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- Title
Sensitivity analysis of a data-driven model of ocean temperature.
- Authors
Furner, Rachel; Haynes, Peter; Munday, Dave; Paige, Brooks; Jones, Daniel C.; Shuckburgh, Emily
- 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 datadriven models are making predictions is critical to developing trust in these alternative prediction systems. We develop and interpret a simple regression model of ocean temperature evolution, 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 the simple regression model analysed here 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.
- Subjects
SENSITIVITY analysis; ATMOSPHERIC circulation; OCEAN temperature; OCEAN dynamics; WEATHER forecasting; REGRESSION analysis
- Publication
Geoscientific Model Development Discussions, 2021, p1
- ISSN
1991-9611
- Publication type
Article
- DOI
10.5194/gmd-2021-132