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目录 contents

    摘要

    本文分析了中国科学院大气物理研究所年代际气候预测系统IAP DecPreS的海洋同化试验(简称EnOI-IAU试验)在西北太平洋地区的海表面温度(SST)年循环的模拟技巧,并通过对比IAP DecPreS系统自由耦合历史气候模拟试验结果,在包含海气耦合过程的框架下讨论了耦合模式中西北太平洋夏季SST模拟差异,及其对亚洲季风区夏季季风降水模拟的影响。结果表明,EnOI-IAU试验较好地模拟出了西北太平洋各个季节的SST空间分布,并显著减小了原存在于历史气候模拟试验中持续全年的SST冷偏差。混合层热收支诊断分析表明,包含同化过程在内的海洋过程的模拟差异对西北太平洋海温的模拟提升有重要贡献。夏季,EnOI-IAU试验模拟的印度季风伴随的低层西风较观测偏东、偏强,且高估了赤道西太平洋区域的降水量值、低估了印度洋区域的降水量值。水汽收支分析显示,气旋式环流异常造成的水汽辐合异常是造成亚洲季风区降水模拟差异的主要原因。研究表明,较之历史模拟试验,EnOI-IAU试验中夏季西北太平洋地区SST增暖造成局地对流增强,进而使得局地产生异常上升运动,水汽辐合增强,造成西北太平洋地区降水模拟偏多,激发出低层西风异常及赤道外气旋式环流异常。该低层西风异常导致了北印度洋地区低层辐散异常,减小了原存在于历史试验中印度洋地区的正降水偏差。西北太平洋气旋式环流异常一方面增强了印度夏季风伴随的低层西风,使得更多的水汽从阿拉伯海输送到西太平洋暖池区域,增强了该区域的降水量;另一方面,该气旋式环流异常减小了历史模拟试验中中国南部区域偏强的低层风速,进而提升了模式对东亚低层西南风的模拟能力。

    Abstract

    Based on the oceanic data assimilation experiments (the merge of the Ensemble Optimal Interpolation and Incremental Analysis Update, hereafter EnOI-IAU) using the near-term climate prediction system of the Institute of Atmospheric Physics (IAP DecPreS), the authors first evaluate the model skill for annual cycle simulation of sea surface temperature (SST) over the Northwest Pacific. By comparing with the historical simulation without oceanic data assimilation, the authors investigate the impacts of differences in simulated summer SST over Northwest Pacific on the Asian summer monsoon simulation in the framework with ocean-atmosphere coupling. The results show that the spatial distribution of SST over the Northwest Pacific is generally captured by the EnOI-IAU experiment and the cold SST biases throughout the year in the historical simulation are significantly reduced. According to the mixed-layer heat budget analysis, oceanic processes including the assimilation process are primarily responsible for the improvement in the simulation of SST. During the summer, low-level westerlies associated with the Indian summer monsoon in the EnOI-IAU experiment are stronger and shifted eastward compared with observations. Meanwhile, excessive precipitation is found over the western equatorial Pacific while dry biases are seen over the Indian Ocean region. The moisture budget analysis demonstrates that the anomalous moisture convergence due to changes in circulation plays a crucial role in the simulated rainfall changes over the Asian monsoon region. Compared with the historical simulation, warmed summer SST over the Northwest Pacific in the EnOI-IAU experiment enhances local convection and induces anomalous local ascending motions, which are favorable for the development of anomalous low-level westerly flow and off-equatorial cyclonic circulation anomalies. The low-level westerly anomalies induce low-level divergence anomalies over the Indian Ocean and partly reduce the corresponding wet biases in the historical simulation. The cyclonic circulation anomalies over the Northwest Pacific, on the one hand, enhance low-level westerlies associated with the Indian summer monsoon, which transport more water vapor form the Arabian Sea to the western Pacific warm pool and increase precipitation over this region. On the other hand, the enhanced low-level westerlies reduce the overly strong low-level southerly winds over southern China in historical simulation, and subsequently improve the simulation of low-level southwesterly winds over East Asia.

  • 1 引言

    亚洲夏季风是亚洲地区夏季最重要的环流系统,也是全球气候系统中最为活跃的成员之一。亚洲夏季风异常会造成我国旱涝变化,从而对社会、经济产生重要影响。因此,亚洲夏季风的变化一直是气候学界关注的重要课题之一(Tao and Chen,1987Webster et al.,1998Ding et al.,2008Zhou et al.,2009a)。

    气候系统模式是理解亚洲季风变化机理、预测和预估其变化的重要工具。但是当前的气候模式在亚洲季风的模拟上存在显著偏差(Sperber et al.,2013Zhou et al.,2017),其原因包括多个方面,其中之一是当前气候系统模式对影响亚洲季风的西北太平洋和印度洋SST的模拟存在偏差(Marathayil et al.,2013Song and Zhou,2014Li et al.,2015)。例如,基于CMIP5多模式模拟结果的分析研究指出,多数模式在阿拉伯海地区存在从冬季持续到春季的SST冷偏差,该偏差可以通过减弱向印度地区的水汽输送,进而推迟模拟的印度夏季风爆发时间,并减弱印度夏季降水(Levine and Turner,2012)。通过对比CMIP5耦合与非耦合模式模拟结果,Song and Zhou(2014)的研究指出,CMIP5全球耦合模式(CGCMs)在西北太平洋地区的SST模拟冷偏差,在一定程度上改进了模式对东亚夏季风(EASM)气候态的模拟性能。SST冷偏差减小了西北太平洋表层蒸发,增强了西北太平洋副热带高压,使得梅雨带降水增多、西北太平洋地区降水减少。相似的结论在对比耦合和非耦合区域气候模拟研究中也存在(Zou et al.,2016)。

