Relationship between Vertical Convection Structure and Precipitation Simulation Bias in the Tropical Atmosphere: An Analysis Based on GAMIL3 Model
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摘要: 针对LASG/IAP发展的大气环流模式GAMIL（Grid-point Atmospheric Model of IAP LASG）的两个版本GAMIL2（G2）和GAMIL3（G3），评估了其对热带降水气候态以及对流垂直结构的模拟能力，在此基础上探究了新版本模式降水模拟改进的原因以及热带对流垂直结构与降水模拟偏差的关系。两个版本的GAMIL模式都较好地捕捉到了热带降水的主要特征，且G3的模拟结果整体优于G2。新版本的主要改进在于显著减小了热带西北太平洋正降水偏差。水汽收支诊断显示，模式降水偏差主要来源于蒸发项和水汽垂直平流动力项，而后者的偏差则来自于对流强度和对流垂直结构的共同作用。对流垂直结构偏差主要存在于赤道印度洋与赤道大西洋区域，表现为大气低层辐合分量偏小，对流卷出层高度偏高；在热带西北太平洋与赤道东太平洋区域，模式较好地还原了典型的“头重型”和“脚重型”对流垂直结构，但依然存在有整体性的对流偏深。湿静力能（MSE）收支显示，热带西北太平洋区域过量的净能量通量是模式垂直运动偏差的主要来源。而对流垂直结构偏深造成的总湿稳定度（Gross Moist Stability，简称GMS）偏大，在一定程度上抵消了模式中的净能量通量偏差，抑制了模拟的对流强度。诊断结果显示，G3中热带西北太平洋区域的降水改善主要源于对流强度正偏差的减小。G3中对流阈值和层云阈值的下调，使得对流发生频率增加，从而抑制了过大的对流强度。热带对流垂直结构与降水偏差有着紧密且多样的联系，在未来模式发展中应当予以重视。
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关键词:
- GAMIL模式 /
- 热带海洋降水 /
- 对流垂直结构 /
- 总湿稳定度（GMS）
Abstract: The simulation ability of tropical precipitation and convective vertical structure was evaluated using the two version of GAMIL (Grid-point Atmospheric Model of IAP LASG). In this work, we focused on the differences between GAMIL2 (G2) and GAMIL3 (G3), and the reasons for improved precipitation simulation in the G3 and the relationship between the convective vertical structure of tropical convection and precipitation simulation deviations were investigated. Both GAMIL versions accurately represent the key features of tropical precipitation, with G3 being more globally accurate than G2. The updated version greatly reduces the positive precipitation bias in the tropical Northwest Pacific Ocean. The water vapor budget diagnosis reveals that the precipitation deviation is primarily caused by the evaporation and the vertical advection dynamic terms, where the latter is attributed to the combined influence of vertical motion intensity and profiles. The vertical structure deviation of convection is most prevalent in the equatorial Indian Ocean and Atlantic Ocean areas, which primarily corresponds to small convergence component in the lower atmosphere and a larger height of detrainment. The traditional “top-heavy” and “bottom-heavy” vertical motion profile characteristics are strongly represented in the tropical northwest Pacific and equatorial Eastern Pacific Ocean, although deeper convection than reanalysis data is still evident. The moist static energy budget reveals that the estimated vertical motion divergence is primarily caused by the excess net energy flow across the tropical northwest Pacific Ocean. Alternatively, the deeper vertical convective structure leads to a larger gross moist stability, which offsets the net energy flux deviation and inhibits the convection in G3. This offset effect has significantly improved precipitation simulation in the tropical Northwest Pacific Ocean due to a decrease in the positive deviation of convective intensity. The down-regulation of convective stratus thresholds in G3 increases the frequency of convection and inhibits excessive vertical motion intensity. The vertical structure of tropical convection has various intimate links with precipitation deviation, which should be considered for future model development. -
