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AMERICAN METEOROLOGICAL SOCIETY Journal of Climate

EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: 10.1175/JCLI-D-16-0372.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. If you would like to cite this EOR in a separate work, please use the following full citation: Yao, J., T. Zhou, Z. Guo, X. Chen, L. Zou, and Y. Sun, 2017: Improved performance of High-Resolution Atmospheric Models in simulating the EastAsian Summer Monsoon Rainbelt. J. Climate. doi:10.1175/JCLI-D-16-0372.1, in press.

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Improved performance of High-Resolution Atmospheric Models in

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simulating the East-Asian Summer Monsoon Rainbelt

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Junchen YAO1,2 Tianjun ZHOU1 Zhun GUO1,3 Xiaolong Chen1 Liwei ZOU1 Yong SUN1

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1 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

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2 University of Chinese Academy of Sciences, Beijing 100049

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3 CCRC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

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Corresponding author:

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Tianjun Zhou

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LASG, Institute of Atmospheric Physics

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Chinese Academy of Sciences

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Beijing 100029, China.

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Phone: 86-10-8299-5279

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E-mail: [email protected]

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Abstract

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Simulating the East-Asian summer monsoon (EASM) rainbelt has been proven challenging

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for climate models. In this study, the impacts of high-resolution to the simulation of spatial

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distributions and rainfall intensity of the EASM rainbelt are revealed based on Atmospheric

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Model Intercomparison Project (AMIP) simulations from Coupled Model Intercomparison

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Project Phase 5 (CMIP5) models. A set of sensitivity experiments is further performed to

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eliminate the potential influences of differences among CMIP5 models. The results show that

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the high-resolution models improve the intensity and the spatial pattern of the EASM rainfall

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compared to the low-resolution models, further valid in the sensitivity experiments. The

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diagnosis of moist static energy (MSE) balance and moisture budgets is further performed to

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understand the mechanisms underlying the enhancements. Both analyses indicate that the

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improved EASM rainfall is benefitted from the intensified meridional convergence along the

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EASM rainbelt simulated by the high-resolution models. In addition, such convergence is

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mainly contributed by intensified stationary meridional eddy northerly flows over the mid-

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north of China and southerly flows on the south of Japan due to increased model resolution,

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which is robust in the sensitivity experiments. Further analysis indicates that the stationary

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meridional eddy flow changes in high-resolution simulations are related to the barotropic

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Rossby wave downstream the Tibetan Plateau due to increased resolution.

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Keywords EASM precipitation, atmospheric moisture budget, moist static energy diagnosis,

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high resolution impact

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1. Introduction

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The East-Asian summer monsoon (EASM) is the most important climate system over East

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Asia and it is characterized by a frontal rainbelt called Meiyu in China and Baiu in Japan,

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respectively. The seasonal migration of the EASM rainbelt is closely related to the large-scale

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circulation. Following the northward movement of the western Pacific subtropical high, the

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monsoon rainbelt extends from the Indochina Peninsula in the South China Sea to the Yangtze

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River valley from early June to mid-July (Ding 1992; Ding and Chan 2005; Zhou et al. 2009b;

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Sampe and Xie 2010). Extreme climate events (e.g., north China floods in 2012 and heat wave

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of central east China in 2013) occuring over East Asia, are closely related to the variability in

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EASM precipitation (Waliser, 2006; Sampe and Xie, 2010; Zhou et al. 2009a, 2013, 2014).

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Therefore, the mechanism of EASM precipitation formation has been a major research focus

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of the monsoon study community (Sampe and Xie, 2010; Molnar et al. 2010; Chen and Bordoni

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2014a, 2014b).

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Climate models are powerful tools for monsoon research. Many studies have demonstrated

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the models’ basic performance in simulating the EASM precipitation (Zhou and Li 2002; Chen

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et al. 2010; Zhou and Zou 2010; Zhou et al. 2013). However, significant biases exist in

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simulating both the rainfall intensity and spatial distribution, especially for low-resolution

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general circulation models (GCMs), such as remarkable deficiencies in reproducing the

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location, amount, and seasonal evolution of precipitation over East Asia (Kang et al. 2002;

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Zhou and Li 2002; Chang 2004; Zhou et al. 2009a; Huang et al. 2013). These biases are related

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to both the imperfect representation of basic physical and dynamical processes (e.g., moist

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convection and large-scale circulation) (Bony et al.2013; Huang et al. 2013; Dai et al. 2013) 3

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and the complex topography over East Asia (Chen 1983; Boos and Hurley, 2013).

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The resolution of a GCM is proposed to be a major factor that affects a model’s performance

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in simulating EASM precipitation (Solomon et al. 2007; Huang et al. 2013). Previous studies

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have confirmed that high-resolution models more effectively reproduce the temperature, large-

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scale circulation, atmospheric teleconnections and precipitation (Cherchi and Navarra 2007;

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Dirmeyer et al. 2011; Hertwig et al. 2015; Johnson et al. 2015). The spatial distribution and

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rainfall intensity of EASM precipitation are one of the dramatic improvements in high-

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resolution models (Zhou and Li, 2002; Kitoh and Kusunoki, 2006; Gao et al. 2006). However,

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the physical and dynamic mechanisms of increasing resolution responsible for the

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improvements remain unknown.

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The exact physical mechanisms underlying the formation and maintenance of the Meiyu-

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Baiu (MB) rainbelt are still controversial. Some studies emphasize the role of the subtropical

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westerly jet, which triggers the MB by transporting warm advection from the Tibetan Plateau

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to the MB domain (Sampe and Xie 2010). However, recent studies indicate that one of the

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essential factors affecting the EASM rainbelt is the stationary eddy meridional velocity (Chen

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and Bordoni 2014a, 2014b). The related meridional advection of atmospheric dry enthalpy and

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moisture flux convergence sustains and intensifies the MB rainfall system. Which mechanism

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dominates the improvement of MB simulation from low- to high-resolution climate models

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deserves study.

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The purposes of this study are to answer the following two questions: 1) Whether the high-

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resolution models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) can

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better reproduce the MB rainbelt compared to the low-resolution counterparts. 2) If so, based

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on dynamical diagnoses, whether the large-scale circulation or the humidity overwhelm the

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rainfall improvements should be revealed. 3) If increased model resolution does have impact

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on the large-scale circulation or the humidity, what are the related dynamical mechanisms? Are

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the related mechanisms merely related to the changes of horizontal resolutions? For the third

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scientific question, a set of sensitivity experiments with identical physical schemes, in order to

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avoid potential influences from CMIP5 model differences among physical schemes, would be

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included.

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The remainder of this study is organized as follows. Section 2 describes the reanalysis

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datasets, models and analysis method used in this study. In section 3, we present our main

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results. Section 4 provides concluding remarks.

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2. Data and Methods

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a. Data description

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Monthly precipitation data were collected from the Global Precipitation Climatology Project

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(GPCP) Version 2.2 Monthly Combined Precipitation Dataset (Adler et al. 2003; Huffman et

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al. 2009). Wind, temperature, specific humidity, heat flux and radiation variables were collected

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from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis

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(ERA) Interim (Dee et al. 2011). The surface heat flux and radiation datasets are 3-h forecast

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fields obtained at 0000 and 1200UTC. Also the wind, temperature, specific humidity variables

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were collected from the Japanese 55-year Reanalysis (JRA55) for the comparison (Ebita et al.

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2011). These observation and reanalysis datasets are chosen due to their relatively high

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resolution (around 1 degree).

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In this study, in order to eliminate the sea surface temperature biases from coupled models

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(Song and Zhou 2009), monthly data of 16 AGCMs participating in the CMIP5 are examined.

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The AGCMs are divided into two groups by horizontal resolution. i.e., high horizontal

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resolution (higher than 1 degree) models (HRM) and low horizontal resolution (lower than 1

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degree) models (LRM). Since AGCMs with resolution lower than and around 1 degree do not

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show improvements of rainfall as the high-resolution models do, thus 1 degree is chosen as a

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threshold. Detailed information on the AGCMs is listed in Table 1.

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Data from 1979 to 2004 are used in this study. b. Sensitivity experiment

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Considering the different physical schemes employed in the used CMIP5 models may

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obscure the results, we perform three sensitive experiments at 1.9×2.5 (CAM5-2deg),

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0.9×1.25 (CAM5-1deg) and 0.47×0.63 (CAM5-0.5deg) by using CAM5.3, an AGCM

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developed by the National Center for Atmospheric Research, which only differs by the

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resolution but with the same framework and physical schemes. The model employs default

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finite-volume dynamical core, Zhang and McFarlane’s deep convection scheme (Zhang and

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McFarlane 1995, Neale et al 2008), Park and Bretherton’s (2009) shallow convection scheme

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and Morrison and Gettelman (MG) microphysics scheme (Morrison and Gettelman, 2008;

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Gettelman et al. 2010). All configurations have 30 vertical levels. We integrate the model for

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eight year with the three kinds of horizontal resolution, forced by the same prescribed

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climatological sea surface temperatures (SST) with annual cycle. The climatological SST were

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chosen because the focus of this study is the impacts of horizontal resolutions on the EASM

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rainfall.

