%0 Journal Article %1 liu2023physical %A Liu, Glenn %A Wang, Peidong %A Kwon, Young-Oh %D 2023 %J Geophysical Research Letters %K ML amv moc-var %N 24 %P e2023GL106278 %R https://doi.org/10.1029/2023GL106278 %T Physical Insights From the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks %U https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023GL106278 %V 50 %X Abstract North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.

@article{liu2023physical, abstract = {Abstract North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.}, added-at = {2024-01-02T12:39:34.000+0100}, author = {Liu, Glenn and Wang, Peidong and Kwon, Young-Oh}, biburl = {https://www.bibsonomy.org/bibtex/2a9abad158b388591c7f6fdecdac6ce53/laura.jackson}, doi = {https://doi.org/10.1029/2023GL106278}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2023GL106278}, interhash = {b399bfdf326e90be2b452f2a1ac96163}, intrahash = {a9abad158b388591c7f6fdecdac6ce53}, journal = {Geophysical Research Letters}, keywords = {ML amv moc-var}, note = {e2023GL106278 2023GL106278}, number = 24, pages = {e2023GL106278}, timestamp = {2024-01-02T12:39:34.000+0100}, title = {Physical Insights From the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023GL106278}, volume = 50, year = 2023 }