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Summary Slide for Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Networks

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Dec. 31, 2020
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Exploring explainable machine learning for detecting changes in climate
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Summary Slide for Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Networks

Dec. 31, 2020
• 0 likes • 118 views

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The relative roles of individual forcings on large-scale climate variability remain difficult to disentangle within fully-coupled global climate model simulations. Here, we train an artificial neural network (ANN) to classify the climate forcings of a new set of CESM1 initial-condition large ensembles that are forced by different combinations of aerosol (industrial and biomass burning), greenhouse gas, and land-use/land-cover forcings. As a result of learning the regional responses of internal variability to the different external forcings, the ANN is able to successfully classify the dominant forcing for each model simulation. Using recently developed explainable AI methods, such as layerwise relevance propagation, we then compare the patterns of climate variability identified by the ANN between different external climate forcings that are learned by the neural network. Further, we apply this ANN architecture on additional climate simulations from the multi-model large ensemble archive, which include all anthropogenic and natural radiative forcings. From this collection of initial-condition ensembles, the ANN is also able to detect changes in atmospheric internal variability between the 20th and 21st centuries by training on climate fields after the mean forced signal has already been removed. This ANN framework and its associated visualization tools provide a novel approach to extract complex patterns of observable and projected climate variability and trends in Earth system models. (from https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379553)

The relative roles of individual forcings on large-scale climate variability remain difficult to disentangle within fully-coupled global climate model simulations. Here, we train an artificial neural network (ANN) to classify the climate forcings of a new set of CESM1 initial-condition large ensembles that are forced by different combinations of aerosol (industrial and biomass burning), greenhouse gas, and land-use/land-cover forcings. As a result of learning the regional responses of internal variability to the different external forcings, the ANN is able to successfully classify the dominant forcing for each model simulation. Using recently developed explainable AI methods, such as layerwise relevance propagation, we then compare the patterns of climate variability identified by the ANN between different external climate forcings that are learned by the neural network. Further, we apply this ANN architecture on additional climate simulations from the multi-model large ensemble archive, which include all anthropogenic and natural radiative forcings. From this collection of initial-condition ensembles, the ANN is also able to detect changes in atmospheric internal variability between the 20th and 21st centuries by training on climate fields after the mean forced signal has already been removed. This ANN framework and its associated visualization tools provide a novel approach to extract complex patterns of observable and projected climate variability and trends in Earth system models. (from https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379553)

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Summary Slide for Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Networks

  1. 1. 1. Using explainable AI methods with artificial neural networks (ANNs) reveals climate patterns in large ensemble simulations 2. Metric proposed for quantifying the uncertainty of an ANN visualization method that extracts signals from different external forcings 3. ANN trained using a large ensemble simulation without time- evolving aerosols makes more accurate predictions of real world data Zachary M. Labe & Elizabeth A. Barnes Department of Atmospheric Science at Colorado State University 14 January 2021 – 1:15 PM to 1:20 PM (EST) 20th Conference on Artificial Intelligence for Environmental Science 101st AMS Annual Meeting (Paper 11.2) DISENTANGLING CLIMATE FORCING IN SINGLE-FORCING LARGE ENSEMBLES USING NEURAL NETWORKS Aerosol-driven Greenhouse gas-driven All forcings LRP – (LRP)

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