基于统计动力反演的东亚亚热带季风变化驱动机制研究

doi:10.13249/j.cnki.sgs.2017.03.015

地理科学 ›› 2017, Vol. 37 ›› Issue (3): 445-454.doi: 10.13249/j.cnki.sgs.2017.03.015

基于统计动力反演的东亚亚热带季风变化驱动机制研究

侯立春^1,^2,³(), 林振山^1,³(), 刘翔^1,³, 罗虎明⁴

1.南京师范大学地理科学学院环境与生态建模研究室,江苏南京 210023
2.上饶师范学院历史地理与旅游学院,江西上饶 334001
3.江苏省地理信息资源开发与利用协同创新中心,江苏南京 210023
4.西北农林科技大学理学院,陕西杨凌 712100

收稿日期:2016-03-13 修回日期:2016-04-20 出版日期:2017-03-20 发布日期:2017-03-20
作者简介:
作者简介：侯立春（1965-）,男,江苏新沂人,副教授,主要从事地学建模与全球变化方向研究。 E-mail:houlchjs@163.com
基金资助:
国家自然科学基金项目（31470519）、江苏省自然科学基金项目（BK20131399)和江苏省高校优势学科建设工程项目资助

Driving Mechanism of East Asian Subtropical Monsoon Changes Based on Statistical-dynamical Inversion Model

Lichun Hou^1,^2,³(), Zhenshan Lin^1,³(), Xiang Liu^1,³, Huming Luo⁴

1.College of Geographical Science, Nanjing Normal University, Nanjing 210023, Jiangsu, China
2.College of History Geography and Tourism, Shangrao Normal College, Shangrao 334000, Jiangxi, China
3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China
4.College of Sciences, Northwest A & F University, Yangling 712100, Shaanxi, China

Received:2016-03-13 Revised:2016-04-20 Online:2017-03-20 Published:2017-03-20
Supported by:
National Natural Science Foundation of China(31470519), Natural Science Foundation of Jiangsu Province (BK20131399), The Priority Academic Program of Jiangsu Higher Education Institutions

摘要/Abstract

摘要：

利用观测数据,运用非线性统计-动力学方法,反演系统各因子之间的相互关系,建立了东亚亚热带季风变化的动力方程,为研究东亚亚热带季风的驱动机制提供了量化参考。研究发现：过去2 000 a东亚亚热带季风是多因子通过反馈机制相互作用影响且具有耦合效应的复杂非线性动力系统,其驱动力主要来源于普若岗日冰芯δ¹⁸О代表的青藏高原热力作用强迫、太阳黑子活动、ENSO、温室气体单因子CO₂和CH₄浓度、北极温度和CH₄及北极温度与7月太阳辐射的耦合作用机制;反馈调节作用主要源于7月太阳辐射与太阳黑子活动、CH₄浓度、中国陆地地表温、CH₄与7月太阳辐射以及CO₂和CH₄的耦合调节作用。并通过动力反演机制推论热带西太平洋对亚热带季风有一定驱动作用,但并不是主要驱动力,即驱动亚热带季风变化的主源地并不在热带西太平洋海区,石笋δ¹⁸О指代的也主要是夏季风信息。

关键词: 东亚亚热带季风, 统计动力学反演, 驱动机制, 和尚洞石笋

Abstract:

In this article, a nonlinear statistical-dynamical model was used to inverse the interaction between different factors and the dynamic equation of the East Asian subtropical monsoon is established by using the observed data. It provides a quantitative reference for study on the driving mechanism of the subtropical monsoon in East Asia. Research shows that the East-Asian subtropical monsoon is a complex nonlinear dynamical system with multi-factors interacting by feedback mechanisms and coupling effectsover the past two millennia. Its driving force mainly comes from the Tibetan Plateau thermal forcing with the Puruogangri ice core δ¹⁸О, sunspot activity, ENSO, greenhouse gas CO₂ and CH₄ concentrations, and the coupling mechanism between the Arctic temperature and CH₄, as well as the Arctic temperature and the solar radiation in July. Feedback regulation mainly comes from the solar radiation in July and sunspot activity, CH₄concentration, land surface temperature in China, the coupling regulation mechanism between the solar radiation in July and CH₄, as well as CO₂ and CH₄. It is inferred that the tropical western Pacific has a certain driving effect on the subtropical monsoon of East Asia through the dynamic inversion mechanism, but not the main driving force, namely the main source that drives the subtropical monsoon changes is not in the sea area of tropical western Pacific, and stalagmite δ¹⁸О is mainly referred to the features of summer monsoon information.

Key words: subtropical monsoon in East Asian, statistical-dynamical inversion, driving mechanism, Heshang Cave stalagmite

中图分类号:

P462

侯立春, 林振山, 刘翔, 罗虎明. 基于统计动力反演的东亚亚热带季风变化驱动机制研究[J]. 地理科学, 2017, 37(3): 445-454.

Lichun Hou, Zhenshan Lin, Xiang Liu, Huming Luo. Driving Mechanism of East Asian Subtropical Monsoon Changes Based on Statistical-dynamical Inversion Model[J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(3): 445-454.

