Earthly Machine Learning

• FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution

  FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution*Qiusheng Huang, Yuan Niu, Xiaohui Zhong, Anboyu Guo, Lei Chen, Dianjun Zhang, Xuefeng Zhang, Hao Li*---* **First Data-Driven Sub-Daily Global Forecast:** FuXi-Ocean is the first deep learning-based global ocean forecasting model to achieve six-hour temporal resolution at an eddy-resolving 1/12° spatial resolution, with vertical coverage extending up to 1500 meters. This capability addresses a crucial need for high-frequency predictions that traditional numerical models struggle to deliver efficiently.* **Adaptive Temporal Modeling Innovation:** A key component of the model is the **Mixture-of-Time (MoT) module**, which adaptively integrates predictions from multiple temporal contexts based on variable-specific reliability. This mechanism is crucial for accommodating the diverse temporal dynamics of different ocean variables (e.g., fast-changing surface variables vs. slowly evolving deep-ocean processes) and effectively mitigates the accumulation of forecast errors in sequential prediction.* **Superior Performance and Efficiency:** The model demonstrates superior skill in predicting key variables (temperature, salinity, and currents) compared to state-of-the-art operational numerical forecasting systems (like HYCOM, BLK, and FOAM) at sub-daily intervals. Furthermore, it achieves this high performance with remarkable data efficiency, requiring only approximately 9 years of training data and relying solely on ocean variables (T, S, U, V, SSH) as input, without external data dependencies like atmospheric forcing.* **High-Impact Applications:** By providing accurate, high-resolution, sub-daily forecasts, FuXi-Ocean creates critical opportunities for maritime operations, including improved navigation, search and rescue, oil spill trajectory tracking, and enhanced marine resource management, particularly due to its comprehensive vertical coverage (0-1500 m).

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• Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model

  Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model*By Helge Heuer, Tom Beucler, Mierk Schwabe, Julien Savre, Manuel Schlund, and Veronika Eyring** This paper presents a **successful proof-of-concept for transferring a machine learning (ML) convection parameterization**—trained on the ClimSim dataset—to the ICON-A climate model. The resulting hybrid ML-physics model achieved stable and accurate simulations in long-term AMIP-style runs lasting at least 20 years.* A core innovation is the **confidence-guided mixing scheme**, which allows the Neural Network (NN) to predict its own error. When the NN's predicted confidence is low (e.g., in moist, unstable regimes or high-variability areas), its prediction is mixed with the conventional Tiedtke convection scheme. This mechanism improves reliability, prevents unphysical outputs by detecting potential extrapolation beyond the training domain, and makes the hybrid model tunable against observations.* The scheme's robustness and accuracy were further enhanced through the **use of a physics-informed loss function**—which encourages adherence to conservation laws like enthalpy and mass—and **noise-augmented training**. These techniques mitigate stability issues commonly faced by ML parameterizations and significantly improve physical consistency compared to purely data-driven models.* In evaluation against observational data, several hybrid configurations **outperformed the default Tiedtke scheme**, demonstrating improved precipitation statistics and showing a better representation of global climate variables. The confidence-guided approach demonstrated a fundamental change in the model's behavior, with the ML component contributing approximately 67% of the convective tendencies on average.

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• Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere

  Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere(By Noah D. Brenowitz, Tao Ge, Akshay Subramaniam, Peter Manshausen, Aayush Gupta, David M. Hall, Morteza Mardani, Arash Vahdat, Karthik Kashinath, Michael S. Pritchard, NVIDIA* The paper introduces **Climate in a Bottle (cBottle)**, a generative diffusion-based AI framework capable of synthesizing full global atmospheric states at an unprecedented $\mathbf{5 \text{ km resolution}}$ (over 12.5 million pixels per sample). Unlike prevailing auto-regressive paradigms, cBottle samples directly from the full distribution of atmospheric states without requiring a previous time step, thereby avoiding issues like drifts and instabilities inherent to time-stepping models.* cBottle utilizes a **two-stage cascaded diffusion approach**: a global coarse-resolution generator conditioned on minimal climate-controlling inputs (such as monthly sea surface temperature and solar position), followed by a patch-based 16x super-resolution module.* The model demonstrates **foundational versatility** by being trained jointly on multiple data modalities, including ERA5 reanalysis and ICON global cloud-resolving simulations. This enables various zero-shot applications such as climate downscaling, channel infilling for missing or corrupted variables, bias correction between datasets, and translation between these modalities.* cBottle proposes a new form of **interactive climate modeling** through the use of guided diffusion. By training a classifier alongside the generator, users can steer the model to conditionally generate physically plausible **extreme weather events, such as Tropical Cyclones**, at specified locations on demand, circumventing the need to sift through petabytes of output to find rare events.* The model exhibits **high climate faithfulness** across a battery of tests, including reproducing diurnal-to-seasonal scale variability, large-scale modes of variability (like the Northern Annular Mode), and tropical cyclone statistics. Furthermore, it achieves **extreme distillation** by encapsulating massive datasets into a few GB of neural network weights, offering a 256x compression ratio per channel.