    值得注意的是,以上关于SST模拟偏差对季风的模拟影响研究结论,大多是通过比较耦合与非耦合模式的模拟差异得到。但是,耦合模式同单独的大气模式模拟结果差异,并不能完全解释为SST模拟偏差的影响,亦有可能来自是否包含海气耦合过程。此前的一些研究将耦合模式模拟得到的SST再驱动单独大气模式,然后与耦合模式模拟结果比较,以此解释海气耦合过程的贡献(Wang et al.,2005Zou and Zhou,2013)。然而,如何在包含海气耦合过程的框架下,考察耦合模式海温模拟偏差对亚洲夏季风模拟的影响,此前未有相关研究,这也是本文试图回答的问题。

    在耦合模式里纠正海温模拟偏差,最直接的方法是同化观测海温。Wu et al.(2018)将Incremental Analysis Update(IAU)方案和集合最优插值(Ensemble Optimal Interpolation,EnOI)方案相结合,发展了适用于年代际预测的海洋初始化方法(EnOI-IAU),并应用于中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(IAP/LASG)的气候模式FGOALS-s2(Flexible Global Ocean-Atmosphere-Land System model, Spectral Version 2)中,从而建立起中国科学院大气物理研究所年代际气候预测系统IAP DecPreS。

    年代际气候预测系统IAP DecPreS的初始化试验,本质就是在进行海气耦合模式的历史气候模拟试验时,在海洋模式中同时同化进去历史观测记录,因此,实际是考虑了海洋同化的耦合模拟试验,它属于弱耦合同化试验。基于这组试验,陈晓龙等(2017)评估了FGOALS-s2海洋同化系统中东亚夏季风和前冬厄尔尼诺—南方涛动(ENSO)关系的年代际变化,结果表明,模式可再现观测中的1970年代末EASM与前冬ENSO关系的增强,但未能再现1990年代中期EASM与前冬ENSO关系的减弱。郭准和周天军(2017)分析了EnOI-IAU试验所模拟的海温和云分布的关系,结果表明,EnOI-IAU试验可较好地模拟出SST和低云的气候态空间分布,但SST模拟偏高,特别在副热带东大洋沿岸和南大洋,且低估了主要层积云区的低云云量及云水。邹立维等(2018)研究发现,EnOI-IAU同化试验对夏季亚洲季风区大部分海域SST年际变化有较高的模拟技巧,在西北太平洋夏季降水—SST相关关系方面,同化试验的模拟技巧介于自由耦合模拟试验和观测海温驱动试验之间。上述工作侧重气候变率,目前尚没有研究工作关注海洋同化在改进耦合模式海温模拟偏差方面的作用。

    本文将基于年代际气候预测系统IAP DecPreS的海洋同化耦合模拟试验,在评估其对亚洲季风区SST模拟能力的基础上,与IAP DecPreS系统所依托的耦合模式FGOALS-s2的历史模拟试验相比,重点关注夏季,探讨耦合模式夏季西太平洋SST模拟偏差对亚洲夏季风模拟的影响。文章其余部分安排如下:第二部分介绍使用的模式、数据和方法,第三部分介绍主要的分析结果,第四部分是结论。

  • 2 模式、资料和方法

  • 2.1 模式

    中国科学院大气物理研究所年代际气候预测系统IAP DecPreS依托的耦合模式系统是FGOALS-s2,它是IAP/LASG发展的耦合气候系统模式,其大气分量是IAP/LASG开发的SAMIL2模式(Bao et al.,2013),垂直方向采用混合坐标,分26层,水平分辨率约为2.81°(经度)×1.66°(纬度)。FGOALS-s2海洋分量是IAP/LASG开发的LICOM2模式(Liu et al.,2012),垂直方向为z坐标,分30层,其水平分辨率约为1°×1°,其中赤道区域加密到0.5°×0.5°。陆面模块为CLM3(Oleson et al., 2004),海冰模块为CSIM5(Briegleb et al.,2004),耦合器采用NCAR的CPL6。关于FGOALS耦合模式的技术细节,参见有关文献(Bao et al.,2013Zhou et al.,2014)。该模式被广泛应用于气候变率和东亚季风等研究中(Song and Zhou, 2013Song et al.,2013周天军等,2013Zhou et al.,2013Zou and Zhou,2015Wu et al.,2017a2017b)。

  • 2.2 EnOI-IAU初始化方案

    基于耦合气候系统模式FGOALS-s2和EnOI-IAU同化方案的初始化试验本质上是同化海洋观测资料,再通过海—陆—气耦合过程调节气候系统的其他分量,最终实现对整个气候系统初始化的弱耦合同化试验。

    EnOI-IAU方案同化的观测资料是由英国哈德来中心(Hadley Centre)提供的,经过质量控制的格点化SST资料HadISST1.1以及原始海洋上层700米盐度和温度廓线资料EN4_v1.1(Good et al., 2013)。同化区域为70°S~70°N(其中高纬度60°~70°为缓冲区),同化循环窗口宽度为1个月。同化循环步骤如下:(1)模式自由积分得到预报场(背景场);(2)结合观测资料和预报场,使用集合最优插值(EnOI)方法计算得到分析增量;(3)将模式回到同化循环的起始点重新积分,并在积分过程中逐步引入分析增量。

    EnOI方法基于集合卡尔曼滤波(EnKF)理论发展而来,可视为EnKF的次优解,其优点是不需要进行集合模拟(Oke et al.,2002Evensen,2003)。构造EnOI背景误差协方差矩阵所需集合成员来自历史气候模拟试验。

  • 2.3 试验及观测资料介绍

    本文使用了基于不同初值的3组海洋同化试验集合平均结果以及历史模拟试验(historical)3个成员的集合平均结果。其中,historical试验为“自由耦合历史试验”,本文简称“历史试验”;EnOI-IAU试验在考虑海气耦合过程的同时,部分地同化了观测海洋数据,本质上属于基于耦合模式的“弱耦合同化试验”,本文简称“同化试验”。分析时段为1984~2005年。