图/表 6

表1

表2

方程（4）反演结果"

系数	a1	a2	a3	a4	a5	a6	a7
系数反演值	-0.10055	-0.02913	-0.01081	0.005285	-0.01142	0.02959632	0.056275
相对贡献率	0.16798	0.01348	0.00337	0.000767	0.00406	0.019062	0.02241
系数	a8	a9	a10	a11	a12	a13	a14
系数反演值	-0.08866	-0.02563	0.031175	0.015778	-0.00844	-0.01303	-0.06627
相对贡献率	0.02649	0.00728	0.02701	0.00481	0.000628	0.005129	0.081259
系数	a15	a16	a17	a18	a19	a20	a21
系数反演值	0.025305	0.01484	-0.00878	0.019919	0.009474	0.10939	0.007487
相对贡献率	0.034365	0.013161	0.007513	0.016384	0.000439	0.025516	0.000667
系数	a22	a23	a24	a25	a26	a27	a28
系数反演值	-0.001	0.006777	0.00068	-0.04707	0.004708	-0.01395	-0.0027
相对贡献率	6.89E-05	0.001923	0.000167	0.022363	0.000357	0.002691	0.000125
系数	a29	a30	a31	a32	a33	a34	a35
系数反演值	-0.03946	0.009177	-0.10255	0.010578	-0.00341	0.012104	0.016208
相对贡献率	0.022328	0.000277	0.020959	0.000654	0.000145	0.001384	0.001388
系数	a36	a37	a38	a39	a40	a41	a42
系数反演值	-0.00204	-0.01043	0.010397	-0.01391	0.020316	-0.13212	-0.02057
相对贡献率	6.53E-05	0.001589	0.001336	0.0027	0.001725	0.04733	0.00244
系数	a43	a44	a45	a46	a47	a48	a49
系数反演值	-0.02771	0.080616	-0.00914	-0.0126	-0.01155	-0.03064	0.006057
相对贡献率	0.01001	0.056586	0.000315	0.003257	0.003792	0.016001	0.00019
系数	a50	a51	a52	a53	a54	a55	a56
系数反演值	-0.03766	0.020391	0.019536	-0.02404	-0.00797	-0.00275	0.01068
相对贡献率	0.002652	0.004034	0.007764	0.007658	0.000364	0.000165	0.002665
系数	a57	a58	a59	a60	a61	a62	a63
系数反演值	0.014724	-0.03827	0.01665	-0.00057	-0.01608	-0.00568	0.012554
相对贡献率	0.001451	0.004024	0.002169	5.97E-06	0.003168	0.000213	0.002846
系数	a64	a65	a66	a67	a68	a69	a70
系数反演值	0.014113	-0.02446	-0.00161	-0.00441	-0.00499	0.00077	0.028955
相对贡献率	0.001104	0.00221	2.83E-05	0.000566	0.00034	4.73E-06	0.004246
系数	a71	a72	a73	a74	a75	a76	a77
系数反演值	-0.10141	-0.05228	-0.00698	-0.02653	-0.02617	-0.12942	0.053111
相对贡献率	0.023636	0.016062	0.000708	0.010153	0.00374	0.029033	0.010645
系数	a78	a79	a80	a81	a82	a83	a84
系数反演值	0.006918	-0.08445	-0.05064	-0.09445	0.01539	0.163461	0.042832
相对贡献率	0.000277	0.025373	0.003357	0.024295	0.000726	0.069235	0.002009
系数	a85	a86	a87	a88	a89	a90
系数反演值	-0.0377	0.040118	-0.00131	0.000559	-0.00079	0.009332
相对贡献率	0.013109	0.01552	4.64E-06	4.76E-06	3.70E-06	0.00047

表2

图1

图2

图3

图4

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驱动类型	驱动因子	代用数据	代码	分辨率（a）
海陆热力差异	石笋δ¹⁸О	和尚洞石笋δ¹⁸О^[16]	M	1~3
	热带西太平洋海表温	Tropical southwest Pacific SST^[17]	T_p	2~10
	中国陆地地表温	北京石花洞石笋与温度重建^[18]	T	1
	北极温度	GISP2 Ice Core TR^[19]	T_a	1
太阳辐射与太阳活动变化	7月30^oN太阳辐射量	Total solar irradiance reconstruction^[20]	S₁
太阳辐射与太阳活动变化	太阳黑子	Sunspot（Solar Variability）^[21]	S₂	1~10
大气环流	ENSO事件	ENSO（Pacific Ocean）^[22]	E_s	1
温室气体	CO₂质量浓度	Law Dome Ice Core CO₂^[23]	C_o	1
	CH₄质量浓度	Law Dome Ice Core CH₄^[24]	C_h	1
	N₂O质量浓度	Law Dome Ice Core N₂O^[23]	N_o	1
青藏高原	高原热力	青藏高原普若岗日冰芯δ¹⁸О^[24]	Q	5
火山活动	Volcanic sulfate	GISP2 Ice Core Volcanic markers^[25]	V	1~3

基于统计动力反演的东亚亚热带季风变化驱动机制研究

Driving Mechanism of East Asian Subtropical Monsoon Changes Based on Statistical-dynamical Inversion Model

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