CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates

• URL: http://arxiv.org/abs/2509.00010v1
• Date: Fri, 15 Aug 2025 20:28:04 GMT
• Title: CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates
• Authors: Shuchang Liu, Paul A. O'Gorman,
• Abstract summary: Robust generalization under climate change remains a major challenge for machine learning applications in climate science.<n>We present CERA (Climate-invariant climate representation through Representation), a machine learning framework consisting of an autoencoder with explicit latent-space alignment.<n>Without training on labeled data from a +4K climate, CERA leverages labeled control-climate data and unlabeled warmer-climate inputs to improve generalization to the warmer climate.
• Score: 1.205087107092304
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Robust generalization under climate change remains a major challenge for machine learning applications in climate science. Most existing approaches struggle to extrapolate beyond the climate they were trained on, leading to a strong dependence on training data from model simulations of warm climates. Use of climate-invariant inputs improves generalization but requires challenging manual feature engineering. Here, we present CERA (Climate-invariant Encoding through Representation Alignment), a machine learning framework consisting of an autoencoder with explicit latent-space alignment, followed by a predictor for downstream process estimation. We test CERA on the problem of parameterizing moist-physics processes. Without training on labeled data from a +4K climate, CERA leverages labeled control-climate data and unlabeled warmer-climate inputs to improve generalization to the warmer climate, outperforming both raw-input and physically informed baselines in predicting key moisture and energy tendencies. It captures not only the vertical and meridional structures of the moisture tendencies, but also shifts in the intensity distribution of precipitation including extremes. Ablation experiments show that latent alignment improves both accuracy and the robustness across random seeds used in training. While some reduced skill remains in the boundary layer, the framework offers a data-driven alternative to manual feature engineering of climate invariant inputs. Beyond parameterizations used in hybrid ML-physics systems, the approach holds promise for other climate applications such as statistical downscaling.

Related papers

• Toward generative machine learning for boosting ensembles of climate simulations [0.0]
  We develop a conditional Variational Autoencoder (cVAE) trained on a limited sample of climate simulations to generate arbitrary large ensembles.<n>We show that the cVAE model learns the underlying distribution of the data and generates physically consistent samples that reproduce realistic low and high moment statistics.
  arXiv  Detail & Related papers  (2026-02-06T00:54:19Z)
• Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales [0.3032942517187112]
  Article establishes protocols for the proper preprocessing of input data for AI/ML models designed for climate prediction.<n>Three aims are to: educate researchers, developers, and end users on the effects that preprocessing has on climate predictions.<n>Ultimately, implementing the recommended practices will enhance the robustness and transparency of AI/ML in climate prediction studies.
  arXiv  Detail & Related papers  (2025-08-09T17:55:38Z)
• A Physics-guided Multimodal Transformer Path to Weather and Climate Sciences [59.05404971880922]
  Many problems in meteorology can now be addressed using AI models.<n>Data-driven algorithms have significantly improved accuracy compared to traditional methods.<n>We propose a new paradigm where observational data from different perspectives are treated as multimodal data and integrated via transformers.
  arXiv  Detail & Related papers  (2025-04-19T04:31:35Z)
• ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method [61.76389719956301]
  We contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns time series climate data from ERA5, extreme weather events data from NOAA, and satellite image data from NASA.<n>Under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks.
  arXiv  Detail & Related papers  (2025-04-10T02:22:23Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution [6.919867656801517]
  ClimDetect is a standardized dataset comprising 1.17M daily climate snapshots paired with target climate change indicator variables.<n>The dataset is curated from both CMIP6 climate model simulations and real-world observation-assimilated reanalysis datasets.<n>We also explore the application of vision transformers (ViT) to climate data -- a novel approach that, to our knowledge, has not been attempted before for climate change detection tasks.
  arXiv  Detail & Related papers  (2024-08-28T17:58:53Z)
• Towards Physically Consistent Deep Learning For Climate Model Parameterizations [46.07009109585047]
  parameterizations are a major source of systematic errors and large uncertainties in climate projections. Deep learning (DL)-based parameterizations, trained on data from computationally expensive short, high-resolution simulations, have shown great promise for improving climate models. We propose an efficient supervised learning framework for DL-based parameterizations that leads to physically consistent models.
  arXiv  Detail & Related papers  (2024-06-06T10:02:49Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Climate-Invariant Machine Learning [0.8831201550856289]
  Current climate models require representations of processes that occur at scales smaller than model grid size. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. We propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms.
  arXiv  Detail & Related papers  (2021-12-14T07:02:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.