研究论文 正式出版 版本 1 Vol 10 (6) : 527-542 2019
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人为气溶胶排放导致最近80年东亚夏季风在过去四个世纪以来空前减弱*
Anthropogenic aerosols cause recent pronounced weakening of Asian Summer Monsoon relative to last four centuries*
: 2019 - 12 - 15
: 2019 - 12 - 16
: 2019 - 12 - 16
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摘要&关键词
摘要:亚洲夏季风(Asian Summer Monsoon,ASM)对亚洲数十亿人口的生存、亚洲生态系统和生物多样性的分布、以及农业生产(粮食安全)和工业活动影响严重。因此了解ASM过去时空变化及其动力学过程对陆地生态系统、水资源、森林和景观研究至关重要。近几十年,器测记录显示以降水量为代表的ASM强度一直在减弱,但这一减弱趋势的起始时间和动力学过程尚不清楚。为此,第一次集成了ASM西部-中部边缘带10个对降水敏感的树木年轮宽度年表,重建了公元1566年以来反映ASM强度变化的降水序列。重建结果不仅捕捉到了ASM过去4个世纪以来强弱变化历史,也反映出历史上蝗灾与弱季风的关联。特别是发现了最近80年具有过去448年中前所未有的、最为强烈的、显著且持续时间最长的ASM强度减弱趋势。这一减弱趋势与在温室效应影响下ASM本该增强的预期大相庭径。耦合气候模型实验表明,北半球人为硫酸盐气溶胶排放的逐渐增加,对ASM减弱起了决定性作用。
关键词:东亚夏季风;树木年轮;降水重建;人为气溶胶;ASM减弱趋势
Abstract & Keywords
Abstract: Background, aim, and scope The Asian Summer Monsoon (ASM) affects ecosystems, biodiversity, and food security of billions of people. In recent decades, ASM strength (as represented by precipitation) has been decreasing, but instrumental measurements span only a short period of time. The initiation and the dynamics of the recent trend are unclear. As a result, the properties of the recent ASM decreasing trend, including whether it is a part of a longer-term trend must be understood. Several forcing factors may affect the strength of the ASM, including solar variability, volcanic eruptions, and anthropogenic aerosols. So how aerosols and the ASM interact will also be examined given that concentration of aerosols in northwest China has been increasing over the past several decades. Materials and methods Here for the first time, we use an ensemble of 10 tree ring-width chronologies from the west-central margin of ASM, to reconstruct detail of monsoonal precipitation variability from July of previous year to June of current year (PJJ) back to 1566 CE. The 10 tree ring-width chronologies are selected on the basis that they are sensitive only to rainfall, providing not only a higher-resolution but also an appropriate and direct proxy of the ASM over reconstruction from previous studies. Results The reconstructed PJJ time series is a proxy for the ASM, measuring the ASM strength over its marginal zone. The reconstruction captures weak/strong ASM events, and it is found that historical severe droughts and locust plague disasters both appear during weak ASM events. Notably, we found an unprecedented 80-year trend of decreasing ASM strength within the context of the 448-year reconstruction, which is contrary to what is expected from greenhouse warming. Comparison of two sets of historical model experiments (10 runs each) with and without increasing anthropogenic aerosols shows that this unprecedented decreasing trend is likely due to increasing anthropogenic aerosols, highlighting that the ASM-weakening effect of increasing anthropogenic sulfate aerosols could more than offset the ASM-enhancing effect of increasing greenhouse gases. Discussion Modeling is the only way to identify likely causes of the decreasing trend, and the results support a mechanism that would otherwise be difficult to measure directly. Our work further confirmed that anthropogenic aerosol’s role in the ASM weakening in a longer period of 1934—2013, compared with previous study which spans 44 years (1958—2001). Besides the increase of sulfate aerosols, other factors, such as PDO and NAO, might have influences on the monsoon weakening, especially during historical periods without anthropogenic aerosols. Conclusions Our reconstruction provides an important time series to study the ASM over the past 448 years. The time series confirms known properties of the ASM (e.g., the 24-year frequency spectrum), reproduces known historical extreme climate events, and offers opportunities to understand less-known events. Further, the reconstruction can contribute to the debate regarding the recent behavior of the ASM and help evaluate the relative importance of anthropogenic radioactive forcing factors. Recommendations and perspectives We expect that the time series will find a wide range of utility for understanding past climate variability and for predicting future climate change.
Keywords: the Asian Summer Monsoon; tree ring; reconstruction; anthropogenic aerosols; ASM decreasing trend
*Copyright (2019) Wiley. Used with permission from (Liu et al, Anthropogenic Aerosols Cause Recent Pronounced Weakening of Asian Summer Monsoon Relative to Last Four Centuries, Geophysical Research Letters, John Wiley and Sons).
