Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning

Sonnewald, Maike; Lguensat, Redouane

doi:10.1029/2021ms002496

Cited by 24 publications

(48 citation statements)

References 76 publications

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“…These applications are based mostly on instantaneous or very short-term relationships and do not address the problem of how these products can be used to improve our ability to understand and forecast the oceanic system. Further use for current reconstruction using ML [155], heat fluxes [99], the 3-dimensional circulation [206], and ocean heat content [125] are also being explored.…”

Section: Ocean Observationsmentioning

confidence: 99%

“…Objective analysis that can be understood as IAI can also be applied to explore theoretical branches of oceanography, revealing novel structures [45,207,217]. Examples where ML and theoretical exploration have been used in synergy by allowing interpretability, explainability, or both within oceanography include [206,243], and the concepts are discussed further in sections 6.2 and 6.…”

Section: Exchanges Between Observations and Theorymentioning

confidence: 99%

“…Many variables needed for analysis are effectively unavailable due the difficulty in saving or sharing the data. ML has the capacity to efficiently analyze large datasets as shown in section 2 and 3, but it has also been used to infer, for example, information about subsurface currents [50,155], eddy heat fluxes [99] and full 3-dimensional dynamics in CMIP6 [206]. ML in many forms has the potential to be highly valuable for researchers interested in the analysis of data that is increasingly large, potentially sparse, and partially unavailable for logistical reasons [85].…”

Section: Computational Challengesmentioning

confidence: 99%

“…ML based mapping tools could also be useful to inform where more observational data is needed, for example when deciding where to sample on a cruise or where to send autonomous platforms. To date, satellite images are largely used, but added guidance from ML techniques could be very valuable, particularly if sub-surface observations are the target [155,49,206].…”

Section: Decision Supportmentioning

confidence: 99%

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Bridging observation, theory and numerical simulation of the ocean using Machine Learning

Sonnewald,

Lguensat,

Jones

et al. 2021

Preprint

Self Cite

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Section: Ocean Observationsmentioning

confidence: 99%

Section: Exchanges Between Observations and Theorymentioning

confidence: 99%

Section: Computational Challengesmentioning

confidence: 99%

Section: Decision Supportmentioning

confidence: 99%

See 2 more Smart Citations

Bridging observation, theory and numerical simulation of the ocean using Machine Learning

Sonnewald,

Lguensat,

Jones

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

“…Further, a growing number of explainable artificial intelligence (XAI) methods have been adapted for applications in weather and climate science (McGovern et al., 2019; Toms et al., 2020), which can retrospectively trace the decisions of neural networks and assist scientists in comparing the attribution of input features to known physical mechanisms in the Earth system. Besides evaluating trust and credibility to the machine learning prediction, XAI methods can also be used for physics‐guided scientific discovery and hypothesis testing (Ebert‐Uphoff & Hilburn, 2020; Sonnewald & Lguensat, 2021; Toms et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

Labe

Barnes

2022

Geophysical Research Letters

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The global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long‐term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content (OHC) at the onset. Through a machine learning explainability method, we find the ANN is learning off‐equatorial patterns of anomalous OHC that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations.

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Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives

Materia,

García,

van Straaten

et al. 2024

WIREs Climate Change

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Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are particularly challenging to predict accurately due to their rarity and chaotic nature, and because of model limitations. However, recent studies have shown that there might be systemic predictability that is not being leveraged, whose exploitation could meet the need for reliable predictions of aggregated extreme weather measures on timescales from weeks to decades ahead. Recently, numerous studies have been devoted to the use of artificial intelligence (AI) to study predictability and make climate predictions. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large‐scale and local drivers. Machine and deep learning have been explored to enhance prediction, while causal discovery and explainable AI have been tested to improve our understanding of the processes underlying predictability. Hybrid predictions combining AI, which can reveal unknown spatiotemporal connections from data, with climate models that provide the theoretical foundation and interpretability of the physical world, have shown that improving prediction skills of extremes on climate‐relevant timescales is possible. However, numerous challenges persist in various aspects, including data curation, model uncertainty, generalizability, reproducibility of methods, and workflows. This review aims at overviewing achievements and challenges in the use of AI techniques to improve the prediction of extremes at the subseasonal to decadal timescale. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development.This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Social Status of Climate Change Knowledge > Climate Science and Decision Making

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Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning

Cited by 24 publications

References 76 publications

Bridging observation, theory and numerical simulation of the ocean using Machine Learning

Bridging observation, theory and numerical simulation of the ocean using Machine Learning

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives

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