印度洋偶极子预报技巧在多模式中的对比研究

雷蕾; 伍艳玲; 唐佑民

doi:10.3969/j.issn.0253-4193.2020.07.005

印度洋偶极子预报技巧在多模式中的对比研究

doi: 10.3969/j.issn.0253-4193.2020.07.005

雷蕾^1,,
伍艳玲^1, ,,
唐佑民^{1, 2}

1.
自然资源部第二海洋研究所卫星海洋环境动力学国家重点实验室，浙江杭州 310012
2.
北不列颠哥伦比亚大学环境科学与工程学院，加拿大不列颠哥伦比亚省乔治王子城 V2N4Z9

基金项目: 国家自然科学基金（41530961）；国家自然科学基金青年科学基金（41805066）。

详细信息

作者简介:
雷蕾（1993-），女，河南省郑州市人，主要从事物理海洋学方面研究。E-mail：leilei@sio.org.cn

通讯作者:
伍艳玲，女，湖北省天门市人，从事季节到多年际气候可预报性研究。E-mail：ywu@sio.org.cn

中图分类号: P732.4
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- 被引次数: 0
出版历程
- 收稿日期: 2019-06-12
- 修回日期: 2019-12-02
- 网络出版日期: 2020-11-18
- 刊出日期: 2020-07-25

A comparison of Indian Ocean dipole prediction skill in a multi-model ensemble

Lei Lei^1
,,
Wu Yanling^{1
, ,},
Tang Youmin^{1, 2}

1.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2.
College of Environmental Science and Engineering, University of Northern British Columbia, Prince George, BC V2N4Z9, Canada

摘要

摘要: 本文采用北美多模式集合产品数据，分析了印度洋偶极子指数在不同模式中实际预报技巧和潜在可预报性的差异，并进一步探究其可能的原因。结果表明，印度洋偶极子的有效预报时效在不同模式中差别较大，从2个月到4个月不等。其中东极子海温异常在不同模式中预报技巧的差别较西极子海表面温度异常更明显，表明模式误差和初始误差对东极子海表面温度异常演变的影响更为显著。另外，印度洋偶极子的实际预报技巧和潜在预报技巧存在明显的线性关系，潜在预报技巧高的模式，其实际预报技巧也高。最后，本文诊断、分析了厄尔尼诺对印度洋偶极子预报技巧的影响，发现在厄尔尼诺和印度洋偶极子相关性较高的气候模式中，印度洋偶极子实际预报技巧也较高。
- 印度洋偶极子 /
- 实际预报技巧 /
- 潜在可预报性
Abstract: In this work, we compare the actual prediction skills and potential predictability of the Indian Ocean Dipole (IOD) index among different models, using the North American Multi-Model Ensemble dataset. If defined by a 0.5 anomaly correlation skill, we find that skillful predictions of the IOD strongly vary among models, ranging from 2- to 4-month leads. The diversity in the prediction skills of Sea Surface Temperature Anomalies (SSTA) over the east pole of the IOD is more obvious than that over the west pole, suggesting the model errors and the initial errors have more significant effects on SSTA over the east pole region. Also, there is a significant linear relationship between the actual prediction skills and potential predictability of the IOD. Finally, we find the actual prediction skills diversity is due to the El Niño/Southern Oscillation (ENSO) influences onto the IOD in different models. The models with enhanced ENSO/IOD link tend to have high actual prediction skills.
- Indian Ocean dipole /
- actual prediction skill /
- potential predictability

HTML全文

图 1 1982−2010年观测（黑线）和模式预报（红线）的DMI时间序列

预报的DMI时间序列是超前时间为1～3个月预报的平均。预报的和观测的时间序列均去掉季节内和7年以上频率的信号

Fig. 1 Evolution of observed (black) and predicted (red) DMI from 1982 to 2010

The predicted DMI is at 1−3 months of lead time. A 4−84-month bandpass filter is applied to remove the intraseasonal and long-term variations for both the predicted and observed time series

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图 2 1982−2010年观测（黑线）和模式预报（红线）的DMI时间序列

预报的DMI时间序列来自超前时间为4～6个月的平均。预报的和观测的时间序列均去掉季节内和7年以上频率的信号

Fig. 2 Evolution of observed (black) and predicted (red) DMI from 1982 to 2010

The predicted DMI is at 4−6 months of lead time. A 4-84-month bandpass filter is applied to remove the intraseasonal and long-term variations for both the predicted and observed time series

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图 3 NMME中10个模式和多模式集合（MEE）对印度洋偶极子指数（a，b）、西极子（c，d）和东极子（e，f）的模式预报与观测之间的均方根误差以及距平相关系数

