AMERICAN METEOROLOGICAL SOCIETY Journal of Climate
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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
(2)
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) pq 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
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∗ ̅̅̅̅̅̅̅ of the advection of eddy dry energy by eddy zonal flows (−< ̅̅̅ 𝑢∗ ⋅ 𝜕 x 𝑇 >) and the advection
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∗ ̅̅̅̅̅̅ of eddy moist energy by eddy zonal flows (−< ̅̅̅ 𝑢∗ ⋅ 𝜕 x 𝑞 >), the underestimation in the HRMs
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is associated with the weakened eddy zonal velocity compared to the LRMs (figure not shown
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here).
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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
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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
783
removed) flow (shaded; units: m/s) averaged from 25° and 35°N between AMIP high- and low-
784
resolution models (a), CAM5-1deg and 2deg (b), CAM5-0.5deg and CAM5-2deg (c).
50
