About

Ryan P. Abernathey is an Associate Professor of Earth and Environmental Sciences at Columbia University and the Lamont-Doherty Earth Observatory. He received his Ph.D. from MIT in 2012 and his B.A. from Middlebury College. After completing a postdoctoral fellowship at Scripps Institution of Oceanography, he joined Columbia in 2013. His research focuses on physical oceanography, specifically the large-scale ocean circulation and its relationship with Earth's climate. A central theme of his work is understanding how ocean mesoscale turbulence, including eddies, waves, and jets on scales of tens to hundreds of kilometers, contributes to the transport of momentum, heat, and geochemically relevant tracers. His regional focus is the Southern Ocean, which surrounds Antarctica and links the three main ocean basins. Abernathey employs high-resolution numerical modeling and satellite remote sensing in his research, which has fostered an interest in high-performance computing and big data. He has been recognized with several awards, including an Alfred P. Sloan Research Fellowship in Ocean Sciences and an NSF CAREER award for his project on the evolution of mesoscale turbulence in a changing climate. Additionally, he has received a NASA New Investigator Award and a Columbia Research Initiatives for Science and Engineering (RISE) grant to apply machine learning techniques to ocean satellite observations. Abernathey is an active advocate for open source software, open data, and reproducible science.

Research topics

• Computer Science
• Geology
• Artificial Intelligence
• Meteorology
• Oceanography
• Ecology
• Environmental science
• Algorithm
• Data science
• Geophysics
• Operating system
• Biology
• Geography
• Physics
• Computer vision
• World Wide Web
• Climatology

Selected publications

• xarray

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-13

  otherOpen access

  N-D labeled arrays and datasets in Python.

  PublisherDOI

• Rapid changes in terrestrial carbon dioxide uptake captured in near-real time from a geostationary satellite: The ALIVE framework

  Remote Sensing of Environment · 2025-04-14 · 5 citations

  articleOpen access

  The terrestrial carbon cycle responds to human activity, ecosystem dynamics, and weather and climate variability including extreme events. Satellite remote sensing has transformed our ability to estimate ecosystem carbon dioxide uptake, the gross primary productivity (GPP), with increasing accuracy and spatial resolution. Many aspects of terrestrial carbon cycling happen quickly on sub-daily or daily scales. These dynamics may not be captured at the temporal scales of typical remote sensing products from polar orbiting satellites – often multiple days or longer. Imagers onboard geostationary satellites measure the Earth system at “hypertemporal” time scales of minutes or less and often have the spectral capabilities to estimate GPP and other surface-atmosphere fluxes using established approaches. Here, we use observations and data products from the Advanced Baseline Imager (ABI) on the Geostationary Environmental Operational Satellite – R Series (GOES-R) to create ALIVE GPP ( A dvanced Baseline Imager L ive I maging of V egetated E cosystems), a GPP product that provides open data on the native five-minute basis of GOES-R CONUS scenes with latency under one day. Our machine learning model, trained on GPP estimates from 111 eddy covariance flux towers with 276 site-years of data spanning tropical to boreal ecosystems, captures up to 70 % of the observed variability when 20 % of tower sites are withheld, with R 2 values of 0.78 (0.82) when aggregating to daily (weekly) periods. We compared ALIVE GPP predictions against eight-day MODIS MOD17A2 GPP estimates and daily GPP estimates from the Breathing Earth System Simulator v2 (BESSv2) and demonstrate how ALIVE GPP simulates the impacts of phenological transitions, flash drought, and hurricanes. Advancements to geostationary satellite imagery, machine learning, and cloud computing make it possible to estimate carbon flux in near real-time and provide new ways to understand the ever-changing carbon cycle and the processes that define it. • We created ‘ALIVE GPP ’, a five-minute GPP mapping framework with <1 day latency. • Gradient boosting models were trained on eddy covariance and GOES-R data across CONUS. • ALIVE GPP R 2 is 0.67 to 0.70 for half-hourly intervals and 0.78 for daily sums. • Rapid regional GPP responses to extreme events matched expectations.

  PublisherDOI

• The Thermodynamics of the 2023 Gulf of Mexico Marine Heatwave

  Geophysical Research Letters · 2025-02-20 · 1 citations

  articleOpen access

  Abstract This study aims to understand the mechanisms of the activation and evolution of the marine heatwave (MHW) that occurred in the Gulf of Mexico (GoM) during summer 2023. We quantified contributions of the thermodynamic processes that transformed surface waters in the GoM into an unprecedented large volume of extremely warm water . Through water mass transformation analysis of reanalyses data, we find that the genesis of this MHW was due to the compounding effect of anomalously warm winter surface water priming the region for a MHW, coupled with greater exposure to strong solar radiation. Transformation due to total surface fluxes (sensible and latent heat, solar and longwave radiation) contributed to the MHW volume at a peak rate of 17.0 Sv ( = Sv), while the residual term (including mixing) countered the effect by 22.3 Sv at its peak. Total transformation during this 2023 MHW peaked at 4.9 Sv.

