Research

Themes

My group’s research focuses on paleoclimatology – the study of past climates – along the following themes. For more details, see our publications.

Climate Reconstruction

Climate Reconstruction

Our group has been involved with reconstructions of El Niño-Southern Oscillation (ENSO, [1,2]), global mean surface temperature [3,4], and multiple fields [5,6]. We have also been active in benchmarking reconstruction techniques [7–9].

Climate Modeling

Climate Modeling

Our group has participated in the development [10] and evaluation of climate models, examining how models represent ENSO [11], simulate volcanic eruptions [12], fractionate water isotopes [13,14], and simulate the climate continuum [15]. Paleoclimatology is particularly suited for model evaulation, as past climates provide an out-of-sample test of model predictions. Picture credit: Quanta Magazine.

Data Science

Data Science

We develop methods for paleoclimate data assimilation [5,16], climate field imputation [17,18], uncertainty quantification [19,20], and time series analysis [21,22].

Proxy Interpretation

Proxy Interpretation

We have furthered understanding of how paleoclimate “proxies” encode climate signals through proxy system models [10,23–25]. Alumni Jun Hu and Alex James have investigated speleothem records [14,26] on orbital [27] and shorter [28] timescales.

Open Science

Open Science

A throughline of our research is open science, exemplified by data compilations [29,30], data standards [31–33], and open-source software [16,22,34,35]. More at the LinkeEarth website.

Current Projects

LMR4D: P4CLIMATE — Connecting Seasonal to Millennial Timescales through Strongly Coupled Data Assimilation

NSF P4CLIMATE AGS-2402475 | Co-PI (PI: G. Hakim, UW) | 2024–2026

LMR4D is the latest iteration of the Last Millennium Reanalysis implementing a 4D variational (4DVAR) data assimilation framework — the same approach used in operational numerical weather prediction — to produce seasonally resolved temperature reconstructions over the Common Era. LMR4D will leverage data from new sources, including documentary evidence, marine sediments, annually-resolved marine bivalve chronologies, as well as boreholes. The resulting seasonally resolved reconstruction will be applied to (A1) simulate tropical cyclone track and intensity statistics using deep-learning weather models (Pangu-Weather, FourCastNet, DLWP), extending TC records by centuries and enabling the first millennial-scale simulations of tropical cyclone statistics, and (A2) characterize sources of climate variability across the seasonal-to-centennial continuum, including volcanic forcing responses and constraints on equilibrium climate sensitivity. LMR4D products will be publicly accessible via PReSto and NOAA NCEI; the 4DVAR algorithm and proxy system models will be shared on GitHub, with reproducible workflows distributed via PaleoBooks. The project is training two graduate students and several undergraduates.

NSF Award Page | Publications: [36,37]

PaleoPAL: An AI Research Assistant for Paleoclimatology

NSF CAIG RISE 2425885 | Co-PI (Lead: D. Khider, ISI) | 2025–2027

PaleoPAL leverages a Retrieval-Augmented Generation (RAG) – Large Language Model (LLM) framework to create an AI assistant for paleoclimatology, enabling scientists to search for datasets, methods, and workflows appropriate to their research problem directly from a Jupyter Notebook. The project targets three critical research areas: placing recent El Niño variations in the context of the last 10,000 years, detecting climate tipping points and their potential precursors, and generating empirically-based, low-cost climate projections. By embedding AI directly into the practice of paleoclimatology through a familiar Jupyter interface, PaleoPAL aims to lower technical and conceptual barriers to sophisticated analyses across the paleoclimate community.

NSF Award Page | Project Website

PaleoCube: Enabling Cloud-Based Paleoclimatology

NSF EarthCube ICER 2126510 | Co-PI (Lead: D. Khider, ISI) | 2021-2024

PaleoCube proposes to lower technical and social barriers that prevent full use of paleoclimate observations by bringing scientists to work in the Cloud. The project extends existing cyberinfrastructure (LinkedEarth, Pangeo, Jupyter) to bring cutting-edge capabilities to paleoclimate scientists through cloud-based workflows, hackathons, and community engagement.