亚洲夏季风(ASM)是由季节性太阳辐射驱动引起的海陆热力差异而形成。季风降水与中国大部分地区,尤其是季风活动边缘带的工农业生产密切相关。季风活动边缘带与中国200-mm至400-mm降水等值线基本平行(图1a;Liu et al,2014),它也是介于干旱与半干旱、沙漠与黄土带之间的过渡带。受ASM强烈影响,夏季降水占区域年降水的70%—90%(Wang,2006)。掌握ASM时空变化及相关动力学过程,对理解陆地生态系统、水资源、森林及景观都显得至关重要(Wang,2006;Piao et al,2010;Liu et al,2017)。太阳活动、火山爆发和人为气溶胶等若干强迫因素都可能影响ASM的强度,其中尤其值得关注的是人为气溶胶这一强迫因素。在过去几十年中,中国,特别是中国西北部的气溶胶浓度一直在增加(Lau and Kim,2006;Bollasina et al,2011;Zhu et al,2012a;Ganguly et al,2012;Song et al,2014;Kim et al,2016;Cai et al,2017)。然而,气溶胶和ASM如何相互作用,这一互动机制尚未完全厘清(Li et al,2016;Wu et al,2016)。


图1   亚洲夏季风(ASM)降水PJJ(上年七月至当年六月降水总量)观测站点与树轮采样点分布
Fig.1 Location of the Asian Summer Monsoon (ASM) rainfall observations PJJ (Precipitation from July of previous year to June of current year) and tree ring samples
a:10个树轮样点(红色树形标识)和ASM北部活动边缘带的39个气象站点(黑色圆点,均显示在图中);黄线为200 mm降水等值线,蓝线为400 mm降水等值线。b:1952—2013年树轮重建PJJ序列(红线)与PJJ观测序列(黑线)的对比。 a: Location of 10 tree ring sampling sites (red tree shapes) and 39 meteorological stations (all within the box plotted) at the northern margin of the ASM (black dots). The yellow line is the precipitation isohyet of 200 mm, and the blue is 400 mm. b: Comparison of reconstructed (red line) by tree rings and the observed (black line) precipitation data of PJJ during 1952—2013.
Fig.1 Location of the Asian Summer Monsoon (ASM) rainfall observations PJJ (Precipitation from July of previous year to June of current year) and tree ring samples
了解ASM变化历史对于探索其归因和预测未来的变化都非常重要。基于现代观测记录,对ASM系统的子系统成分、起始时间、季节周期、水汽来源、动力学过程等方面进行了大量研究(Waliser et al,2003;Ding and Chan,2005;Li et al,2015;Ueda et al,2015)。目前的结论是自上世纪70年代末以来,ASM呈减弱趋势(Wang,2001)。但是要进一步研究其长时间的动态变化过程,确定这种变化是由自然还是人为因素造成,那么将这一减弱趋势置于长期历史背景下来研究则至关重要。目前的减弱是否是长期减弱趋势的一部分?然而观测记录覆盖时段过短,无法探寻气候变化长期趋势。
在本文研究中,基于中国黄土高原西部的10个对降水非常敏感的树木年轮宽度年表,重建了自公元1566年以来长达448年的ASM降水序列;讨论过去前所未有的近80年ASM显著减弱趋势,探讨这一趋势与持续增加的人类气溶胶之间的关系。
1   材料和方法
1.1   树轮年表和气象数据
研究中采用ASM边缘地区的10个树轮年表,共310棵树(含584根样芯,图1a和表1)。这些年表中,管涔山年表尚未发表;寿鹿山、首阳山、竺尼山和吐鲁沟的年表均已发表(Liu et al,2013a;Liu et al,2013b;宋慧明等,2017;Sun et al,2018);贺兰山、崆峒山、兴隆山、哈思山的年表为新近更新数据,更新前的数据已发表(Liu et al,2005;Song and Liu,2011;Liu et al,2013c;Ma et al,2015)。每个样点至少采集20棵树,年轮宽度使用精度为0.001 mm宽度测量仪进行测量。根据树木在相同时期和相同气候条件下表现出相似的年际生长变化这一基本原理(Fritts,1976),运用交叉定年的方法确定每一轮的日历年龄。所得每一点的年表都能较好地反映当地降水信号(Liu et al,2005;Fang et al,2010;Song and Liu,2011;Liu et al,2013a;Liu et al,2013b;Liu et al,2013c;Ma et al,2015;Song et al,2017;Sun et al,2018),且各年表之间具有显著相关性(表2)。
表1   亚洲夏季风北部活动边缘带10个树轮采样点信息
序号
No.
