This report describes the data set from the CALYPSO 2022 Campaign and the processing and quality-control steps that were taken in producing the data set. The CALYPSO Campaign was conducted from both the R/V Pourquoi Pas? from February 17-March 12, 2022 and the R/V Pelagia from February 20-March 16.

This report summarizes activities on the French research vessel Pourquoi Pas? operating as part of the Office of Naval Research CALYPSO project during its main 2022 field program in the northwest Mediterranean. Other components of this field program included the Netherland’s research vessel R.V. Pelagia, a fleet of underwater gliders operated by the Scripps Oceanographic Institute and the Balearic Islands Coastal Observing and Forecasting System (SOCIB).

Abstract: Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging: be it predicting if it will rain, knowing which direction a hurricane will go, understanding the implications of melting Arctic ice, or detecting the impacts of humans on the earth’s surface. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery: allowing scientists to ask “why?” but now with the power of machine learning.

Biography: Dr. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. Libby’s research has centered on climate variability, predictability, and change and the data analysis tools used to better understand the Earth system (including machine learning and causal discovery). She teaches graduate courses on statistical analysis methods, machine learning for Earth scientists, and data-driven forecasting from days-to-decades. See Barnes Group.