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c. Methods

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1) Moisture static energy balance

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The moist static energy (MSE) budget is a useful tool for quantifying the roles of temperature,

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specific humidity, radiative processes and large-scale circulation in the spatial distribution of

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rainfall due to deep convection (Chou and Neelin 2003; Neelin 2007; Chou et al. 2013; Chen

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and Bordni 2014a, 2014b; Sun et al. 2015). To understand the main factors that affect the spatial

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distribution of EASM precipitation, the MSE balance analysis is determined. Under a quasi-

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equilibrium assumption of deep convection, a vertically integrated MSE budget averaged over

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a climatological period can be written as



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M h   Fnet   v M      t p

(1)

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where the M=cpT+Lvq is the moist enthalpy, h= cpT+Lvq+gz, Fnet is the net energy entering

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the atmospheric column, and 𝜔 is pressure velocity. T and q refer to air temperature and

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specific humidity, respectively. is a vertical mass integral (i.e., 〈𝑋〉 = 𝑔−1 ∫ 𝑋𝑑𝑝 ), and

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X

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denotes the temporal mean, which is a two-month (June and July) mean. Fnet can be further decomposed as

Fnet  St  St  Ss  Ss  Rt  Rs  Rs  SH  LH

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where the subscripts s and t on the solar (S) and longwave (R) radiative terms denote the surface and model top and SH and LH denote sensible and latent heat flux, respectively. 7

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2) Moisture budget analysis

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Moisture budget analyses are widely used to examine precipitation distributions and

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changes (Chou and Lan 2012). To understand the physical processes that modulate the EASM

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rainfall intensity and related model biases, a moisture budget analysis is conducted.

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Precipitation in the MB region should satisfy the moisture budget: P   t q      ( vq)     pq   E

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(3)

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Compared to the other terms, the tendency term −< ̅̅̅̅̅ 𝜕𝑡 𝑞 > can be ignored due to its small

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̅̅̅̅̅̅̅̅̅̅ climatological mean magnitude. The vertical term −< 𝜕 𝑝 (𝜔𝑞) > can also be neglected due

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to the negligible magnitude of vertical motion at the bottom and top of the atmospheric column.

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̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ The moisture flux term convergence −< ∇ ∙ (𝐯𝑞) > can be further written as zonal moisture

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flux convergence −< ̅̅̅̅̅̅̅̅̅̅ 𝜕𝑥 (𝑢𝑞) > and as meridional moisture flux convergence −
.

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3. Results

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a. Comparison of EASM rainfall pattern between high- and low-resolution models

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Following previous studies (Ding and Chan 2005; Zhou et al. 2009b), the MB season is

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generally defined as the June-July average. As shown in Figure 1a, the precipitation from GPCP

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shows an organized elongated rainbelt spanning over East Asia and the northwestern Pacific.

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The maximum center locates in the Yangtze River valley of China and in the southwestern

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Japan. The weighted average precipitation is 6.7 mm/day over the southeast China region (SC;

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112.5°~122.5°E, 25°~35°N), and it is 7.6 mm/day over the southwestern Japan region (SJ;

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125°~145°E, 27°~37°N). A wedge of deficit rainfall is located south of the Baiu rainbelt, 8

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marking the positioning of the northwestern Pacific subtropical high (~25°N). The spatial

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distribution of ascending motion at 400 hPa matches the MB rainfall patterns well.

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The results of the ensemble mean of the LRMs are shown in Fig. 1b. The elongated rain belt

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is generally captured by LRMs with weaker magnitude over the SC and the SJ region. Rainfall

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amount simulated by LRMS over the SC and SJ are 5.6 mm/day and 4.8 mm/day respectively.

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Compared to the observation, the main biases of precipitation from the LRMs are the deficit

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rainfall between the eastern China and Japan on the ocean, which leads to a bow-like rainfall

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pattern. In addition, the rain belt shows poleward displacement that is related to the northward

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shifted ascending motion. In addition, the weakened ascending motion, especially between the

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eastern China and Japan, results in the underestimation of precipitation in LRMs. The pattern

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correlation coefficient (PCC) over the MB region (box defined in Fig. 1) between the LRMs

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and observation is 0.31, and the root mean square difference (RMSE) is 2.1. As shown in Table

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2, the PCCs between most of the LRMs and observation are generally below 0.50, and the

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RMSEs generally exceed 1.8, except ACCESS1-0 and HadGEM2-A.

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The HRMs also reasonably capture the spatial pattern of the MB rain belt (Fig. 1c). Two

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rainfall centers over the Yangtze River valley in China and over the southwestern Japan are

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also reproduced, though the rainfall intensities are slightly underestimated compared to the

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observation. In HRMs, precipitation over the SC and SJ are 6.0 mm/day and 6.3 mm/day

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respectively. Benefiting from the enhanced rainfall amount over the SJ region, the rain belt

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between the eastern China and Japan is more concentrated in the HRMs. However, there is not

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a significant enhancement in the rainfall over SC when the resolution improves. The ascending

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motion simulated by the HRMs is stronger than it by the LRMs, especially on the east of China, 9

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which leads to the enhancement of precipitation. The PCC between the simulated and observed

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precipitation over the MB region is 0.76, and the RMSE is 1.3. As shown in Table 2, the PCCs

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between HRMs and observation are greater than 0.6, and the RMSEs generally below 1.3,

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except GFDL-HIRAM-C180.

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The regional averaged rainfall amounts over the red box (112.5°~145°E; 25°~37°N) in Fig.

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1 from all models against the mean of horizontal resolutions are shown in Fig. 2. According to

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the observation, the regional averaged rainfall is 7.1 mm/day. The magnitude of rainfall over

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the MB region arises along with the increase of model horizontal resolution. MRI models show

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the nearest simulated rainfall amount to the observations, reaching a magnitude of nearly 6.5

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mm/day. Notably, some of the models do not follow such precipitation-resolution relationship,

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e.g., the CCSM4, CESM1-CAM5 and GFDL-HIRAM, indicating that differences of model

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dynamical frame and physical schemes may obscure the results. The rainfall reproduced by

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sensitivity experiments increase from 5.1mm/day to 6.3mm/day as resolution increases (Fig. 2

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purple markers) that show consistency to the precipitation-resolution relationship, confirming

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the influence of model horizontal resolution on the EASM rainfall. However, as the resolution

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increases, the precipitation improvement seems to have a limit. This implies HRM adjusting

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parameterization scheme may be necessary for better performance in simulating the EASM

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rainfall. Actually, the convection parameterization will be not needed in the convection-

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resolving scale.

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In brief, both the LRMs and HRMs reasonably reproduce the MB rain belt. In addition, the

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magnitude of MB rainfall is enhanced in HRMs, especially over the ocean on the south of Japan,

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compared to the LRMs. However, the enhancement over the SC region is not significant. The 10

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enhanced rainfall intensity over the ocean on the south of Japan in the HRMs is related to the

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enhanced ascending motion. To show which physical processes are responsible for the

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enhancement of convection over the MB region, an MSE balance analysis will be performed

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in the following section.

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b. Moist static energy balance analysis

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The EASM rainfall amount simulated by the HRMs is enhanced compared to that in the

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LRMs. To understand the processes that contribute to the enhancement, an MSE balance

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analysis is performed to reveal the relative roles of temperature, specific humidity, radiative

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processes and large-scale circulation. Since the vertical integration of vertical MSE advection

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̅̅̅̅̅̅̅ () is susceptible to the relatively coarse vertical resolution of models. Similar to

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previous studies (Chou et al., 2013; Chen and Bordoni 2014a), we use a simple assumption that

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the residual is relatively small caused by coarse vertical resolution, thus the vertical MSE

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advection is calculated as ̅̅̅̅̅ 𝐹𝑛𝑒𝑡 −< ̅̅̅̅̅̅̅̅ 𝐯 ∙ ∇𝑀 > according Eq. 1. The vertical MSE advection

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involves pressure velocity 𝜔 and the MSE stratification < ̅̅̅̅̅ 𝜕𝑝 ℎ > . Because the latter is

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generally negative in the stable troposphere, the region of positive < ̅̅̅̅̅̅̅ 𝜔𝜕𝑝 ℎ > represents

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ascending motion (ω ) (Fig. 3b) and net energy flux into the atmosphere column

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̅̅̅̅̅̅ ) (Fig. 3c). The former overwhelms the latter and dominates the spatial patterns of (𝐹𝑛𝑒𝑡

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vertical MSE advection, which is valid in both of the HRMs and LRMs (Fig. 3d-i). Thus, the

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energy triggering the convection over the EASM region are mainly contributed by the

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horizontal moist enthalpy advection.

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Consistent with the bias in simulated MB precipitation (Fig. 2), the vertical MSE advections

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along the rain belt in both of the LRMs and HRMs are underestimated compared with the

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observation, which are mainly contributed by the horizontal advection term (Fig. 3b, 3e, 3h).

