Author
Listed:
- Houxiang Shi
(Laboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
CMA-GDOU Joint Laboratory for Marine Meteorology, South China Sea Institute of Marine Meteorology, Guangdong Ocean University, Zhanjiang 524088, China)
- Yu Zhang
(Laboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
CMA-GDOU Joint Laboratory for Marine Meteorology, South China Sea Institute of Marine Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
Western Guangdong Key Laboratory of Marine Meteorological Disaster Theory and Application, Guangdong Ocean University, Zhanjiang 524088, China)
- Junzhe Chen
(Laboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
Western Guangdong Key Laboratory of Marine Meteorological Disaster Theory and Application, Guangdong Ocean University, Zhanjiang 524088, China
Shenzhen Institute of Guangdong Ocean University, Shenzhen 518120, China)
- Jianjun Xu
(CMA-GDOU Joint Laboratory for Marine Meteorology, South China Sea Institute of Marine Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
Western Guangdong Key Laboratory of Marine Meteorological Disaster Theory and Application, Guangdong Ocean University, Zhanjiang 524088, China
Shenzhen Institute of Guangdong Ocean University, Shenzhen 518120, China)
- Yuyang Xu
(Laboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
CMA-GDOU Joint Laboratory for Marine Meteorology, South China Sea Institute of Marine Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
Key Laboratory of Space Ocean Remote Sensing and Application of Ministry of Natural Resources/Key Laboratory of Climate Resources and Environment in Continental Shelf Sea and Deep Ocean, Zhanjiang 524088, China)
Abstract
Anthropogenic emissions have caused the Antarctic ozone hole, a major global environmental crisis since the late 20th century. Although ozone recovery began in the early 21st century, substantial uncertainty remains regarding the timing of its return to pre-loss levels. This study innovatively develops a “model optimization–bias correction” framework based on spatial pattern (S1) and long-term trend (S2) metrics, assessing 17 Chemistry-Climate Model Initiative Phase 1 (CCMI-1) models using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis for the climate (ERA5). Results: (1) Most models accurately reproduce the Antarctic ozone’s spatial distribution and long-term trends: MRI-ESM1r1 performs best for spatial patterns (S1 = 0.80), GEOSCCM for long-term trends (S2 = 0.82); EMAC-L90MA, UMSLIMCAT, etc., show poor spatial pattern performance (S1 < 0.30), while IPSL and EMAC-L90MA have large trend biases and underperform in trend simulation (S2 < 0.10). (2) Integrating S1 and S2 scores, the Preferred Multi-Model Ensemble comprising the top eight models (PMME8) minimizes ERA5 deviation, outperforming the multi-model ensemble (MME); the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) correction of this ensemble reduces systematic bias by 15–60%. (3) Antarctic ozone recovery time shows a gradual delay following optimal model selection and bias correction. PMME-adjusted projects recovery in October 2063 (2053–2072), later than MME (2052) and PMME (2058), with inter-member uncertainty narrowing from 43 years to 19 years. Similarly, this feature is also found for September, November, and the spring mean. This study provides a reliable methodological foundation for projections of Antarctic ozone recovery and offers scientific support for the compliance assessment and policy adjustment of the Montreal Protocol, thereby advancing environmental sustainability and global ozone governance.
Suggested Citation
Houxiang Shi & Yu Zhang & Junzhe Chen & Jianjun Xu & Yuyang Xu, 2026.
"Delay in Antarctic Ozone Recovery Projection Based on Bias-Corrected Optimal Chemistry-Climate Model Initiative Phase 1 Models,"
Sustainability, MDPI, vol. 18(11), pages 1-21, June.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:11:p:5713-:d:1959915
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