About

Cristian Proistosescu is an Assistant Professor leading the Climate Dynamics & Data Science research group at the University of Illinois Urbana-Champaign, affiliated with the Department of Atmospheric Sciences and the Department of Geology. His research focuses on understanding the dynamics of Earth's climate system and its response to both natural and anthropogenic forcing. The group combines physical theory, numerical model simulations, and observational data with modern data science methods to study climate variability and change across a wide range of timescales, from deep paleoclimate records to future projections of warming and extreme events. Their work addresses key challenges in climate science, including radiative feedbacks, climate sensitivity, and the influence of sea-surface temperature patterns on climate responses. A central theme in their research is the interplay between forced and unforced variability and how this interaction complicates accurate estimates of future warming. The group employs both classical statistical techniques and modern machine learning and AI approaches to integrate analytical models, numerical experiments, and observational data. Current research topics include coupled ocean-atmosphere dynamics, the physics of heat waves, paleoclimate variability, climate model evaluation, and the economics of climate risk. Overall, Proistosescu's work aims to elucidate how uncertainties in the climate system propagate into uncertainties in climate impacts and policy decisions.

Research topics

• Geology
• Climatology
• Environmental science
• Oceanography
• Physics
• Atmospheric sciences
• Remote sensing
• Astrobiology
• Engineering
• Meteorology
• Geophysics
• Geography

Selected publications

• Paleoclimate pattern effects help constrain climate sensitivity and 21st-century warming

  Proceedings of the National Academy of Sciences · 2026-01-22

  articleOpen access

  Paleoclimates provide examples of past climate change that inform estimates of modern warming from greenhouse-gas emissions, known as Earth’s climate sensitivity. However, differences between past and present climate change must be accounted for when inferring climate sensitivity from paleoclimate evidence. The closest paleoclimate analog to near-term warming from greenhouse-gas emissions is the Pliocene (5.3 to 2.6 Ma), a warm epoch with atmospheric CO 2 concentrations similar to today. Recent reconstructions indicate the Pliocene was 1 °C warmer than previously thought, implying higher climate sensitivity, which is also supported by recent reconstructions showing more cooling with reduced CO 2 at the Last Glacial Maximum (LGM; 19 to 23 thousand years ago). However, large-scale patterns of paleoclimate temperature change differ strongly from modern projections. Climate feedbacks and sensitivity depend on temperature patterns, and such “pattern effects” must be accounted for when using paleoclimates to constrain modern climate sensitivity. Here we combine data-assimilation reconstructions with atmospheric general circulation models to show Earth’s climate is more sensitive to Pliocene forcing than modern CO 2 forcing. Pliocene ice sheets, topography, and vegetation alter patterns of ocean warming and excite destabilizing cloud feedbacks, and LGM feedbacks are similarly amplified by the North American ice sheets. Accounting for paleoclimate pattern effects produces a best estimate (median) for modern climate sensitivity of 2.8 °C and 66% CI of 2.4 to 3.4 °C (90% CI: 2.1 to 4.0 °C), substantially reducing uncertainty in projections of 21st-century warming.

  PublisherOA PDFDOI

• Paleoclimate Pattern Effects in the Pliocene and Last Glacial Maximum Help Constrain Modern Climate Sensitivity

  2026-03-14

  articleOpen access

  Paleoclimates provide examples of past climate change that inform estimates of modern warming from greenhouse-gas emissions, known as Earth's climate sensitivity. However, differences between past and present climate change must be accounted for when inferring climate sensitivity from paleoclimate evidence. The closest paleoclimate analog to near-term warming from greenhouse-gas emissions is the Pliocene (5.3-2.6 Ma), a warm epoch with atmospheric CO2 concentrations similar to today. Recent reconstructions indicate the Pliocene was 1°C warmer than previously thought, implying higher climate sensitivity, which is also supported by recent reconstructions showing more cooling with reduced CO2 at the Last Glacial Maximum (LGM; 19-23 thousand years ago).However, large-scale patterns of paleoclimate temperature change differ strongly from modern projections under CO2 forcing. Climate feedbacks and sensitivity depend on temperature patterns, and such "pattern effects" must be accounted for when using paleoclimates to constrain modern climate sensitivity. Here we combine data-assimilation reconstructions with atmospheric general circulation models to show Earth's climate is more sensitive to Pliocene and LGM forcing than modern CO2 forcing. Pliocene ice sheets, topography, and vegetation alter patterns of ocean warming and excite destabilizing cloud feedbacks, and LGM feedbacks are similarly amplified by massive ice sheets. Accounting for paleoclimate pattern effects produces a best estimate (median) for modern climate sensitivity of 2.8°C and 66% confidence interval of 2.4-3.4°C (90% CI: 2.1-4.0°C), substantially revising climate sensitivity's upper bound and projections of 21st-century warming.

