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. With this project, we wish to improve our workflows and extend these algorithms.

In spring 2023, the DSI Scholar will begin by reviewing existing code for the implementation of the pCO2-Residual approach (Bennington et al. 2022, JAMES, doi:10.1029/2021MS002960). They will learn about the past work and develop an improved workflow. Next, they will explore extensions to analysis. We are interested in considering the impact of training the algorithm only on subsets of the data by decades, and in new forms of uncertainty analysis for these predictions.

The DSI Scholar will work with four members of the McKinley group (PI, research scientist, postdoc, graduate student). They will meet weekly to discuss progress and plans.

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

Faculty Advisor

  • Professor: McKinley, Galen
  • Center/Lab: Earth and Environmental Science / LEAP STC
  • Location: Innovation Hub ( 2276 12th Ave ) and Comer 429 (Lamont campus)
  • The McKinley Ocean Carbon Research Group studies how ocean physical and biogeochemical processes impact large-scale carbon cycling and primary productivity. Our primary research tools are numerical models, large historical datasets and machine learning.

Project Timeline

  • Earliest starting date: 4/1/2023
  • End date: 8/31/2023
  • Number of hours per week of research expected during Spring-Summer 2023: ~10

Candidate requirements

  • Skill sets: Fluency in Python. Should be willing to work on improving upon an existing code based as a way to get started in this project.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes