Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Arcodia, Marybeth C.; Barnes, Elizabeth A.; Mayer, Kirsten J.; Lee, Jiwoo; Ordonez, Ana; Ahn, Min-Seop

doi:10.1088/2752-5295/aced60

Title: Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Journal Article · 14 September 2023 · Environmental Research. Climate

DOI: https://doi.org/10.1088/2752-5295/aced60 · OSTI ID:2000195

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^[1]; Mayer, Kirsten J. ^[2];

^[3]; Ordonez, Ana ^[3];

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Colorado State University, Fort Collins, CO (United States)
Colorado State University, Fort Collins, CO (United States); National Center for Atmospheric Research, Boulder, CO (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); University of Maryland, College Park, MD (United States)

Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during forecasts of opportunity. Specifically, this work quantifies subseasonal prediction skill provided by the tropics within the Community Earth System Model Version 2 (CESM2) Large Ensemble and assesses how this skill evolves on decadal timescales. Utilizing the networks' confidence and explainable artificial intelligence, physically meaningful sources of predictability associated with periods of enhanced skill are identified. Using these networks, we find that tropically-driven subseasonal predictability varies on decadal timescales during forecasts of opportunity. Further, we investigate the drivers of the low frequency modulation of the tropical-extratropical teleconnection and discuss the implications. Analysis is extended to ECMWF Reanalysis v5 data, revealing that the relationships learned within the CESM2-Large Ensemble holds in modern reanalysis data. These results indicate that the neural networks are capable of identifying predictable decadal states of the climate system within CESM2 that are useful for making confident, accurate subseasonal precipitation predictions in the real world.

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Research Organization:: University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: SC0022070; AC52-07NA27344; INCITE; No. DE-AC52-07NA27344

OSTI ID:: 2000195

Alternate ID(s):: OSTI ID: 1993872; OSTI ID: 2001107; OSTI ID: 2406147

Report Number(s):: LLNL-JRNL-849547

Journal Information:: Environmental Research. Climate, Vol. 2, Issue 4; ISSN 2752-5295

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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