About

Peter Huybers is a Professor of Environmental Science and Engineering and Chair of Earth and Planetary Sciences at Harvard University. He also serves as the Interim Faculty Dean of Kirkland House. His academic and research activities are based at Harvard University, located at 20 Oxford St., Cambridge, MA. Professor Huybers leads a research group that includes graduate students, research staff, and former students who have completed their PhDs under his supervision. His research focuses on various aspects of climate science, including climate change impacts on agriculture, climate variability, and environmental reconstruction. He has contributed to understanding the effects of climate change on crop yields, water stress, and temperature variability, as well as the attribution of weather events to climate change. His work also involves the development of improved datasets and methodologies for analyzing historical climate data and surface temperature anomalies. Professor Huybers' research group has been involved in projects funded by organizations such as Amazon Web Services and the Harvard Data Science Initiative, highlighting the intersection of climate science and data analysis. His leadership in the field is reflected in his mentorship of students and staff who have gone on to academic and professional positions at institutions like Stanford, UC Berkeley, and the University of British Columbia.

Research topics

• Geography
• Environmental science
• Atmospheric sciences
• Meteorology
• Geology
• Medicine
• Political Science
• Ecology
• Agronomy
• Sociology
• Gerontology
• Economics
• Internal medicine
• Remote sensing
• Economic growth
• Soil science
• Biology
• Environmental health

Selected publications

• How well can we quantify when 1.5 °C of global warming has been exceeded?

  2026-01-28

  articleOpen access

  Abstract. Parties to the 2015 Paris Agreement agreed to limit the long-term increase in global average temperature to well below 2 °C and pursue efforts to keep temperatures below 1.5 °C relative to pre-industrial levels. As the world is fast approaching the 1.5 °C warming level on a sustained basis, and with 2024 likely the first year that was over 1.5 °C warmer than 1850-1900, there is ever increasing interest in how we will know whether and when 1.5 °C warming since pre-industrial has been reached or exceeded with respect to a long-term average. This paper represents a comprehensive community methodological overview, building on the IPCC 6th assessment. It explains why there is no straightforward answer and proposes clear and reasoned ways forward. Existing challenges are as follows. Firstly, the Paris Agreement text contains definitional ambiguities around 'pre-industrial', 'global average temperature', whether the assessment should be on realised or long-term human-induced warming, and over what time frame the long-term temperature goal applies. Then, there are intrinsic limitations of observational records which get more uncertain further back in time due to data sparsity and measurement heterogeneity. Finally, in a non-stationary climate, multidecadal mean indicators of global temperature change will either lag behind the change or must rely on expected future temperature changes (based on extrapolation, initialized predictions, or scenario-based and constrained projections). Our analysis shows that knowing 'whether we are there yet' is a multifaceted and inherently probabilistic problem that includes information on the definition of a specific level of global warming, temperature changes over multiple timescales, and also potentially includes unpacking the attribution of human-caused changes from observed variations. Given the policy relevance of understanding where the world stands relative to 1.5 °C, or any other level of global warming since pre-industrial, there are a number of practical steps which could be taken to increase specificity in answering this critical question in a timely manner, and inform future monitoring and assessment activities. This paper reviews a broad range of approaches, identifies the most pragmatic, robust and transparent, and clarifies requirements for use in real time including how to handle and represent remaining uncertainties. We show that it is possible by combining lines of evidence and several methodologies to estimate the present long-term warming level without delay in a manner that is robust both in retrospective validation of crossing past warming levels and, critically, to divergent warming futures including potential wildcard impacts of large volcanoes which can mask underlying warming for several years. Results are benchmarked against historical exceedances of 0.5 °C and 1 °C warming. Long-term warming as assessed using the approaches developed herein and data up to and including 2024 stands at 1.40 [1.23–1.58] °C, and underlying human-caused warming stands at 1.34 [1.18–1.50] °C. In IPCC quantified likelihood language this means that it was unlikely that long-term realised warming had exceeded 1.5 °C by the end of 2024 and very unlikely that human-induced warming had exceeded 1.5 °C.