    为了与模式的结果进行比较,本文用到如下观测数据:(1)英国Hadley中心提供的再分析数据HadISST1,该数据水平分辨率为1°×1°(Rayner et al.,2003);(2)ERA-interim(European Centre for Medium-Range Weather Forecasts interim reanalysis)再分析资料中的风场、绝对湿度场及表面气压场资料,水平分辨率为1.5°×1.5°(Dee et al.,2011);(3)GPCP(Global Precipitation Climatology Project)降水资料,水平分辨率为2.5°×2.5°(Adler et al.,2003);(4)美国伍兹霍尔海洋研究所(WHOI)提供的OAFlux(objectively analyzed air-sea fluxes)数据集中的月平均湍流热通量,水平分辨率为1°×1°(Yu and Weller,2007Yu et al.,2008);(5)ISCCP(International Satellite Cloud Climatology Project)月平均海表长、短波辐射通量,水平分辨率为1°×1°(Zhang et al.,2004)。根据Liu et al.(2010)的研究,将ISCCP在50°S~30°N的短波辐射通量减小5%。资料的时间跨度均为1984~2005年。为讨论方便,文中将上述资料均统称为“观测资料”。为便于处理,使用双线性插值方法将所有模式数据及观测资料均插值到2.8°×2.8°的规则网格上。

  • 2.4 模式技巧评分

    根据Chen et al.(2013),我们采用了以下的技巧评分公式来客观衡量模式在研究区域的表现:

    Skill=1+R2SDR+1SDR2,
    (1)

    其中,R代表模式与观测的空间相关系数,SDR为模式和观测空间标准差的比值。分数越接近1,技巧越高。

  • 3 结果

  • 3.1 EnOI-IAU试验和历史试验对亚洲季风区SST的模拟

    1给出1984~2005年平均观测SST空间分布及历史和EnOI-IAU试验模拟偏差。由图可见,亚洲季风区SST的季节变化不显著,SST沿纬向呈带状分布,沿经向呈阶梯型分布,低纬度地区海温较暖,中高纬度地区海温较冷(图1a-d)。整体而言,EnOI-IAU试验和历史试验都较好地模拟出了类似观测的SST空间分布(图略),但都存在着海盆尺度的模拟偏差且主要集中于北印度洋、西北太平洋和日本岛以东海域等区域(图1e-l)。在两试验中,印度洋SST偏差的空间分布及量值都较为相似,例如印度洋北部区域全年都为冷偏差且冬、春季最大,赤道印度洋区域则主要表现为暖偏差且夏季最大。就西北太平洋(0°~30°N,120°E~180°)而言,两种试验都模拟出了持续全年的SST冷偏差,其中历史试验中SST冷偏差春、夏季最大,秋、冬季最小,EnOI-IAU试验中SST冷偏差不存在明显的季节变化(图1e-i)。较历史试验,EnOI-IAU试验中西北太平洋SST冷偏差减小,意味着海洋同化在一定程度上提升了模式对该区域海温的模拟效果。值得注意的是,在冬、春季,EnOI-IAU试验模拟的北太平洋北部地区(30°N以北)的海温较观测偏冷,且海温偏差的绝对值较历史试验(暖偏差)明显增大(图1m-p),这说明EnOI-IAU试验对该区域海温的模拟性能较差。这可能是由于:其一,北太平洋北部地区的观测资料较少,造成同化试验无法充分同化该区域的观测资料;其二,北太平洋北部地区受大气的影响较大,而EnOI-IAU试验为海洋同化试验,并未引入大气同化,从而造成该区的海温模拟偏差较大。

    图1
                            1984~2005年平均的(a-d)观测、(e-h)EnOI-IAU试验偏差、(i-l)历史试验偏差、(m-p)EnOI-IAU试验减历史试验的SST变化空间分布。从上到下分别为冬季(DJF)、春季(MAM)、夏季(JJA)和秋季(SON)。打点区域代表通过95%信度水平检验

    图1 1984~2005年平均的(a-d)观测、(e-h)EnOI-IAU试验偏差、(i-l)历史试验偏差、(m-p)EnOI-IAU试验减历史试验的SST变化空间分布。从上到下分别为冬季(DJF)、春季(MAM)、夏季(JJA)和秋季(SON)。打点区域代表通过95%信度水平检验

    Fig. 1 Spatial distributions of (a-d) observed SST (sea surface temperature), (e-h) SST biases in EnOI-IAU (Ensemble Optimal Interpolation and Incremental Analysis Update), (i-l) SST biases in historical experiments, and (m-p) differences in SST simulations between EnOI-IAU and historical experiments (EnOI-IAU-historical simulations) averaged in four seasons during 1984-2005. The first, second, third, and last rows indicate distributions in the winter (DJF), spring (MAM), summer (JJA), and autumn (SON), respectively. Dotted areas are for values at 95% confidence level

    分别取印度洋北部区域(NIO,0°~25°N,50°~100°E)、西北太平洋区域(WNP,0°~30°N,120°E~180°)及西北太平洋北部区域(NWNP,30°~50°N,120°E~180°),定量地研究EnOI-IAU试验和历史试验对SST的模拟情况。泰勒图(Taylor,2001)可以定量表征模式模拟的SST和观测的SST的空间相似程度。在泰勒图中,以原点为圆心的弧线对应的坐标轴上的值表示模式模拟的空间变化标准差与观测的空间变化标准差的比值,各点对应的大弧上的值表示模式与观测的空间相关系数,各点与REF点的距离表示均方根误差。由图2a可见,EnOI-IAU试验对印度洋北部SST空间分布的模拟效果较好,空间相关系数较大且均方根误差较小。就西北太平洋区域而言,较之历史试验,EnOI-IAU试验模拟的SST空间分布与观测更为接近,尤其在夏季,空间相似程度更高且均方根误差更小。但就西北太平洋北部区域而言,尤其在冬季,EnOI-IAU试验对SST空间分布的模拟较之历史试验并无改进。

