Uncertainty maps for model-based global climate classification systems
Abstract Climate classification systems (CCSs) are emerging as essential tools in climate change science for mitigation and adaptation. However, their limitations are often misunderstood by non-specialists. This situation is especially acute when the CCSs are derived from Global Climate Model output...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04387-0 |
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| Summary: | Abstract Climate classification systems (CCSs) are emerging as essential tools in climate change science for mitigation and adaptation. However, their limitations are often misunderstood by non-specialists. This situation is especially acute when the CCSs are derived from Global Climate Model outputs (GCMs). We present a set of uncertainty maps of four widely used schemes -Whittaker-Ricklefs, Holdridge, Thornthwaite-Feddema and Köppen- for present (1980–2014) and future (2015–2100) climate based on 52 models from the Coupled Intercomparison Model Project Phase six (CMIP6). Together with the classification maps, the uncertainty maps provide essential guidance on where the models perform within limits, and where sources of error lie. We share a digital resource that can be readily and freely integrated into mitigation and adaptation studies and which is helpful for scientists and practitioners using climate classifications, minimizing the risk of pitfalls or unsubstantiated conclusions. |
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| ISSN: | 2052-4463 |