IPCC耦合模式对北太平洋海–气系统冬季重现的模拟

赵霞; 杨光

doi:10.3969/j.issn.0253-4193.2019.09.011

IPCC耦合模式对北太平洋海–气系统冬季重现的模拟

doi: 10.3969/j.issn.0253-4193.2019.09.011

赵霞^{1, 3, 5,},
杨光^{2, 4}

1.
中国科学院海洋研究所中国科学院海洋环流与波动重点实验，山东青岛 266071
2.
自然资源部第一海洋研究所海洋与气候研究中心，山东青岛 266061
3.
青岛海洋科学与技术试点国家实验室海洋动力过程与气候功能实验室，山东青岛 266237
4.
青岛海洋科学与技术试点国家实验室区域海洋动力学与数值模拟功能实验室，山东青岛 266237
5.
中国科学院海洋大科学研究中心，山东青岛 266071

基金项目: 国家自然科学基金（41375094）。

详细信息

作者简介:
赵霞（1981—），河南省偃师县人，副研究员，主要从事大尺度海气相互作用及数值模拟研究。E-mail: zhaoxia@qdio.ac.cn

中图分类号: P732.6
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出版历程
- 收稿日期: 2018-09-30
- 修回日期: 2018-11-25
- 网络出版日期: 2021-04-21
- 刊出日期: 2019-09-25

The winter-to-winter recurrence in the North Pacific air-sea system: IPCC-AR4 model evaluation

Zhao Xia^{1, 3, 5
,},
Yang Guang^{2, 4}

1.
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
2.
Center for Ocean and Climate Research, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
3.
Marine Dynamics Process and Climate Function Laboratory, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
4.
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
5.
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China

摘要

摘要: 冬季重现(再现)是中高纬度大尺度海表温度重要的持续性特征，是热带外海洋特有的现象。北太平洋大气环流也存在这一现象，它可能会强迫产生这一海域海温的冬季重现。本文利用IPCC 20C3M耦合模式资料，评估了耦合模式模拟北太平洋海–气系统冬季重现的能力。北太平洋海温冬季重现的空间范围是海盆尺度的，中部重现时间比其周围晚。大气环流场的冬季重现主要是在北太平洋中部，它与海温冬季重现关系密切。大多数IPCC耦合模式基本上可以模拟出太平洋海温大范围的冬季重现现象。与重现范围的模拟相比，耦合模式对重现时间地理差异的模拟都比较差。各模式对大气环流冬季重现时空分布特征的模拟较差，大部分模式未能模拟出大气环流场中主要的重现区域。而且，大气环流冬季重现对海温重现的可能影响并没有体现在这些耦合模式中。耦合模式对北太平洋大气冬季重现的模拟还有待改善。
- 北太平洋 /
- 海–气系统 /
- 持续性 /
- 冬季重现(再现) /
- IPCC模式评估
Abstract: Winter-to-winter recurrence (WWR) is an important persistence characteristic of large-scale sea surface temperature anomalies (SSTAs) in middle-high latitude, which is a unique phenomenon of the extratropical ocean. Its influence on extratropical climate change can not be ignored. WWR also exist in the atmosphere in the North Pacific Ocean, which could induce SSTAs WWR. In this paper, the WWRs of the air-sea system in the North Pacific are evaluated using the model output of 23 coupled models of CMIP in IPCC 4th assessment. Observational results show that, SSTAs WWR occurs over most of the basin of North Pacific, but the recurrence timing is in winter in the central and in fall in other regions. The atmospheric WWR is mainly located in central North Pacific, which is essential for the occurrence of the SSTAs WWR. Most of models can simulate the basinwide SSTAs WWR, but they can’t reproduce the geographical distribution of recurrence timing well. Compared with the SSTAs, little skill is shown in the WWR of the atmospheric circulation in most models. Moreover, the possible effect of the atmospheric WWR on the SSTAs WWR is not reflected in these coupled models. The coupling model has yet to be improved for simulating the atmospheric WWR in the North Pacific.
- North Pacific /
- air-sea system /
- persistence /
- winter recurrence /
- IPCC model evaluation

HTML全文

图 1 北太平洋海温冬季重现的时空分布（起始月为2月）

图中的阴影表示重现时间，浅色为秋季（10–12月），深色为冬季（1–3月）

Fig. 1 Spatiotemporal distribution of the sea surface temperature anomalies winter-to-winter recurrence in the North Pacific Ocean for the starting month of February