图 1 （a）GPCP2.3观测资料、（b）ERA5再分析资料中以及（c）G2、（d）G3模式模拟的1980～2009年平均热带降水场（单位：mm d−1）；（e）G2、（f）G3模式模拟的降水偏差场（相对于ERA5降水资料，单位：mm d−1）。（a, b, c, d）中的红色实线表示ERA5资料中的气候态对流区的边界，即
$-\left\langle{\bar{\omega }{\partial }_{p}\bar{q}}\right\rangle=0$ 的等值线；（d, e）中的红（蓝）色虚线表示模式模拟的偏干（湿）区边界；（b, c, d）右上角标注为各自降水场资料与GPCP降水资料的空间相关系数（PCC）和均方根误差（RMSE，单位：mm d−1）Figure 1. Spatial distribution of 1980–2009 annual mean tropical rainfall (units: mm d−1) from (a) GPCP2.3 observation，(b) ERA5 reanalysis data, (c) G2, and (d) G3, the rainfall bias with of (e) G2 and (f) G3 (respect to the ERA5, units: mm d−1). The red solid line in (a, b, c, d) is
$-\left\langle{\bar{\omega }{\partial }_{p}\bar{q}}\right\rangle=0$ contour, which denotes the boundary of convective zones, and the red (blue) dashed line in (d and e) denotes the boundary of drier (wetter) areas where$ {P}' $ <0 ($ {P}' $ >0) and overlapped with convective zones. The top-right labels in the (b, c, d) are the spatial Pearson correlation coefficient (PCC) and root-mean-square error (RMSE, units: mm d−1) in reference to GPCP
图 2 偏干区与偏湿区降水偏差的水汽收支诊断（单位：W m−2）。蓝（红）色柱代表偏湿（干）区，浅（深）色柱代表G2（G3）； 横坐标刻度上的各项含义参见2.3.1节, 其中NL为
$-\langle{{\omega }'{\partial }_{p}{q}'}\rangle$ 的缩写，Res为Residual的缩写Figure 2. Moisture budget bias averaged over the wetter (drier) areas (units: W m−2).bars in blue (red) color denote the wetter (drier) area, in lighter (heavier) color denote G2 (G3). See Section 2.3.1 for the meanings of the items on the abscissa scale，and
$-\langle{{\omega }'{\partial }_{p}{q}'}\rangle$ is abbreviateed as NL, Residual is abbreviated as Res
图 3 偏干区与偏湿区对垂直平流动力项偏差的进一步分解（单位：W m−2）：G2的模式结果（左列）；G3的模式结果（右列）。棕色、蓝色、红色、紫色实线内区域分别为赤道东印度洋（TEI）、热带西北太平洋（WNP）、热带东太平洋（EP）、赤道大西洋区域（TA）。各项含义参见2.3.2节
Figure 3. the further composition of the dynamic bias (units: W m−2). Left (right) coumn represents the decomposition of G2 (G3). The brown, blue, red, purple solid lines denote the boundary of Tropical Eastern Indian (TEI), Western North Pacific (WNP), Eastern Pacific (EP), Tropical Atlantic (TA) region respectively. See Section 2.3.2 for the meanings of the items on the abscissa scale
图 4 （a1）ERA5再分析资料和（a2）G2、（a3）G3模式结果中对流垂直结构
$ \Omega $ 对垂直运动的方差解释率VΩ分布；（b1）G2与（b2）G3模式结果中的对流降水（单位：mm d−1）的分布；（c）G3与G2模式结果方差解释率之差的分布；（d）G3与G2模式结果中对流降水之差（单位：mm d−1）的分布。图中，棕色、蓝色、红色、紫色实线框内区域分别为TEI、WNP、EP、TA区域Figure 4. the spatial distribution of variance fraction VΩ explained by
$\varOmega$ from (al) ERA5 reanalysis data and (a2) G1, (a3) G2 model simulations; the distribution of the convective precipitation from (b1) G2 and (b2) G3 model simulations; (c) the spatial distribution of difference of${V}_{\varOmega }$ between G3 and G2; (d) the difference of convective precipitation (units: mm d−1) between G3 and G2. The brown, blue, red, purple box in the figures denote TEI, WNP, EP, TA areas respectively
图 5 在WNP（第一行）、EP（第二行）、TEI（第三行）、TA（第四行）四个关键区域内ERA5再分析资料（第一列）、G2模式（第二列）、G3（第三列）各自垂直速度
