中国区域气候变化预估研究进展

Research advances in projections of regional climate change over China

  • 摘要: 气候变化预估研究致力于为决策者提供更可靠、不确定性更小的未来气候变化信息。该综述梳理回顾了近十年来中国在气候变化预估方面的主要进展,并对预估研究的未来发展进行了展望。预估表明,未来中国区域平均气温将升高、降水将增加,且在高排放情景下的增幅最大。未来中国区域极端冷事件将减少,极端高温将增多;极端降水明显增多且强度增强。未来中国区域复合型极端事件也将显著增加,且越罕见的极端事件在未来增加越显著。统计订正、模式加权、基于归因结论的约束和萌现约束等观测约束方法在中国区域气候变化预估中的应用已比较广泛。整体来说,约束预估不会改变模式原始预估的定性结论,但在变化幅度上有所调整。观测约束方法在不同区域、不同变量的约束预估中体现出了减小预估不确定性的能力。为进一步推动中国区域气候变化预估研究的进步,需深化对气候系统及其反馈过程的理解与认识,提升观测资料的质量与气候模式的模拟效果,并加强机器学习等新兴技术的应用。

     

    Abstract: Research on climate change projection aims to provide decision-makers with more reliable and less uncertain information about future climate changes. This paper reviews the main progress made in China over the past decade regarding climate change projections and discusses future prospectives in this field. Climate model projections indicate that both regional average temperatures and precipitation in China will increase, with the largest increases occurring under the scenarios with highest emissions. In the future, extreme cold events in China are expected to decrease, while extreme heat events will become more frequent; extreme precipitation will significantly increase in intensity and frequency. Additionally, compound extreme events will also experience a notable increase, particularly the rarest extreme events, which will rise more significantly in the future. Statistical bias-calibration, model weighting, constraint based on detection and attribution, and emergent constraint have been widely applied in regional climate change projections in China. Overall, constrained projections do not alter the qualitative conclusions of the raw model projections, but adjust the magnitude of change. The observational constraint methods have demonstrated the ability to reduce uncertainty in projections across various regions and variables in China. To further advance regional climate change projection research in China, it is essential to deepen understanding of the climate system and its feedback processes, improve the quality of observational data and the performance of climate model simulations, and enhance the application of emerging technologies such as machine learning.

     

/

返回文章
返回