样点
Site
树种
Species
纬度
Latitude
经度
Longitude
海拔
Elevation /m
起始年
Starting year
结束年
Ending year
年代长度
Years
树芯数量
Cores
文献
References
1贺兰山
Mt.Helan
(HL)
松Pine38.31°N105.46°E2400—250017422013272125Liu et al. (2005) **
2寿鹿
Shoulu
(SL)
云杉1 Spruce137.08°N103.44°E24981828200718055Liu et al. (2013a) *
3吐露沟
Tulugou
(TLG01)
松Pine36.63°N102.79°E23701721200828856Liu et al. (2013b) *
4吐露沟
Tulugou
(TLG02)
松Pine36.69°N102.73°E21461726200828351Liu et al. (2013b) *
5兴隆山
Mt.Xinglong
(XL)
云杉1 Spruce135.78°N104.07°E23141580201343448Liu et al. (2013c) **
6崆峒山
Mt.Kongtong
(KT)
松Pine35.50°N106.50°E1500—21231547201346773Song &Liu (2011) **
7哈思山
Mt.Hasi
(HS)
松Pine37.00°N104.40°E2400—27001666201334862Ma et al. (2015) **
8贵清山
Mt.Guiqing
(GQ)
松Pine34.63°N104.47°E24601492201152056***
9首阳山
Mt.Shouyang
(SY)
云杉2 Spruce234.99°N104.24°E23401683201333129Sun et al. (2018) *
10竺尼寺
Zhunisi
(ZNS)
松Pine35.14 °N103.94 °E24201880201313429宋慧明等. (2017) *
Song et al. (2017) *
松:油松(Pinus tabulaeformis Carr.);云杉1:青海云杉(Picea crassifolia Kom.);云杉2:紫果云杉(Picea purpurea Mast.)。
*年表数据已发表。**新近更新年表数据,原先该地年表数据已发表。***年表数据未发表。
Pine: Pinus tabulaeformis Carr.; Spruce 1: Picea crassifolia Kom.; Spruce 2: Picea purpurea Mast.
* represents that the chronology was published data. ** represents that the chronology was update data and the old chronologies from these sites were published. *** represents that the chronology was unpublished data.
表2   所有年表间两两相关情况
贵清山
Mt. Guiqing
崆峒山
Mt. Kongtong
兴隆山
Mt. Xinglong
哈思山
Mt. Hasi
首阳山
Mt. Shouyang
吐露沟01
Tulugou 01
吐露沟02
Tulugou 02
贺兰山
Mt. Helan
寿鹿山
Mt. Shoulu
崆峒山
Mt. Kongtong
0.51,
465
兴隆山
Mt. Xinglong
0.50,
432
0.37,
434
哈思山
Mt. Hasi
0.41,
346
0.35,
348
0.45,
348
首阳山
Mt. Shouyang
0.45,
329
0.34,
331
0.47,
331
0.25,
331
吐露沟01
Tulugou 01
0.40,
288
0.37,
288
0.46,
288
0.53,
288
0.29,
288
吐露沟02
Tulugou 02
0.27,
283
0.24,
283
0.45,
283
0.44,
283
0.26,
283
0.63,
283
贺兰山
Mt. Helan
0.23,
270
0.25,
272
0.28,
272
0.45,
272
0.25,
272
0.42,
267
0.37,
267
寿鹿山
Mt. Shoulu
0.32,
180
0.36,
180
0.51,
180
0.58,
180
0.29,
180
0.62,
180
0.53,
180
0.41,
180
竺尼寺
Zhunisi
0.49,
131
0.61,
133
0.57,
133
0.31,
133
0.48,
133
0.43,
128
0.44,
128
0.27,
133
0.35,
127
表中含相关系数r与样本量n,所有p值小于0.001。
r , n, and all at p<0.001.
在研究中,将10个样点的所有样芯合并使用ARSTAN程序(Cook and Kairiukstis,1990)生成能够代表ASM边缘带的树轮年表。选用直线或负指数函数拟合树木生长趋势,将每个样芯每一轮的原始测量值除以生长趋势进行标准化,采用双权重平均法合成区域年表。ARSTAN程序能够生成三种类型的区域年表:标准年表(STD)、差值年表(RES)和自回归年表(ARS)。因为STD同时包含高频和低频信息,最终选择STD进行进一步分析和研究。按照年表子样本强度>0.85(Wigley et al,1984)确定的年表有效时段为1566—2013年(起始年代包含9根样芯),并以该时段作为年表的重建时段(图2和表3)。


图2   亚洲夏季风边缘区域树轮宽度标准年表
a:年表与样本量;b:滑动表达样本信号(EPS)与序列间公共时段的树间平均相关(Rbar)。 a: Chronology and sample size; b: Running expressed population signal (EPS) and average correlation among trees for the common overlap period among series (Rbar).