$ \omega $ 的季节循环（单位：Pa s−1）以及区域平均的对流垂直结构Ω（第四列）。右上角标注为区域平均的方差解释率VΩ，黑色点划线表示ERA5再分析资料、红色（蓝色）实线表示G2（G3）模式结果，对流廓线已经过标准化处理，因而仅代表垂直结构，不反映量级Figure 5. Seasonal cycle of vertical velocity ω (units: Pa s−1) of ERA5 (left column), G2 (second column) and G3 (third column) respectively in WNP (top row), EP (second row), TEI (third row) and TA (bottom row). In the upper right corner is the regional averaged variance fraction VΩ. Vertical arrangement of vertical motion on a regional average ω (right column). ERA5 is represented by a black dot-dashed line, whereas G2 (G3) isrepresented by a red (blue) solid line. the convection profile has been standardized in such a way that it reflects just the vertical structure andnot the magnitude
图 6 （a、c）G2和（b、d）G3模式结果中WNP区域的湿静力能收支诊断及净能量通量（单位：W m−2）诊断：（a、b）水汽动力项偏强区
$-\langle{{\omega }{{'}}{\partial }_{p}\bar{q}}\rangle > 0$ ；（c、d）水汽动力项偏弱区$-\langle{{\omega }{{'}}{\partial }_{p}\bar{q}}\rangle < 0$ 。各收支项为对应区域内平均的结果，橙色柱体代表正偏差项，蓝色柱体代表负偏差项，未填色的柱体表示${F}_{\mathrm{n}\mathrm{e}\mathrm{t}}'$ 的分项。各项含义参见2.3.2节, 其中NL为$-\langle{{\omega }'{\partial }_{p}{h}'}\rangle$ 的缩写， Res为Residual的缩写Figure 6. Moist static budget terms averaged over WNP area (units: W m−2) from (a, c) G2 and (b, d) G3 models: (a, b) Area with stronger convection of G2; (c, d) area with weaker convection. The orange bars represent positive bias, the blue bars represent negative bias, the unshaded bars represent the components of
${F}_{\mathrm{n}\mathrm{e}\mathrm{t}}'$ . See section 2.3.2 for the meanings of the items on the abscissa scale，and$-\langle{{\omega }'{\partial }_{p}{h}'}\rangle$ is abbreviateed as NL, Residual is abbreviated as Res
图 7 （a）G2和（b）G3模式由700 hPa积分至1000 hPa得到的低层湿焓平流非线性偏差
$-\int {\boldsymbol{V}}\cdot \nabla \left({q}'+{T}'\right)\mathrm{d}p$ （填色，只对绝对值大于8 W m−2的区域进行填色）、850 hPa潜热能偏差（深蓝色等值线，加粗实线为0线，等值线间隔为600 J kg−1）以及850 hPa风速（黑色箭头；单位：m s−1）。黄色斜线阴影区域为WNP区域Figure 7. Integral of low level moist enthalpy advection deviation
$-\int {\boldsymbol{V}}\cdot \nabla \left({q}'+{T}'\right)\mathrm{d}p$ (shading; units: W m−2) from (a) G2 and (b) G3 models, only absolute values larger than 8 W m−2 are shown; latent heat energy on 850 hPa (contour intervals: 600 J kg−1), the thick solid contour denotes zero, thin solid contours denote positive values and dashed, negative, and the 850 hPa wind field (vectors, units: m s−1). Yellow hatching denote the WNP area
图 8 WNP区域（a）总湿稳定度（GMS）偏差率与对流强度偏差率的空间散点分布，（b）区域平均的GMS偏差收支诊断。（a）中椭圆内为2-σ的置信区域，椭圆中点处的五角星代表了相应颜色散点的平均点，
${\omega }_{I}'/{\bar{\omega }}_{I}$ 是对流强度的相对偏差（相对于观测的偏差百分比），${M}'/\bar{M}$ 是GMS的相对偏差，右上角的标注是两类偏差率的相关系数，标注*代表通过95%显著性检验；（a, b）中，红色代表G2，蓝色代表G3。GMS小于0或是相对偏差大于240%的散点已经被剔除Figure 8. (a) Scatterplot for the bias proportion of GMS (Gross Moist Stability,
