The ocean significantly mitigates climate change by absorbing fossil fuel carbon from the atmosphere. Cumulatively since the preindustrial times, the ocean has absorbed 40% of emissions. To understand past changes, diagnose ongoing changes, and to predict the future behavior of the ocean carbon sink, we must understand its spatial and temporal variability. However, the ocean is poorly sampled and so we cannot do this directly from in situ measurements.

In the McKinley group, we have developed several data science techniques to reconstruct ocean carbon data based on association to satellite-based full-field driver data. We have also developed the Large Ensemble Testbed, a compilation of Earth System simulations designed for the evaluation of ocean carbon reconstructions (Gloege et al., in review; Stamell et al., in review). In this Spring 2021 project, the DSI Scholar will apply the Large Ensemble Testbed to assess how regional disaggregation of reconstructions may allow some regions to have substantially lower uncertainties than if only global algorithms are used. For this project, the data will be 100 realizations of simulated surface ocean pCO2, subsampled as the real data (SOCAT, www.socat.info), as well as simulated sea surface temperature, chlorophyll, salinity, and mixed layer depth for 1982-2016. All datasets are already in use, thus data preparation will not be a significant task.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Galen McKinley
  • Department/School: Earth and Environmental Science / A&S
  • Location: Virtual for spring 2021
  • The McKinley Ocean Carbon Research Group studies how ocean physical and biogeochemical processes impact large-scale carbon cycling and primary productivity. These studies encompass fluid dynamics, climate processes, biogeochemistry and ecology. Our primary research tools are numerical models and large historical datasets.

Project Timeline

  • Earliest starting date: 3/1/2021
  • End date:
  • Number of hours per week of research expected during Spring 2021: ~10
  • Project will extend through one of spring or summer terms.

Candidate requirements

  • Skill sets: Python, machine learning basics (NN, XGB)
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes
  • Additional comments: interest in learning about the oceans and climate