Fig.2 Regional tree-ring standard chronology in the fringe region of the Asian Summer Monsoon
表3   亚洲夏季风北部边缘区域树轮宽度标准年表(STD)的统计特征
统计量StatisticsSTD
均值Mean sensitivity0.26
标准差Standard deviation0.26
偏度Skewness-0.08
峰度Kurtosis2.60
一阶自相关系数First-order autocorrelation0.36
所有序列间平均相关系数Mean correlation among all series0.36
表达样本信号Expressed population signal (EPS)> 0.851566年(9个芯)
1566 (9 cores)
气象数据采用ASM西北缘39个气象站1951—2013年的降水和气温观测数据(图1a)。为了研究ASM边缘带树木生长对区域气候的响应情况,采用全部气象站的均值来代表区域气候条件。研究区逐月降水量和气温如图3a所示。


图3   气象数据与相关函数分析
Fig.3 Climatic data and correlation analysis
a:39个气象站平均降水与平均气温记录的月分布(1951—2013年)。b:轮宽指数与亚洲夏季风北部边缘区39个气象站月均气温(红柱)和降水(绿柱)记录的相关分析(1951—2013年)。PJJ为上年七月至当年6月的年降水量。虚线为95%置信限。 a: Monthly precipitation and temperature distribution of the averaged observation data from 39 meteorological stations (1951—2013). b: Correlation between ring width index and monthly averaged mean temperature (red bars) and precipitation (green bars) meteorological data from 39 stations at the northern margin of the Asian Summer Monsoon (1951—2013). PJJ is the precipitation from previous July to current June. The dashed line is the 95% confidence limit.
1.2   转换方程的建立及检验
相关函数分析表明,在1952—2013年所有气象站共同观测期间,区域树轮年表对季风边缘带的降水响应明显,STD与上年7月至当年6月的区域降水呈显著正相关(PJJ,图3b)。因此运用线性回归模型设计转换方程:
PJJ = 140.644×Wt + 198.455
r = 0.766, R2 = 0.586, R2adj = 0.579, N = 62, p < 0.0001)
式中:Wt代表t年的STD值。
在校验期(1952—2013年),重建降水对观测降水的方差解释量为58.6%。图1b表明重建降水与观测记录相关较好。利用分段检验法对重建方程的稳定性和可靠性进行检验(Cook et al,1999;Fritts,1991)。分别采用1952—1981年和1984—2013年的气候数据进行建模,并用剩余的1982—2013年和1952—1983年的数据对相应的建模结果进行检验。检验过程用到的参数有相关系数(r)、方差解释量(R2)、符号检验(ST)、误差缩减值(RE)、效率系数(CE)、乘积均值检验(t),等。检验统计参数表明回归模型具有较高的稳定性和可靠性(表4;Cook et al,1999),特别是RE和CE值均远大于0。区域年表与重建PJJ序列之间线性相关,表明PJJ重建序列能够反映ASM边缘带的区域降水情况。
表4   PJJ重建的分段检验统计结果
建模期Calibration校验期Verification
时段PeriodrR2STt时段PeriodrR2RECESTt
1952—19810.802**0.64322*4.591982—20130.661**0.4370.570.36216.16
1984—20130.686**0.47121*6.221952—19830.787**0.6190.660.5924**4.73
1952—20130.766**0.58647**6.86
* 0.05显著性水平,** 0.01显著性水平;r:相关系数;R2r的平方;ST:符号检验;RE:误差缩减值;CE:有效系数;t:乘积平均值。
* Significant level 0.05, ** Significant level 0.01; r: correlation coefficient; R2: r square; ST: sign test; RE: reduction of error test; CE: coefficient of efficiency; t: product means test.

2   结果
2.1   东亚夏季风水汽来源和重建应用
ASM北缘地区降水来源于南亚季风和东亚季风的水汽输送(Ding and Chan,2005)。与长江下游相似,ASM其他区域7—9月700 hPa以下(海拔3000 m及以下)的水汽来源于印度洋和西太平洋。然而,由于对横贯东亚的太平洋-日本模式的罗斯比波响应,在弱ASM期间,北太平洋上空存在反气旋异常,同时在其北部存在一个气旋异常(Feng et al,2014)。气旋异常的西侧存在北风异常。这种环流异常虽使得中国北方水汽输送减少,但会增加长江下游水汽辐合(Wang et al,2008)。但是,由于辐合异常呈带状分布,在不同的大气环流异常下,辐合异常可能并不位于同一纬度(Wang et al,2001;Feng et al,2014)。一旦ASM减弱,由于水汽补充不足,北方可能会经历干旱,但南方却不一定各地都呈湿润状态。因此,北部边缘地区的降水对ASM的增强/减弱更为敏感(Ding et al,2008;Wang et al,2008);从1951—2013年季风边缘地区39个站点的观测结果来看,上年7—9月的降水量占上年7月到当年6月降水总量(PJJ)的56.7%。因此,北部边缘地区的降水可指示ASM强度变化。此外,观测PJJ与中国北方大部分地区7—9月降水存在显著的相关性(图4a)。在同一时段,重建PJJ与上一年7月、8月和9月的实测降水相关性最强,这也是ASM活动强烈的季节(图5)。重建PJJ序列与前一年7—9月降水的相关性达到0.574。同时,重建PJJ序列与北方前一年7—9月降水也显著相关(图4b)。因此,重建的PJJ序列可视为指示ASM强度变化的一个代用指标,指示ASM在其活动边缘区域上的强度(图4),高PJJ值意味着较强的ASM活动(Ding et al,2008)。由于重建PJJ是代表ASM边缘地区的,而其他ASM指数主要是针对在东亚/中国或南亚/中国的,因此它们之间会存在一定差异(Li and Zeng,2002;Wang and Fan,1999)。目前,基于树轮的ASM重建研究大多是仅基于一两条青藏高原地区的树轮序列(Li et al,2010;Grießinger et al,2011;Xu et al,2012)所得的结果。尽管该区域有一条季风序列使用了季风区多个样点的树轮资料,但却是主要针对南亚季风变化(Shi et al,2014;Shi et al,2017)的研究。本文则使用了10个树轮年表进行重建研究,且所选年表对ASM边缘带的季风降水均存在较强响应,因此本文的重建序列对季风变化更为敏感,也与其他ASM重建序列有明显差异(图6)。