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The horizontal moist enthalpy advection center between the eastern China and Japan is

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weakened and shifted to the north of Japan in the LRMs (Fig. 3e). Such bias is considerably

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reduced in the HRMs (Fig. 3j). The most prominent improvement is located south of Japan

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which is averagely 38 W/m2 (55%) larger than the LRMs, to which the horizontal moist

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enthalpy advection contributes 29 W/m2 (76%). Meanwhile, the horizontal moist enthalpy

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advection is suppressed in the north of the East Asian region, leading to a more realistic moist

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enthalpy advection around the front in the HRMs (Fig. 3k). However, the horizontal moist

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enthalpy advection over the SC region is suppressed in the HRMs compared to the LRMs.

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To more clearly show the differences between the HRMs and the LRMs, we compare the

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differences of MSE advections with precipitation (Fig. 3j-l). The enhancements of precipitation,

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accompanied with the MSE advection enhancement, mainly locates in the SJ region in the

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HRMs. The two patterns coincidently match each other. Hence, the improvement of the EASM 12

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rain belt over the SJ region can be explained by the enchaned horizontal moist enthalpy

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advection (Fig. 3k).

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c. Decomposition of horizontal moist enthalpy advection

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Above evidences show that, the moist enthalpy advection plays a critical role in improving

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the EASM rainfall in the HRM. To clearly identify relative importance of mean advection, eddy

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advection, and transient advection to the moist enthalpy advection, we decompose the

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advection term into 5 terms follow Equation A.1(detailed information seen in the Appendix).

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Because of the small magnitude of the pure zonal mean moist enthalpy advection (−< v̅ ∙

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̅̅̅̅̅ ∇𝑀 >), it is ignorable in the following analysis. The remained 4 terms are the advection of

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stationary eddy energy by zonal-mean flow −< ̅̅̅̅ [v] ∙ ̅̅̅̅̅̅ ∇𝑀∗ >, the advection of zonal-mean

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energy by eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ [∇𝑀] > , the advection of stationary eddy energy by the

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stationary eddy flow, or pure eddy advection, −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀∗ >, and the advection of transient

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eddy energy by the transient eddy flow, or transient advection, −< ̅̅̅̅̅̅̅̅̅̅ 𝐯 ′ ∙ ∇𝑀′ >.

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Along the EASM rain belt, the contribution from the advection of zonal-mean energy by

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eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ [∇𝑀] > and the pure eddy advection −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀∗ > overwhelm the

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̅̅̅̅ ∙ ̅̅̅̅̅̅ advection of stationary eddy energy by zonal-mean flow < [v] ∇𝑀∗ > and the transient

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advection −< ̅̅̅̅̅̅̅̅̅̅ 𝐯 ′ ∙ ∇𝑀′ > in the reanalysis data, which is valid in both of the HRMs and

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LRMs (Fig. 4). As shown in Fig. 4, both of the LRMs and HRMs reasonably reproduce the

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spatial pattern of 4 terms. In terms of the advection of stationary eddy energy by zonal-mean

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flow < ̅̅̅̅ [v] ∙ ̅̅̅̅̅̅ ∇𝑀∗ > (Fig. 4a-c) and transient advection −< ̅̅̅̅̅̅̅̅̅̅ 𝐯 ′ ∙ ∇𝑀′ > (Fig. 4j-l), the HRMs

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(LRMs) simulated them by 15.1 W/m2 (17.5 W/m2) and -19.5 W/m2 (-15.6 W/m2) over the MB

13

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region, compared to 24.1 W/m2 and -3.5 W/m2 in the reanalysis data results. The LRMs

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underestimate their magnitude, which are further underestimated in the HRMs. Thus, the

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enhancement of the total moist enthalpy advection along the EASM rain belt in the HRMs are

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contributed by the advection of zonal-mean energy by the eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ [∇𝑀] > and the

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pure eddy advection −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀∗ >,which averaged are 11.8 W/m2 and 19.8 W/m2 higher

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than that in the LRMs. Although, the magnitude of these two terms are enhanced in the HRMs,

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there is no significant added values in their spatial distribution from the LRMs, especially the

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advection of the zonal-mean energy by eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ [∇𝑀] > . This term is

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underestimated and northward shifted in the LRMs over the SJ region. However, it is

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overestimated on the south of Japan and underestimated over the east mainland of China in the

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HRMs. In terms of the pure eddy advection −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀∗ >, the HRMs well capture its spatial

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distribution, while the LRMs show insufficient skills around the SJ region.

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To clearly identify relative importance of zonal influence, meridional influence, dry

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enthalpy and moist enthalpy to the advection of the zonal-mean energy by eddy flow −
and the pure eddy advection −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀 ∗ >, we decompose the total horizontal

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moist enthalpy advection term into 20 terms (Eq. A.2). Two terms are consisted of 8 terms (the

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9th to 16th terms at right hand of Eq. A.2). The results of the decomposition averaged over

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enhanced rainfall area (25°~ 34°N, 120°~ 155°E) are shown in Fig. 5. The results indicate the

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enhanced energy advection in the HRMs is mainly contributed by two terms, which are the

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advection of eddy moist energy by eddy meridional flows (−< ̅̅̅ 𝑣 ∗ ⋅ ̅̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗ >, referred to moist

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term) and the advection of zonal-mean dry energy by eddy meridional flows (−< ̅̅̅ 𝑣 ∗ ⋅ ̅̅̅̅̅̅̅̅ [𝜕𝑦 𝑇] >,

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referred to dry term). The moist (dry) term in the HRMs enhances the energy advection by 18.5 14

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W/m2 (7.1 W/m2) compared to the LRMs. In addition, they explain 88% of the enhancement

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from the horizontal moist advection. Thus, we focus on them in the following analysis. In terms

299

∗ ̅̅̅̅̅̅̅ of the advection of eddy dry energy by eddy zonal flows (−< ̅̅̅ 𝑢∗ ⋅ 𝜕 x 𝑇 >) and the advection

300

∗ ̅̅̅̅̅̅ of eddy moist energy by eddy zonal flows (−< ̅̅̅ 𝑢∗ ⋅ 𝜕 x 𝑞 >), the underestimation in the HRMs

301

is associated with the weakened eddy zonal velocity compared to the LRMs (figure not shown

302

here).

303

The results of dry and moist terms are shown in Fig. 6. The spatial pattern of the dry and

304

moist term indicates the warm air and moisture transported from tropical ocean by eddy

305

meridional velocity sustaining the convection along the MB rain belt in the reanalysis data (Fig.

306

6a, 5d).

307

For the dry term, it is mainly affected by the vertically integrated stationary eddy meridional

308

velocity < ̅̅̅ 𝑣 ∗ > (Fig. 5a contours), which is valid in models (Fig. 6b, 6c contours). The

309

deficiency in simulating the dry term, especially over the SJ region, is mainly resulted by the

310

weak and northward shifted vertically integrated stationary eddy meridional velocity < ̅̅̅ 𝑣∗ >

311

in the LRMs (Fig. 6b). Furthermore, compared to that in the LRMs, the vertically integrated

312

stationary eddy meridional velocity < ̅̅̅ 𝑣 ∗ > in the HRMs exhibits enhanced northerly wind

313

over the mid-north of China and enhanced southerly wind on the south of Japan. Such spatial

314

pattern results in stronger meridional convergence along the MB rainblet (Fig. 6h, 6i contours).

315

For the moist term, in the SJ region, the LRMs underestimate its magnitude compared to

316

that in the reanalysis data, while the HRMs show opposite results (Fig. 6e). The moist term is

317

mainly affected by the vertically integrated eddy meridional moisture gradient −< ̅̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗>, of

15

318

which spatial distribution well explains the moist term (Fig. 6g-i). Previous study (Zhou et al.

319

2010) indicates that such moisture gradient over EASM region is highly related to the

320

circulation convergence. As shown in Fig. 6g-i, the convergence of vertically integrated eddy

321

meridional moisture gradient −< ̅̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗> along the EASM rain belt is mainly due to the

322

stationary eddy meridional velocity convergence ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ . Thus, the enhancement of the moist

323

term on the south of Japan in the HRMs is also related to the enhanced northerly over the mid-

324

north of China and enhanced southerly on the south of Japan. Such enhanced northerly and

325

∗ ̅̅̅̅̅̅ southerly lead to stronger stationary eddy meridional velocity convergence 𝜕 𝑦 𝑣 along the

326

rain belt, especially over the SJ, in the HRMs.

