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. 2020 Nov 25;10:20514. doi: 10.1038/s41598-020-76873-x

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Relative variable importance plots from random forest analysis for thermal metric trends. Relative variable importance for (a) deepwater temperature trends, (b) mean water column temperature trends, (c) surface water temperature trends, and (d) density difference trends. Solid circles in each panel indicate the relative increase in mean squared error (MSE) due to a random permutation compared to the most important variable, in order of decreasing importance. Variables marked with “X” had no increase in MSE and are statistically equivalent to random prediction. Random forest for thermocline depth resulted in 0% explanatory power, so no additional analysis was conducted.