MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses

Hideo Shiogama, Hiroaki Tatebe, Michiya Hayashi, Manabu Abe, Miki Arai, Hiroshi Koyama, Yukiko Imada, Yu Kosaka, Tomoo Ogura, Masahiro Watanabe
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Abstract

Abstract. Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.
MIROC6大集成(MIROC6- le):实验设计和初步分析
摘要单模式初始条件大集合(LEs)是理解强迫响应和内部变率在历史和未来气候变化中的作用的有用方法。在这里,我们使用MIROC6耦合大气-海洋全球气候模式(MIROC6- le)制作了迄今为止最大的集合之一。MIROC6-LE的总实验周期超过76000年。MIROC6-LE包括一个工业化前的长期控制运行、50个成员的历史模拟、8个10或50个成员的单一强迫历史实验、5个50个成员的未来情景实验和3个50个成员的单一强迫未来实验。在这里,我们描述了实验设计。大部分实验的输出数据都是免费向公众开放的。这个数据集将对广泛的研究团体有用。我们还演示了一些初始分析的例子。具体而言,我们通过分析历史完全强迫运行和单次强迫运行的总和,证实了1850-2020年地表气温和降水的年平均值和极端指数的强迫响应关系具有线性可加性。为了分离2000-2020年相对于1850-1900年的年平均温度和极端温度的历史人为信号,在世界大部分地区,集合大小分别为4和15就足够了。年平均降水和极端降水的历史人为信号分别在76%和69%的地区具有显著意义。14个成员足以检验世界大部分地区共享社会经济路径(ssp)、ssp585和ssp126之间的年平均值和极端温度和降水指数的变化差异。研究ssp126和ssp119之间年平均和极端降水变化的差异需要大于50个成员的集合。以偏离整体平均值为代表的内部变率的历史和未来变化,重点分析了El Niño/南方涛动(ENSO)和全球年平均温度和降水。在所有未来实验中,从工业化前条件到1951-2000年,从1951-2000年到2051-2100年,以及从低排放到高排放的未来情景实验中,31的集合大小足以检测ENSO的增强。由27个成员组成的单一强迫历史实验可以隔离人为温室气体和气溶胶强迫引起的ENSO强化。在未来的所有实验中,有23个成员可以识别全球平均温度变率的未来变化,而50个成员不足以检测ssp119和ssp126的全球平均降水变率的变化。我们还证实,由于对历史自然强迫的年际气候响应,当使用来自长期平均值的非趋势异常时,这些温度和降水变化并没有得到精确分析,这突出了大集合对评估内部变率的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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