${M}'/\bar{M}$ ) and (b) the budget of regional averaged GMS bias in the WNP area. In (a), the relative bias of$ {\omega }_{I} $ (${\omega }_{I}{{'}}/{\bar{\omega }}_{I}$ ), GAMIL relative to the ERA5 reanalisis, ellipses denote the boundary of 2-σ confidence; the star centered in the ellipse denote the mean of the scatters with the corresponding color; the top-right labels denote the correlation coefficient between$({M}')/\bar{M}$ and${\omega }_{I}{{'}}/{\bar{\omega }}_{I}$ , corr of both models are significant as the 95% confidence level. In (a, b), red points represents G2, blue triangle represents G3; the grids$ M $ <0 or relative bias larger than 240% have been removed
图 9 GAMIL模式中对流垂直结构偏差
${\varOmega }'$ 与降水偏差${P}'$ 的关系Figure 9. Relationship between convective vertical structure deviation
${\varOmega }'$ and precipitation deviation${P}'$ in GAMIL
图 10 （a）ERA5再分析资料、（b）G2和（c）G3模式依照月平均降水分箱的月平均标准化垂直运动
$ \omega $ 分布谱。横坐标为1至100百分位，分箱区域为热带对流区（参考图1与表1）；填色为各箱平均的月均标准化垂直运动$ \omega $ ，仅反映结构不代表量级Figure 10. Binned normalized
$ \omega $ by monthly mean precipitation intensity from 1 to 100 percentiles bins over the tropical convection region (refer to Fig. 1 and Table 1) from (a) ERA5 reanalysis data, (b) G2 and (c) G3 models. Shading is bin-averaged normalized$ \omega $ , which only reflect the structure of$ \omega $ don’ t represent its magnitude表 1 研究区域定义
Table 1. Definition of study areas
区域定义 $-\langle{\bar{\omega }{\partial }_{p}\bar{q} }\rangle$（ERA5） ${P}'$（GAMIL-ERA5） 对流区 >0 − 偏湿区 >0 >0 偏干区 >0 <0 
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表 2 关键区域定义
Table 2. Definition of key study areas
区域 经纬度范围 $-\langle{\bar{\omega }{\partial }_{p}\bar{q} }\rangle$（ERA5） ${V}_{\varOmega }$(G2、G3、ERA5） 赤道东印度洋（TEI） （3°S～3°N，50°E～80°E）和（3°S～8°N，80°E～100°E） >0 − 热带西北太平洋（WNP） （2.5°N～20°N，127°E～160°E） >0 >60 热带东太平洋（EP） （5°S～20°N，150°W～90°W） >0 − 赤道大西洋区域（TA） （5°S～5°N, 60°W～10°W） >0 −
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[1] Abbott T H, Cronin T W, Beucler T. 2020. Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium [J]. J. Atmos. Sci., 77(5): 1637−1660. doi: 10.1175/JAS-D-19-0197.1 [2] Adler R F, Huffman G J, Chang A, et al. 2003. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present) [J]. Journal of Hydrometeorology, 4(6): 1147−1167. doi: 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2 [3] Arakawa A. 2004. The cumulus parameterization problem: Past, present, and future [J]. J. Climate, 17(13): 2493−2525. doi: 10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2 [4] Arakawa A, Schubert W H. 1974. Interaction of a cumulus cloud ensemble with the large-scale environment, Part I [J]. J. Atmos. Sci., 31(3): 674−701. doi: 10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2 [5] Arakawa A, Jung J H. 2011. Multiscale modeling of the moist-convective atmosphere—A review [J]. Atmospheric Research, 102(3): 263−285. doi: 10.1016/j.atmosres.2011.08.009 [6] Back L E, Bretherton C S. 2006. Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific [J]. Geophys. Res. Lett., 33(17): L17810. doi: 10.1029/2006GL026672 [7] Back L E, Bretherton C S. 2009a. A simple model of climatological rainfall and vertical motion patterns over the tropical oceans [J]. J. Climate, 22(23): 6477−6497. doi: 10.1175/2009JCLI2393.1 [8] Back L E, Bretherton C S. 2009b. On the relationship between SST gradients, boundary layer winds, and convergence over the tropical oceans [J]. J. Climate, 22(15): 4182−4196. doi: 10.1175/2009JCLI2392.1 [9] Back L E, Hansen Z, Handlos Z. 2017. Estimating vertical motion profile top-heaviness: Reanalysis compared to satellite-based observations and stratiform rain fraction [J]. J. Atmos. Sci., 74(3): 855−864. doi: 10.1175/JAS-D-16-0062.1 [10] Betts A K. 1986. A new convective adjustment scheme. Part I: Observational and theoretical basis [J]. Quart. J. Roy. Meteor. Soc., 112(473): 677−691. doi: 10.1002/qj.49711247307 [11] Bony S, Stevens B, Frierson D M W, et al. 2015. Clouds, circulation and climate sensitivity [J]. Nature Geoscience, 8(4): 261−268. doi: 10.1038/ngeo2398 [12] Bretherton C S, Park S. 2009. A new moist turbulence parameterization in the community atmosphere model [J]. J. Climate, 22(12): 3422−3448. doi: 10.1175/2008JCLI2556.1 [13] Bretherton C S, McCaa J R, Grenier H. 2004. A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: Description and 1D results [J]. Mon. Wea. Rev., 132(4): 864−882. doi: 10.1175/1520-0493(2004)132<0864:ANPFSC>2.0.CO;2 [14] Bui H X, Yu J Y. 2021. Impacts of model spatial resolution on the simulation of convective spectrum and the associated cloud radiative effect in the tropics [J]. J. Meteor. Soc. Japan, 99(4): 789−802. doi: 10.2151/jmsj.2021-039 [15] Bui H X, Yu J Y, Liu H W, et al. 2019. Convective structure changes over the equatorial Pacific with highly increased precipitation under global warming simulated in the HiRAM [J]. SOLA, 15: 119−124. doi: 10.2151/sola.2019-022 [16] Chen C A, Yu J Y, Chou C A. 2016. Impacts of vertical structure of convection in global warming: The role of shallow convection [J]. J. Climate, 29(12): 4665−4684. doi: 10.1175/JCLI-D-15-0563.1 [17] Chen Y C, Yu J Y. 2021. Modes of tropical convection and their roles in transporting moisture and moist static energy: Contrast between deep and shallow convection [J]. Climate Dyn., 57(7–8): 1789–1803. doi:10.1007/s00382-021-05777-x [18] Chen Z M, Zhou T J, Zhang L X, et al. 2020. Global land monsoon precipitation changes in CMIP6 projections [J]. Geophys. Res. Lett., 47(14): e2019GL086902. doi: 10.1029/2019GL086902 [19] Chou C A, Neelin J D, Chen C A, et al. 2009. Evaluating the “Rich-Get-Richer” mechanism in tropical precipitation change under global warming [J]. J. Climate, 22(8): 1982−2005. doi: 10.1175/2008JCLI2471.1 [20] Dai A G. 2006. Precipitation characteristics in eighteen coupled climate models [J]. J. Climate, 19(18): 4605−4630. doi: 10.1175/JCLI3884.1 [21] Emanuel K A. 2007. Quasi-equilibrium dynamics of the tropical atmosphere [M]//Schneider T, Sobel A H. The Global Circulation of the Atmosphere. Princeton: Princeton University Press, 186–218. [22] Fuchs-Stone Ž, Raymond D J, Sentić S. 2020. OTREC2019: Convection over the East Pacific and southwest Caribbean [J]. Geophys. Res. Lett., 47(11): e2020GL087564. doi: 10.1029/2020GL087564 [23] Fueglistaler S, Dessler A E, Dunkerton T J, et al. 2009. Tropical tropopause layer [J]. Rev. Geophys., 47(1): RG1004. doi: 10.1029/2008RG000267 [24] Gill A