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1. Climate-invariant machine learning

   https://doi.org/10.1126/sciadv.adj7250  

   Beucler, Tom; Gentine, Pierre; Yuval, Janni; Gupta, Ankitesh; Peng, Liran; Lin, Jerry; Yu, Sungduk; Rasp, Stephan; Ahmed, Fiaz; O’Gorman, Paul A.; et al (February 2024, Science Advances)

   Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes. 

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2. Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud‐Forming Particles

   https://doi.org/10.1029/2021GL094133  

   Nair, Arshad Arjunan; Yu, Fangqun; Campuzano‐Jost, Pedro; DeMott, Paul J.; Levin, Ezra J. T.; Jimenez, Jose L.; Peischl, Jeff; Pollack, Ilana B.; Fredrickson, Carley D.; Beyersdorf, Andreas J.; et al (November 2021, Geophysical Research Letters)

   Abstract Cloud condensation nuclei (CCN) are mediators of aerosol‐cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model‐simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi‐campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol‐cloud interactions (ERF_aci) and improving confidence in assessment of anthropogenic contributions and climate change projections. 

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3. Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

   https://doi.org/10.1098/rstb.2024.0252  

   Singhal, Rajneesh; Izquierdo, Paulo; Ranaweera, Thilanka; Segura_Abá, Kenia; Brown, Brianna NI; Lehti-Shiu, Melissa D; Shiu, Shin-Han (May 2025, Philosophical Transactions of the Royal Society B: Biological Sciences)

   Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’. 

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4. A review on machine learning-guided design of energy materials

   https://doi.org/10.1088/2516-1083/ad7220  

   Kim, Seongmin; Xu, Jiaxin; Shang, Wenjie; Xu, Zhihao; Lee, Eungkyu; Luo, Tengfei (September 2024, Progress in Energy)

   Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome. 

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5. Power and accountability in reinforcement learning applications to environmental policy

   Chapman, Melissa; Scoville, Caleb; Lapeyrolerie, Marcus; Boettiger, Carl (January 2021, Conference Proceedings on Neural Information Processing Systems, 2021)

   Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry. 

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