图4   CRU降水格点数据(CRU TS V4.01)与PJJ序列的空间相关分析(1951—2012年)
Fig.4 Correlation pattern between the CRU TS V4.01 grid precipitation data and PJJ during the period 1951—2012
a:器测PJJ与格点PJAS的空间相关;b:树轮序列(即重建PJJ)与格点PJAS的空间相关。PJJ为上年七月至当年6月的年降水量,PJAS为上年七月至九月的降水量。两种空间相关模式对应较好,进一步反映出亚洲夏季风北部边缘降水重建PJJ的可靠性。图中仅显示90%置信水平以上的区域。 a: The instrumental PJJ versus gridded PJAS; b: The tree-ring series (i.e., the reconstructed PJJ) versus gridded PJAS. PJJ is the precipitation from previous July to current June, and PJAS is the July—September precipitation of previous year. The two spatial patterns correspond well, reinforcing the notion that the reconstructed PJJ is a reliable proxy for the precipitation of the northern margin of the Asian Summer Monsoon. Only areas above the 90% confidence level are shown.


图5   树轮宽度序列与39个气象站月均降水观测值的相关性分析(1952—2013年)
Fig.5 Correlation between ring-width chronology and observed monthly averaged mean precipitation data from 39 stations in the fringe region (1951—2013)
虚线代表95%置信限。 The dashed line denotes the 95% confidence limit. The reconstructed PJJ time series is a proxy for the ASM.


图6   本文降水重建序列(红线)与其他亚洲夏季风(ASM)序列(黑线)的对比
Fig.6 Comparisons between our reconstructed precipitation series (red line) and other series (black line) representing Asian Summer Monsoon (ASM)
a:南亚夏季风指数(Shi et al,2014;Shi et al,2017);b:青藏高原东缘红原树轮δ18O序列(Xu et al,2012)。 a: South ASM index (Shi et al, 2014; Shi et al, 2017); b: Tree ring δ18O at Hongyuan, eastern edge of the Tibet Plateau (Xu et al, 2012).
2.2   历史文献及现有资料对重建AMS序列的校验
中国旱涝指数是基于具有气候描述的地方编年史等历史文献记录建立的。作为ASM代用指标的PJJ序列,捕捉到历史文献记录(袁林,1994;中国气象灾害大典编委会,2006)和中国旱涝指数(DWI)序列(中国气象科学研究院,1981;张德二等,2003)中的许多极端事件。发现当DWI增高时,PJJ呈下降趋势(图7a)。在ASM北部活动边缘带相关区域,包括陕西、甘肃和宁夏地区在内的1586/1587、1759、1928/1929等几次大规模的严重干旱事件(袁林,1994;中国气象灾害大典编委会,2006;图1b中画点区域),均被推设为ASM急剧减弱的结果。文献记载,1586年至1587年甘肃东部超过一半的人口逃离家园,报道有人食人的现象发生;1759年,粮食价格飞涨,严重的饥荒困扰着甘肃地区(袁林,1994);1928年和1929年,陕西、甘肃和宁夏三省(区)遭受严重干旱,饥荒造成超过50万人死亡,且有关于狗食人尸、整户自杀的记载(袁林,1994;中国气象灾害大典编委会,2006;Ge et al,2016)。重建的PJJ序列捕捉到了这些干旱事件(表5),这为ASM减弱造成干旱提供了支持。


图7   重建PJJ序列的特性
Fig.7 Properties of the reconstructed PJJ index
表5   1566—2013年的极干(mean − 2σ)年和极湿(mean +2σ)年
极干年
Extremely drought year
降水量
Precipitation /mm
极湿年
Extremely wet year
降水量
Precipitation /mm
19282401605428
15862411781421
19292461804420
17592471604414
19322531956411
19662581786409
20002581897409
17472591675407
1566—2013年的重建降水均值(mean)为333 mm,一个标准差(1σ)为37 mm。定义降水量低于296 mm(mean − 1σ)为干旱年,降水量大于370 mm(mean + 1σ)为湿润年。在过去448年中,湿润年和干旱年分别占15.6%(70年)和16.1%(72年)。定义降水量低于259 mm(mean − 2σ)为极干年,大于407 mm(mean + 2σ)为极湿年。
The mean precipitation of the reconstruction is 333 mm for the period from 1566 to 2013 AD, and 1σ is 37 mm. We defined a dry year as having a value lower than 296 mm (mean − 1σ), and a wet year as having a value higher than 370 mm (mean + 1σ). In the past 448 years, wet and dry years accounted for 15.6 % (70 years) and 16.1 % (72 years), respectively. Extremely dry or wet years were defined as having a value lower than 259 mm (mean − 2σ) or higher than 407 mm (mean + 2σ).