327

Based on the analysis above, the stationary eddy meridional velocity convergence ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗

328

shows its importance in improving the EASM rainbelt in the HRMs. Its PCC between the

329

HRMs and the reanalysis data is 0.78 compared to 0.64 for the LRMs, whereas the PCC of

330

vertically integrated stationary eddy meridional velocity < ̅̅̅ 𝑣 ∗ > is 0.75 and 0.73 for the

331

HRMs and LRMs, respectively, indicating more importance of ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ instead of < ̅̅̅ 𝑣 ∗ > in

332

the improved EASM rainfall due to high resolution. Moreover, the intensified northerly over

333

the mid-north of China and the intensified southerly wind on the south of Japan in the HRMs

334

play an important role in improving the simulation of ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ , despite its PCC show negligible

335

improvement compared to that from the LRMs. Such spatial distribution of < ̅̅̅ 𝑣 ∗ > creates

336

stronger gradients over the SC and SJ region in the HRMs, exactly where the MB rainbelt

337

locates. Thus the MB rainbelt simulation is improved in the HRMs.

338

d. Moisture budget analysis

16

339 340

To further investigate the role of meridional convergence played in the enhancement of the EASM rainfall intensity in the HRMs, a moisture budget analysis is performed.

341

The area averaged (MB domain) moisture budget results based on reanalysis data and

342

models are shown in Fig. 7a. The results indicate that the precipitation over MB domain is

343

mainly contributed by the local surface evaporation (𝐸̅ ) and the meridional moisture flux

344

convergence (−< ̅̅̅̅̅̅̅̅̅̅ 𝜕𝑦 (𝑣𝑞) >) in both reanalysis data and AGCMs. The improvement from

345

HRMs of the evaporation (0.34 mm/day) is not as large as the meridional moisture flux

346

convergence (3.3 mm/day). Thus, the improvements of rainfall amount over MB domain is

347

attributed to intensified meridional moisture flux convergence in the HRMs.

348

To clearly identify the effects of meridional flow convergence in moisture transport, same

349

decomposition method is applied to the meridional moisture flux convergence (detailed

350

information seen in the Appendix). As shown in Fig. 7b, four terms contributed to the

351

improvement of the meridional moisture flux convergence −< ̅̅̅̅̅̅̅̅̅̅ 𝜕𝑦 (𝑣𝑞) > . The main

352

contributors of the improvements of the meridional moisture flux convergence from HRMs are

353

three terms: stationary eddy meridional moisture transported via meridional flow convergence

354

( −< ̅̅̅ 𝑞 ∗ ⋅ ̅̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ > ), zonal-mean moisture transported via stationary eddy meridional flow

355

convergence (−< ̅̅̅̅ [𝑞] ⋅ ̅̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ >) and pure stationary eddy meridional moisture advection (−
). The third term is discussed in Section 3c. Thus, we focus on the former two terms

357

in the following analysis. The −< ̅̅̅ 𝑞 ∗ ⋅ ̅̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ > term (the−< ̅̅̅̅ [𝑞] ⋅ ̅̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ > term) contributes

358

63% (21%) enhancements to the meridional moisture flux convergence in the HRMs. Moreover,

359

both of two terms are affected by the stationary eddy meridional flow convergence, confirming

360

it plays an important role in enhanced rainfall amount in the HRMs. 17

361

The distribution of stationary eddy meridional velocity in HRMs largely enhances

362

meridional convergence over the MB domain. As a result, the moisture transport along the

363

rainbelt is intensified, thereby the enhancement of rainfall intensity of the EASM rainfall in the

364

HRMs.

365

e. Results of sensitivity experiments

366

In order to avoid the potential influence from model physics and further investigate the

367

resolution impact on the stationary eddy flow and its convergence, we performed a series of

368

sensitivity experiments using CAM5 model with the same dynamic core and physics scheme

369

at 3 different resolutions (details seen in section 2).

370

During the EASM season, all the experiments using CAM5 models at different resolutions

371

can reasonably reproduce the EASM rainbelts. However, the EASM rainfall intensity in

372

CAM5-2deg is significantly underestimated (Fig. 8a), especially over western south of Japan,

373

compared to the observation. The rainbelt over the eastern China and the southern Japan is

374

separated by an artificial minimum center (Fig. 7a), similar to the pattern observed in the

375

CMIP5 LRMs (Fig. 1b). Such a bow-like spatial distribution of precipitation also exists in

376

CAM5-1deg. When the resolution of CAM5 increases to 0.5 degree (CAM5-0.5deg), the

377

precipitation intensity is further enhanced, closer to the observation. The precipitation over

378

East China Sea and south of Japan, which is underestimated in the low resolution simulations

379

(CAM5-2deg and CAM5-1deg), is significantly enhanced in CAM5-0.5deg. Consequently, the

380

bow-like pattern over East Asia in low resolution simulations is improved in CAM5-0.5deg.

381

The PCCs of the monsoon rainbelt between CAM5-2deg, CAM5-1deg, CAM5-0.5deg and

18

382

observation are 0.26, 0.39 and 0.52, increasing with resolution. For the RMSE, the values are

383

1.81, 1.62, 1.60, respectively, decreasing with resolution. The enhancement of precipitation,

384

especially over the SJ, in CAM5 with increased resolution, are similar to those of CMIP5

385

AGCMs, indicating that model resolution does have impact on simulating EASM rainfall.

386

As mentioned in the previous sections, the precipitation is actually dominated by the

387

∗ ̅̅̅̅̅̅ stationary eddy meridional velocity convergence 𝜕 𝑦 𝑣 . As described in previous section, the

388

HRMs reproduce stronger northerly wind over the mid-north of China, and stronger southerly

389

wind over the south of Japan, thereby the intensified meridional convergence along the MB

390

rainbelt, compared to that in the LRMs. Such phenomenon would be verified by the CAM5

391

experiments.

392

As shown in Fig.8, the vertically integrated stationary eddy meridional velocity < ̅̅̅ 𝑣∗ >

393

decreased over the mid-north of China, and increased over the south of Japan, with resolution

394

increasing (Fig. 8 black contours), which is similar with the CMIP5 AGCMs. Furthermore,

395

such northerly (southerly) flow over the mid-north of China (south of Japan) gradually

396

enhanced from CAM5-2deg to CAM5-0.5deg, leading to stronger meridional convergence

397

over the MB rainbelt region (Fig.8 red contours). The regional weighted averaged stationary

398

eddy meridional velocity convergences ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ over the MB rainbelt region in CAM5-2deg,

399

CAM5-1deg, CAM5-0.5deg are -1.12 ∙ 10-5s-1, -1.33 ∙ 10-5s-1, -1.45 ∙ 10-5s-1, respectively,

400

approaching -1.63∙10-5 s-1 in the reanalysis data. Such enhancements in meridional convergence

401

are also similar to those of the CMIP5 AGCMs due to increasing resolution. Thus, the

402

sensitivity experiments confirm the resolution impact on meridional flow convergence, and

403

further enhances the EASM rainfall over the SJ region. 19

404

To further investigate the meridional circulation change resulting in stronger meridional

405

convergence due to resolution impact, the differences of eddy geopotential height and

406

meridional eddy flow at 500 hPa between the low- and high- resolution simulations from the

407

CMIP5 AGCMs and sensitivity experiments are shown in Fig. 9. A Rossby wave train is

408

organized from mainland of China into the western north Pacific. In order to depict the

409

propagation direction of the wave train, the wave activity flux (WAF) at 500 hPa is calculated

410

according to Takaya and Nakamura (2001) with the differences between the high- and low-

411

resolution models (Fig. 9). As is shown, in CMIP5 HRMs, CAM5-1degree, and CAM5-

412

0.5degree, the WAFs downstream the Tibetan Plateau and over north of the south China sea

413

converge over the eastern mainland of China around 120°E, and propagate further east into the

414

western north Pacific (Fig. 9a). Such wave pattern corresponds to enhanced northerly

415

anomalies over the mid-north of China and southerly anomalies in the south of Japan, induces

416

the enhanced meridional flow convergence along the EASM rain belts. The wave patterns of

417

meridional eddy flow and eddy geopotential height are consistent between AGCMs and CAM5

418

sensitivity experiments.

419

The vertical structure of the wave train is quasi-barotropic as shown in Fig. 10, which is the

420

differences of eddy meridional eddy flow averaged from 25° to 35°N between low- and high-

421

resolution simulations from all models. Such well-organized barotropic Rossby wave structure

422

in the troposphere indicating that it is not likely resulted by the enhanced EASM rainfall, since

423

the Rossby wave driven by diabatic heating is usually organized in baroclinic structure. Notably,

424

such barotropic Rossby wave is responsible for the enhanced northerly anomalies over the mid-

425

north of China (110°~120°E) and southerly anomalies in the south of Japan (130°~140°E). 20

426

Moreover, the results of CMIP5 AGCMs are consistent with the sensitivity experiments,

427

indicating such barotropic Rossby wave is due to the increasing model resolution rather than

428

potential influences from diversities of model dynamic core and physics scheme.