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• Probabilistic Measures for Fair AI and NWP Model Comparison

  Probabilistic measures afford fair comparisons of AIWP and NWP model output (Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva-Maria Walz, Alexander I. Jordan, Sebastian Lerch, June 10, 2025)Introduction of a New Fair Comparison Metric: The paper introduces the Potential Continuous Ranked Probability Score (PC), a new measure designed to allow fair and meaningful comparisons between single-valued output from data-driven Artificial Intelligence based Weather Prediction (AIWP) models and physics-based Numerical Weather Prediction (NWP) models. This approach addresses concerns that traditional loss functions (like RMSE) may unfairly favor AIWP models, which often optimize their training using these metrics. Methodology Based on Probabilistic Postprocessing: PC is calculated by applying the same statistical postprocessing technique—specifically Isotonic Distributional Regression (IDR), also known as Easy Uncertainty Quantification (EasyUQ)—to the deterministic output of both AIWP and NWP models. PC is then defined as the mean Continuous Ranked Probability Score (CRPS) of these newly generated probabilistic forecasts. Measure of Potential Skill and Invariance: PC quantifies potential predictive performance. A key property of PC is that it is invariant under strictly increasing transformations of the model output, treating both forecasts equally and facilitating comparisons where the pre-specification of a loss function might otherwise place competitors on unequal footings. AIWP Outperformance and Operational Proxy: When applied to WeatherBench 2 data, the PC measure demonstrated that the data-driven GraphCast model outperforms the leading physics-based ECMWF high-resolution (HRES) model. Furthermore, the PC measure for the HRES model was found to align exceptionally well with the mean CRPS of the operational ECMWF ensemble, confirming that PC serves as a reliable proxy for the performance of real-time operational probabilistic products.

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• Jigsaw: Training Multi-Billion-Parameter AI Weather Models With Optimized Model Parallelism

  Jigsaw: Training Multi-Billion-Parameter AI Weather Models With Optimized Model ParallelismAuthors: Deifilia Kieckhefen, Markus Götz, Lars H. Heyen, Achim Streit, and Charlotte Debus (Karlsruhe Institute of Technology, Helmholtz AI)The paper introduces WeatherMixer (WM), a multi-layer perceptron (MLP)-based architecture designed for atmospheric forecasting, which serves as a competitive alternative to Transformer-based models. WM's workload scales linearly with input size, addressing the scaling challenges and quadratic computational complexity associated with the self-attention mechanism in Transformers when dealing with gigabyte-sized atmospheric data.• A novel parallelization scheme called Jigsaw parallelism is proposed, combining both domain parallelism and tensor parallelism to efficiently train multi-billion-parameter models. Jigsaw is optimized for large input data by fully sharding the data, model parameters, and optimizer states across devices, eliminating memory redundancy. Jigsaw effectively mitigates hardware bottlenecks, particularly I/O-bandwidth limitations frequently encountered in training large scientific AI models. Due to its partitioned data loading (domain parallelism), the scheme achieves superscalar weak scaling in I/O-bandwidth-limited systems. The method demonstrates excellent scaling behavior on high-performance computing systems, exceeding state-of-the-art performance in strong scaling in computation–communication-limited systems. The training was successfully scaled up to 256 GPUs, reaching peak performances of 9 and 11 PFLOPs.• Beyond hardware efficiency, Jigsaw improves predictive performance: by partitioning the model across more GPUs (model parallelism) instead of relying solely on data parallelism, it naturally enforces smaller global batch sizes, which empirically helps mitigate the problematic large-batch effects observed in AI weather models, leading to lower loss values.

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