Understanding pattern scaling errors across a range of emissions pathways
In one sentence. Decomposes pattern scaling errors from the MESMER emulator into scenario pattern and timeseries components, showing timeseries error is about 20% for high emissions but comparable to pattern error for low emissions.
Abstract
Abstract. The regional impacts of multiple possible future emission scenarios can be estimated by combining a few Earth System Model (ESM) simulations with a linear pattern scaling model such as MESMER which uses the pattern of local temperature responses per degree global warming. Here we use MESMER to emulate the future regional pattern of surface temperature response based on historical single-forcer and future Shared Socioeconomic Pathway (SSP) CMIP6 simulations. Pattern scaling errors are decomposed into two components: differences in scaling patterns between scenarios, and intrinsic timeseries differences between local and global responses in the target scenario. The timeseries error is relatively small for high-emissions scenarios, contributing around 20 % of the total error, but is similar in magnitude to the pattern error for lower-emission scenarios. This irreducible timeseries error limits the efficacy of pattern scaling for emulating strong mitigation pathways and reduces the dependence on the predictor pattern used. The results help guide the choice of predictor scenarios and where to target introducing other dependent variables beyond global surface temperature into pattern scaling models.
Labels
- Topic
- Climate emulation, Climate analysis & attribution
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Global, Regional
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.5194/esd-14-817-2023 OpenAlex:W4385950693
- Added
- 2026-10-22
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