Version 1
: Received: 5 February 2024 / Approved: 5 February 2024 / Online: 5 February 2024 (16:31:38 CET)
How to cite:
Deng, L.; Borhara, K.; Wang, S. Explainable AI in Assessing Multi-Year ENSO Predictability from the Western North Pacific Precursor. Preprints2024, 2024020302. https://doi.org/10.20944/preprints202402.0302.v1
Deng, L.; Borhara, K.; Wang, S. Explainable AI in Assessing Multi-Year ENSO Predictability from the Western North Pacific Precursor. Preprints 2024, 2024020302. https://doi.org/10.20944/preprints202402.0302.v1
Deng, L.; Borhara, K.; Wang, S. Explainable AI in Assessing Multi-Year ENSO Predictability from the Western North Pacific Precursor. Preprints2024, 2024020302. https://doi.org/10.20944/preprints202402.0302.v1
APA Style
Deng, L., Borhara, K., & Wang, S. (2024). Explainable AI in Assessing Multi-Year ENSO Predictability from the Western North Pacific Precursor. Preprints. https://doi.org/10.20944/preprints202402.0302.v1
Chicago/Turabian Style
Deng, L., Krishna Borhara and Shih-Yu Wang. 2024 "Explainable AI in Assessing Multi-Year ENSO Predictability from the Western North Pacific Precursor" Preprints. https://doi.org/10.20944/preprints202402.0302.v1
Abstract
The application of Explainable AI (XAI) to improve understanding of complex non-linear SST teleconnections and gain predictive skills is at the frontier of climate research. In this study, we attempt to refine the applications of XAI advancements to ENSO prediction by including SSTA in the western North Pacific (WNP) as a precursor. Our analyses indicate that the baseline accuracy increases from the 60% threshold previously reported for ENSO to >85% for moderate warm, cold, and neutral ENSO states one year in advance when WNP SSTA is included. Precision increases to over 90% for higher magnitude El Niños and La Niñas. Experiments conducted at 4-year lead times yield a strong WNP SSTA signal. A moderate SSTA signal is observed at 3-year lead time with no predictability; however, predictability spikes to over 80% when stratospheric zonal winds are included with WNP SSTA. While additional scientific work is required to make robust connections between XAI and dynamical processes, our results demonstrate a potential to improve long term predictability of ENSO through the ability of XAI in capturing SST patterns evolving at extended spatial and temporal scales.
Keywords
ENSO Predictability; Machine Learning; Explainable AI; Western North Pacific
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
