Three-type MJO initiation processes over the Western Equatorial Indian Ocean

Thirty strong Madden-Julian Oscillation (MJO) events in boreal winter 1982-2001 are selected to investigate the triggering processes of MJO convection over the western equatorial Indian Ocean (IO). These MJO events are classified into three types, according to their dynamic and thermodynamic precursor signals in situ. In Type I, a remarkable increase in low-level moisture occurs, on average, 7 days prior to the convection initiation. This low-level moistening is mainly due to the advection of the background mean moisture by easterly wind anomalies over the equatorial IO. In Type II, lower-tropospheric ascending motion anomalies develop, on average, 4 days prior to the initiation. The cause of this ascending motion anomaly is attributed to the anomalous warm advection, set up by a suppressed MJO phase in the equatorial IO. In Type III, there are no clear dynamic and thermodynamic precursor signals in situ. The convection might be triggered by energy accumulation in the upper layer associated with Rossby wave activity fluxes originated from the midlatitudes.     

MJO与中间层-低热层风场潮汐DE3季节内变化性的关联

本文结合TIMED-TIDI卫星与流星雷达子午链风场观测数据,分析了中间层-低热层（MLT）区域非迁移潮汐DE3的季节内变化性及其与对流层MJO的可能关联.结果揭示了DE3风场潮汐广泛存在显著的宽频带季节内变化信号,且强度存在季节依赖.纬向风DE3潮汐（DE3-U）季节内变化在北半球冬季具有较强幅值,可达季节平均的1~2倍,而在其他季节通常在20%以内.结合MJO指数进一步讨论了不同季节下DE3-U对MJO的响应.统计结果表明,在影响较大的北半球冬季,DE3-U通常在MJO第4—6位相时有更大的幅值（+10%~+40%）,而在其余位相时更小（-10%~-40%）.这表明对流层与MJO相关的对流活动通过潜热释放的纬向变化可影响高空大气非迁移潮汐的强度.

     

An Assessment of MJO and Tropical Waves Simulated by Different Versions of the GAMIL Model

Simulated outgoing longwave radiation (OLR) outputs by two versions of the grid-point atmospheric general circulation model (GAMIL) were analyzed to assess the influences of improvements in cloud microphysics and convective parameterization schemes on the simulation of the Madden-Julian oscillation (MJO) and other tropical waves. The wavenumber-frequency spectral analysis was applied to isolate dominant modes of convectively coupled equatorial waves, including the MJO, Kelvin, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertio-gravity (IG) waves. The performances of different versions of the GAMIL model (version 1.0 (GAMIL1.0) and version 2.0 (GAMIL2.0)) were evaluated by comparing the power spectrum distributions of these waves among GAMIL1.0, GAMIL2.0, and observational data. GAMIL1.0 shows a weak MJO signal, with the maximum variability occurring separately at wavenumbers 1 and 4 rather than being concentrated on wavenumbers 1–3, suggesting that GAMIL1.0 could not effectively capture the intraseasonal variability. However, GAMIL2.0 is able to effectively reproduce both the symmetric and anti-symmetric waves, and the significant spectra of the MJO, Kelvin, and MRG waves are in agreement with observational data, indicating that the ability of GAMIL2.0 to simulate the MJO and other tropical waves is enhanced by improving the cloud microphysics and convective parameterization schemes and implying that such improvements are crucial to further improving this model’s performance.     

