Predicting Critical Biogeochemistry of the Southern Ocean for Climate
Monitoring
- URL: http://arxiv.org/abs/2111.00126v1
- Date: Sat, 30 Oct 2021 00:13:46 GMT
- Title: Predicting Critical Biogeochemistry of the Southern Ocean for Climate
Monitoring
- Authors: Ellen Park, Jae Deok Kim, Nadege Aoki, Yumeng Melody Cao, Yamin
Arefeen, Matthew Beveridge, David Nicholson, Iddo Drori
- Abstract summary: We train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location.
We apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network.
- Score: 1.8689461238197955
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Biogeochemical-Argo (BGC-Argo) program is building a network of globally
distributed, sensor-equipped robotic profiling floats, improving our
understanding of the climate system and how it is changing. These floats,
however, are limited in the number of variables measured. In this study, we
train neural networks to predict silicate and phosphate values in the Southern
Ocean from temperature, pressure, salinity, oxygen, nitrate, and location and
apply these models to earth system model (ESM) and BGC-Argo data to expand the
utility of this ocean observation network. We trained our neural networks on
observations from the Global Ocean Ship-Based Hydrographic Investigations
Program (GO-SHIP) and use dropout regularization to provide uncertainty bounds
around our predicted values. Our neural network significantly improves upon
linear regression but shows variable levels of uncertainty across the ranges of
predicted variables. We explore the generalization of our estimators to test
data outside our training distribution from both ESM and BGC-Argo data. Our use
of out-of-distribution test data to examine shifts in biogeochemical parameters
and calculate uncertainty bounds around estimates advance the state-of-the-art
in oceanographic data and climate monitoring. We make our data and code
publicly available.
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