429

The quasi-barotropic wave is downstream the Tibetan Plateau, and shows somewhat

430

similarity to that due to orographic forcing (Held and Ting 1989, Cook and Held 1992, Lutsko

431

and Held 2016). Thus, more realistic topography in the high-resolution simulations may

432

responsible for such wave train pattern. However, whether the barotropic Rossby wave in

433

higher resolution simulations is induced by finer topography may a set of idealized experiments

434

for further investigation

435

4. Summary

436

Climate models with higher resolutions generally exhibit enhanced performance in terms of

437

simulating the EASM rainbelt, especially on the south of Japan. To understand the underlying

438

physical mechanisms, we conducted an MSE balance analysis and moisture budget analysis to

439

examine the spatial distribution and rainfall levels of the EASM rainbelt reproduced by low-

440

and high-resolution AGCMs. Sensitivity experiments using CAM5 with identical dynamic core

441

and physics scheme but different resolutions are also performed to validate the robustness of

442

rainfall improvements and related mechanisms apart from the potential influences from

443

different physical schemes employed in CMIP5 models. Main conclusions are summarized as

444

follows.

445

(1) The EASM rainbelt simulated by LRMs is underestimated compared to the observation,

446

especially on the south of Japan. Such biases are reduced in the HRMs. The rainfall intensity

21

447

is generally enhanced by 1.0 mm/day in HRMs compared to LRMs, to which the enhancement

448

over the SJ region contributes 1.5 mm/day. The increase of PCC (0.76 for HRMs and 0.31 for

449

LRMs) and the decrease of RMSE (1.3 for HRMs and 2.1 for LRMs) indicate such

450

enhancement due to increased model resolution is robust. The results of sensitivity experiments

451

further confirm such rainfall intensity enhancements.

452

(2) By conducting an MSE balance analysis, the factor dominating improvements of EASM

453

rainfall simulation in the HRMs was found to be the horizontal MSE advection. The

454

decomposition results show that the enhanced horizontal moist enthalpy advection in the

455

HRMs results from the intensified northerly flow over the mid-north of China and southerly

456

flow on the south of Japan. Such meridional eddy stationary flow enhancements lead to

457

stronger stationary eddy meridional flow convergence along the MB rain belt in the HRMs.

458

The intensified convergence along the MB front region in the HRMs strengthens the moist

459

energy transport from the tropics, thereby the improved MB rainbelt.

460

(3) A moisture budget analysis confirms that the enhanced rainfall intensity is mainly

461

contributed by enhanced stationary eddy meridional flow convergence in HRMs. Such

462

enhanced convergence intensifies the moisture transport into the MB rainbelt in HRMs, thereby

463

the enhanced rainfall intensity.

464

(4) The above improvements of MB rainbelt and stationary eddy meridional flow

465

convergences due to increased resolution are verified by sensitivity experiments apart from

466

potential influences from different physical schemes employed in CMIP5 AGCMs. Further

467

analysis indicates that the stationary eddy meridional flow convergence along the MB rainbelt

22

468

is intensified as model resolution increased. The convergence enhancements are related to the

469

intensified northerly (southerly) flow over the mid-north of China (the south of Japan). Such

470

meridional flow changes are resulted by the barotropic Rossby wave due to increased model

471

resolution. Such barotropic wave downstream the Tibetan Plateau propagates from the east

472

China to the western North Pacific, creating stronger meridional convergence along the MB

473

rainbelt. However, whether such quasi-barotropic wave is forced by the finer topography of

474

Tibetan Plateau need further investigation.

475

476

Appendix

477

The Decomposition of Advection Term

478

As explained in section 3, during EASM season both net energy flux and horizontal moist

479

enthalpy advection play essential roles in sustaining the monsoon rainfall. However, due to the

480

significant improvements of moist enthalpy advection term and moisture reproduced by high-

481

resolution models, a method clearly differentiating the contribution from stationary eddy fluxes

482

and zonal mean fluxes should be introduced in order to seek out the improved fluxes influenced

483

by model horizontal resolution. Hence, the time mean advection term < ̅̅̅̅̅̅̅̅ v ∙ ∇𝑋 > can be

484

decomposed as

485

 v X  [ v]  [X ]    [ v] X *    v*  [X ]    v* X *    v 'X '  (A. 1)

486

Here, (𝑋)′ denotes the deviation from time 𝑋̅ (two-month June and July mean for each

487

individual year), and (𝑋)∗ denotes the deviation from the global zonal mean [𝑋], and 〈𝑋〉

488

are same as that in section 2. 23

489

As explained in Section 3c, the moist enthalpy advection term < ̅̅̅̅̅̅̅̅ 𝐯 ∙ ∇𝑀 > plays an

490

essential role in improvements of monsoon rainfall from high- resolution models. Thus, by

491

using the decomposition method, the moist enthalpy advection term can be decomposed as

492

 v M  [u]  [ x T]    [v]  [ y T]    [u]  [ x q]    [v]  [ y q]    u *  [ x T] 

493

  v*  [ y T]    u *  [ x q]    v*  [ y q]    [u]   x T*    [v]  Y T* 

494

  [u]   x q*    [v]   y q*    u *   x T*    v*   y T*    u *   x q* 

495

+++++ (A. 2)

496

Here, the first four terms on the right-hand side are the zonal-mean energy advection by the

497

̅̅̅̅ ⋅ ̅̅̅̅̅̅̅ [𝑢] ⋅ ̅̅̅̅̅̅̅ [𝑢] ⋅ ̅̅̅̅̅̅̅ zonal-mean flow (i.e., < ̅̅̅̅ [𝜕𝑥 𝑇] >, < [𝑣] [𝜕𝑦 𝑇] >, < ̅̅̅̅ [𝜕𝑥 𝑞] >, < ̅̅̅̅ [𝑣] ⋅ ̅̅̅̅̅̅̅ [𝜕𝑦 𝑞] >);

498

the second four terms on the right hand side denote the advection of the zonal-mean energy by

499

̅̅̅̅̅̅̅ ̅̅̅∗ ̅̅̅̅̅̅̅ ̅̅̅∗ ̅̅̅̅̅̅̅ ̅̅̅∗ the stationary eddy flow (i.e. , < ̅̅̅ 𝑢∗ ⋅ [𝜕 𝑥 𝑇] > , < 𝑣 ⋅ [𝜕𝑦 𝑇] > , < 𝑢 ⋅ [𝜕𝑥 𝑞] > , < 𝑣 ⋅

500

̅̅̅̅̅̅̅ [𝜕𝑦 𝑞] >); the third four terms on the right hand side denote the advection of the stationary eddy

501

̅̅̅̅ ⋅ ̅̅̅̅̅̅ ̅̅̅̅ ⋅ energy by the zonal-mean flow (i.e., < ̅̅̅̅ [𝑢] ⋅ ̅̅̅̅̅̅ 𝜕𝑥 𝑇 ∗ >, < [𝑣] 𝜕𝑦 𝑇 ∗ >, < ̅̅̅̅ [𝑢] ⋅ ̅̅̅̅̅̅ 𝜕𝑥 𝑞 ∗ >, < [𝑣]

502

̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗ >); the fourth four terms on the right hand side denote the advection of stationary eddy

503

energy by the stationary eddy flow (i.e., < ̅̅̅ 𝑢∗ ⋅ ̅̅̅̅̅̅ 𝜕𝑥 𝑇 ∗ >, < ̅̅̅ 𝑣 ∗ ⋅ ̅̅̅̅̅̅ 𝜕𝑦 𝑇 ∗ >, < ̅̅̅ 𝑢∗ ⋅ ̅̅̅̅̅̅ 𝜕𝑥 𝑞 ∗ >,
); and the last four terms on the right hand side denote the advection of transient

505

̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅ eddy energy by transient eddies (i.e., < 𝑢′ 𝜕𝑥 𝑇 ′ > , < 𝑣 ′ 𝜕𝑦 𝑇 ′ > , < 𝑢′ 𝜕𝑥 𝑞 ′ > ,
). T and q are considered in energy units in A. 2.

507

̅̅̅̅ ̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅ ̅̅̅̅ ̅̅̅̅̅̅̅ ̅̅̅̅ [𝑢] ⋅ [𝜕 The zonal-mean terms(i.e., < ̅̅̅̅ 𝑥 𝑇] >, < [𝑣] ⋅ [𝜕𝑦 𝑇] >, < [𝑢] ⋅ [𝜕𝑥 𝑞] >, < [𝑣] ⋅

508

̅̅̅̅̅̅̅ [𝜕𝑦 𝑞] >)are generally small compared to the other terms; the transient terms (i.e.,
, < 𝑣 ′ 𝜕𝑦 𝑇 ′ >, < 𝑢′ 𝜕𝑥 𝑞 ′ >, < 𝑣 ′ 𝜕𝑦 𝑞 ′ >) are also very small due to the 24

510

long-temporal mean, and they can all thus be neglected. Besides, the contribution from
, < [𝑣] 𝜕𝑦 𝑞 ∗ >, < [𝑣] 𝜕𝑦 𝑇 ∗ >, < ̅̅̅ 𝑢∗ ⋅ ̅̅̅̅̅̅̅ [𝜕𝑥 𝑇] >, < ̅̅̅ 𝑢∗ ⋅ ̅̅̅̅̅̅̅ [𝜕𝑥 𝑞] > and < ̅̅̅ 𝑣∗ ⋅

512

̅̅̅̅̅̅ 𝜕𝑦 𝑇 ∗ > to the total advection are much smaller compared to other terms, and they are also

513

neglected in the Section 3c.