  PublisherDOI

• The Impact of Sub‐Grid Heterogeneity on Air‐Sea Turbulent Heat Flux in Coupled Climate Models

  Geophysical Research Letters · 2025-06-30 · 2 citations

  articleOpen accessSenior author

  Abstract Understanding air‐sea interaction is crucial for our ability to predict future states of the climate system, and for decision‐making. However, the representation of air‐sea interactions in climate models is limited by structural errors. Coarse‐resolution climate models do not resolve small‐scale structures at the air‐sea state, which, due to nonlinearities in the coupling bulk formulas, can impact the large‐scale air‐sea exchange—a mechanism that has received little attention in the literature. Since observations at the temporal and spatial coverage needed to study this problem do not yet exist, we quantify the impact of this small‐scale heterogeneity on the large‐scale air‐sea heat flux by analyzing 1/10° coupled simulations. This effect systematically cools the ocean by about 4 globally, with regional impacts reaching up to 100 . Key contributors are atmospheric wind and oceanic temperature heterogeneity, with the former causing widespread cooling and the latter introducing more spatially variable effects.

  PublisherOA PDFDOI

• The Overlooked Sub-Grid Air-Sea Flux in Climate Models

  2024-05-18 · 2 citations

  preprintOpen accessSenior author

  Understanding air-sea interaction is crucial for our ability to predict future states of the climate system, and to inform economic and societal decision-making. However, the representation of air-sea interactions in climate models is limited by structural errors associated with model resolution. Coarse-resolution climate models do not resolve small-scale structures in the air-sea state, which, due to strong nonlinearities in the coupling formulae, can impact the large-scale air-sea exchange—a mechanism that has received little attention and is the focus of this paper. Since observations at the temporal and spatial coverage needed to study this problem do not yet exist, we quantify the impact of this small-scale heterogeneity on the large-scale air-sea heat flux by analyzing 1/10° coupled climate simulations. This effect systematically cools the ocean by about 4W/m2 globally—with large spatio-temporal variations—and mostly enhances the large-scale heat flux. By identifying an overlooked contribution to air-sea heat flux in climate models, we open a promising new direction for addressing biases in climate simulations and thus improving future climate predictions. Furthermore, future observations, like the newly proposed satellite mission ODYSEA35, could potentially observe and quantify this effect directly.

  PublisherOA PDFDOI

• Learning Machine Learning with Lorenz-96

  Journal of Open Source Education · 2024-12-26 · 1 citations

  articleOpen access

  International audience

  PublisherOA PDFDOI

• A global Lagrangian eddy dataset based on satellite altimetry&amp;#160;

  2024-03-08

  preprintOpen accessSenior authorCorresponding

  The methods used to identify coherent ocean eddies are either Eulerian or Lagrangian in nature, and nearly all existing eddy datasets are based on the Eulerian method. In this study, millions of Lagrangian particles are advected by satellite-derived surface geostrophic velocities over the period of 1993&amp;#8211;2019. Using the method of Lagrangian-averaged vorticity deviation (LAVD), we present a global Lagrangian eddy dataset&amp;#160;(GLED v1.0). This open-source dataset contains not only the general features (eddy center position, equivalent radius, rotation property, etc.) of eddies with lifetimes of&amp;#160;30, 90, and 180&amp;#8201;days, but also the trajectories of particles trapped by coherent eddies over the lifetime. We present the statistical features of Lagrangian eddies and compare them with those of the most widely used sea surface height (SSH) eddies, focusing on generation sites, size, and propagation speed. A remarkable feature is that Lagrangian eddies are generally smaller than SSH eddies, with a radius ratio of about&amp;#160;0.5. Also, the validation using Argo floats indicates that coherent eddies from GLED v1.0 exist in the real ocean and have the ability to transport water parcels. Our eddy dataset provides an additional option for oceanographers to understand the interaction between coherent eddies and other physical or biochemical processes in the Earth system.

  PublisherDOI

• The Thermodynamics of the 2023 Gulf-of-Mexico Marine Heatwave

  2024-08-08

  preprintOpen accessSenior author

  This study aims to understand the mechanisms of the activation and evolution of the marine heatwave (MHW) that occurred in the Gulf of Mexico (GOM) during summer 2023. We quantified contributions of the thermodynamic processes that transformed surface waters in the GOM into an unprecedented large volume of extremely warm water (&gt; 31.8 o C). Through water mass transformation analysis of reanalyses data, we find that the genesis of this MHW was due to the compounding effect of anomalously warm winter surface water priming the region for a MHW, coupled with greater exposure to strong solar radiation. Transformation due to total surface fluxes (sensible and latent heat, solar and longwave radiation) contributed to the MHW volume at a peak rate of 17.7 Sv (Sv = 10 6 m 3 s -1 ), while mixing countered the effect by 14.6 Sv at its peak. Total transformation during this 2023 MHW peaked at 4.9 Sv.

