AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.
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A PMP-based evaluation framework for testing deep-learning Earth system models on climate-relevant diagnostics beyond short-range forecasts.
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AIMIP Phase 1: systematic evaluations of AI weather and climate models
AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.
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A PMP-inspired Evaluation Framework for Assessing Deep-Learning Earth System Models
A PMP-based evaluation framework for testing deep-learning Earth system models on climate-relevant diagnostics beyond short-range forecasts.