  PublisherDOI

• Software in support of "How sea level paces faulting at fast-spreading mid-ocean ridges"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  otherOpen accessSenior author

  Matlab and C code.

  PublisherDOI

• Supplementary material to "How well can we quantify when 1.5 °C of global warming has been exceeded?"

  2026-01-28

  articleOpen access
  PublisherDOI

• Climate Change Alters Teleconnections

  Geophysical Research Letters · 2026-01-14 · 2 citations

  articleOpen access

  Abstract Internal modes of climate variability, such as El Niño and the North Atlantic Oscillation (NAO), can have strong influences upon distant weather patterns, effects that are referred to as “teleconnections.” The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covariance fingerprinting approach to demonstrate that shifts in teleconnection patterns affecting monthly temperatures between the periods 1960–1990 and 1990–2020 are attributable to anthropogenic forcing. We further apply multilinear regression to assess the regional contributions and statistical significance of changes in five key climate modes: the El Niño‐Southern Oscillation, NAO, Southern Annular Mode, Indian Ocean Dipole, and the Pacific Decadal Oscillation. In many regions, observed changes exceed what would be expected from natural variability alone, further implicating an anthropogenic influence. Finally, we provide projections of how these teleconnections will alter in response to further changes in climate.

  PublisherOA PDFDOI

• Climate change alters teleconnections Supporting Information

  2026-03-13

  articleOpen access

  Internal modes of climate variability, such as El Niño and the North Atlantic Oscillation, can have a strong influence on distant weather patterns, effects that are referred to as “teleconnections”. The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covariance fingerprinting approach to demonstrate that shifts in teleconnection patterns affecting monthly temperatures between the periods 1960–1990 and 1990–2020 are attributable to anthropogenic forcing. We further apply multilinear regression to assess the regional contributions and statistical significance of changes in five key climate modes: the El Niño–Southern Oscillation, North Atlantic Oscillation, Southern Annular Mode, Indian Ocean Dipole, and the Pacific Decadal Oscillation. In many regions, observed changes exceed what would be expected from natural variability alone, further implicating an anthropogenic influence.

  PublisherDOI

• SST Dataset Choice Affects Estimates of Historical Climate Variability

  2026-03-14

  articleOpen access

  Understanding the origins of climate variability (e.g., ENSO and AMV) and their interactions across timescales, as well as assessing model performance in simulating them, relies on robust sea-surface temperature (SST) datasets. Yet, there are numerous instrumental SST products that differ in their bias adjustments and gridding/infilling strategies. These structural choices propagate to key inferences about the climate system such as climate variability indices, the separation of internal and forced components, and teleconnection magnitudes and spatial patterns. Here, we explain why instrumental SST products differ, what these differences imply for climate variability and teleconnection analyses, and which products are best suited for specific applications. We review recent advances in bias adjustment and gridding/infilling of in situ data and assess the implications of these methods for global mean SST evolution and regional variability indices. We find substantial discrepancies in trends during the satellite era among older products, whereas state-of-the-art datasets are much more consistent. State-of-the-art SST datasets are also more consistent with signals from CMIP-class climate models in global mean SST during World War II, Atlantic multidecadal variability indices, and trends in the tropical Pacific zonal gradient — demonstrating the need to carefully choose SST datasets when investigating climate variability. Disagreements persist, however, for early-20th-century warming, which has implications for separating forced response from internal variability, and in data-sparse regions such as the Southern Ocean and Arctic. To support robust, physically interpretable teleconnection diagnostics, we articulate practical principles for dataset selection and highlight the NCAR Climate Data Guide as an evolving resource for updated SST benchmarking.