    图2
                            (a)EnOI-IAU试验(蓝色)和历史试验(红色)模拟的印度洋北部(NIO,0°~25°N,50°~100°E)、西北太平洋(WNP,0°~30°N,120°E~180°)及西北太平洋北部(NWNP,30°~50°N,120°E~180°)SST泰勒图,横纵坐标为标准差,各点与REF的距离为均方根误差(RMSE);(b)印度洋北部、(c)西北太平洋、(d)西北太平洋北部SST偏差年循环

    图2 (a)EnOI-IAU试验(蓝色)和历史试验(红色)模拟的印度洋北部(NIO,0°~25°N,50°~100°E)、西北太平洋(WNP,0°~30°N,120°E~180°)及西北太平洋北部(NWNP,30°~50°N,120°E~180°)SST泰勒图,横纵坐标为标准差,各点与REF的距离为均方根误差(RMSE);(b)印度洋北部、(c)西北太平洋、(d)西北太平洋北部SST偏差年循环

    Fig. 2 (a) Taylor diagram for pattern statistics of SST simulations in the northern Indian Ocean (NIO, 0°-25°N, 50°-100°E), the Northwest Pacific Ocean (WNP, 0°-30°N, 120°E-180°), the northern Northwest Pacific Ocean (NWNP, 30°-50°N, 120°E-180°) in the EnOI-IAU (blue) and historical (red) experiments. The abscissa and ordinate both show normalized standard deviation, the distance from REF indicates root mean square error (RMSE). Annual cycles of SST biases are shown in (b) NIO, (c) WNP, (d) NWNP

    2b定量地给出了两种试验模拟的印度洋北部区域、西北太平洋区域及西北太平洋北部区域SST偏差年循环。由图可见,两种试验中印度洋北部区域SST偏差年循环特征相似,冬、春季冷偏差最大,夏季冷偏差最小甚至为暖偏差。就西北太平洋而言,两种试验模拟的SST全年都偏冷,其中历史试验中SST冷偏差在七月达到最大值,约为 −1.03 K,二月达到最小值,约−0.46 K;EnOI-IAU试验中SST冷偏差最大值同样出现在七月,约为 −0.65 K,最小值出现在一月,约−0.43 K。夏季,EnOI-IAU试验中的SST冷偏差较历史试验减小最为明显,约为0.41 K。但在西北太平洋北部区域,历史试验模拟的SST全年都偏暖约1.90 K,而EnOI-IAU试验则全年偏冷约−1.41 K,其中二月冷偏差最大,约−3.67 K。

    以上分析表明,较之历史试验,EnOI-IAU试验对亚洲季风区的各大海域SST的模拟能力不同。EnOI-IAU试验对北印度洋区域SST的模拟能力未有显著改进,但提升了模式对西北太平洋海温的模拟技巧,主要体现在:空间分布与观测更为接近,SST冷偏差减小。另外,EnOI-IAU试验对西北太平洋北部地区冬、春季的海温模拟较差。那么,从海洋混合层热收支的角度,较之历史试验,造成EnOI-IAU试验模拟海温技巧差异的可能原因是什么?

    对于气候态的SST,混合层热收支诊断方程可表示如下:

    D0'=-Qnet'C,
    (2)

    其中,C=cp0ρ0H是混合层热容量,cp0为混合层比热(假设混合层压力定常),ρ0为海水密度,H为混合层深度,Qnet'代表从海表面进入海洋内部的净热通量的变化(向下为正),D0'表示由于海洋平流、混合等过程造成的海洋热输送效应的变化(Xie et al.,2010Schneider and Fan,2012)。

    海表热通量包含四个基本物理量:短波辐射通量(QS)、长波辐射通量(QL)、感热通量(QH)和潜热通量(QE)。潜热通量是SST反馈和大气强迫共同作用的结果(Xie et al., 2010)。例如,在其他气象要素保持不变的情况下,风速减小造成蒸发减弱,海洋会通过升高SST来释放对应的潜热通量以保持热量收支平衡。这种风—蒸发—SST(WES)反馈调节机制证明,表层蒸发潜热是海洋过程和大气强迫共同作用的结果。因此,为了得到SST的变化,将海表潜热通量的变化(QE')分解为海洋响应(QEo')和大气强迫(QEa')两部分(De Szoeke et al., 2007; Du and Xie, 2008)。QEo'实际上代表了牛顿冷却效应:

    QEo'=αQE¯T',
    (3)

    其中,牛顿冷却系数αQE¯代表海洋通过蒸发(表面空气湿度差、表面风速等变量的控制)来限制SST变化的能力,QE¯为平均的气候态潜热通量,系数α根据Clausius-Clapeyron方程计算得到,本文中α约为0.06 K−1。因此,与大气过程有关的海表潜热通量可以表示为

    QEa'=QE'-αQE¯T'.
    (4)

    从而,大气过程的效应(Qa')可以表示为

    Qa'=QS'+QL'+QH'+QEa',
    (5)

    其中,QS'QL'QH'分别代表海表面短波辐射、长波辐射和感热通量的变化。最终SST的变化可以通过计算与大气过程有关的海表热通量的变化以及海洋热输送的变化得到。因此可以得到SST的变化公式:

    T'=CDo'+Qa'αQE¯.
    (6)

