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Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal climate variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large scale spatial averaging. This paper demonstrates a pattern recognition method (forced pattern filtering) that extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Ni˜no, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El-Ni˜no-like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to effectively identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.
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This content will become publicly available on March 20, 2026
Fingerprinting the recovery of Antarctic ozone
The Antarctic ozone “hole” was discovered in 1985, and man-made ozone- depleting substances (ODS) are its primary cause. Following reductions of ODSs under the Montreal Protocol, signs of ozone recovery have been reported, based largely on observations and broad yet compelling model-data comparisons. While such approaches are highly valuable, they don't provide rigorous statistical detection of the temporal and spatial structure of Antarctic ozone recovery in the presence of internal climate variability. Here, we apply pattern-based detection and attribution methods as employed in climate change studies to separate anthropogenically forced ozone responses from internal variability, relying on trend pattern information as a function of month and height. The analysis uses satellite observations together with single-model and multi-model ensemble simulations to identify and quantify the month-height Antarctic ozone recovery “fingerprint”. We demonstrate that the data and simulations show remarkable agreement in the fingerprint pattern of the ozone response to decreasing ODSs since 2005. We also show that ODS forcing has enhanced ozone internal variability during the austral spring, influencing detection of forced responses and their time of emergence. Our results provide robust statistical and physical evidence that actions taken under the Montreal Protocol to reduce ODSs are indeed resulting in the beginning of Antarctic ozone recovery, defined as increases in ozone consistent with expected month-height patterns.
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- PAR ID:
- 10582450
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature
- Volume:
- 639
- Issue:
- 8055
- ISSN:
- 0028-0836
- Page Range / eLocation ID:
- 646 to 651
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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