泛滥的蝗虫吞噬了宝贵的粮食作物,加剧了干旱对农作物产量的影响,导致严重饥荒。历史记录的蝗灾均发生在PJJ序列中的干旱年份(图7b),特别与极端干旱时段一致(表6)。因此,ASM边缘带的蝗灾历史记录(李钢,2008;Tian et al,2011)进一步验证了PJJ序列可作为ASM的代用序列。
表6   极干年份里重建PJJ、旱涝指数(中国气象科学研究院,1981;张德二等,2003)以及蝗灾指数(李刚,2008)的对比
极干年份
Extremely drought year
降水量
Precipitation
/mm
旱/涝等级*
Dryness/Wetness grade*
蝗灾年份
Locust disaster year
蝗灾级别**
Locust disaster level**
1928240519283
1586241515873
1929246519304
1759247517613
1932253319322
19662583
20002585
1747259417483
* 20世纪20年代,数百名中国气候学家查阅了全国范围内2200余种地方志,辑录史料超过二百二十万字(数据),将过去510年的干湿状况划分为5个等级(中国气象科学研究院,1981):
1级——涝;2级——偏涝;3级——正常;4级——偏旱;5级——旱。
** 蝗灾级别定义(李钢,2008):
1级——出现级,仅有局部一两个小区域发现一代蝗虫,几乎没有造成危害(能收八九成以上)。2级——干扰级,两个或两个以上区域发生一代蝗灾,对农业生产有危害(可收六到八成)。3级——危害级,多个区域多次发生一到两代较大蝗灾,有迁飞扩散能力,对农业生产造成较大破坏(仅收三到五成),灾区生产难以恢复。4级——灾难级,多个区域或几近全国的范围发生两代以上特大蝗灾,频繁迁飞扩散,对农业生产(绝收,或仅余一二成)和百姓生活带来巨大灾难,往往与饥荒、瘟疫、战争相伴。
* During the 1920s, hundreds of Chinese climatologists processed more than 2200 local annals and many other historical writings nationwide and abstracted more than 2200000 characters (data). The degree of annual dryness and wetness during the last 510 years was classified into 5 grades (Chinese Academy of Meteorological Sciences, 1981):
Grade 1— very wet; Grade 2— wet; Grade 3— normal; Grade 4— dry; and Grade 5— drought.
** The definition level of locust plagues (Li, 2008):
Grade 1— Occurrence grade. The first generation of locusts appeared in only one or two small areas, and there was almost no harm to agriculture output. Crop harvest reached about 80%—90%.
Grade 2— Impact grade. The first generation of locusts appeared in two or more areas, and there was harm to agriculture, with crop harvest about 60%—80%.
Grade3— Hazard grade. The first and the second generation of locusts appeared in many areas, and there was huge damage to agricultural production. Crops harvest was about 30%—50%, and production was difficult to restore.
Grade 4— Disastrous grade. More than two generations of locusts appeared in multiple areas to almost national scope. There were enormous disasters to agriculture output with no or less than 10%—20% harvest, and the people could not survive. The disaster was often accompanied by famine, plague and war.
2.3   ASM减弱与人为气溶胶的影响
ASM对气候变暖如何响应的问题。因此这个重建序列的作用远不止用于直接检验、证实和再现历史事件。现代降水观测记录显示ASM存在减弱趋势(如:30—40年减弱趋势;Xu et al,2006;Wang et al,2012),PJJ序列在近几十年来同样显示出了下降趋势,但观测记录涵盖的时段则相对较短。为了找出ASM最明显且持续时间最长的减弱趋势,分别计算了1566—2013年PJJ重建序列50年、55年、60年、65年、70年、75年、80年、85年和90年等各个步长的滑动趋势计算。结果发现近几十年中,持续时间最长、最显著的下降趋势出现在80年滑动趋势分析中。因此,在80年滑动窗口下,1934—2013年显著的80年下降趋势(图8a)是整个448年间最大的(图8b),且80年的下降趋势与印度季风区近100年来降水显著减弱的趋势相似(Xu et al,2016)。这是因为本研究的降水同时代表了印度季风和东亚季风的变化,尽管PJJ序列在近100年呈下降趋势(约0.14 mm/a),但是并未超过95%的显著性水平。


图8   PJJ全序列重建
Fig.8 The reconstructed PJJ time series
a:过去四个世纪以来的PJJ重建(红色曲线)。倾斜黑线指示1934—2013年PJJ的80年下降趋势。b:重建PJJ的80年滑动序列(红线)。灰色区域代表95%置信区间。蓝色水平线代表最大的80年降水减少量,约0.62 mm/a。 a: The reconstructed PJJ during the past four centuries (red curve). The sloping black line denotes an 80-year decreasing trend of PJJ during the period 1934 to 2013. b: Eighty-year running trend (red line) for reconstructed PJJ. The gray area denotes the 95% confidence intervals. The blue horizontal line indicates the greatest 80-year decline in precipitation of approximately 0.62 mm/a.