E. 1980. Some simple solutions for heat-induced tropical circulation [J]. Quart. J. Roy. Meteor. Soc., 106(449): 447−462. doi: 10.1002/qj.49710644905 [25] Guo Z, Zhou T J. 2014. An improved diagnostic stratocumulus scheme based on estimated inversion strength and its performance in GAMIL2 [J]. Science China Earth Sciences, 57(11): 2637−2649. doi: 10.1007/s11430-014-4891-7 [26] Hartmann D L, Larson K. 2002. An important constraint on tropical cloud - climate feedback [J]. Geophys. Res. Lett., 29(20): 1591. doi: 10.1029/2002GL015835 [27] Held I M, Soden B J. 2006. Robust responses of the hydrological cycle to global warming [J]. J. Climate, 19(21): 5686−5699. doi: 10.1175/JCLI3990.1 [28] Hersbach H, Bell B, Berrisford P, et al. 2020. The ERA5 global reanalysis [J]. Quart. J. Roy. Meteor. Soc., 146(730): 1999−2049. doi: 10.1002/qj.3803 [29] Holtslag A A M, Boville B A. 1993. Local versus nonlocal boundary-layer diffusion in a global climate model [J]. J. Climate, 6(10): 1825−1842. doi: 10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2 [30] John V O, Soden B J. 2007. Temperature and humidity biases in global climate models and their impact on climate feedbacks [J]. Geophys. Res. Lett., 34(18): L18704. doi: 10.1029/2007GL030429 [31] Kang I S, Jin K, Wang B, et al. 2002. Intercomparison of the climatological variations of Asian summer monsoon precipitation simulated by 10 GCMs [J]. Climate Dyn., 19(5–6): 383–395. doi:10.1007/s00382-002-0245-9 [32] Klein S A, Hartmann D L. 1993. The seasonal cycle of low stratiform clouds [J]. J. Climate, 6(8): 1587−1606. doi: 10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2 [33] Larson K, Hartmann D L, Klein S A. 1999. The role of clouds, water vapor, circulation, and boundary layer structure in the sensitivity of the tropical climate [J]. J. Climate, 12(8): 2359−2374. doi: 10.1175/1520-0442(1999)012<2359:TROCWV>2.0.CO;2 [34] Larson V E, Golaz J C. 2005. Using probability density functions to derive consistent closure relationships among higher-order moments [J]. Mon. Wea. Rev., 133(4): 1023−1042. doi: 10.1175/MWR2902.1 [35] Li L J, Dong L, Xie J B, et al. 2020. The GAMIL3: Model description and evaluation [J]. J. Geophys. Res.: Atmos., 125(15): e2020JD032574. doi: 10.1029/2020JD032574 [36] Ma J, Chadwick R, Seo K H, et al. 2018. Responses of the tropical atmospheric circulation to climate change and connection to the hydrological cycle [J]. Annual Review of Earth and Planetary Sciences, 46(1): 549−580. doi: 10.1146/annurev-earth-082517-010102 [37] Ma J, Zhou L, Foltz G R, et al. 2020. Hydrological cycle changes under global warming and their effects on multiscale climate variability [J]. Ann. N. Y. Acad. Sci., 1472(1): 21−48. doi: 10.1111/nyas.14335 [38] Neelin J D. 2007. Moist dynamics of tropical convection zones in monsoons, teleconnections, and global warming [M]//Schneider T, Sobel A H. The Global Circulation of the Atmosphere. Princeton: Princeton University Press, 267–301. [39] Neelin J D, Held I M. 1987. Modeling tropical convergence based on the moist static energy