Regionalizing the Sea-level Budget With Machine Learning Techniques
Published in Ocean Science, 2023
Recommended citation: Camargo, C. M. L., Riva, R. E. M., Hermans, T. H. J., Schütt, E. M., Marcos, M., Hernandez-Carrasco, I., and Slangen, A. B. A.: Regionalizing the sea-level budget with machine learning techniques, Ocean Sci., 19, 17–41, https://doi.org/10.5194/os-19-17-2023, 2023 https://doi.org/10.5194/os-19-17-2023
Closing the sea level budget (between observed rise and the sum of its causes) has been a challenge, is an ongoing effort and has primarily concerned the global mean. In this paper we use machine learning to identify sub-areas with similar trends to close the sea level budget on a regional level, with much reduced errors compared with 1-degree grid points. (Text adapted from ‘Co-editor-in-chief’ highlight statement)