    3给出了与历史试验相比,EnOI-IAU试验模拟的混合层热收支变化空间分布。由图可见,海洋和大气合过程的模拟异常(图3a-d)与SST异常(图1m-p)空间分布相似。从大气过程和海洋过程两方面来看,西北太平洋地区的SST暖异常主要由包含同化过程在内的海洋过程的模拟异常引起,与大气过程有关的海表热通量的模拟异常则起到冷却作用。而西北太平洋北部地区较强的SST冷异常同样由包含同化过程在内的海洋过程的模拟异常引起,与大气过程有关的海表热通量的模拟异常则倾向于造成SST暖异常(图3e-l)。

    图3
                            与历史试验相比,EnOI-IAU试验模拟的混合层热收支变化空间分布:大气过程造成的净海表热通量变化(中列)和海洋热输送过程变化(右列)及二者之和(左列)。正值(红色)代表加热作用,负值(蓝色)代表冷却作用。从上到下分别为冬季、春季、夏季和秋季

    图3 与历史试验相比,EnOI-IAU试验模拟的混合层热收支变化空间分布:大气过程造成的净海表热通量变化(中列)和海洋热输送过程变化(右列)及二者之和(左列)。正值(红色)代表加热作用,负值(蓝色)代表冷却作用。从上到下分别为冬季、春季、夏季和秋季

    Fig. 3 Spatial distributions of mixed layer heat budget differences between EnOI-IAU and historical experiments: The net sea surface heat flux changes caused by atmospheric process change (middle column), the ocean heat transfer process change (right column), the sum of both (left column). Positive values (red) indicate the heating effect, negative values (blue) indicate the cooling effect. The first, second, third, and last rows indicate differences in the winter, spring, summer, and autumn, respectively

  • 3.2 EnOI-IAU试验和历史试验对亚洲夏季风、降水的模拟

    4分别给出1984~2005年观测、历史试验和EnOI-IAU试验夏季平均降水及850 hPa风场空间分布。由图可见,历史试验和EnOI-IAU试验都较好地模拟出了类似观测的降水及850 hPa风场空间分布。使用方程(1),定量地评估两种试验对整个亚洲季风区(10°S~40°N,40°E~180°)夏季降水及850 hPa风场的模拟技巧。结果显示,历史试验对亚洲季风区夏季降水(850 hPa风场)的模拟技巧为0.74(0.81),EnOI-IAU试验模拟技巧为0.66(0.86)。就子季风区而言,历史试验对西高止山以东和孟加拉湾地区模拟的降水偏多,EnOI-IAU试验对此有一定的改进;历史试验对西北太平洋季风区降水及季风槽强度的模拟更为接近观测,而EnOI-IAU试验显著高估了赤道西太平洋区域的降水量,对季风槽的模拟亦偏强。

    图4
                            1984~2005年观测的、历史试验和EnOI-IAU试验模拟的夏季平均降水量(填色,单位: mm d−1)及850 hPa风场(矢量,单位: m s−1)空间分布

    图4 1984~2005年观测的、历史试验和EnOI-IAU试验模拟的夏季平均降水量(填色,单位: mm d−1)及850 hPa风场(矢量,单位: m s−1)空间分布

    Fig. 4 Spatial distributions of JJA (June, July, Autumn) mean precipitation (shadings, units: mm d−1) and 850-hPa wind (vectors, units: m s−1) derived from (a) observations, (b) historical experiments, (c) EnOI-IAU experiments during 1984-2005

    为了理解考虑海洋同化后季风环流及其降水的变化原因,我们使用水汽收支诊断的方法,分析了较之历史试验EnOI-IAU试验模拟的亚洲季风区夏季降水异常的原因。降水变化可由以下方程决定:

    ΔP=-ΔVq-ΔqV+ΔE,
    (7)

    其中,P为降水,V为水平风矢量,q为绝对湿度,E为蒸发,Δ代表EnOI-IAU试验和历史试验之差,表示从1000 hPa到300 hPa的垂直积分,为水平梯度算子。根据方程(7),降水的变化由水汽水平平流项、水汽辐合项及表层蒸发的变化三项贡献。

    由图5可知,相比历史试验,EnOI-IAU试验模拟的降水在西太平洋区域偏多,尤其是海洋大陆东岸及赤道西太平洋区域,在印度洋区域偏少,尤其是东阿拉伯海地区(图5a)。水汽收支诊断结果显示,水汽辐合异常(图5d)是造成降水异常的主要因素,两者的空间相关系数为0.91。另外,表层蒸发异常(图5b)也在一定程度上影响降水异常,但量值较小。

    图5
                            与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均水汽收支变化空间分布:(a)降水异常;(b)表层蒸发异常;(c)水汽水平平流项、(d)水汽辐合项对降水异常的贡献;(e)水汽变化、(g)环流变化导致的水汽水平平流变化;(f)水汽变化、(h)环流变化导致的水汽辐合变化。打点区域代表通过95%的信度水平检验

    图5 与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均水汽收支变化空间分布:(a)降水异常;(b)表层蒸发异常;(c)水汽水平平流项、(d)水汽辐合项对降水异常的贡献;(e)水汽变化、(g)环流变化导致的水汽水平平流变化;(f)水汽变化、(h)环流变化导致的水汽辐合变化。打点区域代表通过95%的信度水平检验

    Fig. 5 Distributions of in JJA mean (a) precipitation differences, (b) evaporation differences, contributions to precipitation differences from (c) horizontal moisture advection and (d) moisture flux convergence, contributions to horizontal moisture advection from (e) moisture differences and (g) circulation differences, contributions to moisture convergence differences from (f) the moisture differences and (h) the circulation differences between ENOI-IAU experiments and historical experiments during 1984-2005. Dotted areas are for values at 95% confidence level

    由于大气中的水汽和环流的变化都会对水汽平流和水汽辐合造成影响,根据方程(8)和(9):

