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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.06642 |
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| _version_ | 1866912501599305728 |
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| author | Harris, Trevor Sriver, Ryan |
| author_facet | Harris, Trevor Sriver, Ryan |
| contents | Ensembles of General Circulation Models (GCMs) are the primary tools for investigating climate sensitivity, projecting future climate states, and quantifying uncertainty. GCM ensembles are subject to substantial uncertainty due to model inadequacies, resolution limits, internal variability, and inter-model variability, meaning rigorous climate risk assessments and informed decision-making require reliable and accurate uncertainty quantification (UQ). We introduce conformal ensembles (CE), a new approach to climate UQ that quantifies and constrains projection uncertainty with conformal prediction sets and observational data. CE seamlessly integrates climate model ensembles and observational data across a range of scales to generate statistically rigorous, easy-to-interpret uncertainty estimates. CE can be applied to any climatic variable using any ensemble analysis method and outperforms existing inter-model variability methods in uncertainty quantification across all time horizons and most spatial locations under SSP2-4.5. CE is also computationally efficient, requires minimal assumptions, and is highly robust to the conformity measure. Experiments show that it is effective when conditioning future projections on historical reanalysis data compared with standard ensemble averaging approaches, yielding more physically consistent projections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying uncertainty in climate projections with conformal ensembles Harris, Trevor Sriver, Ryan Applications Machine Learning Ensembles of General Circulation Models (GCMs) are the primary tools for investigating climate sensitivity, projecting future climate states, and quantifying uncertainty. GCM ensembles are subject to substantial uncertainty due to model inadequacies, resolution limits, internal variability, and inter-model variability, meaning rigorous climate risk assessments and informed decision-making require reliable and accurate uncertainty quantification (UQ). We introduce conformal ensembles (CE), a new approach to climate UQ that quantifies and constrains projection uncertainty with conformal prediction sets and observational data. CE seamlessly integrates climate model ensembles and observational data across a range of scales to generate statistically rigorous, easy-to-interpret uncertainty estimates. CE can be applied to any climatic variable using any ensemble analysis method and outperforms existing inter-model variability methods in uncertainty quantification across all time horizons and most spatial locations under SSP2-4.5. CE is also computationally efficient, requires minimal assumptions, and is highly robust to the conformity measure. Experiments show that it is effective when conditioning future projections on historical reanalysis data compared with standard ensemble averaging approaches, yielding more physically consistent projections. |
| title | Quantifying uncertainty in climate projections with conformal ensembles |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2408.06642 |