CMIP6年代际试验对中国气温预测能力的初步评估

doi:10.12006/j.issn.1673-1719.2020.029

气候变化研究进展 ›› 2021, Vol. 17 ›› Issue (2): 162-174.doi: 10.12006/j.issn.1673-1719.2020.029

CMIP6年代际试验对中国气温预测能力的初步评估

汤秭晨¹^,², 李清泉¹^,², 王黎娟¹(), 伍丽泉³

1 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,南京 210044
2 国家气候中心中国气象局气候研究开放实验室,北京 100081
3 广西壮族自治区气候中心,南宁 530022

收稿日期:2020-02-21 修回日期:2020-08-23 出版日期:2021-03-30 发布日期:2021-04-02
通讯作者: 王黎娟
作者简介:汤秭晨,女,硕士研究生
基金资助:
国家重点基础研究发展计划(2016YFA0602200);国家“第二次青藏高原综合科学考察研究”(2019QZKK0208);中国科学院战略性先导科技专项(XDA20100304);国家自然科学基金重大项目(41790471);国家自然科学基金青年基金项目(41376030)

Preliminary assessment on CMIP6 decadal prediction ability of air temperature over China

TANG Zi-Chen¹^,², LI Qing-Quan¹^,², WANG Li-Juan¹(), WU Li-Quan³

1 Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
2 Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
3 Guangxi Climate Center, Nanning 530022, China

Received:2020-02-21 Revised:2020-08-23 Online:2021-03-30 Published:2021-04-02
Contact: WANG Li-Juan

摘要/Abstract

摘要：

利用参加第六次国际耦合模式比较计划（CMIP6）年代际气候预测计划（DCPP）的加拿大CanESM5模式和日本MIROC6模式的结果,评估了模式对中国近地面气温的预测能力。在年代际尺度上,两个模式年代际试验对近地面气温的回报技巧均高于历史试验的模拟能力,采用海温初始化可以提高模式对中国近地面气温的年代际预报技巧。对年代际回报试验的进一步分析表明,两个模式均能较好地预测年平均气温的变化;对季节平均气温,模式在秋季的回报技巧最高,在冬季较低。就区域平均气温而言,两个模式对中国各个地区年平均和季节平均气温都有较高的回报技巧,其中我国南方和西部地区的气温回报技巧比北方高。年平均以及春季、冬季的气温年代际回报技巧总体随提前时间的增加而降低,夏季和秋季的气温回报技巧随提前时间的增加提高。各区域内年代际预测技巧随提前时间的变化特征与全国整体基本一致。

关键词: CMIP6, 气候系统模式, 年代际预测, 中国近地面气温

Abstract:

The prediction ability of air temperature over China is evaluated in this paper based on the outputs of the Canadian CanESM5 model and the Japanese MIROC6 model participating in the Decadal Climate Prediction Project (DCPP) of the sixth Coupled Model Intercomparison Project (CMIP6). Comparing decadal prediction with historical simulation, both models’ decadal hindcasts show higher prediction skill for surface air temperature (SAT), which proves oceanic initialization improves the prediction skill of SAT in China on decadal scale. The models can capture the variation of annual mean temperature, and the prediction skill of seasonal mean temperature is the highest in autumn and comparatively lower in winter. Although both models have good performances in predicting annual and seasonal mean temperatures in various regions of China, the prediction skills are higher in the southern and western China than the northern China. As the lead time increases, the prediction skills of annual, spring and winter mean temperatures decrease, while those of summer and autumn mean temperature increase. The prediction skills of subregions share the same characteristics with the whole country.

Key words: CMIP6, Climate model, Decadal prediction, Near-surface air temperature over China

汤秭晨, 李清泉, 王黎娟, 伍丽泉. CMIP6年代际试验对中国气温预测能力的初步评估[J]. 气候变化研究进展, 2021, 17(2): 162-174.

TANG Zi-Chen, LI Qing-Quan, WANG Li-Juan, WU Li-Quan. Preliminary assessment on CMIP6 decadal prediction ability of air temperature over China[J]. Climate Change Research, 2021, 17(2): 162-174.

图/表 10

图1 中国区域划分

Fig. 1 Sub-region of China

图2 观测、CanESM5模式历史试验模拟以及MIROC6模式年代际试验提前1~5年回报的1961—2010年平均的年平均及四季平均气温

Fig. 2 1961-2010 averaged temperatures of observation (a, d, g, j, m), CanESM5’s historical simulation (b, e, h, k, n) and MIROC6’s decadal hindcasts (c, f, i, l, o) at lead years 1-5 (a-c) annual, (d-f) spring, (g-i) summer, (j-l) autumn, (m-o) winter

表1 1961—2010年平均的CanESM5与MIROC6模式历史试验模拟和年代际试验提前1~5年回报的年及季节平均气温与观测的空间相关系数

Tab.1 Average (1961-2010) pattern correlation coefficient (PCC) of annual and seasonal mean temperatures between model results (decadal and historical experiments of CanESM5 and MIROC6 models) and observations at lead years of 1-5

图3 CanESM5模式历史试验与年代际试验提前5~9年的年平均及四季平均回报气温与观测的ACC 注：黑点表示通过0.1的显著性水平。

Fig. 3 Anomaly correlation coefficient (ACC) of annual and seasonal mean temperature between observation and CanESM5 historical experiment (a-e) and decadal reforecast (f-j) at lead years 5-9 (Spotted area denotes passing significant test at 0.1 level) (a, f) annual, (b, g) spring, (c, h) summer, (d, i) autumn, (e, j) winter

图4 MIROC6模式历史试验与年代际回报试验提前5~9年的年平均及四季平均回报气温与观测的RMSE

Fig. 4 Root mean square error (RMSE) of annual and seasonal mean temperature between observation and MIROC6 historical experiment (a-e) and decadal reforecast (f-j) at lead years 5-9 (a, f) annual, (b, g) spring, (c, h) summer, (d, i) autumn, (e, j) winter

图5 CanESM5模式提前(a) 1~5,(b) 2~6,(c) 3~7,(d) 4~8,(e) 5~9,(f) 6~10年回报的年平均气温与观测的ACC 注：黑点表示通过0.01的显著性水平。

Fig. 5 ACC between annual mean temperature of observation and that of CanESM5 reforecast at lead years (a) 1-5, (b) 2-6, (c) 3-7,(d) 4-8, (e) 5-9, (f) 6-10 (Spotted area denotes passing significant test at 0.01 confident level)

表2 模式年代际试验回报的全国平均年平均气温RMSE

Tab.2 China’s average RMSE of annual mean temperature from model reforecasts at different lead years ℃

表3 模式年代际试验回报的季节平均气温与观测的全国平均ACC

Tab.3 China’s average ACC of seasonal mean temperature from model reforecasts at different lead years

图6 两个模式平均春(a)、夏(b)、秋(c)、冬(d)季提前1~5年回报气温的RMSE

Fig. 6 RMSE of seasonal mean temperature of two model’s average reforecast at lead year 1-5 (a) spring, (b) summer, (c) autumn, (d) winter

图7 两个模式平均的年平均及季节平均不同区域平均气温回报与观测的ACC 注：点划线表示ACC通过0.1的显著性检验。

Fig. 7 ACC between observation temperature and average temperature of two models’ reforecasts in various regions of China (Black chain lines represent thresholds at 0.1 significance level) (a) annual, (b) spring, (c) summer, (d) autumn, (e) winter

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CMIP6年代际试验对中国气温预测能力的初步评估

Preliminary assessment on CMIP6 decadal prediction ability of air temperature over China

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