赤道MJO活动对南海夏季风爆发的影响

利用1979-2013年NCEP/DOE再分析资料的大气多要素日平均资料、美国NOAA日平均向外长波辐射资料和ERSST月平均海温资料,分析赤道大气季节内振荡(简称MJO)活动对南海夏季风爆发的影响及其与热带海温信号等的协同作用.结果表明,赤道MJO活动与南海夏季风爆发密切联系,MJO的湿位相(即对流活跃位相)处于西太平洋位相时,有利于南海夏季风爆发,而MJO湿位相处于印度洋位相时,则不利于南海夏季风爆发.赤道MJO活动影响南海夏季风爆发的物理过程主要是大气对热源响应的结果,当MJO湿位相处于西太平洋位相时,一方面热带西太平洋对流加强使潜热释放增加,导致处于热源西北侧的南海-西北太平洋地区对流层低层由于Rossby响应产生气旋性环流异常,气旋性环流异常则有利于西太平洋副热带高压的东退,另一方面菲律宾附近热源促进对流层高层南亚高压在中南半岛和南海北部的建立,使南海地区高层为偏东风,从而有利于南海夏季风建立;当湿位相MJO处于印度洋位相时,热带西太平洋对流减弱转为大气冷源,情况基本相反,不利于南海夏季风建立.MJO活动、孟加拉湾气旋性环流与年际尺度海温变化协同作用,共同对南海夏季风爆发迟早产生影响,近35年南海夏季风爆发时间与海温信号不一致的年份,基本上是由于季节转换期间的MJO活动特征及孟加拉湾气旋性环流是否形成而造成,因此三者综合考虑对于提高季风爆发时间预测水平具有重要意义.     

Impacts of a GCM＇s Resolution on MJO Simulation

Long-term integrations are conducted using the Spectral Atmospheric Model （referred to as SAMIL）, which was developed in the Laboratory for Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASG） in the Institute of Atmospheric Physics （IAP）, with different resolutions to inves-tigate sensitivity of the Madden-Julian Oscillation （MJO） simulations to the model＇s resolution （horizontal and vertical）. Three resolutions of the model, R15L9, R42L9 and R42L26, with identical physical processes, all produced the basic observed features of the MJO, including the spatiotemporal space-time spectra and eastward propagation. No fundamental differences among these simulations were found. This indicates that the model resolution is not a determining factor for simulating the MJO. Detailed differences among these modeling results suggest, however, that model resolution can substantially affect the simulated MJO in certain aspects. For instance, at a lower horizontal resolution, high frequency disturbances were weaker and the structures of the simulated MJO were better defined to a certain extent. A higher vertical resolution led to a more realistic spatiotemporal spectrum and spatial distribution of MJO precipitation. Meanwhile, increasing the model＇s resolution improved simulation of the climatology. However, increasing the resolution should be based on improving the cumulus parameterization scheme.     

Implications from Subseasonal Prediction Skills of the Prolonged Heavy Snow Event over Southern China in Early 2008

An exceptionally prolonged heavy snow event(PHSE) occurred in southern China from 10 January to 3 February2008, which caused considerable economic losses and many casualties. To what extent any dynamical model can predict such an extreme event is crucial for disaster prevention and mitigation. Here, we found the three S2 S models(ECMWF,CMA1.0 and CMA2.0) can predict the distribution and intensity of precipitation and surface air temperature(SAT)associated with the PHSE at 10-day lead and 10-15-day lead, respectively. The success is attributed to the models' capability in forecasting the evolution of two important low-frequency systems in the tropics and mid-latitudes [the persistent Siberian High and the suppressed phase of the Madden-Julian Oscillation(MJO)], especially in the ECMWF model. However, beyond the 15-day lead, the three models show almost no skill in forecasting this PHSE.The bias in capturing the two critical circulation systems is responsible for the low skill in forecasting the 2008 PHSE beyond the 15-day lead. On one hand, the models cannot reproduce the persistence of the Siberian High, which results in the underestimation of negative SAT anomalies over southern China. On the other hand, the models cannot accurately capture the suppressed convection of the MJO, leading to weak anomalous southerly and moisture transport, and therefore the underestimation of precipitation over southern China.The Singular Value Decomposition(SVD) analyses between the critical circulation systems and SAT/precipitation over southern China shows a robust historical relation, indicating the fidelity of the predictability sources for both regular events and extreme events(e.g., the 2008 PHSE).     

