Cristian Proistosescu

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…

Research topics

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

Selected publications

• An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence

  Reviews of Geophysics · 2020 · 1275 citations

  , in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.

  DOI

• Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models

  Journal of Climate · 2020 · 170 citations

  Abstract Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than t…

  DOI

• Magnitudes and Spatial Patterns of Interdecadal Temperature Variability in CMIP6

  Geophysical Research Letters · 2020 · 105 citations

  Senior authorCorresponding

  Abstract Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more G…

  DOI

• Estimating the timing of geophysical commitment to 1.5 and 2.0 °C of global warming

  Nature Climate Change · 2022 · 73 citations

  PublisherDOI

• To What Extent Does Discounting ‘Hot’ Climate Models Improve the Predictive Skill of Climate Model Ensembles?

  Earth s Future · 2024-10-01 · 6 citations

  articleOpen accessSenior author

  Abstract It depends. The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called ‘model democracy’ by discounting climate models that are too warm over the historical period (i.e., models that ‘run hot’) when making projections of global temperature change. However, the IPCC did not address whether this procedure is reliable for other quantities. Here, we explore the implications of weighting climate models according to their skill in re…

  PublisherOA PDFDOI

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

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