514

As explained in Section 3d, the meridional moisture convergence flux < ̅̅̅̅̅̅̅̅̅̅ 𝜕𝑦 (𝑣𝑞) > is

515

another essential factor affected improvements from high- resolution models. Same as moist

516

enthalpy advection, it can be decomposed as

517

  y (vq)  v*   y q*    [v]   y q*    v*  [ y q]    [v]  [ y q] 

518

  q*  y v*    [q]  y v*    q*  [ y v]    [q]  [ y v] 

519

  v'  y q'    q'  y v' 

(A. 3)

520

̅̅̅̅ ⋅ ̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅ [𝑞] ⋅ [𝜕 The mean terms ( < [𝑣] [𝜕𝑦 𝑞] > , < ̅̅̅̅ 𝑦 𝑣] > ) are associated with planet scale

521

circulation and humidity, thus are generally small and neglected. The stationary eddy terms (
, < [𝑣] ⋅ 𝜕𝑦 𝑞 > , < 𝑞 ⋅ [𝜕𝑦 𝑣] > , < 𝑞 ⋅ 𝜕𝑦 𝑣 > ) are associated with

523

meridional stationary eddy flows and meridional humidity distributions. The previous study

524

̅̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅ (Chen and Bordoni 2014a) indicate that the transient terms (< 𝑣 ′ 𝜕𝑦 𝑞 ′ >, < 𝑞′𝜕𝑦 𝑣′ >)

525

play a minor role compared to the other terms due to the long-temporal mean, and they are

526

neglected in Section 3d. In addition, the contribution from these eddy terms (< ̅̅̅ 𝑣 ∗ ⋅ ̅̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗ >,

527

< ̅̅̅ 𝑞 ∗ ⋅ ̅̅̅̅̅̅̅̅ [𝜕𝑦 𝑣] >) to the total meridional moisture convergence flux are gradually less than 0.1

528

mm/day, thus can be neglected.

529

25

530

Acknowledgments. This work was jointly supported by R&D Special Fund for Public

531

Welfare Industry (meteorology) (GYHY201506012), National Natural Science Foundation of

532

China (NFSC) under Grant No. 41420104006 and No. 41125017.

26

533 References

534 Adler, R.F., and coauthors, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) 535

Monthly Precipitation Analysis (1979-Present). J. Hydrometeor., 4,1147-1167.

536 Bony, S., and coauthors, 2013: Carbon dioxide and climate: Perspectives on a scientific 537

assessment. Climate Science for Serving Society, edited by G. R. Asrar and J. W. Hurrell,

538

Springer, Dordecht, 391-413.

539 Boos, W.R., J.V. Hurley, 2013: Thermodynamic bias in the multimodel mean boreal summer 540

monsoon. J. Climate. 26: 2279-2287.

541 Chang, C.P., 2004: East Asian Monsoon vol. 2. World Sci., Singapore, pp. 301–331.

542 Chen, G.J., 1983: Observational aspects of the Mei-Yu phenomena in subtropical China. J. 543

Meteor. Soc. Japan, 61, 306-312.

544 Chen, H.M., T.J. Zhou, R. B. Neale, X.Q. Wu, G.J. Zhang, 2010: Performance of the New NCAR 545

CAM3.5 in East Asian Summer Monsoon Simulations: Sensitivity to Modifications of the

546

Convection Scheme. J. Climate. 23, 3657-3675.

547 Chen J., S. Bordoni, 2014a: Orographic effects of the Tibetan Plateau on the East Asian summer 548

monsoon: An energetic perspective. J. Climate. 27, 3052-3072.

549 Chen J., S. Bordoni, 2014b: Intermodel spread of East Asian summer monsoon simulations in 550

CMIP5. Geophys. Res. Lett., 41, 1314-1321. doi:10.1002/2013GL058981.

551 Cherchi, A., and Navarra, A., 2007: Sensitivity of the Asian summer monsoon to the horizontal 552

resolution: differences between AMIP-type and coupled model experiments. Climate Dyn., 27

553

28, 273-290.

554 Chou C., J. Neelin, 2003: Mechanisms Limiting the Northward Extent of the Northern Summer 555

Monsoons over North America, Asia, and Africa. J. Climate., 16, 406-425, doi:10.1175/ 1520-

556

0442(2003)016,0406:MLTNEO.2.0.CO;2.

557 Chou C., C. Lan, 2012: Changes in the annual range of precipitation under global warming. J. 558

Climate., 25, 222-235. doi:10.1175/jcli-d-11-00097.1.

559 Chou C., T. Wu, P. Tan, 2013: Changes in gross moist stability in the tropics under global 560

warming. Climate Dyn.. 41, 2481-2496. doi: 10.1007/s00382-013-1703-2.

561 Cook, K. H., I. M. Held, 1992: The stationary response to large-scale orography in a general 562

circulation model and a linear model. J. Atmos. Sci., 49, 525-539.

563 Dai, A.G., H.M. Li, Y. Sun, L.C. Hong, L. Ho, C. Chou, and T.J. Zhou, 2013: The Relative Roles 564

of Upper and Lower Tropospheric Thermal Contrasts and Tropical Influences in Driving

565

Asian Summer Monsoons, J. Geophys. Res.: Atmos., 118, 7024-7045, doi:10.1002/jgrd.50565.

566 Dee, D., and Coauthors, 2011: The ERA‐Interim reanalysis: Configuration and performance of 567

the data assimilation system. Quart. J. R. Meteorol. Soc., 137, 553-597. doi:10.1002/qj.828.

568 Ding, Y., 1992: Summer monsoon rainfalls in China. J. Meteor. Soc. Japan, 70, 373–396.

569 Ding, Y., and J. Chan, 2005: The East Asian summer monsoon: Anoverview. Meteor. Atmos. 570

Phys., 89, 117–142, doi:10.1007/s00703-005-0125-z.

571 Dirmeyer, P. A., and Coauthors, 2012: Simulating the diurnal cycle of rainfall in global climate 572

models: Resolution versus parameterization. Climate Dyn., 39, 399-418. 28

573 Ebita, A., and Coauthors. 2011: The Japanese 55-year Reanalysis" JRA-55": an interim report. 574

Sola, 7, 149-152.

575 Gettelman, A., and Coauthors, 2010: Global simulations of ice nucleation and ice supersaturation 576

with an improved cloud scheme in the Community Atmosphere Model. J. Geophys. Res.:

577

Atmos., 115(D18).

578 Gao, X., Y. Xu, Z. Zhao, J.S. Pal, and F. Giorgi, 2006: On the role of resolution and topography 579

in the simulation of East Asia precipitation. Theor. Appl. Climatol., 86, 173-185.

580 Held, I. M., M. Ting, 1990: Orographic versus thermal forcing of stationary waves: The 581

importance of the mean low-level wind. J. Atmos. Sci., 47, 495-500.Hertwig, E., von Storch,

582

J. S., Handorf, D., Dethloff, K., Fast, I., and Krismer, T., 2015: Effect of horizontal resolution

583

on ECHAM6-AMIP performance. Climate Dyn., 45, 185-211, doi: 10.1007/s00382-014-

584

2396-x.

585 Huang, D. Q., J. Zhu, Y.C. Zhang and A.N. Huang, 2013: Uncertainties on the simulated summer 586

precipitation over eastern China from the CMIP5 models. J. Geophys. Res., 118, 9035-9047.

587 Huffman, G.J, R.F. Adler, D.T. Bolvin, G. Gu 2009: Improving the Global Precipitation Record: 588

GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

589 Johnson, S. J., and Coauthors, 2015: The resolution sensitivity of the South Asian monsoon and 590

Indo-Pacific in a global 0.35° AGCM. Climate Dyn., 46, 807-831, doi: 10.1007/s00382-015-

591

2614-1.

592 Kang, I., and Coauthors, 2002: Intercomparison of the climatological variations of Asian summer

29

593

monsoon precipitation simulated by 10 GCMs. Climate Dyn.. 19, 383-395.

594 Kitoh, A., S. Kusunoki, 2008: East Asian summer monsoon simulation by a 20-km mesh AGCM. 595

Climate Dyn.. 31, 389-401.

596 Kobayashi, C., M. Sugi, 2004: Impact of horizontal resolution on the simulation of the Asian 597

summer monsoon and tropical cyclones in the JMA global model. Climate Dyn.. 23, 165-176.

598 Lutsko, N. J., I. M. Held, 2016: The Response of an Idealized Atmosphere to Orographic Forcing: 599

Zonal versus Meridional Propagation. J. Atmos. Sci., 73, 3701-3718.

600 Molnar, P., W. Boos, D. Battisti, 2010: Orographic controls on climate and paleoclimate of Asia: 601

thermal and mechanical roles for the Tibetan Plateau. Annu. Rev. Earth Planet. Sci., 38, 77,

602

doi:10.1146/annurev-earth-040809-152456.