Christensen et al,2013),ASM在近几十年会有所增强,但这与本文所得结果不一致,因此极可能有其他强迫导致ASM强度减弱。在寻找其动力学原因时,发现无论是PDO(Watanabe and Yamazaki,2014)还是NAO(Lu et al,2006)都无法对80年减弱趋势(图9)做出解释。


图9   1881—2013年重建PJJ、太平洋十年涛动指数(PDO)及北大西洋涛动指数(NAO)
Fig.9 The reconstructed PJJ, Pacific Decadal Oscillation and North Atlantic Oscillation during the period 1881—2013
a:亚洲夏季风北部边缘带重建PJJ。b:上年7月到当年6月的NAO指数(Lu et al,2006)。c:上年7月到当年6月的PDO指数(Watanabe and Yamazaki, 2014)。黑线代表1934—2013年的线性趋势。该对比表明,PJJ的下降趋势并非由NAO或PDO引起。 a: The reconstructed PJJ in the fringe region of the Asian Summer Monsoon. b: Monthly North Atlantic Oscillation (NAO) index (Lu et al, 2006) from previous July to current June. c: Monthly Pacific Decadal Oscillation (PDO) index (Watanabe and Yamazaki, 2014) from previous July to current June. The black lines represent their linear trends during the period 1934—2013. This demonstrates that the decreasing trend of PJJ is not caused by any trends of NAO and PDO.
Bollasina et al,2011)。尽管西亚上空的黑碳排放增加了印度次大陆的季风前期降水,但却抑制了整个东亚地区的季风前期降水(Lau et al,2006),同时硫酸盐排放也已被证实会迫使ASM强度下降(Menon et al,2002;Lau et al,2006)。因而,假设重建PJJ序列中ASM表现的80年减弱趋势是由硫酸盐气溶胶所主导,即人为气溶胶的增加在ASM减弱过程中抵消了温室气体增加导致的ASM增强效应。
CSIRO模式(Rotstayn et al,2007)中的两种耦合气候模型来验证上述假设,每组模型都包含8个耦合因子,时间范围为1871—1999年。第一组模型包括随时间变化的所有强迫因素,如:全球太阳辐射强迫、温室气体、臭氧、火山活动和人为气溶胶排放等因素。第二组除了将人为气溶胶排放量设定为工业化前水平的常量外,其他与第一组相同。对气溶胶的所有直接和间接影响都考虑在内并进行参数化。正如Rotstayn et al(2007)指出的那样:“该模型设计了一个全面的和相互作用的气溶胶方案,包含硫酸盐和碳质气溶胶、矿物粉尘、海盐和平流层火山气溶胶的排放。”二战后,从1940年开始全球排放量经历了大幅增长,到1980年前后除亚洲外世界各地的硫酸盐排放量都有所减少,而到目前为止亚洲的排放量却在持续增加(Smith et al,2011)。将两组模型的总体均值进行比较,结果表明,在包括ASM边缘带的中国大部分地区,1940年以来的夏季降水在没有人为硫酸盐气溶胶辐射强迫的情况下都会增加(图10a),而当加入硫酸盐气溶胶强迫时,ASM区域(包括ASM边缘带)降水则会显著减少(图10b)。在中国北方及AMS活动边缘区域,人为硫酸盐气溶胶的影响在21年滑动平均序列上表现十分明显。1920—1940年的降水低值与辐射强迫变化无关(图10c),很可能是由正相位PDO导致的(图9)。在这个模型中,下降趋势的起始时间大约从1940年开始(图10c),这与PJJ重建序列中所表现出的时间大致对应。因此,北半球人为硫酸盐气溶胶排放量的增加可能是导致本文重建ASM序列中显著80年减弱趋势的主要因素。


图10   用耦合气候模式模拟1940年以来北方夏季降水趋势(Rotstayn et al,2007)
Fig.10 Boreal summer rainfall trends since 1940 simulated by a coupled climate model (Rotstayn et al, 2007)
a:包含除人为气溶胶以外的所有强迫因素。b:包含全球太阳辐射、温室气体、臭氧、火山活动和人为气溶胶排放在内的所有强迫因素。蓝色矩形框为中国北方区域,黑点代表降水趋势超过95%显著性水平。c:中国北方区域PJJ与降水趋势对比。黑线和红线均代表根据气候模式模拟的中国北方区域降水变化的21年滑动平均序列,蓝线为PJJ21年滑动平均序列。结果显示1940—2013年的PJJ下降趋势可能是由人为气溶胶引起。JJA指六月至八月。 a: All forcing but without anthropogenic aerosols. b: All forcing including global emissions of solar irradiance, greenhouse gases, ozone, volcanic aerosols, and anthropogenic aerosols. Blue rectangular outline is the northern regions of China, and the black dot indicates that the rainfall trend passes the 95% significant level. c: Comparisons between PJJ and rainfall trends for the northern regions of China. The black and red lines indicate the 21-year running averages of rainfall changes of the northern regions of China simulated from climate model, and the blue line is the PJJ series after applying 21-year running averages. The result shows that the decreasing trend in PJJ from 1940 to 2013 is potentially contributed by anthropogenic aerosols. JJA = June—August.