Fig. 3 The root mean square error and anomaly correlation coefficient for the DMI (a, b), the west pole (c, d), and the east pole (e, f) for the 10 models in the NMME and the MME

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图 4 NMME中10个模式和多模式集合的潜在可预报性

a图为信噪比方法得到的潜在相关系数，b图为信息熵方法得到的互信息距平相关系数

Fig. 4 The DMI potential predictability of 10 models in NMME and MME

The potential correlation coefficient is obtained by the signal-to-noise ratio method in a, and the mutual information anomaly correlation coefficient is estimated using the information entropy theory in b

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图 5 集合成员数对模式实际预报技巧的影响

随意选取的6个集合成员所得到的实际预报技巧（红色虚线）及其平均（黑色实线），各个模式所有集合成员计算得到的实际预报技巧（蓝色实线，也是图3中的DMI的距平相关系数）

Fig. 5 The influences of ensemble members on the actual prediction skills

The actual prediction skills (red dashed lines) obtained by randomly-selected six members and their average (black solid line), the actual prediction skills calculated by all the ensemble members of individual model (blue solid line, also the anomaly correlation coefficient of the DMI in Fig.3)

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图 6 NMME中去除CCSM3模式后剩余的模式成员关于IOD的实际预报技巧和潜在可预报性的散点图

横轴为不同模式成员在超前时间为0～6个月的距平相关系数平均值，纵轴为超前时间为0～6个月的潜在相关系数的平均值The x axis is the mean of the DMI ACC at the lead time of 0−6 months, while the y axis is the mean of DMI PCORR at the lead time of 0−6 months

Fig. 6 Scatterplot of the actual prediction skills against potential predictability (PCORR) of the DMI (without CCSM3)

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图 7 所有模式成员IOD的实际预报技巧以及ENSO/IOD关系的散点图

纵轴为不同模式成员在超前时间为0～6个月的距平相关系数（ACC）平均值，横轴为ENSO/IOD的关系。ENSO/IOD的关系为同期（9−11月）的Niño3.4指数以及DMI的相关系数

Fig. 7 Scatterplot of the DMI actual prediction skills at lead time 0−6 months against the strength of ENSO/IOD link

The actual prediction skill (y axis) is the mean of the DMI ACC at the lead time of 0−6 months. The ENSO/IOD link (x axis) is represented by the simultaneous correlation coefficient between Niño 3.4 index and the DMI during Sptember to November

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图 8 IOD预报时效最短（CCSM3/ECHAM-AC, a）及最长（GFDL-CM2.1/GFDL-CM2.5，b）的两对模式降水异常与Niño 3.4指数的同期回归系数（9-11月）

所有的颜色区域均超过95%信度检验

Fig. 8 The regression patterns for the total precipitation anomaly (mm/d) onto the Niño3.4 index during Sptember to November in the worst performance models (CCSM3/ECHAM-AC, a) and the best performance models (GFDL-CM2.1/GFDL-CM2.5, b)

Areas shown in color are significant at 95% confidence level

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图 9 IOD预报时效最短（CCSM3/ECHAM-AC，a）及最长（GFDL-CM2.1/GFDL-CM2.5，b）的两对模式SSTA与Niño3.4指数的同期回归系数（9−11月）

所有的颜色区域均超过95%信度检验

Fig. 9 The regression patterns for SSTA onto the Niño3.4 index during Sptember to November in the worst performance models (CCSM3/ECHAM-AC, a) and the best performance models (GFDL-CM2.1/GFDL-CM2.5, b)

Areas shown in color are significant at 95% confidence level

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表 1 NMME中10个模式的介绍

Tab. 1 The introduction of 10 models in NMME

模式	模式全称	来源组织	超前时间/月	集合成员数目
CanCM3	Third Generation Canadian Coupled Global Climate Model	CMC	0～11	10
CanCM4	Fourth Generation Canadian Coupled Global Climate Model	CMC	0～11	10
CCSM3	Community Climate System Model, version 3	NCAR	0～11	6
CCSM4	Community Climate System Model, version 4	NCAR	0～11	10
CESM1	Community Earth System Model, version 1	NCAR	0～11	10
CFSv2	Climate Forecast System, version 2	NCEP	0～9	24
ECHAM-AC	European Centre Hamburg Model	IRI	0～7	12
ECHAM-DC	European Centre Hamburg Model	IRI	0～7	12
GFDL-CM2.1	Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1	GFDL	0～11	20
GFDL-CM2.5	Geophysical Fluid Dynamics Laboratory Climate Model, version 2.5	GFDL	0～11	24

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