  PublisherDOI

• Optimizing Objective Model Calibration Approaches using Single Column Models

  arXiv (Cornell University) · 2026-04-04

  preprintOpen accessSenior author

  Sub-grid scale parameterizations in atmospheric models involve numerous uncertain parameters that must be tuned to align simulations with observations. Here, we propose a framework for assessing objective tuning frameworks using the Single Column Atmosphere Model (SCAM), which retains key physical parameterizations of general circulation models (GCMs) while greatly reducing computational cost. We conduct a perfect-model experiment where we run SCAM with a known "true" parameter set to generate synthetic observations that mimic Atmospheric Radiation Measurement (ARM) Intensive Observation Periods. Perturbed parameter ensembles are constructed by varying microphysics, convection, and aerosol parameters, and cloud-radiation fields are evaluated over the Southern Great Plains. We find that point estimates find solutions that greatly reduce model-observation misfit without recovering the true parameter values. In contrast, a Bayesian framework using a Gaussian Process emulator with Markov Chain Monte Carlo sampling yields tighter constraints on some parameters and more consistent recovery across experiments and variables. The perfect model framework allows to assess which observables yield most information, which parameters are recoverable given a certain set of observations, and what is the minimum observational record needed. Although this study focuses on a single location with synthetic observations, such experiments provide a controlled setting to evaluate and identify robust calibration frameworks, which can then be extended to multiple locations and real observations with greater confidence.

  PublisherDOI

• The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-24

  otherOpen access1st authorCorresponding

  Data and code to reproduce the results shown in the manuscript "The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming".

  PublisherDOI

• The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-24

  otherOpen access1st authorCorresponding

  Data and code to reproduce the results shown in the manuscript "The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming".

  PublisherDOI

• Optimizing Objective Model Calibration Approaches using Single Column Models

  arXiv (Cornell University) · 2026-04-04

  articleOpen accessSenior author

  Sub-grid scale parameterizations in atmospheric models involve numerous uncertain parameters that must be tuned to align simulations with observations. Here, we propose a framework for assessing objective tuning frameworks using the Single Column Atmosphere Model (SCAM), which retains key physical parameterizations of general circulation models (GCMs) while greatly reducing computational cost. We conduct a perfect-model experiment where we run SCAM with a known "true" parameter set to generate synthetic observations that mimic Atmospheric Radiation Measurement (ARM) Intensive Observation Periods. Perturbed parameter ensembles are constructed by varying microphysics, convection, and aerosol parameters, and cloud-radiation fields are evaluated over the Southern Great Plains. We find that point estimates find solutions that greatly reduce model-observation misfit without recovering the true parameter values. In contrast, a Bayesian framework using a Gaussian Process emulator with Markov Chain Monte Carlo sampling yields tighter constraints on some parameters and more consistent recovery across experiments and variables. The perfect model framework allows to assess which observables yield most information, which parameters are recoverable given a certain set of observations, and what is the minimum observational record needed. Although this study focuses on a single location with synthetic observations, such experiments provide a controlled setting to evaluate and identify robust calibration frameworks, which can then be extended to multiple locations and real observations with greater confidence.

  PublisherOA PDF

• The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming

  arXiv (Cornell University) · 2026-03-25

  preprintOpen access1st authorCorresponding

  The Pattern Effect describes the dependence of top-of-atmosphere radiation anomalies on changes in the pattern of sea surface temperatures. The emerging consensus in the field explains the impact of Pacific warm pool temperature on radiation using Convective Quasi-Equilibrium Weak Temperature Gradient (QE-WTG) theory: warm pool warming leads to increase in free-tropospheric temperatures across the tropics, a strengthening of inversion, increased cloud cover in the East Pacific low cloud decks, and negative radiative anomalies. Here we call on overlooked past results and new simulations from the Energy Exascale Earth System model to show that Rossby waves dominate the low-cloud response over the subtropical East Pacific low cloud decks, leading to decrease cloud cover in the low cloud decks. While the global radiative response is negative and consistent with QE-WTG, it is dominated by the response of the deep tropics, rather than the subtropical low cloud decks.

  PublisherDOI

• Making sense of ZEC and TCRE: A conceptual model for the coupled climate response to carbon emissions. 