Simulation of state-dependent high-frequency atmospheric variability associated with ENSO

High-frequency atmospheric variability depends on the phase of El Nino/Southern Oscillation (ENSO). Recently, there is increasing evidence that state-dependent high-frequency atmospheric variability significantly modulates ENSO characteristics. Hence, in this study, we examine the model simulations of high-frequency atmospheric variability and, further, its dependency on the El Nino phase, using atmospheric and coupled GCMs (AGCM and CGCM). We use two versions of physical packages here—with and without convective momentum transport (CMT)—in both models. We found that the CMT simulation gives rise to a large climatological zonal wind difference over the Pacific. Also, both the climate models show a significantly improved performance in simulating the state-dependent noise when the CMT parameterization is implemented. We demonstrate that the better simulation of the state-dependent noise results from a better representation of anomalous, as well as climatological, zonal wind. Our further comparisons between the simulations, demonstrates that low-frequency wind is a crucial factor in determining the state-dependency of high-frequency wind variability. Therefore, it is suggested that the so-called state-dependent noise is directly induced by the low-frequency wind anomaly, which is caused by SST associated with ENSO.     

MJO对我国东部春季降水影响的分析

利用澳大利亚气象局的MJO(Madden-Julian Oscillation)指数,通过位相合成及对比分析研究了MJO对我国东部春季降水的影响.研究表明,当MJO传播至中东印度洋时,我国长江中下游地区的春季降水为正异常,当其进一步东传至中南半岛-印尼群岛一带时,我国华南地区的春季降水为正异常,而在其他活动阶段不利于我国东部的春季降水.对比分析表明,MJO的活动主要通过引起大尺度环流异常、对流层中低层涡度及水汽输送的异常,进而对我国东部春季降水产生明显的影响.     

2018年春末南方极端持续高温及MJO影响

本文分析了2018年春末我国南方地区持续性极端高温过程及其影响系统。本次事件自5月15日首先在江淮等地迅速发展,17日之后扩展至华南并在该地区维持至6月初。江南南部和华南东部等地高温日数比常年同期偏多6～10 d,累计近两千站次出现了35℃以上的高温天气,约是之前最多年（1991年）的2.6倍。研究区域内有超过三分之一的测站高温日数突破5月历史极值。统计分析结果显示,春末南方高温日数与该地区上空位势高度、其西侧经向风及其上空对流活动均有显著关系,但对流的影响更为超前,约在一周左右。除和华南地区对流呈显著正相关外,春末南方高温还和热带印度洋OLR（Outgoing Longwave Radiation）呈显著的负相关,这种相关模态和MJO（Madden-Julian Oscillation）的第一、二位相对应的对流异常分布一致。对2018年的个例分析支持这一统计结果。2018年5月,MJO有20 d持续位于这两个位相,且振幅均超过1,是有MJO监测以来5月这两个位相最多的一年。因此,无论是统计分析还是个例都表明MJO对本次春末南方高温有重要影响。     

Impact of convective momentum transport by deep convection on simulation of tropical intraseasonal oscillation

This paper focuses on the impacts of convective momentum transport (CMT) on simulations of the tropical intraseasonal oscillation (TIO) in SAMIL. Two sets of experiments are performed, which give different reality of the Madden-Julian Oscillation (MJO). The Tiedtke cumulus parameterization scheme is used for all experiments. It is found that simulations of the TIO can be influenced by CMT, and the impacts on the simulated TIO depend on the model capability in simulating the MJO. CMT tends to have large influences to the model that can simulate the eastward propagation of the MJO. CMT can further influence the long-term mean of zonal wind and its vertical shear. Zonal wind suffers from easterlies biases at low level and westerlies biases at upper level when CMT is introduced. Such easterlies biases at low level reduce the reality of the simulated tropical intraseasonal oscillation. When CMT is introduced in the model, MJO signals disappear but the model’s mean state improves. Therefore, a more appropriate way is needed to introduce CMT to the model to balance the simulated mean state and TIO signals.