Presenter(s):
David Trossman (NOAA STAR/NESDIS) and Robert Tyler (NASA GSFC Geodesy and Geophysics Laboratory and UMBC JCEST)

Sponsor(s):
STAR Science Seminar Series

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Meeting number: 199 183 0790
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Abstract:
A new remote sensing-based approach to monitor ocean heat content (OHC) anomalies is proposed to overcome challenges with observing OHC over the entire ocean. The output of an ocean state estimate – using the Estimating the Circulation & Climate of the Ocean (ECCO) framework – is assumed to be perfect observational data and used to identify prospective variables that could be calculated from remotely monitored characteristics of the ocean. The depth-integrated electrical conductivity (potentially derived from magnetometry) is shown to be highly predictive of OHC in poorly observed regions – such as those covered by sea ice – so it is used together with sea surface heights (from altimetry) and ocean bottom pressures (from gravimetry) to estimate OHC. The seafloor depth, sea surface height anomalies, ocean bottom pressure, and depth-integrated electrical conductivity explain virtually all of the variance in OHC. To demonstrate the feasibility of a method that uses these ocean characteristics – inferable from global satellite coverage – to monitor OHC, the output of ECCO is sampled along historical hydrographic transects, a machine learning algorithm – called a Generalized Additive Model or GAM – is trained on these samples, and OHC is estimated everywhere. This remote monitoring method can estimate global OHC within 0.15% spatial root-mean-square error (RMSE) on a bi-decadal time scale. This RMSE is sensitive to the spatial variance in OHC that gets sampled by hydrographic transects, the variables included in the GAM, and their measurement errors when inferred from satellite data – in particular the noise levels of depth-integrated electrical conductivity and ocean bottom pressure. OHC could be remotely monitored over sufficiently long time scales when enough spatial variance in OHC is explained in the training data over those time scales. This method could potentially supplement existing methods to monitor OHC.

Bio(s):
David Trossman is a physical oceanographer, by training. He received his PhD at the University of Washington in Seattle, did a postdoc at the University of Michigan in Ann Arbor, did another postdoc at McGill University, was a researcher jointly at NASA Goddard Space Flight Center and Johns Hopkins University through the GESTAR cooperative agreement, was a researcher at the University of Texas in Austin’s Oden Institute for Computational Engineering and Sciences, and is currently a senior scientist at NOAA STAR/NESDIS through Global Science & Technology. In general, his research has taken two trajectories. 1) He has studied the physical and biogeochemical consequences of ocean circulation and mixing as well as the interactions between the ocean and other components of the Earth system in order to understand and improve the realism of Earth system models. 2) He has also probed the information content of physical and biogeochemical observational data sources to advance the reconstruction of the ocean’s historical conditions through statistical techniques.

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Seminar Contact:
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