  PublisherDOI

• Comment on essd-2025-825 section 4.5

  2026-02-06

  peer-reviewOpen access

  <strong class="journal-contentHeaderColor">Abstract.</strong> Parties to the 2015 Paris Agreement agreed to limit the long-term increase in global average temperature to well below 2 &deg;C and pursue efforts to keep temperatures below 1.5 &deg;C relative to pre-industrial levels. As the world is fast approaching the 1.5 &deg;C warming level on a sustained basis, and with 2024 likely the first year that was over 1.5 &deg;C warmer than 1850-1900, there is ever increasing interest in how we will know whether and when 1.5 &deg;C warming since pre-industrial has been reached or exceeded with respect to a long-term average. This paper represents a comprehensive community methodological overview, building on the IPCC 6th assessment. It explains why there is no straightforward answer and proposes clear and reasoned ways forward. Existing challenges are as follows. Firstly, the Paris Agreement text contains definitional ambiguities around 'pre-industrial', 'global average temperature', whether the assessment should be on realised or long-term human-induced warming, and over what time frame the long-term temperature goal applies. Then, there are intrinsic limitations of observational records which get more uncertain further back in time due to data sparsity and measurement heterogeneity. Finally, in a non-stationary climate, multidecadal mean indicators of global temperature change will either lag behind the change or must rely on expected future temperature changes (based on extrapolation, initialized predictions, or scenario-based and constrained projections). Our analysis shows that knowing 'whether we are there yet' is a multifaceted and inherently probabilistic problem that includes information on the definition of a specific level of global warming, temperature changes over multiple timescales, and also potentially includes unpacking the attribution of human-caused changes from observed variations. Given the policy relevance of understanding where the world stands relative to 1.5 &deg;C, or any other level of global warming since pre-industrial, there are a number of practical steps which could be taken to increase specificity in answering this critical question in a timely manner, and inform future monitoring and assessment activities. This paper reviews a broad range of approaches, identifies the most pragmatic, robust and transparent, and clarifies requirements for use in real time including how to handle and represent remaining uncertainties. We show that it is possible by combining lines of evidence and several methodologies to estimate the present long-term warming level without delay in a manner that is robust both in retrospective validation of crossing past warming levels and, critically, to divergent warming futures including potential wildcard impacts of large volcanoes which can mask underlying warming for several years. Results are benchmarked against historical exceedances of 0.5 &deg;C and 1 &deg;C warming. Long-term warming as assessed using the approaches developed herein and data up to and including 2024 stands at 1.40 [1.23&ndash;1.58] &deg;C, and underlying human-caused warming stands at 1.34 [1.18&ndash;1.50] &deg;C. In IPCC quantified likelihood language this means that it was<em> unlikely</em> that long-term realised warming had exceeded 1.5 &deg;C by the end of 2024 and <em>very unlikely </em>that human-induced warming had exceeded 1.5 &deg;C.

  PublisherDOI

• <scp>DCENT</scp> ‐I: A Globally Infilled Extension of the Dynamically Consistent <scp>ENsemble</scp> of Temperature Dataset

  Geoscience Data Journal · 2026-02-12 · 2 citations

  articleOpen accessSenior author

  ABSTRACT A spatially infilled Dynamically Consistent Ensemble of surface Temperature (DCENT‐I) has been created by infilling land‐air and sea‐surface temperatures from DCENT using ordinary kriging with anisotropic and heterogeneous kernels. By incorporating air temperature anomalies over sea‐ice areas, DCENT‐I provides spatially complete monthly temperature fields at 5° resolution from 1850 to the present (currently the end of 2024) as a 200‐member ensemble. Uncertainty estimates that account for the need to infill for missing observations are made using a Multivariate Gaussian Process, and these are consistent with estimates derived from masking climate model simulations. The use of anisotropic and heterogeneous kernels leads to a reconstruction of El Niño variability whose spatial pattern and temporal variance are generally consistent throughout the record. As compared with taking the unfilled average, infilling increases the global mean surface temperature (GMST) warming estimate over 2005–2024 relative to a 1850–1900 baseline from 1.09 [0.96, 1.18] (95% range across members) to 1.17 [1.05, 1.26]°C, largely because of infilling in rapidly warming high‐latitude regions. Compared with HadCRUT5, GISTEMP v4, NOAA Global Temp v6, and Berkeley Earth, DCENT‐I shows a steadier and slightly faster GMST warming trend, reflecting the bias‐adjustments inherited from DCENT.

