In lieu of an invited speaker for the GML seminar, we are going to have an extended seminar on March 8th from 2:30-4pm with 5 speakers who will share their AGU or AMS presentations. The schedule is as follows:

Galen McKinley is Professor of Earth and Environmental Sciences Columbia University and the Lamont Doherty Earth Observatory. She is an ocean, carbon cycle and climate scientist. Her research focuses on the physical, chemical and ecological drivers of the global ocean’s uptake of anthropogenic carbon. Regional and global ocean and climate models, and data science techniques applied to large community-compiled datasets are her primary tools.

Professor McKinley earned a BS in Civil Engineering from Rice University (1995) and a PhD in Climate Physics and Chemistry from MIT (2002). Her postdoctoral work was at Instituto Nacional de Ecologia in Mexico and Princeton University. From 2004-2017, she served on the faculty in Atmospheric and Oceanic Sciences at University of Wisconsin – Madison. Selected honors include the 2020 Ocean Science Voyager award from the American Geophysical Union, 2012-2013 Defense Science Study Group, and the Class of 1955 Teaching Award at UW-Madison in 2011.

In addition to research and teaching at Columbia University and Lamont Doherty Earth Observatory, Professor McKinley frequently contributes to national and international scientific coordination, and to scientific advising for the US federal government. She also enjoys opportunities to share climate and ocean science with the general public.

The ocean has absorbed approximately 40% of industrial-age fossil carbon emissions, and thus has substantially damped climate change. Understanding decadal variability in the ocean carbon sink is crucial for accurate diagnosis of the global carbon cycle, and will improve confidence in future predictions. I use ocean models, machine-learning based reconstructions of the global ocean carbon fluxes from sparse pCO2 data, and theory to quantify recent decadal variability and to understand remaining uncertainties.

Ensembles of hindcast ocean models and ensembles of pCO2 observation-based products indicate globally-coherent changes in the sink since the 1990s. There was a sharp increase in the ocean sink in the early 1990s. Next, there was a decline to a sink minimum in 2001, and then the sink recovered. By developing an upper ocean diagnostic box model that replicates these variations, I can attribute these patterns to two external forcings: the variable growth rate of atmospheric pCO2, and the 1991 eruption of Mt. Pinatubo.

Ocean models suggest smaller amplitude decadal flux variability than do observation-based products. Observation-based products are based on very sparse pCO2 data, with only about 2% coverage of the global ocean. Thus, errors may be significant when machine learning is applied to extrapolate to full coverage. To understand these errors, we have created a testbed using four climate model Large Ensembles which we can evaluate extrapolation skill. Due to the very sparse sampling, we show that the amplitude of decadal flux variability is overestimated by all machine learning approaches. Thus, ocean models may not be significantly underestimating flux variability as has been previously suggested.