    -ΔVq=-VpdΔq-qpdΔV-ΔqΔV,
    (8)
    -ΔqV=-ΔqVpd-qpdΔV-ΔqΔV,
    (9)

    分别将水汽平流和水汽辐合项拆分为水汽的变化(第一项)、环流的变化(第二项)以及水汽和环流共同变化的非线性项(第三项)进行研究,其中下角标pd代表EnOI-IAU试验和历史试验的均值。图5e-h分别给出了水汽平流项和水汽辐合项的水汽变化(图5e、f)、环流变化(图5g、h)的空间分布,由图可知,水汽辐合项中的环流的变化(图5h)是造成降水异常的最主要因素,两者的空间相关系数达到0.93。

    6定量的给出了西太平洋中低纬度区域(0°~20°N,120°E~180°)和印度洋区域(5°S~20°N,60°~90°E)的夏季降水异常及各个因素对降水异常的贡献值。西太平洋区域(WPO)存在大约2.21 mm d−1的降水正异常,而印度洋区域(IO)则有约−2.04 mm d−1的降水负异常。水汽收支诊断分析表明,水汽辐合项中的环流异常造成的降水异常(约1.88 mm d−1)以及蒸发异常(约0.72 mm d−1)是引起西太平洋区域降水正异常的最主要因素,而印度洋区域的降水负异常则主要受水汽辐合项中的环流异常造成的降水异常(约−1.58 mm d−1)所控制。

    图6
                            与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均西太平洋区域(蓝色,0°~20°N,120°E~180°)和印度洋区域(红色,5°S~20°N,60°~90°E)降水(Precip)变化及各个因素(图6a:蒸发异常(Evapor)、水汽水平平流项(Advection)、水汽辐合项(Convergence);图6b:水汽辐合项中的水汽变化(Moisture)、环流变化(Circulation)、非线性项(Nonlinear))对降水变化的贡献值

    图6 与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均西太平洋区域(蓝色,0°~20°N,120°E~180°)和印度洋区域(红色,5°S~20°N,60°~90°E)降水(Precip)变化及各个因素(图6a:蒸发异常(Evapor)、水汽水平平流项(Advection)、水汽辐合项(Convergence);图6b:水汽辐合项中的水汽变化(Moisture)、环流变化(Circulation)、非线性项(Nonlinear))对降水变化的贡献值

    Fig. 6 Differences in JJA mean precipitation (Precip) and moisture-related factors (in Fig. 6a, evaporation (Evapor), horizontal moisture advection (Advection), total moisture flux convergence (Convergence); in Fig. 6b, moisture change (Moisture), circulation change (Circulation), the nonlinear product (Nonlinear)) responsible for the precipitation over the western Pacific Ocean (blue, 0-20°N, 120°E-180°) and the Indian Ocean (red, 5°S- 20°N, 60°-90°E) during 1948-2005

    总之,水汽收支分析表明,环流的变化造成的水汽辐合异常是造成西太平洋区域和印度洋区域降水异常的主要原因。

  • 3.3 EnOI-IAU试验中SST的模拟对季风降水模拟的影响

    上文分析表明,EnOI-IAU试验显著提升了夏季西北太平洋SST的模拟,减小了原存在于历史试验中的SST冷偏差。此外,EnOI-IAU试验显著增强了西太平洋地区夏季降水量,减少了印度洋区域夏季降水量,水汽收支诊断分析表明,环流异常造成的水汽辐合异常是造成亚洲季风区降水异常的主要原因。那么,SST异常和环流、降水异常之间有怎样的联系?

    200 hPa速度势的变化和热带地区对流的变化相一致。图7a给出了较之历史试验,EnOI-IAU试验模拟的夏季200 hPa速度势及对应辐散风异常空间分布。由图可见,赤道西太平洋区域上层辐散异常,而赤道印度洋区域上层辐合异常。较之历史试验,EnOI-IAU试验模拟的西北太平洋地区SST偏暖(图2b),增暖的SST造成局地对流增强,进而使得局地产生异常上升运动,水汽辐合异常,造成西北太平洋地区降水模拟增强。高层异常风场在印度洋区域上空辐合下沉,使得局地低层水汽辐散异常,降水减少。

    图7
                            (a)与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均200 hPa速度势(填色,单位:107 m2 s−1)及对应辐散风(矢量,单位:m s−1)异常空间分布。(b)同(a),但为降水(填色,单位:mm d−1)及850 hPa风场(矢量,单位:m s−1)异常

    图7 (a)与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均200 hPa速度势(填色,单位:107 m2 s−1)及对应辐散风(矢量,单位:m s−1)异常空间分布。(b)同(a),但为降水(填色,单位:mm d−1)及850 hPa风场(矢量,单位:m s−1)异常

    Fig. 7 (a) Spatial distributions of differences in JJA mean 200-hPa velocity potential (shadings, units: 107 m2 s−1) and corresponding divergent wind (vectors, units: m s−1) between ENOI-IAU and historical experiments during 1984-2005. (b) As in (a), but for 850-hPa wind (vectors, units: m s−1) and precipitation (shadings, units: mm d−1)

    7b给出了较之历史试验,EnOI-IAU试验模拟的夏季降水及850 hPa风场变化空间分布。根据Matsuno-Gill理论(Matsuno, 1966;Gill, 1980 ),西太平洋区域增强的对流引起局地上升运动进而激发出低层西风异常及赤道外气旋式环流异常。该低层西风异常导致了北印度洋地区低层辐散异常,减小了原存在于历史试验中印度洋地区的正降水偏差。西北太平洋气旋式环流异常则一方面增强了印度季风伴随的低层西风,使得更多的水汽从阿拉伯海输送到西太平洋暖池区域,增强了该区域的降水量;另一方面,该气旋式环流异常减小了历史试验中中国南部区域偏强的低层风速,一定程度改进了模式对东亚地区低层西南风的模拟能力。