budget [J]. Mon. Wea. Rev., 115(1): 3−12. doi: 10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2 [40] Neelin J D, Yu J Y. 1994. Modes of tropical variability under convective adjustment and the Madden-Julian oscillation. Part I: Analytical theory [J]. J. Atmos. Sci., 51(13): 1876−1894. doi: 10.1175/1520-0469(1994)051<1876:MOTVUC>2.0.CO;2 [41] Siebesma A P, Soares P M M, Teixeira J. 2007. A combined eddy–diffusivity mass–flux approach for the convective boundary layer [J]. J. Atmos. Sci., 64(4): 1230−1248. doi: 10.1175/JAS3888.1 [42] Sun W Q, Li L J, Wang B. 2016. Reducing the biases in shortwave cloud radiative forcing in tropical and subtropical regions from the perspective of boundary layer processes [J]. Science China Earth Sciences, 59(7): 1427−1439. doi: 10.1007/s11430-016-5290-z [43] Tian B J, Fetzer E J, Kahn B H, et al. 2013. Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology [J]. J. Geophys. Res.: Atmos., 118(1): 114−134. doi: 10.1029/2012JD018607 [44] Wang B, Ding Q H, Fu X H, et al. 2005. Fundamental challenge in simulation and prediction of summer monsoon rainfall [J]. Geophys. Res. Lett., 32(15): L15711. doi: 10.1029/2005GL022734 [45] Wills R C, Levine X J, Schneider T. 2017. Local energetic constraints on Walker circulation strength [J]. J. Atmos. Sci., 74(6): 1907−1922. doi: 10.1175/JAS-D-16-0219.1 [46] Wu B, Zhou T J, Li T M. 2017. Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part I: Maintenance mechanisms [J]. J. Climate, 30(23): 9621−9635. doi: 10.1175/jcli-d-16-0489.1 [47] Wu X Q, Deng L P, Song X L, et al. 2007. Impact of a modified convective scheme on the Madden-Julian Oscillation and El Niño–Southern Oscillation in a coupled climate model [J]. Geophys. Res. Lett., 34(16): L16823. doi: 10.1029/2007GL030637 [48] Yu J Y, Neelin J D. 1997. Analytic approximations for moist convectively adjusted regions [J]. J. Atmos. Sci., 54(8): 1054−1063. doi: 10.1175/1520-0469(1997)054<1054:AAFMCA>2.0.CO;2 [49] Yu J Y, Chou C, Neelin J D. 1998. Estimating the gross moist stability of the tropical atmosphere [J]. J. Atmos. Sci., 55(8): 1354−1372. doi: 10.1175/1520-0469(1998)055<1354:ETGMSO>2.0.CO;2 [50] Zhang G J, Wu X Q. 2003. Convective momentum transport and perturbation pressure field from a cloud-resolving model simulation [J]. J. Atmos. Sci., 60(9): 1120−1139. doi: 10.1175/1520-0469(2003)060<1120:CMTAPP>2.0.CO;2 [51] 周天军, 陈晓龙, 吴波. 2019a. 支撑“未来地球”计划的气候变化科学前沿问题 [J]. 科学通报, 64(19): 1967−1974. doi: 10.1360/N972018-00818Zhou T J, Chen X L, Wu B. 2019a. Frontier issues on climate change science for supporting Future Earth [J]. Chinese Science Bulletin (in Chinese) (in Chinese), 64(19): 1967−1974. doi: 10.1360/N972018-00818 [52] 周天军, 邹立维, 陈晓龙. 2019b. 第六次国际耦合模式比较计划(CMIP6)评述 [J]. 气候变化研究进展, 15(5): 445−456. doi: 10.12006/j.issn.1673-1719.2019.193Zhou T J, Zou L W, Chen X L. 2019b. Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6) [J]. Climate Change Research (in Chinese) (in Chinese), 15(5): 445−456. doi: 10.12006/j.issn.1673-1719.2019.193 -
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