Computer models that use artificial intelligence (AI) cannot forecast record-breaking weather as well as traditional...
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Carbon Brief
Traditional models still ‘outperform AI’ for extreme weather forecasts
Abatify Summary
Nature & Climate Perspective
**Traditional numerical weather prediction remains the superior tool for safeguarding biodiversity and carbon sinks against unmodeled catastrophic climate events. **
- The inability of AI to predict unprecedented extreme weather threatens the integrity of LULUCF projects by underestimating the risk of carbon reversals from wildfires and storms.
- Reliable extreme weather forecasting is essential for the long-term environmental stability of blue carbon and reforestation initiatives that rely on stable climatic envelopes.
- Accurate baseline modeling of physical climate risks is necessary to prevent the sudden collapse of localized ecosystems that host high-value biodiversity assets.
Market & Policy Outlook
**The current performance gap in AI forecasting complicates the robust quantification of 'Permanence' required by ICVCM Core Carbon Principles (CCPs). **
- Under ICVCM CCPs, the failure to accurately model tail-risk events could lead to the under-capitalization of insurance buffer pools for carbon credits.
- Corporate compliance and SBTi-aligned risk disclosures remain dependent on traditional physics-based models to ensure market pricing reflects true climate volatility.
- Financial liquidity in the voluntary carbon market may be hampered if AI-driven risk assessments lead to the mispricing of Nature-Based Solutions (NBS) due to ignored extreme weather variables.
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