A sensitivity analysis of a regression model of ocean temperature

Furner, Rachel ORCID: https://orcid.org/0000-0003-0535-4388; Haynes, Peter; Munday, Dave ORCID: https://orcid.org/0000-0003-1920-708X; Paige, Brooks; Jones, Daniel C. ORCID: https://orcid.org/0000-0002-8701-4506; Shuckburgh, Emily. 2022 A sensitivity analysis of a regression model of ocean temperature. Environmental Data Science, 1, e11. 28, pp. https://doi.org/10.1017/eds.2022.10

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Abstract/Summary

There has been much recent interest in developing data-driven models for weather and climate predictions. However, there are open questions regarding their generalizability and robustness, highlighting a need to better understand how they make their predictions. In particular, it is important to understand whether data-driven models learn the underlying physics of the system against which they are trained, or simply identify statistical patterns without any clear link to the underlying physics. In this paper, we describe a sensitivity analysis of a regression-based model of ocean temperature, trained against simulations from a 3D ocean model setup in a very simple configuration. We show that the regressor heavily bases its forecasts on, and is dependent on, variables known to be key to the physics such as currents and density. By contrast, the regressor does not make heavy use of inputs such as location, which have limited direct physical impacts. The model requires nonlinear interactions between inputs in order to show any meaningful skill—in line with the highly nonlinear dynamics of the ocean. Further analysis interprets the ways certain variables are used by the regression model. We see that information about the vertical profile of the water column reduces errors in regions of convective activity, and information about the currents reduces errors in regions dominated by advective processes. Our results demonstrate that even a simple regression model is capable of learning much of the physics of the system being modeled. We expect that a similar sensitivity analysis could be usefully applied to more complex ocean configurations.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1017/eds.2022.10
Additional Keywords:	Data science, interpretable ML, model sensitivity, oceanography, regression model
Date made live:	11 Jan 2023 10:32 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530116

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