Award: OCE-2048509

We have used a global database of measurements of ocean chemistry that allow us to trace the contact between the ocean and the atmosphere in order to determine how long all of the water in the global ocean has been at depth. From this, we were able to use machine learning and our understanding of the exchange of gases between the atmosphere and the ocean to create a program that can quantiatively estimate the amount of carbon in seawater that is present because of human CO2 emissions to the atmosphere.  We've made this code freely available online at Github, and we intend to incorporate this code into several ongoing monitoring efforts to better account for ocean carbon uptake when we calibrate sensors on remote sampling platforms.  We anticipate that our estimation strategy will improve the calibrations on these sensors and will be a broadly useful tool for science and communication in the years to come.  We are nearing submission of a manuscript that will fully document our estimation routines and assess how accurately they can reproduce known distributions of extra carbon in models.  We also compare our results to results from many alternative methods in literature, and find that our estimates have surprisingly high fidelity.  This summary only covers the portion of the award related to activities conducted at the University of Washington.  Complementary work conducted at MBARI will be detailed in a parallel report.

Last Modified: 10/23/2024
Modified by: Brendan R Carter