Earthly Machine Learning

• AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation

  🎙️ Episode 24: AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation🔗 DOI: https://doi.org/10.1038/s41561-024-01527-w🌐 AbstractDespite decades of progress, Earth system models (ESMs) still face significant gaps in accuracy and uncertainty, largely due to challenges in representing small-scale or poorly understood processes. This episode explores a transformative vision for next-generation climate modeling—one that embeds AI across multiple scales to enhance resolution, improve model fidelity, and better inform climate mitigation and adaptation strategies.📌 Bullet points summaryExisting ESMs struggle with inaccuracies in climate projections due to subgrid-scale and unknown process limitations.A new approach is proposed that blends AI with multiscale modeling, combining fine-resolution simulations with coarser hybrid models that capture key Earth system feedbacks.This strategy is built on four pillars:Higher resolution via advanced computingPhysics-aware machine learning to enhance hybrid modelsSystematic use of Earth observations to constrain modelsModernized scientific infrastructure to operationalize insightsAims to deliver faster, more actionable climate data to support urgent policy needs for both mitigation and adaptation.Envisions hybrid ESMs and interactive Earth digital twins, where AI helps simulate processes more realistically and supports climate decision-making at scale.💡 The Big IdeaIntegrating AI into climate models across scales is not just an upgrade—it’s a shift towards smarter, faster, and more adaptive climate science, essential for responding to the climate crisis with precision and urgency.📖 CitationEyring, Veronika, et al. "AI-empowered next-generation multiscale climate modelling for mitigation and adaptation." Nature Geoscience 17.10 (2024): 963–971.

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• FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators

  🎙️ Episode 23: FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators🔗 DOI: https://doi.org/10.1145/3592979.3593412🌍 AbstractAs climate change intensifies extreme weather events, traditional numerical weather prediction (NWP) struggles to keep pace due to computational limits. This episode explores FourCastNet, a deep learning Earth system emulator that delivers high-resolution, medium-range global forecasts at unprecedented speed—up to five orders of magnitude faster than NWP—while maintaining near state-of-the-art accuracy.📌 Bullet points summaryFourCastNet outpaces traditional NWP with forecasts that are not only faster by several magnitudes but also comparably accurate, thanks to its data-driven deep learning approach.Powered by Adaptive Fourier Neural Operators (AFNO), the model efficiently handles high-resolution data, leveraging spectral convolutions, model/data parallelism, and performance optimizations like CUDA graphs and JIT compilation.Scales excellently across supercomputers such as Selene, Perlmutter, and JUWELS Booster, reaching 140.8 petaFLOPS and enabling rapid training and large-scale ensemble forecasts.Addresses long-standing challenges in weather and climate modeling, including limits in resolution, complexity, and throughput, paving the way for emulating fine-scale Earth system processes.Enables "Interactivity at Scale"—supporting digital Earth twins and empowering users to explore future climate scenarios interactively, aiding science, policy, and public understanding.💡 The Big IdeaFourCastNet revolutionizes weather forecasting by merging the power of deep learning and spectral methods, unlocking interactive, ultra-fast, and high-fidelity Earth system simulations for a changing world.📖 CitationKurth, Thorsten, et al. "Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators." Proceedings of the Platform for Advanced Scientific Computing Conference. 2023.

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• Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

  🎙️ Episode 22: Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems🔗 DOI: https://doi.org/10.1038/s41467-023-43860-5🧠 AbstractImproving the accuracy and scalability of carbon cycle quantification in agroecosystems is essential for climate mitigation and sustainable agriculture. This episode discusses a new Knowledge-Guided Machine Learning (KGML) framework that integrates process-based models, high-resolution remote sensing, and machine learning to address key limitations in conventional approaches.📌 Bullet points summaryIntroduces KGML-ag-Carbon, a hybrid model combining process-based simulation (ecosys), remote sensing, and ML to improve carbon cycle modeling in agroecosystems.Outperforms traditional models in capturing spatial and temporal carbon dynamics across the U.S. Corn Belt, especially under data-scarce conditions.Delivers high-resolution (250m daily) estimates for critical carbon metrics such as GPP, Ra, Rh, NEE, and crop yield, with field-level precision.Benefits from pre-training with synthetic data, remote sensing assimilation, and a hierarchical architecture with knowledge-guided loss functions for better accuracy and interpretability.Shows promise for broader applications including nutrient cycle modeling, large-scale carbon assessment, and scenario testing under various management and climate conditions.💡 The Big IdeaKGML-ag-Carbon represents a leap in modeling agroecosystem carbon cycles, blending scientific knowledge with data-driven insights to unlock precision and scalability in climate-smart agriculture.📖 CitationLiu, Licheng, et al. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems." Nature Communications 15.1 (2024): 357.

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• AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning

  🎙️ Episode 21 — AtmoRep: A Stochastic Model of Atmospheric Dynamics Using Large-Scale Representation LearningThis week, we explore AtmoRep, a novel task-independent AI model for simulating atmospheric dynamics. Built on large-scale representation learning and trained on ERA5 reanalysis data, AtmoRep delivers strong performance across a variety of tasks—without needing task-specific training.🔍 Highlights from the episode:Introduction to AtmoRep, a stochastic computer model leveraging AI to simulate the atmosphere.Zero-shot capabilities for nowcasting, temporal interpolation, model correction, and generating counterfactuals.Outperforms or matches state-of-the-art models like Pangu-Weather and even ECMWF's IFS at short forecast horizons.Fine-tuning with additional data, like radar observations, enhances performance—especially for precipitation forecasts.Offers a computationally efficient alternative to traditional numerical models, with potential for broader scientific and societal applications.📚 Read the paper: https://doi.org/10.48550/arXiv.2308.13280✍️ Citation:Lessig, Christian, et al. "AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning." arXiv:2308.13280 (2023)

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• Finding the Right XAI Method—A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science

  🎙️ Episode 20: Finding the Right XAI Method—Evaluating Explainable AI in Climate Science🔗 DOI: https://doi.org/10.48550/arXiv.2303.00652🧩 AbstractExplainable AI (XAI) methods are increasingly used in climate science, but the lack of ground truth explanations makes it difficult to evaluate and compare them effectively. This episode dives into a new framework for systematically evaluating XAI methods based on key properties tailored to climate research needs.📌 Bullet points summaryIntroduces XAI evaluation for climate science, offering a structured approach to assess and compare explanation methods using key desirable properties.Identifies five critical properties for XAI in this context: robustness, faithfulness, randomization, complexity, and localization.Evaluation shows that different XAI methods perform differently across these properties, with performance also depending on model architecture.Salience methods often score well on faithfulness and complexity but lower on randomization.Sensitivity methods typically do better on randomization but at the expense of other properties.Proposes a framework to guide method selection: assess the importance of each property for the research task, compute skill scores for available methods, and rank or combine methods accordingly.Highlights the role of benchmark datasets and evaluation metrics in supporting transparent and context-specific XAI adoption in climate science.💡 The Big IdeaThis work empowers climate researchers to make informed, task-specific choices in explainable AI, turning a fragmented XAI landscape into a guided, comparative process rooted in scientific needs.📖 CitationBommer, Philine Lou, et al. "Finding the right XAI method—A guide for the evaluation and ranking of explainable AI methods in climate science." Artificial Intelligence for the Earth Systems 3.3 (2024): e230074.

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