3   讨论与结论
本文基于ASM活动边缘带10个对降水变化响应显著的树轮年表,重建了一条可以反映过去448年ASM强度变化的降水序列。与以往研究相比,其重要性不仅体现在具有更高的重建方差解释量,还体现在具有更宽广的空间场,能直接代表ASM强度的变化。本文的重建序列是研究448年以来ASM变化的重要资料。重建ASM序列再现了许多重要的ASM特征(Qian et al,2011;如:24年周期)、已知的历史极端气候事件(Ge et al,2016),并对了解未知气候事件提供了可能途径(Tian et al,2011)。
此外,重建结果可以对近年来关于ASM变化的争论提供论据(Zhu et al,2012b),并对评价人为辐射强迫因素的重要性提供支持(Song et al,2014)。本研究发现了在过去448年中前所未有的、最为强烈的、显著的持续80多年的ASM强度减弱趋势,这与受温室效应影响ASM应该增强的预期大相庭径。两组气候模型实验(有/无人为气溶胶排放增加,每组运行10次)对比表明,这一前所未有的减弱趋势很可能是由人为硫酸盐气溶胶排放增加引起,人为气溶胶硫酸盐排放引起的ASM减弱效应可能抵消了温室气体引发的ASM增强效应。模拟是识别造成ASM减弱可能原因的唯一方法,实验结果为上述假设提供了其他方法无法提供的支持。Song et al(2014)首次定量比较了1958—2001年所有导致ASM减弱的外部作用中人为气溶胶的影响。这项工作进一步证实了人为气溶胶在1934—2013年,这一较长时间内对ASM减弱的作用。因此,认为北半球人为硫酸盐气溶胶排放量的增加可能是导致近80年来ASM减弱的主要因素,而此前,在没有人为气溶胶排放的历史时期,PDO和NAO等其他因素可能对ASM减弱/增强产生影响。
综上所述,重建的ASM时间序列将在了解季风活动边缘带过去气候变化、研究当前气候变化检测和归因、评估人为强迫和预测未来气候变化等方面起到重要作用。
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稿件与作者信息
刘禹
LIU Yu
刘禹,E-mail: liuyu@loess.llqg.ac.cn
蔡文炬
CAI Wenju
孙长峰
SUN Changfeng
宋慧明
SONG Huiming
Kim M. Cobb
Kim M. Cobb
李建平
LI Jianping
Steven W. Leavitt
Steven W. Leavitt
吴立新
WU Lixin
蔡秋芳
CAI Qiufang
刘若时
LIU Ruoshi
Benjamin Ng
Benjamin Ng
Paolo Cherubini
Paolo Cherubini
Ulf Büntgen
Ulf Büntgen
宋怡
SONG Yi
王国建
WANG Guojian
雷莺
LEI Ying
晏利斌
YAN Libin
李强
LI Qiang
马永永
MA Yongyong
方丛羲
FANG Congxi
孙军艳
SUN Junyan
李旭祥
LI Xuxiang
Deliang Chen
Deliang Chen
Hans W. Linderholm
Hans W. Linderholm
基金项目:国家自然科学基金项目(41630531);大气重污染成因与治理攻关项目(DQGG0104);中科院项目(QYZDJ–SSW–DQC021,XDPB05,GJHZ1777);黄土与第四纪地质国家重点实验室项目(SKLLQGZD1701)。
National Natural Science Foundation of China (41630531); National Research Program for Key Issues in Air Pollution Control (DQGG0104); Grant from Chinese Academy of Sciences (QYZDJ–SSW–DQC021, XDPB05, GJHZ1777), Grant from Institute of Earth Environment, Chinese Academy of Sciences, and State Key Laboratory of Loess and Quaternary Geology (SKLLQGZD1701).
出版历史
出版时间: 2019年12月16日 (版本1
参考文献列表中查看
地球环境学报
Journal of Earth Environment