Comparison of Indian Ocean warming simulated by CMIP5 and CMIP6 models

ABSTRACT

Under the ongoing global warming, the sea surface temperature (SST) over the entire Indian Ocean (IO) has been warming saliently at a rate of 0.014°C yr⁻¹ since the 1950s, which is larger than that in other regions of the globe. The salient IO warming reflects the synergistic effect of global warming and the internal variability of the climate system, and the warming could lead to climate anomalies in peripheral regions. The simulation performance of the sustained IO warming was evaluated by comparing 37 CMIP5 and 37 CMIP6 models with observed data. The results show that the warming in the IO can be captured by nearly all the CMIP models, but most tend to underestimate the magnitude of IO warming trends. There is no qualitative improvement in the simulation of the salient IO warming from CMIP5 to CMIP6. In addition, six metrics were used to investigate the performance of all models. Concerning the spatial pattern of warming trends, the CMIP5 models reveal a better simulation performance than those in CMIP6 models. Only nine best models (seven CMIP5 models and two CMIP6 models) can simulate a high warming trend in the IO region of 0.014 ± 0.001°C yr⁻¹ during 1950–2005, but these nine models still have some disadvantages among other metrics. The overall evaluation here provides necessary information for future investigation about the mechanism of the sustained IO warming based on the climate models with better performances.

Graphical Abstract

摘要

在全球变暖背景下, 印度洋增温有着独特的特征:自 20 世纪 50 年代以来, 整个印度洋的海表温度以 0.014°C yr⁻¹ 的速度显著升高, 比全球其他地区的变暖幅度都大。 本文利用 37 个 CMIP5 模式和 37 个 CMIP6 模式的模拟结果, 分析评估了当今国际主流模式对印度洋显著变暖现象的模拟性能, 并与观测结果进行比较。 结果表明, 几乎所有 CMIP 模式都能刻画出印度洋变暖现象, 但大多数模式都低估了其变暖趋势的幅度。 进一步, 本文定义了六个客观指标用以比较模式的模拟性能, 在变暖趋势的空间分布方面, CMIP5 模式显示出比 CMIP6 更好的模拟结果。 本研究结果为后续基于气候模式开展印度洋显著变暖现象的机理研究提供了必要的信息。

KEYWORDS:

• Indian Ocean
• salient warming
• CMIP5
• CMIP6

关键词:

• 印度洋
• 显著增暖
• CMIP5 模式
• CMIP6 模式

Acknowledgments

The authors wish to thank the two anonymous reviewers for their constructive and helpful comments. The ERSST.v5 data were obtained from http://www.esrl.noaa.gov. The CMIP6 model data and CMIP5 model data are available from the ESGF at https://esgf-node.llnl.gov/search/.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Key R&D Program of China [grant numbers 2016YFA0600602 and 2019YFC1510002] and the National Natural Science Foundation of China [grant number 41776039].