NSF Award Page | [22,38] | CyberPaleo | PaleoBook Gallery

PReSto: A Paleoclimate Reconstruction Storehouse

NSF Geoinformatics EAR 1948822 | Co-PI (Lead: N. McKay, NAU) | 2020-2026

Developing a continuously-updated platform for paleoclimate reconstructions with broad web access to accelerate scientific inference. PReSto will connect growing digital paleoclimate data to evolving methodologies and distribute results through responsive web interfaces.

NSF Award Page | Publications | Project Info

Past Projects

FROGS: Facilitating Reproducible Open GeoScience

NSF GEO OSE Track 1 RISE-2324732 | Co-PI (Lead: D. Khider, ISI) | 2024-2026

Building open science capacity in the geosciences through the LeapFROGS training platform and a series of hands-on workshops (PyRATES, FAIRLeap, Open Geoscience Hackathon). Three workshops engaged 56 participants across more than a dozen geoscience subfields and career stages, producing 8 reproducible notebooks and 9 open-source software packages. Post-event surveys showed over 90% of participants gained confidence in applying FAIR and reproducible research methods.

A Big Data Approach to Fundamental Paleoclimate Questions

NSF P2C2 AGS 2002556 | Lead PI | 2020-2023

Applying Big Data approaches to address fundamental questions in climate dynamics: (1) the spatial extent of abrupt changes in hydroclimate, and (2) how knowledge of past temperature variations can help reduce uncertainty in twenty-first century climate projections. Links paleoclimate data to CMIP6 climate model projections.

NSF Award Page | [21,22,27,28,39]

The Global Climate Response to Volcanic Eruptions

NOAA Climate Program Office NA18OAR4310426 | Lead PI | 2018-2020

Investigating volcanic climate impacts using the Last Millennium Reanalysis framework.

Abrupt Change in Climate and Ecosystems

Belmont Forum via NSF ICER 1929554 | Co-PI (Lead: N. McKay, NAU) | 2019-2022

International collaboration investigating tipping points in climate and ecosystems through integrated analysis approaches. Part of the Belmont Forum’s focus on understanding where critical thresholds exist in Earth system components.

NSF Award Page |

LinkedEarth: Crowdsourcing Data Curation & Standards

NSF EarthCube ICER 1541029 | Lead PI | 2015-2017

Community platform development for paleoclimate data standards and knowledge curation.

Last Millennium Climate Reanalysis Project

NOAA Climate Program Office NA14OAR4310175 | Co-PI (Lead: G. Hakim, UW) | 2014-2017

Developing data assimilation methods for paleoclimate reconstructions over the Common Era.

GeoChronR: Open-Source Tools for Time-Uncertain Data

NSF Geoinformatics EAR 1347213 | Co-PI (Lead: N. McKay, NAU) | 2014-2017

Analysis, visualization and integration tools for geochronological data with age uncertainties.

Efficient High Dimensional Bayesian Methods for Climate Field Reconstruction

NSF Mathematical Geophysics DMS 1025464 | Co-PI (Lead: B. Rajaratnam, Stanford) | 2010-2015

Statistical method development for spatially complete climate reconstructions.

Multiproxy Reconstructions as A Missing-Data Problem

NSF P2C2 GEO 1003818 | Lead PI | 2010-2015

New techniques for multiproxy climate reconstructions and their application to regional climates of the past millennium.

Maximizing the Potential of Tropical Climate Proxies

NOAA Climate Program Office NA10OAR4310115 | Lead PI | 2010-2014

Integrated climate-proxy forward modeling to maximize the climate information extractable from tropical paleoclimate proxies.