  PublisherDOI

• XDGGS: Xarray Extension for Discrete Global Grid Systems (DGGS)

  2024-03-09

  preprintOpen access

  Traditional geospatial representations of the globe on a 2-dimensional plane often introduce distortions in area, distance, and angles. Discrete Global Grid Systems (DGGS) mitigate these distortions and introduce a hierarchical structure of global grids. Defined by ISO standards, DGGSs serve as spatial reference systems facilitating data cube construction, enabling integration and aggregation of multi-resolution data sources. Various tessellation schemes such as hexagons and triangles cater to different needs - equal area, optimal neighborhoods, congruent parent-child relationships, ease of use, or vector field representation in modeling flows.The fusion of Discrete Global Grid Systems (DGGS) and Datacubes represents a promising synergy for integrated handling of planetary-scale data.The recent Pangeo community initiative at the ESA BiDS'23 conference has led to significant advancements in supporting Discrete Global Grid Systems (DGGS) within the widely used Xarray package. This collaboration resulted in the development of the Xarray extension XDGGS (https://github.com/xarray-contrib/xdggs). The aim of xdggs is to provide a unified, high-level, and user-friendly API that simplifies working with various DGGS types and their respective backend libraries, seamlessly integrating with Xarray and the Pangeo scientific computing ecosystem. Executable notebooks demonstrating the use of the xdggs package are also developed to showcase its capabilities.This development represents a significant step forward, though continuous efforts are necessary to broaden the accessibility of DGGS for scientific and operational applications, especially in handling gridded data such as global climate and ocean modeling, satellite imagery, raster data, and maps.Keywords: Discrete Global Grid Systems, Xarray Extension, Geospatial Data Integration, Earth Observation, Data Cube, Scientific Collaboration

  PublisherDOI

• XDGGS: A community-developed Xarray package to support planetary DGGS data cube computations

  ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2024-06-20 · 1 citations

  articleOpen access

  Abstract. Traditional map projections introduce distortions, especially for global data. Discrete Global Grid Systems (DGGS) offer an alternative by dividing the Earth into equal-area grid cells at different resolutions. This paper describes xdggs, a new Xarray extension that simplifies working with DGGS. Xdggs provides a unified API for various DGGS libraries and integrates seamlessly with the Pangeo ecosystem through extending the widely used Xarray library to use the DGGS-specific cell identifiers as an index. This development makes DGGS more accessible and will lead to facilitating data analysis on a planetary scale.Xdggs aims to provide a user-friendly API that hides the implementation complexities of different DGGS libraries. And because it integrates seamlessly with Xarray, a popular tool for geospatial data analysis, xdggs promotes FAIR data practices by simplifying data access and interoperability and can become a valuable tool for geospatial scientists and application developers working with global datasets.

  PublisherOA PDFDOI

Recent grants

• CAREER: Evolution of Ocean Mesoscale Turbulence in a Changing Climate

  NSF · $763k · 2016–2022

• Collaborative Research: The Upper Branch of the Southern Ocean Overturning in the Southern Ocean State Estimate: Water Mass Transformation and the 3-D Residual Circulation

  NSF · $102k · 2014–2019

• Collaborative Research: Ocean Transport and Eddy Energy

  NSF · $49k · 2019–2022

• Collaborative Proposal: EarthCube Integration: Pangeo: An Open Source Big Data Climate Science Platform

  NSF · $795k · 2017–2021

Frequent coauthors

• Dhruv Balwada

  Lamont-Doherty Earth Observatory

  68 shared

• K. Shafer Smith

  57 shared

• Takaya Uchida

  Université Grenoble Alpes

  46 shared

• Qiyu Xiao

  Hunan Cancer Hospital

  45 shared

• Joseph Hamman

  33 shared

• Jonathan Gula

  Ifremer

  32 shared

• Aurélien Ponte

  Ifremer

  31 shared

• Tongya Liu

  Second Institute of Oceanography

  30 shared

Education

• Ph.D., Earth, Atmospheric, and Planetary Sciences

  Massachusetts Institute of Technology

  2012

• BA, Physics

  Middlebury College

  2004

Awards & honors

• Alfred P. Sloan Research Fellowship in Ocean Sciences (2016)
• NSF CAREER award for a project entitled “Evolution of Mesosc…
• NASA New Investigator Award (2013)
• Columbia Research Initiatives for Science and Engineering (R…