Preprints
https://doi.org/10.5194/gmd-2021-132
https://doi.org/10.5194/gmd-2021-132

Submitted as: model description paper 02 Jun 2021

Submitted as: model description paper | 02 Jun 2021

Review status: this preprint is currently under review for the journal GMD.

Sensitivity analysis of a data-driven model of ocean temperature

Rachel Furner1,2, Peter Haynes1, Dave Munday2, Brooks Paige3, Daniel C. Jones2, and Emily Shuckburgh1 Rachel Furner et al.
  • 1University of Cambridge, Cambridge, UK
  • 2British Antarctic Survey, Cambridge, UK
  • 3University College London, London, UK

Abstract. There has been much recent interest in developing data-driven models for weather and climate predictions. These have shown reasonable success in modelling atmospheric dynamics over short time scales, however there are open questions regarding the sensitivity and robustness of these models. Using model interpretation techniques to better understand how data-driven models are making predictions is critical to developing trust in these alternative prediction systems. We develop a simple regression model of ocean temperature evolution, Ocean Temperature Regressor v1.0, and investigate its sensitivity to improve understanding of whether data-driven models are capable of learning the complex underlying dynamics of the systems being modelled.

We investigate model sensitivity in a variety of ways and find that Ocean Temperature Regressor v1.0 behaves in ways which are, for the most part, in line with our knowledge of the ocean system being modelled. Specifically we see that the regressor heavily bases its forecasts on, and is dependent on, variables which we know are key to the physical dynamics inherent in the system, such as the currents and density. By contrast, inputs to the regressor which have limited direct dynamic impact, such as location, are not heavily used by the regressor. We also find that the regression model requires non-linear interactions between inputs in order to show any meaningful predictive skill – in line with out knowledge of the highly nonlinear dynamics of the ocean. Further sensitivity analysis is carried out to interpret that the ways in which certain variables are used by the regression model. Results here are again mostly in line with our physical knowledge of the system, for example, we see that information about the vertical profile of the water column reduces errors in areas associated with convective activity, and information about the currents is used by the regressor to reduce errors in regions dominated by advective processes.

Our results show that even a simple regression model is capable of “learning” much of the physical dynamics inherent in the ocean system being modelled, which gives promise for the sensitivity and generalisability of data-driven models more generally.

Rachel Furner et al.

Status: open (until 28 Jul 2021)

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  • CC1: 'Comment on gmd-2021-132', Paul PUKITE, 06 Jun 2021 reply

Rachel Furner et al.

Rachel Furner et al.

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Short summary
Traditional weather & climate models are built from physics-based equations, while data-driven models are built from patterns found in datasets using Machine Learning or statistics. There is growing interest in using data-driven models for weather & climate prediction, but confidence in their use depends on understanding the patterns they're finding. We look at this with a simple regression model of ocean temperature and see the patterns found by the regression model are similar to the physics.