References

1.
Emile-Geay J, Cobb KM, Mann ME, Wittenberg AT. Estimating Central Equatorial Pacific SST variability over the Past Millennium. Part 2: Reconstructions and Implications. J Clim. 2013;26:2329–52. doi:10.1175/JCLI-D-11-00511.1
2.
Zhu F, Emile-Geay J, Anchukaitis KJ, Hakim GJ, Wittenberg AT, Morales MS, et al. A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation. Nature Communications. 2022;13(1):747. doi:10.1038/s41467-022-28210-1
3.
Barboza LA, Emile-Geay J, Li B, He W. Efficient Reconstructions of Common Era Climate via Integrated Nested Laplace Approximations. Journal of Agricultural, Biological and Environmental Statistics. 2019. doi:10.1007/s13253-019-00372-4
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12.
Zhu F, Emile-Geay J, Hakim GJ, King J, Anchukaitis KJ. Resolving the differences in the simulated and reconstructed temperature response to volcanism. Geophysical Research Letters. 2020;47(8):e2019GL086908. doi:10.1029/2019GL086908
13.
Hu J, Emile-Geay J, Nusbaumer J, Noone D. Impact of convective activity on precipitation  in isotope-enabled general circulation models. Journal of Geophysical Research: Atmospheres. 2018;123(23):13, 595–13, 610. doi:10.1029/2018JD029187
14.
Hu J, Emile-Geay J, Tabor C, Nusbaumer J, Partin J. Deciphering oxygen isotope records from chinese speleothems with an isotope-enabled climate model. Paleoceanography and Paleoclimatology. 2019;34(12):2098–112. doi:10.1029/2019PA003741
15.
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16.
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19.
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20.
Emile-Geay J, Hakim GJ, Viens F, Zhu F, Amrhein DE. Temporal comparisons involving paleoclimate data assimilation: Challenges & remedies. Journal of Climate. 2025. doi:10.1175/JCLI-D-24-0101.1
21.
James A, Emile-Geay J, Malik N, Khider D. Detecting Paleoclimate Transitions With Laplacian Eigenmaps of Recurrence Matrices (LERM). Paleoceanography and Paleoclimatology. 2024 Jan;39(1):e2023PA004700. doi:10.1029/2023PA004700
22.
Khider D, Emile-Geay J, Zhu F, James A, Landers J, Ratnakar V, et al. Pyleoclim: Paleoclimate timeseries analysis and visualization with python. Paleoceanography and Paleoclimatology. 2022;37(10):e2022PA004509. doi:10.1029/2022PA004509
23.
Thompson DM, Ault TR, Evans MN, Cole JE, Emile-Geay J. Comparison of observed and simulated tropical climate trends using a forward model of coral. Geophys Res Lett. 2011;38:L14706. doi:10.1029/2011GL048224
24.
Dee SG, Steiger NJ, Emile-Geay J, Hakim GJ. On the utility of proxy system models for estimating climate states over the common era. Journal of Advances in Modeling Earth Systems. 2016;8. doi:10.1002/2016MS000677
25.
Dee SG, Parsons LA, Loope GR, Overpeck JT, Ault TR, Emile-Geay J. Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: Implications for multi-decadal variability. Earth and Planetary Science Letters. 2017;476(Supplement C):34–46. doi:10.1016/j.epsl.2017.07.036
26.
Hu J, Emile-Geay J, Partin J. Correlation-based interpretations of paleoclimate data – where statistics meet past climates. Earth and Planetary Science Letters. 2017 Feb;459:362–71. doi:10.1016/j.epsl.2016.11.048
27.
James A, Emile-Geay J, Partin JW, Khider D. Global speleothem analysis reveals state-dependent hydrological response to orbital forcing. Paleoceanography and Paleoclimatology. 2025;40(8):e2024PA005098. doi:10.1029/2024PA005098
28.
James A, Hu J, Emile-Geay J, Partin JW, Scroxton N, Malik N, et al. Regime Shifts in Holocene Paleohydrology as Recorded by Asian Speleothems. Paleoceanography and Paleoclimatology. 2025;40(1):e2024PA004974. doi:10.1029/2024PA004974
29.
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34.
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35.
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