603 Morrison, H., A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics 604

scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and

605

numerical tests. J. Climate., 21, 3642-3659.

606 Neale, R. B., J. H. Richter, M. Jochum, 2008: The impact of convection on ENSO: From a 607

delayed oscillator to a series of events. J. Climate., 21, 5904-5924.

608 Neelin, J., 2007: dynamics of tropical convection zones in monsoons, teleconnections, and global 609

warming. The Global Circulation of the Atmosphere., Princeton University Press, 267-301.

610 Park, S., C. S. Bretherton, 2009: The University of Washington shallow convection and moist 611

turbulence schemes and their impact on climate simulations with the Community Atmosphere

612

Model. J J. Climate., 22, 3449-3469.

30

613 Sampe, T., S. Xie, 2010: Large-scale dynamics of the Meiyu-Baiu Rainbelt: environmental 614

forcing by the westerly jet. J. Climate., 23, 113-134, doi:10.1175/2009JCLI3128.1.

615 Solomon, S., and Coauthors, 2007: Climate change 2007: The physical science basis. Cambridge 616

University Press, 996 pp.

617 Song, F., T. Zhou, 2014: The climatology and interannual variability of East Asian summer 618

monsoon in CMIP5 coupled models: Does air–sea coupling improve the simulations? J.

619

Climate., 27, 8761-8777.

620 Sun, Y., T.J. Zhou, G. Ranstein, C. Contoux, Z. Zhang, 2015: Drivers and mechanisms for 621

enhanced summer monsoon precipitation over East Asia during the mid-Pliocene in the IPSL-

622

CM5A, Climate Dyn., 46,1437-1457, doi: 10.1007/s00382-015-2656-4.

623 Takaya, K., H. Nakamura, 2001: A formulation of a phase-independent wave-activity flux for 624

stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. J. Atmos.

625

Sci., 58, 608-627.Waliser, D., 2006: The Asian Monsoon. Intraseasonal variability in the

626

atmosphere-ocean climate system, edited by Wang B. Springer Berlin Heidelberg, 203-257.

627 Zhang, G. J., N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization 628

of cumulus convection in the Canadian Climate Centre general circulation model.

629

Atmosphere-ocean, 33, 407-446.

630 Zhou, T.J., Z. Li, 2002: Simulation of the East Asian summer monsoon using a variable resolution 631

atmospheric GCM. Climate Dyn., 19, 167-180.

632 Zhou, T.J., B. Wu, B. Wang, 2009a: How well do atmospheric general circulation models capture

31

633

the leading modes of the interannual variability of the Asian-Australian monsoon?. J. Climate.,

634

22, 1159-1173.

635 Zhou, T.J., D. Gong, J. Li, and B. Li, 2009b: Detecting and understanding the multi-decadal 636

variability of the East Asian Summer Monsoon–Recent progress and state of affairs. Meteorol.

637

Z., 18, 455-467.

638 Zhou, T.J., Zou, L., 2010: Understanding the predictability of East Asian summer monsoon from 639

the reproduction of land-sea thermal contrast change in AMIP-type simulation. J. Climate.,

640

23, 6009-6026.

641 Zhou, T.J., F. Song, R. Lin, X. Chen, and X. Chen, 2013: The 2012 North China floods: 642

Explaining an extreme rainfall event in the context of a longer-term drying tendency. Bull.

643

Amer. Meteor. Soc., 94, 49-51.

644 Zhou, T.J., S. Ma, L.W. Zou, 2014: Understanding a hot summer in central eastern China: 645

Summer 2013 in context of multimodel trend analysis. Bull. Amer. Meteor. Soc., 95, 54-57.

646 Zhou, X., Y. Ding, P. Wang, 2010: Moisture transport in the Asian summer monsoon region and 647

its relationship with summer precipitation in China. J. Meteorol. Res., 24, 31-42.

32

648

Figure Captions

649

Fig. 1 Climatology of June-July precipitation (shaded; units: mm/day) and vertical velocity

650

(contour; units: Pa/s; contour interval -0.02Pa/s, thick lines denote a vertical velocity equal to

651

-0.01Pa/s) at 400 hPa derived from (a) GPCP and mean of ERA-Interim and JRA55, (b) the

652

low-resolution models and (c) the high-resolution models. The red box marks the Meiyu-Baiu

653

(MB) region.

654

Fig. 2 Area averaged precipitation for the MB region for June-July (y-axis) and model

655

resolutions. Black, red, blue and purple markers refer to GPCP, low-resolution models high-

656

resolution models and sensitivity experiments, respectively.

657

Fig. 3 Climatology of the June-July vertically integrated MSE budget. The difference between

658

̅̅̅̅̅̅ and < ̅̅̅̅̅̅̅̅ 𝐹𝑛𝑒𝑡 𝐯 ∙ ∇𝑀 > for vertically integrated vertical MSE advection < ̅̅̅̅̅̅̅ 𝜔𝜕𝑝 ℎ > (Left),

659

vertically integrated horizontal moist enthalpy advection −< ̅̅̅̅̅̅̅̅ 𝐯 ∙ ∇𝑀 > (center), net energy

660

flux into the atmospheric column ̅̅̅̅̅̅ 𝐹𝑛𝑒𝑡 (right) for the ERA-interim (first row), the low-

661

resolution multi-model ensemble mean (second row), the high-resolution multi-model

662

ensemble mean (third row), and the differences between high- and low- resolution multi-model

663

ensemble mean (last row) (shaded, units: W/m2). Contours denote the differences of

664

climatology of June-July precipitation between high- and low-resolution models (contours

665

range from 0.5 to 2.5 by 0.5; units: mm/day).

666

Fig. 4 Decomposition of the vertically integrated horizontal moist enthalpy advection −
for the ERA-interim (left), the low-resolution multi-model ensemble mean (center),

668

the high-resolution multi-model ensemble mean (right). Rows indicate the advection of

33

669

[𝐯] ∙ ∇𝑀∗ > (top row), the advection of stationary eddy energy by the zonal-mean flow −< ̅̅̅̅̅̅̅̅̅̅̅̅

670

zonal-mean energy by eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ [∇𝑀] > (second row), the advection of stationary

671

eddy energy by the stationary eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀∗ > (third row) and the advection of

672

transient eddy energy by the transient eddy flow −< ̅̅̅̅̅̅̅̅̅̅ 𝐯 ′ ∙ ∇𝑀′ > (bottom row). (Color shading

673

is in W/m2).

674

Fig. 5 Value of main variable derived from decomposed horizontal moist enthalpy advection

675

averaged over the enhanced rainfall region (25°~ 34°N, 120°~ 155°E; units: W/m2). Each

676

variable is vertically integrated. The observation is the mean of ERA-interim and JRA55.

677

Fig. 6 The advection of zonal mean dry enthalpy by the stationary eddy meridional velocity−
(top row; shaded, units: W/m2), advection of eddy moist enthalpy by meridional 𝑣 𝑦

679

∗ ∙ 𝜕 𝑞 ∗ > (second row; shaded, units: W/m2), and, and ̅̅̅̅̅̅̅̅̅̅̅ eddy meridional velocity −< 𝑣 𝑦

680

vertically integrated eddy meridional moisture gradient −< ̅̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗> (bottom row; shaded,

681

units: 105 kg/(m∙s2)) for ERA-interim (left), the low-resolution multi-model ensemble mean

682

(center), the high-resolution multi-model ensemble mean (right). Contours in top row denote

683

vertically integrated stationary eddy meridional velocity < ̅̅̅ 𝑣 ∗ > (range from -20.0 to 20.0 by

684

4.0, solid and dashed contours denote positive and negative value; units: 106 kg/(m∙s)), and

685

contours in bottom row denote stationary eddy meridional velocity convergence ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ at 700

686

hPa (range from -4.0 to -1.0 by -1.0; units: 10-5 s-1).

687

Fig. 7 Results of moisture budget (a) and values of main variable decomposed from the

688

meridional moisture flux averaged over the MB region (b). Each variable is vertically

689

integrated. The observation is the mean of ERA-interim and JRA55.

34

690

Fig. 8 Climatology of June-July precipitation (shaded; units: mm/day), vertically integrated

691

meridional eddy velocity< ̅̅̅ 𝑣 ∗ > (black contour, range from -20.0 to 20.0 by 4.0, solid and

692

dashed contours denote positive and negative value; units: 106 kg/(m∙s)) and stationary eddy

693

meridional velocity convergence ̅̅̅̅̅̅ 𝜕𝑦 𝑣 ∗ at 700 hPa (red contour, range from -4.0 to -1.0 by -

694

1.0; units: 10-5 s-1) from (a) CAM5-2deg, (b) CAM5-1deg, and (c) CAM5-0.5deg, respectively.

695

The Tibetan Plateau is represented gray shaded area.