  2026-03-14

  articleOpen access1st author

  Effective climate policy requires quantifying the temperature response to CO2 emissions. The current policy framework centers around Remaining Carbon Budgets, and depends heavily on there being a linear Transient Climate Response to Cumulative Emissions (TCRE) and a low Zero Emission Commitment (ZEC). The linearity of TCRE and the smallness of ZEC are based on emergent behaviors of a small number of Earth System Models (ESMs) and lack both conceptual understanding and uncertainty quantification. Here we present an analytically tractable conceptual model for the coupled interaction of the thermal component of the climate system with the carbon cycles. Unlike previous decompositions our model is built by assembling dynamical energy balance and carbon flux models. Thus, we obtain closed-form approximations for TCRE and ZEC in terms of well-established conceptual parameters such as the radiative feedback, ocean heat uptake efficiency, the average timescale ocean carbon uptake, the Q10 temperature sensitivity of respiration, etc. We derive conditions for both long-term (millennial-scale) low ZEC, as well as conditions for transient (centennial-scale) low ZEC, along with conditions for the near-linearity of TCRE. We find that there is no intrinsic physical reason for a low ZEC or a linear TCRE, and they arise from fortuitous compensations between unrelated parameters. We also show the system has the potential for significant centennial-scale transient amplification, arising from non-normal system dynamics.In addition to providing conceptual insight, the model allows us to easily explore the limits of the traditional assumptions surrounding TCRE and ZEC. For example, we show that a pattern effect derived from models with observed Sea Surface Temperature patterns (AMIP), can lead to a much larger ZEC than that derived from coupled ESMs.

  PublisherDOI

• Bayesian methods for fitting spectral models to noisy, sparse, proxy data

  2026-03-14

  articleOpen access1st authorCorresponding

  The background continuum of climate variability recorded in proxy records is often modelled using parametric spectral models, such as power-laws, auto-regressive processes, or stochastic differential equations.However, fitting such models to proxy data is usually done in an ad-hoc manner, such as by using least-squares fitting in log-log space.Here I will discuss two formal Bayesian methods for fitting parametric stochastic models to proxy data. One is a spectral-domain approach based the Whittle likelihood. The other is a time-domain approach based on Gaussian Processes.In both cases, I show how the standard approaches can be modified to account for some of the ways in which climate proxies alter spectral slopes: measurement error, time uncertainty, uneven sampling, and smoothing (e.g. from diffusion or bioturbation). Finally, I use synthetic data generated from power-law and Matern processes, and proxy-system models, to show expected skill of the two approaches for different proxies.I find that these formal approaches provide significant bias reduction relative to typical ad-hoc approaches, allowing for much more accurate calibration of stochastic models of climate variability across scales.

  PublisherDOI

• The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming

  arXiv (Cornell University) · 2026-03-25

  articleOpen access1st authorCorresponding

  The Pattern Effect describes the dependence of top-of-atmosphere radiation anomalies on changes in the pattern of sea surface temperatures. The emerging consensus in the field explains the impact of Pacific warm pool temperature on radiation using Convective Quasi-Equilibrium Weak Temperature Gradient (QE-WTG) theory: warm pool warming leads to increase in free-tropospheric temperatures across the tropics, a strengthening of inversion, increased cloud cover in the East Pacific low cloud decks, and negative radiative anomalies. Here we call on overlooked past results and new simulations from the Energy Exascale Earth System model to show that Rossby waves dominate the low-cloud response over the subtropical East Pacific low cloud decks, leading to decrease cloud cover in the low cloud decks. While the global radiative response is negative and consistent with QE-WTG, it is dominated by the response of the deep tropics, rather than the subtropical low cloud decks.

  PublisherOA PDF

Recent grants

• Collaborative Research: Quantifying the sea-surface temperature pattern effect for Last Glacial Maximum and Pliocene constraints on climate sensitivity

  NSF · $82k · 2020–2026

Frequent coauthors

• Kyle C. Armour

  41 shared

• Yue Dong

  Lamont-Doherty Earth Observatory

  34 shared

• Peter Huybers

  Harvard University

  25 shared

• Gerard H. Roe

  University of Washington

  22 shared

• Malte F. Stuecker

  University of Hawaii System

  20 shared

• David S. Battisti

  University of Washington

  19 shared

• David Paynter

  NOAA Geophysical Fluid Dynamics Laboratory

  17 shared

• Piers M. Forster

  University of Leeds

  16 shared

Labs

• CDDS @ UIUCPI

  Climate Dynamics and Data Science research group at the University of Illinois Urbana-Champaign

Education

• PhD, Earth and Planetary Sciences

  Harvard University

  2017

• BA, Physics

  Princeton University

  2009