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On the Foundations of Earth and Climate Foundation Models

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arxiv 2405.04285 v1 pith:M42O6IU4 submitted 2024-05-07 cs.AI eess.SP

classification cs.AIeess.SP
keywords foundationearthmodelmodelsclimatefeaturesidealachieve
verification ladder T0 review T1 audit T2 compute T3 formal

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Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirable Earth and climate foundation model. Crafting the ideal Earth foundation model, we define eleven features which would allow such a foundation model to be beneficial for any geoscientific downstream application in an environmental- and human-centric manner.We further shed light on the way forward to achieve the ideal model and to evaluate Earth foundation models. What comes after foundation models? Energy efficient adaptation, adversarial defenses, and interpretability are among the emerging directions.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 16 citations worldwide. Full citation record

  1. Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction

    eess.IV 2025-07 conditional novelty 6.0 of 10

    An additive model of annual temperature cycle, linear ERA5 amplification, and a U-Net residual reconstructs all-weather daily land surface temperature at 30 m with 2-3 K accuracy.

  2. ExEBench: Benchmarking Foundation Models on Extreme Earth Events

    cs.LG 2025-05 conditional novelty 6.0 of 10

    ExEBench packages seven extreme-event datasets into standardized forecasting and mapping tasks and benchmarks several AI foundation models under different fine-tuning strategies.

  3. Multi-Agent Geospatial Copilots for Remote Sensing Workflows

    cs.LG 2025-01 conditional novelty 5.0 of 10

    A hybrid multi-agent orchestrator (composition plus iterative reassessment) reports 60.3% agentic correctness on generated remote sensing workflows, about 17 percentage points above the single-agent GeoLLM-Engine baseline.

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