Climate analysis & attribution
Exploring a Data‐Driven Approach to Identify Regions of Change Associated With Future Climate Scenarios
In one sentence. Neural network classifies temperature and precipitation maps by radiative forcing scenario using SPEAR output, with attribution identifying regional fingerprints of climate change.
Abstract
A key consideration for evaluating climate projections is uncertainty in radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare those observations with specified climate scenarios, it remains less obvious on how to connect regional climate patterns with these scenarios in real time. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use an attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. The neural network learns to identify “fingerprint” patterns that associate signals of climate change with the scenarios. We illustrate this using output from additional mitigation experiments and highlight regions that are critical for associating the new simulations with likely radiative forcing scenarios.
Labels
- Topic
- Climate analysis & attribution, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1029/2024jh000327 OpenAlex:W4404049225
- Added
- 2026-10-22
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