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Risks and solutions when using non-stationary training data in earth system science

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    • Date
    8 December 2025
    Timeframe
    17:00 - 18:00 CET Geneva
    Duration
    60 minutes

    Earth system science increasingly relies on machine learning to analyze complex, multivariate, and spatiotemporal data. However, the validity of these models critically depends on the assumption that training and deployment data share similar statistical properties – a condition often violated in real-world environmental applications. This presentation addresses the risks associated with non-stationary training data distributions, arising from climate change, evolving land use, or sensor shifts over time. We show how such distribution shifts can lead to degraded model performance, biased predictions, and misleading scientific conclusions. Through different examples, we illustrate the mechanisms and consequences of non-stationarity. We then discuss methodological solutions, including domain adaptation, continual learning, and uncertainty quantification techniques, that help mitigate these effects and improve model robustness. By combining insights from machine learning and earth system science, this talk aims to foster awareness of distributional risks and promote the development of adaptive, interpretable, and trustworthy models for understanding and predicting Earth’s dynamic systems.

     

    Session Objectives:

    By the end of this session, participants will be able to:

    • Explain the concept of non-stationary data distributions and their relevance in Earth system science.
    • Identify common sources of distribution shifts in environmental data.
    • Evaluate the impact of non-stationarity on machine learning model performance.
    • Apply at least one methodological approach (e.g., domain adaptation, continual learning) to mitigate distributional risks.
    • Interpret model outputs while considering uncertainty and distributional shifts.

    Recommended Mastery Level / Prerequisites: 

    • Basic understanding of machine learning concepts (training, validation, testing)
    • Familiarity with Earth system science datasets and spatiotemporal data
    • Optional: prior exposure to Python and common ML libraries (scikit-learn, PyTorch, TensorFlow)
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