  • 4 结论

    本文通过对比FGOALS-s2自由耦合历史试验结果(无同化试验)和EnOI-IAU同化试验结果(弱耦合同化试验),分别考察了两种试验对亚洲季风区气候态SST及亚洲夏季季风降水的模拟情况,并讨论了耦合模式中西北太平洋夏季SST模拟差异对亚洲季风区夏季季风降水模拟的影响。主要结论如下:

    (1)相比历史试验,EnOI-IAU试验提升了模式对西北太平洋区域各个季节SST的模拟技巧,模拟的空间分布与观测更为接近、SST冷偏差减小。但同化试验对西北太平洋北部地区的海温模拟性能较差。混合层热收支诊断分析表明,包含同化过程在内的海洋过程的模拟偏差对整个西北太平洋地区海温的模拟有重要影响。

    (2)EnOI-IAU试验和历史试验都较好地模拟出了亚洲季风区夏季季风降水的空间分布。在子季风区中,历史试验对西高止山以东和孟加拉湾地区模拟的降水偏多,EnOI-IAU试验对此有一定的改进;而历史试验对西北太平洋季风区降水及季风槽强度的模拟更为接近观测,EnOI-IAU试验则显著高估了赤道西太平洋的降水量,对季风槽的模拟亦偏强。水汽收支分析表明,气旋式环流异常造成的水汽辐合异常是造成亚洲季风区降水模拟差异的主要原因。

    (3)较之历史试验,EnOI-IAU试验中西北太平洋地区夏季SST增暖造成局地对流增强,进而使得局地产生异常上升运动,水汽辐合异常,使得西北太平洋地区降水模拟偏多,激发出低层西风异常及赤道外气旋式环流异常。该低层西风异常导致了北印度洋地区低层辐散异常,减小了原存在于历史试验中印度洋地区的正降水偏差。西北太平洋气旋式环流异常则一方面增强了印度季风伴随的低层西风,使得更多的水汽从阿拉伯海输送到西太平洋暖池区域,增强了该区域的降水量;另一方面,该气旋式环流异常减小了历史试验中中国南部区域偏强的低层风速,进而提升了模式对东亚地区低层西南风的模拟能力。

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      周天军, 孙丹, 薛峰. 2013. 气候系统模式FGOALS-s2对南半球气候的模拟和预估[J]. 大气科学, 37(2): 499-517. Zhou T J, Sun D, Xue F. 2013. Present and future climate in the Southern Hemisphere simulated by IAP/LASG climate system model FGOALS-s2[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 37(2): 499-517. doi:10.3878/j.issn.1006-9895.2012.12320

    • 41

      Zhou T J, Song F F, Chen X L. 2013. Historical evolution of global and regional surface air temperature simulated by FGOALS-s2 and FGOALS-g2: How reliable are the model results? [J] Advances in Atmospheric Sciences, 30(3): 638-657. doi:10.1007/s00376-013-2205-1

    • 42

      Zhou T J, Yu Y Q, Liu Y M, et al. 2014. Flexible Global Ocean-Atmosphere-Land System Model: A Modeling Tool for the Climate Change Research Community [M]. Berlin Heidelberg: Springer. doi:10.1007/978-3-642-41801-3

    • 43

      Zhou T J, Chen X L, Wu B. et al. 2017. A robustness analysis of CMIP5 models over the East Asia-western North Pacific domain [J]. Engineering, 3(5): 773-778. doi:10.1016/J.ENG.2017.05.018

    • 44

      Zou L W, Zhou T J. 2013. Can a regional ocean-atmosphere coupled model improve the simulation of the interannual variability of the western North Pacific summer monsoon? [J] J. Climate, 26(7): 2353-2367. doi:10.1175/JCLI-D-11-00722.1

    • 45

      Zou L W, Zhou T J. 2015. Asian summer monsoon onset in simulations and CMIP5 projections using four Chinese climate models[J]. Advances in Atmospheric Sciences, 32(6): 794-806. doi:10.1007/s00376-014-4053-z

    • 46

      Zou L W, Zhou T J, Peng D D. 2016. Dynamical downscaling of historical climate over CORDEX East Asia domain: A comparison of regional ocean-atmosphere coupled model to stand-alone RCM simulations[J]. J. Geophys. Res., 121(4): 1442-1458. doi:10.1002/2015JD023912

    • 47

      邹立维, 李东欢, 周天军, 等. 2018. FGOALS海洋同化试验对西北太平洋夏季SST—降水关系的模拟评估[J]. 气候与环境研究, 2018(02): 139-149. Zou Liwei, Li Donghuan, Zhou Tianjun,et al. 2018. Summer rainfall-SST relationships in the western North Pacific simulated by the FGOALS model with ocean assimilation[J]. Climatic and Environmental Research (in Chinese), 23(2): 139-149. doi:10.3878/j.issn.1006-9585.2017.17006

王晨琦

机 构:

1. 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029

2. 自然资源部国家海洋环境预报中心国家海洋局海洋灾害预报技术研究重点实验室,北京100081

Affiliation:

1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

2. Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing 100081

邮 箱:wangcq@lasg.iap.ac.cn

作者简介:王晨琦,男,1991年出生,硕士研究生,主要从事海气相互作用研究。E-mail: wangcq@lasg.iap.ac.cn

邹立维

机 构:中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029

Affiliation:State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

角 色:通讯作者

Role:Corresponding author

邮 箱:zoulw@lasg.iap.ac.cn

作者简介:邹立维,E-mail: zoulw@lasg.iap.ac.cn

周天军

机 构:

1. 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029

3. 中国科学院大学,北京100049

Affiliation:

1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

3. University of Chinese Academy of Sciences, Beijing 100049

吴波

机 构:中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029

Affiliation:State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

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图1 1984~2005年平均的(a-d)观测、(e-h)EnOI-IAU试验偏差、(i-l)历史试验偏差、(m-p)EnOI-IAU试验减历史试验的SST变化空间分布。从上到下分别为冬季(DJF)、春季(MAM)、夏季(JJA)和秋季(SON)。打点区域代表通过95%信度水平检验

Fig. 1 Spatial distributions of (a-d) observed SST (sea surface temperature), (e-h) SST biases in EnOI-IAU (Ensemble Optimal Interpolation and Incremental Analysis Update), (i-l) SST biases in historical experiments, and (m-p) differences in SST simulations between EnOI-IAU and historical experiments (EnOI-IAU-historical simulations) averaged in four seasons during 1984-2005. The first, second, third, and last rows indicate distributions in the winter (DJF), spring (MAM), summer (JJA), and autumn (SON), respectively. Dotted areas are for values at 95% confidence level

图2 (a)EnOI-IAU试验(蓝色)和历史试验(红色)模拟的印度洋北部(NIO,0°~25°N,50°~100°E)、西北太平洋(WNP,0°~30°N,120°E~180°)及西北太平洋北部(NWNP,30°~50°N,120°E~180°)SST泰勒图,横纵坐标为标准差,各点与REF的距离为均方根误差(RMSE);(b)印度洋北部、(c)西北太平洋、(d)西北太平洋北部SST偏差年循环

Fig. 2 (a) Taylor diagram for pattern statistics of SST simulations in the northern Indian Ocean (NIO, 0°-25°N, 50°-100°E), the Northwest Pacific Ocean (WNP, 0°-30°N, 120°E-180°), the northern Northwest Pacific Ocean (NWNP, 30°-50°N, 120°E-180°) in the EnOI-IAU (blue) and historical (red) experiments. The abscissa and ordinate both show normalized standard deviation, the distance from REF indicates root mean square error (RMSE). Annual cycles of SST biases are shown in (b) NIO, (c) WNP, (d) NWNP

图3 与历史试验相比,EnOI-IAU试验模拟的混合层热收支变化空间分布:大气过程造成的净海表热通量变化(中列)和海洋热输送过程变化(右列)及二者之和(左列)。正值(红色)代表加热作用,负值(蓝色)代表冷却作用。从上到下分别为冬季、春季、夏季和秋季

Fig. 3 Spatial distributions of mixed layer heat budget differences between EnOI-IAU and historical experiments: The net sea surface heat flux changes caused by atmospheric process change (middle column), the ocean heat transfer process change (right column), the sum of both (left column). Positive values (red) indicate the heating effect, negative values (blue) indicate the cooling effect. The first, second, third, and last rows indicate differences in the winter, spring, summer, and autumn, respectively

图4 1984~2005年观测的、历史试验和EnOI-IAU试验模拟的夏季平均降水量(填色,单位: mm d−1)及850 hPa风场(矢量,单位: m s−1)空间分布

Fig. 4 Spatial distributions of JJA (June, July, Autumn) mean precipitation (shadings, units: mm d−1) and 850-hPa wind (vectors, units: m s−1) derived from (a) observations, (b) historical experiments, (c) EnOI-IAU experiments during 1984-2005

图5 与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均水汽收支变化空间分布:(a)降水异常;(b)表层蒸发异常;(c)水汽水平平流项、(d)水汽辐合项对降水异常的贡献;(e)水汽变化、(g)环流变化导致的水汽水平平流变化;(f)水汽变化、(h)环流变化导致的水汽辐合变化。打点区域代表通过95%的信度水平检验

Fig. 5 Distributions of in JJA mean (a) precipitation differences, (b) evaporation differences, contributions to precipitation differences from (c) horizontal moisture advection and (d) moisture flux convergence, contributions to horizontal moisture advection from (e) moisture differences and (g) circulation differences, contributions to moisture convergence differences from (f) the moisture differences and (h) the circulation differences between ENOI-IAU experiments and historical experiments during 1984-2005. Dotted areas are for values at 95% confidence level

图6 与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均西太平洋区域(蓝色,0°~20°N,120°E~180°)和印度洋区域(红色,5°S~20°N,60°~90°E)降水(Precip)变化及各个因素(图6a:蒸发异常(Evapor)、水汽水平平流项(Advection)、水汽辐合项(Convergence);图6b:水汽辐合项中的水汽变化(Moisture)、环流变化(Circulation)、非线性项(Nonlinear))对降水变化的贡献值

Fig. 6 Differences in JJA mean precipitation (Precip) and moisture-related factors (in Fig. 6a, evaporation (Evapor), horizontal moisture advection (Advection), total moisture flux convergence (Convergence); in Fig. 6b, moisture change (Moisture), circulation change (Circulation), the nonlinear product (Nonlinear)) responsible for the precipitation over the western Pacific Ocean (blue, 0-20°N, 120°E-180°) and the Indian Ocean (red, 5°S- 20°N, 60°-90°E) during 1948-2005

图7 (a)与历史试验相比,EnOI-IAU试验模拟的1984~2005年夏季平均200 hPa速度势(填色,单位:107 m2 s−1)及对应辐散风(矢量,单位:m s−1)异常空间分布。(b)同(a),但为降水(填色,单位:mm d−1)及850 hPa风场(矢量,单位:m s−1)异常

Fig. 7 (a) Spatial distributions of differences in JJA mean 200-hPa velocity potential (shadings, units: 107 m2 s−1) and corresponding divergent wind (vectors, units: m s−1) between ENOI-IAU and historical experiments during 1984-2005. (b) As in (a), but for 850-hPa wind (vectors, units: m s−1) and precipitation (shadings, units: mm d−1)

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