean ridge magma supply"Science, 352(6292), 1405, 2016 · link
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Identification and interpretation of nonnormality in atmospheric time seriesGeophysical Research Letters, 43(10), 5425-5434, 2016 · link
2012
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To tune or not to tune: Detecting orbital variability in Oligo-Miocene climate recordsEarth and Planetary Science Letters, 325, 100-107, 2012 · link
Teaching
Applied AI for Earth Sciences
A graduate-level course on modern AI/ML methods in earth sciences. It cover fundamental ML concepts and architectures (e.g. regularization, convolutional neural nets, transformers), Python infrastructure (e.g. PyTorch) and modern applications to topics such as remote sensing, climate modeling and weather forecasting, and environmental hazard modeling.
The course is taught in a modern AI-assisted programming and analysis framework, teaching students how to effectively integrate generative AI tools (e.g., Large Language Models like CoPilot, and Claude) domain knowledge and conceptual understanding.
Climate Dynamics
A graduate-level class on climate dynamics. Investigates dynamical and physical processes that govern Earth’s past, present, and future climates. Emphasizes fundamental physical principles that determine present climate, and both natural and anthropogenic climate changes across spatial and temporal scales. Observations and climate models are used to examine past changes and potential future impacts.
Climate and Global Change
An introductory undergraduate course on climate change. Introduces climate change focusing, in turn, on mechanisms, impacts, and solutions The goal of the course is to provide you with a good understanding of Earth’s Changing Climate. By the end of the course, you will understand three major aspects:
(1) what physical and socio-economic factors drive climate change
(2) how climate change impacts natural, social, and economic systems
(3) what we can do about it!
Notes
A collection of personal notes and thoughts on climate dynamics and data science.