近日，中国科学院南海海洋研究所彭冬冬团队对CMIP5和CMIP6模型中，华南地区的温度和降水变化进行了详细研究。该项研究成果发表在2024年10月29日出版的《大气科学进展》上。

据了解，揭示区域气候变化对于与适应和减缓气候变化有关的决策活动至关重要。华南是一个经济发达、人口密集的地区，但气候预测的不确定性仍未得到详细评估。

在这里，研究人员基于CMIP5/CMIP6模型，对华南地区气候变化的历史模拟和未来预测做了综合评估。CMIP5/CMIP6模式可以很好地再现年/季节平均温度的观测分布，但对降水的再现能力要低得多。CMIP6在历史模拟中的表现优于CMIP5，这一点可以从更多具有更低偏差幅度和更高技能得分的模型中得到证明。

在RCP8.5和SSP5-8.5情景下，2021-2100年期间，华南地区年平均气温将以0.53（0.42-0.63）和0.59（0.52-0.66）的速率显著增加，降水量将以0.78（0.15-1.56）和1.52（0.91-2.30）的速率小幅增加。

在等效情景下，CMIP6模式比CMIP5模式预估的年/季节平均温度和降水趋势更大。与1850-1900相比，RCP4.5和SSP2-4.5下，2041-2060年间，华南气温将显著升高1.5℃以上，而在RCP8.5和SSP5-8.5下，2081-2100年间，华南气温将显著升高4.5℃以上。气温预测的不确定性主要由模式不确定性和情景不确定性主导，短期内内部不确定性也有一定贡献。降水预估的不确定性主要来自内部不确定性和模式不确定性。对于温度和降水预估的不确定性，主要贡献率的相对大小随时间而变化，并表现出明显的季节差异。

附：英文原文

Title: Temperature and precipitation change over South China in CMIP5 and CMIP6 models: historical simulation and future projection

Author: Dongdong Peng, Tian-Jun ZHOU, Sheng Hu, Lixia Zhang, jiayu zheng, Jingxuan Qu

Issue&Volume: 2024-10-29

Abstract: Revealing regional climate changes is vital for policymaking activities related to climate change adaptation and mitigation. South China is a well-developed region with dense population, but the climate projection uncertainty remains unevaluated in detail. Here, we comprehensively assess the historical simulations and future projection of climate change in South China based on the CMIP5/CMIP6 models. We show evidence that CMIP5/CMIP6 models can well reproduce the observed distributions of annual/seasonal mean temperature but show much lower skills for the precipitation. CMIP6 outperform CMIP5 in the historical simulations, as evidenced by more models with lower bias magnitude and higher skill scores. During 2021-2100, the annual mean temperature over South China would increase significantly at a rate (℃ decade-1) of 0.53 (0.42~0.63) and 0.59 (0.52~0.66), while the precipitation would increase slightly at a rate (% decade-1) of 0.78 (0.15~1.56) and 1.52 (0.91~2.30), under RCP8.5 and SSP5-8.5 scenarios, respectively. CMIP6 models project larger annual/seasonal mean temperature and precipitation trends than CMIP5 models under the equivalent scenarios. South China would robustly increase by more than 1.5 ℃ during 2041-2060 under RCP4.5 and SSP2-4.5 while 4.5 ℃ during 2081-2100 under RCP8.5 and SSP5-8.5 with respect to 1850-1900. The temperature projection uncertainty is mainly dominated by model uncertainty and scenario uncertainty, while internal uncertainty contributes some during the near-term. The precipitation projection uncertainty is mainly from internal uncertainty and model uncertainty. For both temperature and precipitation projection uncertainty, the relative sizes of contributions from the main contributors vary in time and show obvious seasonal differences.

DOI: 10.1007/s00376-024-4375-4

Source: http://www.iapjournals.ac.cn/aas/article/doi/10.1007/s00376-024-4375-4