arXiv:2509.15942v3 Announce Type: replace-cross Abstract: Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale. A typical approach to separating the internal variability from forced climate responses is to generate large ensembles of simulations under different initial conditions. Due to the complexity of Earth System Models, generating these large ensembles is computationally expensive. In this work, we present ArchesClimate, a deep learning-based climate model emulator designed to reduce the cost of exploring internal variab
The increasing computational demands of climate modeling and the rapid advancements in deep learning make this a timely intersection for innovation.
Improving the efficiency and speed of climate predictions is crucial for policymakers and industries to make informed decisions regarding climate change adaptation and mitigation.
The cost and time required to generate comprehensive climate ensembles will be significantly reduced, allowing for more robust and frequent climate scenario planning.
- · Climate scientists
- · Insurance industry
- · Governments (policy makers)
- · Deep learning researchers
- · Traditional climate modeling software vendors (if they don't adapt)
More accurate and faster climate predictions become accessible to a broader range of users.
This democratizes advanced climate modeling, potentially accelerating climate adaptation strategies and investments.
Improved predictability could lead to new financial instruments and risk management strategies based on more reliable long-term climate forecasts.
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Read at arXiv cs.AI