696

Fig. 9 Differences of eddy (zonal mean removed) geopotential height (shaded; units: m) and

697

meridional eddy (zonal mean removed) flow (contour; purple dashed lines denote negative

698

values, black line denote positive values and the zero contour is omitted; contour interval for

699

a, b, c are 0.1, 0.2, 0.2 respectively; units: m/s;) and wave activity flux (vector; units: m2/s2)

700

between from AMIP high- and low-resolution models (a), CAM5-1deg and 2deg (b), CAM5-

701

0.5deg and CAM5-2deg (c).

702

Fig. 10 Pressure-longitude cross sections of differences of meridional eddy (zonal mean

703

removed) flow (shaded; units: m/s) averaged from 25° and 35°N between AMIP high- and low-

704

resolution models (a), CAM5-1deg and 2deg (b), CAM5-0.5deg and CAM5-2deg (c).

35

705

Table 1 Information on the 16 CMIP5-AMIP models used in this study No.

Model

Resolution(lon*lat)

Member

1

CanAM4

2.8*2.8

1

2

IPSL-CM5A-LR

3.75*1.875

6

3

FGOALS-s2

2.8*1.65

2

4

NorESM1-M

2.5*1.875

3

5

MPI-ESM-LR

1.875*1.875

3

6

MPI-ESM-MR

1.875*1.875

3

7

inmcm4

2*1.5

1

8

HadGEM2-A

1.875*1.25

5

9

ACCESS1-0

1.875*1.25

3

10

MIROC5

1.4*1.4

2

11

CCSM4

1.25*1

6

12

CESM1-CAM5

1.25*1

1

13

MRI-AGCM3-2H

0.55*0.55

1

14

GFDL-HIRAM-C180

0.6*0.5

3

15

GFDL-HIRAM-C360

0.3*0.25

2

16

MRI-AGCM3-2S

0.18*0.18

1

706

36

707

Table 2 Pattern correlation coefficient (PCC) and root mean square difference (RMSE)

708

between models and observations over monsoon rainbelt region

709

No.

Model

PCC

RMSE

1

CanAM4

0.17

2.61

2

IPSL-CM5A-LR

0.24

2.31

3

FGOALS-s2

-0.13

2.19

4

NorESM1-M

0.01

2.2

5

MPI-ESM-LR

0.41

1.90

6

MPI-ESM-MR

0.48

1.81

7

inmcm4

-0.07

2.41

8

HadGEM2-A

0.58

1.58

9

ACCESS1-0

0.66

1.49

10

MIROC5

0.12

2.16

11

CCSM4

-0.01

2.43

12

CESM1-CAM5

0.10

2.05

13

MRI-AGCM3-2H

0.78

1.23

14

GFDL-HIRAM-C180

0.56

1.63

15

GFDL-HIRAM-C360

0.69

1.24

16

MRI-AGCM3-2S

0.82

1.32

710 37

711 712

Fig. 1 Climatology of June-July precipitation (shaded; units: mm/day) and vertical velocity

713

(contour; units: Pa/s; contour interval -0.02Pa/s, thick lines denote a vertical velocity equal to

714

-0.01Pa/s) at 400 hPa derived from (a) GPCP and mean of ERA-Interim and JRA55, (b) the

715

low-resolution models and (c) the high-resolution models. The red box marks the Meiyu-Baiu

716

(MB) region.

38

717

718 719

Fig. 2 Area averaged precipitation for the MB region for June-July (y-axis) and model

720

resolutions. Black, red, blue and purple markers refer to GPCP, low-resolution models, high-

721

resolution models and sensitivity experiments, respectively.

39

722 723

Fig. 3 Climatology of the June-July vertically integrated MSE budget. The difference between

724

̅̅̅̅̅̅ and < ̅̅̅̅̅̅̅̅ 𝐹𝑛𝑒𝑡 𝐯 ∙ ∇𝑀 > for vertically integrated vertical MSE advection < ̅̅̅̅̅̅̅ 𝜔𝜕𝑝 ℎ > (Left),

725

vertically integrated horizontal moist enthalpy advection −< ̅̅̅̅̅̅̅̅ 𝐯 ∙ ∇𝑀 > (center), net energy

726

flux into the atmospheric column ̅̅̅̅̅̅ 𝐹𝑛𝑒𝑡 (right) for the ERA-interim (first row), the low-

727

resolution multi-model ensemble mean (second row), the high-resolution multi-model

728

ensemble mean (third row), and the differences between high- and low- resolution multi-model

729

ensemble mean (last row) (shaded, units: W/m2). Contours denote the differences of

40

730

climatology of June-July precipitation between high- and low-resolution models (contours

731

range from 0.5 to 2.5 by 0.5; units: mm/day).

732

41

733 734

Fig. 4 Decomposition of the vertically integrated horizontal moist enthalpy advection −
for the ERA-interim (left), the low-resolution multi-model ensemble mean (center),

736

the high-resolution multi-model ensemble mean (right). Rows indicate the advection of

737

[𝐯] ∙ ∇𝑀∗ > (top row), the advection of stationary eddy energy by the zonal-mean flow −< ̅̅̅̅̅̅̅̅̅̅̅̅

738

zonal-mean energy by eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ [∇𝑀] > (second row), the advection of stationary

739

eddy energy by the stationary eddy flow −< ̅̅̅̅̅̅̅̅̅̅̅ 𝐯 ∗ ∙ ∇𝑀∗ > (third row) and the advection of

740

transient eddy energy by the transient eddy flow −< ̅̅̅̅̅̅̅̅̅̅ 𝐯 ′ ∙ ∇𝑀′ > (bottom row). (Color shading

741

is in W/m2). 42

742

743

744 745

Fig. 5 Value of main variable derived from decomposed horizontal moist enthalpy

746

advection averaged over the enhanced rainfall region (25°~ 34°N, 120°~ 155°E; units:

747

W/m2). Each variable is vertically integrated. The observation is the mean of ERA-interim

748

and JRA55.

43

749 750

Fig. 6 The advection of zonal mean dry enthalpy by the stationary eddy meridional velocity−
(top row; shaded, units: W/m2), advection of eddy moist enthalpy by meridional 𝑣 𝑦

752

∗ ∙ 𝜕 𝑞 ∗ > (second row; shaded, units: W/m2), and vertically ̅̅̅̅̅̅̅̅̅̅̅ eddy meridional velocity −< 𝑣 𝑦

753

integrated eddy meridional moisture gradient −< ̅̅̅̅̅̅̅ 𝜕𝑦 𝑞 ∗> (bottom row; shaded, units: 105

754

kg/(m∙s2)) for ERA-interim (left), the low-resolution multi-model ensemble mean (center), the

755

high-resolution multi-model ensemble mean (right). Contours in top row denote vertically

756

integrated stationary eddy meridional velocity < ̅̅̅ 𝑣 ∗ > (range from -20.0 to 20.0 by 4.0, solid

757

and dashed contours denote positive and negative value ; units: 106 kg/(m∙s)), and contours in

758

∗ ̅̅̅̅̅̅ bottom row denote stationary eddy meridional velocity convergence 𝜕 𝑦 𝑣 at 700 hPa (range

759

from -4.0 to -1.0 by -1.0; units: 10-5 s-1). 44

760

761 762

Fig. 7 Results of moisture budget (Res denotes residual) (a) and values of main variable

763

decomposed from the meridional moisture flux averaged over the MB region (b). Each

764

variable is vertically integrated. The observation is the mean of ERA-interim and JRA55.

45

765 766

Fig. 8 Climatology of June-July precipitation (shaded; units: mm/day), vertically integrated

767

meridional eddy velocity< ̅̅̅ 𝑣 ∗ > (black contour, range from -20.0 to 20.0 by 4.0, solid and

768

dashed contours denote positive and negative value; units: 106 kg/(m∙s)) and stationary eddy

769

∗ ̅̅̅̅̅̅ meridional velocity convergence 𝜕 𝑦 𝑣 at 700 hPa (red contour, range from -4.0 to -1.0 by -

770

1.0; units: 10-5 s-1) from (a) CAM5-2deg, (b) CAM5-1deg, and (c) CAM5-0.5deg, respectively. 46

771

The Tibetan Plateau is represented gray shaded area.

772

47

773 774

Fig. 9 Differences of eddy (zonal mean removed) geopotential height (shaded; units: m) and

775

meridional eddy (zonal mean removed) flow (contour; purple dashed lines denote negative

776

values, black line denote positive values and the zero contour is omitted; contour interval for

777

a, b, c are 0.1, 0.2, 0.2 respectively; units: m/s;) and wave activity flux (vector; units: m2/s2)

48

778

between from AMIP high- and low-resolution models (a), CAM5-1deg and 2deg (b), CAM5-

779

0.5deg and CAM5-2deg (c).

780

49

781 782

Fig. 10 Pressure-longitude cross sections of differences of meridional eddy (zonal mean

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removed) flow (shaded; units: m/s) averaged from 25° and 35°N between AMIP high- and low-

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resolution models (a), CAM5-1deg and 2deg (b), CAM5-0.5deg and CAM5-2deg (c).

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