  PublisherDOI

• Software in support of "How sea level paces faulting at fast-spreading mid-ocean ridges"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-11

  otherOpen accessSenior author

  Code in support of the manuscript "How sea level paces faulting at fast-spreading mid-ocean ridges" submitted to Geophysical Journal International on 11 May 2026. The manuscript is also available on ArXiv.

  PublisherDOI

• Is Water Stress the Root Cause of the Observed Nonlinear Relationship Between Yield Losses and Temperature?

  AGU Advances · 2025-07-03 · 1 citations

  articleOpen accessSenior author

  Abstract Observational analyses consistently find that yields of major rainfed crops increase with temperature up to a threshold of approximately 32C, above which they reduce sharply. Two damage pathways have been suggested to explain this relationship: that high temperatures directly stress crops and drive yield loss, or that high temperatures induce water stress in crops, which in turn drives yield loss. Here we explore a third pathway: that soil water stress limits both agricultural productivity and evaporative cooling, giving rise to the nonlinear relationship between temperature and yield. Determining which of these pathways underpins the yield‐temperature relationship is important for predicting future crop productivity because climate change is expected to alter the co‐variability between temperature and water availability. To examine this third pathway, we use cumulative growing‐season transpiration from an idealized land surface model as a proxy for yield. This approach reproduces the observed yield‐temperature relationship, even though the model includes no mechanisms that limit productivity at high temperatures. In experiments where the influence of temperature on soil moisture is suppressed, yields still decline during hot, dry periods in a manner consistent with the observations. We conclude that water stress, and its influence on evaporative cooling, temperature, and agricultural productivity, drives the yield‐temperature relationship found in crops that experience episodic water stress. This framework explains the muted sensitivity of irrigated yields to high atmospheric temperatures, and suggests that future yield outcomes depend more critically on changes in rainfall than suggested by estimates that attribute yield losses primarily to temperature variations.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: An analysis of 150 years of sea surface and subsurface observations to map whole-ocean temperature and detect circulation change

  NSF · $337k · 2021–2025

• Hydrologic controls on temperature extremes in managed landscapes

  NSF · $349k · 2015–2019

• Collaborative Research: Beyond the Instrumental Record---The Ocean Circulation at the Last Glacial Maximum and the Deglacial sequence

  NSF · $100k · 2011–2014

• P2C2: A Bayesian Approach to Estimating Temperature and Precipitation and Their Inter-Relationship over the Last Millennium

  NSF · $400k · 2009–2013

• Collaborative Research: The Physics and Statistics of Global Sea Level Change

  NSF · $340k · 2010–2015

Frequent coauthors

• A. N. Rhines

  Netflix (United States)

  101 shared

• Ethan E. Butler

  Minnesota Department of Natural Resources

  95 shared

• Nathaniel D. Mueller

  University of Nebraska–Lincoln

  90 shared

• N. Michele Holbrook

  Harvard University

  82 shared

• Duo Chan

  University of Southampton

  79 shared

• J. X. Mitrovica

  66 shared

• Stefan Siebert

  University of Göttingen

  65 shared

• D. K. Ray

  University of Minnesota

  65 shared

Labs

• Peter Huybers LabPI

  Research on climate, extremes, crops, yield, paleoclimate, paleoceanography, glacial cycles, and volcanism

Education

• Ph.D., Atmospheric Chemistry

  Massachusetts Institute of Technology

  1996

• M.S., Atmospheric Chemistry

  Massachusetts Institute of Technology

  1993

• B.S., Earth and Planetary Sciences

  Harvard University

  1991