The shading is the winter-to-winter recurrence timing, dark (light) shading indicates the recurrence timing is in winter (fall)

下载: 全尺寸图片幻灯片

图 2 北太平洋3个格点海温异常滞后相关曲线（2月为起始月）

图中的直线表示95%置信水平

Fig. 2 Lag correlations of the sea surface temperature anomalies of three locations in the North Pacific Ocean for the starting month of February

The thin solid line indicates the 95% confidence level

下载: 全尺寸图片幻灯片

图 3 北太平洋大气环流冬季重现的时空分布

a. 海平面气压异常场; b. 500 hPa位势高度异常场; c. 200 hPa位势高度异常场。图中的阴影表示重现时间，浅色为秋季，深色为冬季

Fig. 3 Spatiotemporal distribution of the winter-to-winter recurrence of atmospheric circulation anomalies in the North Pacific Ocean

a. Sea level pressure anomalies; b. 500 hPa geopotential height anomalies; c. 200 hPa geopotential height anomalies. The shading is the winter-to-winter recurrence timing, dark (light) shading indicates the recurrence timing is winter (fall)

下载: 全尺寸图片幻灯片

图 4 冬季北太平洋中部海平面气压与北太平洋海温的相关

浅（深）色阴影表示负（正）相关系数大于95%置信水平

Fig. 4 Correlation coefficient between the sea level pressure anomalies in the central North Pacific Ocean and the sea surface temperature anomalies in the North Pacific Ocean in winter

The light (dark) shading indicates negative (positive) correlation coefficient values with a confidence level higher than 95%

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图 5 观测与23个模式模拟的北太平洋海温冬季重现的时空分布

Fig. 5 Spatiotemporal distribution of the sea surface temperature anomalies winter-to-winter recurrence in the North Pacific Ocean based on the observation data and 23 climate models

下载: 全尺寸图片幻灯片

图 6 观测与23个模式模拟的北太平洋中部海温的持续性特征

Fig. 6 Persisitence of the sea surface temperature anomalies in the central North Pacific Ocean based on the observation data and 23 climate models

下载: 全尺寸图片幻灯片

图 7 观测与23个模式模拟的海平面气压场冬季重现的时空分布

Fig. 7 Spatiotemporal distribution of the sea level pressure anomalies winter-to-winter recurrence of in the North Pacific Ocean based on the observation data and 23 climate models

下载: 全尺寸图片幻灯片

图 8 观测与23个模式模拟的冬季北太平洋中部海平面气压异常与北太平洋海温异常的相关

Fig. 8 Correlation coefficient between the sea level pressure anomalies in the central North Pacific Ocean and the sea surface temperature anomalies in the North Pacific Ocean in winter based on the observation data and 23 climate models

下载: 全尺寸图片幻灯片

表 1 参加IPCC AR4 20C3M的23个耦合模式的基本情况

Tab. 1 Descriptions of 23 models in the IPCC AR4 20C3M archives

所属单位	国家	耦合模式
皮叶克尼斯气候研究中心	挪威	BCCR-BCM2.0
美国国家大气研究中心	美国	CCSM3
气候模拟与分析中心	加拿大	CGCM3.1-T47
气候模拟与分析中心	加拿大	CGCM3.1-T63
国家气象中心	法国	CNRM-CM3
联邦科学与工业研究组织	澳大利亚	CSIRO-Mk3.0
马普气象研究所	德国	ECHAM5/MPI-OM
波恩大学气象研究所/韩国气象局	德国/韩国	ECHO-G
大气物理研究所	中国	FGOALS-g1.0
国家大气海洋局地球物理流体力学实验室	美国	GFDL-CM2.0
国家大气海洋局地球物理流体力学实验室	美国	GFDL-CM2.1
国家航空航天局/戈达德空间研究所	美国	GISS-AOM
国家航空航天局/戈达德空间研究所	美国	GISS-EH
国家航空航天局/戈达德空间研究所	美国	GISS-ER
地球和火山科学研究所	意大利	INGV-SXG
数值数学研究所	俄罗斯	INM-CM3.0
皮埃尔西蒙拉普拉斯研究所	法国	IPSL-CM4
国家研究所气候系统研究中心	日本	MIROC3.2-hires
国家研究所气候系统研究中心	日本	MIROC3.2-medres
气象研究所	日本	MRI-CGCM2.3.2
国家大气研究中心	美国	PCM
哈德莱气候预测与研究中心	英国	UKMO-HadCM3
哈德莱气候预测与研究中心	英国	UKMO-HadGEM1

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