Published: October 30, 2024; Aithor: Julia Sonrisa
Address: 2276 12th Avenue, Room 206, New York, NY 10027, United States
Abstract: To understand climate impacts from rising carbon emissions, it is critical to accurately quantify the air-sea CO2 exchange and the ocean carbon uptake (i.e., ocean sink) in time and space. Measurements of ocean carbon are sparse because of the vast size of the ocean and limited resources. It is possible to reconstruct ocean carbon values where measurements do not exist by using Machine Learning (ML). However, current estimates of ocean carbon uptake are uncertain and show a large spread.
Earth system models (ESMs) can be used as ‘testbeds’ to better understand the uncertainties in the ocean sink estimates. The target (ocean carbon) and driver variables (e.g., ocean temperature) needed for the ML reconstruction are sampled from the testbed in a time and space pattern identical to real-world measurements. The benefit of a testbed comes from the fact that the full reconstruction can be compared against a correct solution — the full testbed model field — and not just a test set (typically 20% of available data). Furthermore, a testbed can be used to explore various ocean carbon sampling strategies without actually doing the measurements, which would save time and money.
We have used the Pangeo-ESGF CMIP6 Zarr Data 2 (Busecke and Stern, 2024) as a testbed to reconstruct ocean carbon globally over the period 1982-2022. Our work represents a case study on how to utilize the CMIP6 catalog available on the LEAP Pangeo platform. We will show how current and future sampling, as well as algorithmic design choices, can impact ML reconstructions and ocean sink estimates.
Thea Heimdal: Thea is a researcher at Lamont and Columbia University, where she has been for the past two years. She got her PhD from the University of Oslo in 2018, where her research focus was paleoclimate and volcanic carbon emissions. Since starting at Columbia, Thea has shifted her focus to the short-term modern ocean carbon cycle. She combines models, observations, and machine learning to better understand the uptake of carbon in the oceans.
Time: 12:00-2:00 om EST
Free!
Detailed information and discussion of the event.