Improved Performance of High-Resolution ...

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Improved Performance of High-Resolution Atmospheric Models in Simulating the East Asian Summer Monsoon Rain Belt JUNCHEN YAO State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China

TIANJUN ZHOU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

ZHUN GUO State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, and Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

XIAOLONG CHEN, LIWEI ZOU, AND YONG SUN State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China (Manuscript received 11 May 2016, in final form 27 July 2017) ABSTRACT Simulating the East Asian summer monsoon (EASM) rain belt has been proven challenging for climate models. In this study, the impacts of high resolution to the simulation of spatial distributions and rainfall intensity of the EASM rain belt are revealed based on Atmospheric Model Intercomparison Project (AMIP) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. A set of sensitivity experiments is further performed to eliminate the potential influences of differences among CMIP5 models. The results show that the high-resolution models improve the intensity and the spatial pattern of the EASM rainfall compared to the lowresolution models, further valid in the sensitivity experiments. The diagnosis of moist static energy (MSE) balance and moisture budgets is further performed to understand the mechanisms underlying the enhancements. Both analyses indicate that the improved EASM rainfall benefits from the intensified meridional convergence along the EASM rain belt simulated by the high-resolution models. In addition, such convergence is mainly contributed by intensified stationary meridional eddy northerly flows over the central northern areas of China and southerly flows over the south of Japan due to increased model resolution, which is robust in the sensitivity experiments. Further analysis indicates that the stationary meridional eddy flow changes in high-resolution simulations are related to the barotropic Rossby wave downstream of the Tibetan Plateau resulting from increased resolution.

1. Introduction The East Asian summer monsoon (EASM) is the most important climate system over East Asia and it is characterized by a frontal rain belt called mei-yu in

Denotes content that is immediately available upon publication as open access.

Corresponding author: Tianjun Zhou, [email protected]

China and baiu in Japan, respectively. The seasonal migration of the EASM rain belt is closely related to the large-scale circulation. Following the northward movement of the western Pacific subtropical high, the monsoon rain belt extends from the Indochina Peninsula in the South China Sea to the Yangtze River valley from early June to mid-July (Ding 1992; Ding and Chan 2005; Zhou et al. 2009b; Sampe and Xie 2010). Extreme climate events (e.g., the northern China floods in 2012 and the heat wave of central eastern China in 2013) occurring over East Asia are closely related to the variability

DOI: 10.1175/JCLI-D-16-0372.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

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in EASM precipitation (Waliser 2006; Sampe and Xie 2010; Zhou et al. 2009a, 2013, 2014). Therefore, the mechanism of EASM precipitation formation has been a major research focus of the monsoon study community (Sampe and Xie 2010; Molnar et al. 2010; Chen and Bordoni 2014a,b). Climate models are powerful tools for monsoon research. Many studies have demonstrated the models’ basic performance in simulating the EASM precipitation (Zhou and Li 2002; Chen et al. 2010; Zhou and Zou 2010; Zhou et al. 2013). However, significant biases exist in simulating both the rainfall intensity and spatial distribution, especially for low-resolution general circulation models (GCMs), such as remarkable deficiencies in reproducing the location, amount, and seasonal evolution of precipitation over East Asia (Kang et al. 2002; Zhou and Li 2002; Kang 2004; Zhou et al. 2009a; Huang et al. 2013). These biases are related to both the imperfect representation of basic physical and dynamical processes (e.g., moist convection and large-scale circulation) (Bony et al. 2013; Huang et al. 2013; Dai et al. 2013) and the complex topography over East Asia (Chen 1983; Boos and Hurley 2013). The resolution of a GCM is proposed to be a major factor that affects a model’s performance in simulating EASM precipitation (IPCC 2007; Huang et al. 2013). Previous studies have confirmed that high-resolution models more effectively reproduce the temperature, large-scale circulation, atmospheric teleconnections, and precipitation (Cherchi and Navarra 2007; Dirmeyer et al. 2012; Hertwig et al. 2015; Johnson et al. 2016). The spatial distribution and rainfall intensity of EASM precipitation are one of the dramatic improvements in highresolution models (Zhou and Li 2002; Kitoh and Kusunoki 2008; Gao et al. 2006). However, the physical and dynamic mechanisms of increasing resolution responsible for the improvements remain unknown. The exact physical mechanisms underlying the formation and maintenance of the mei-yu–baiu (MB) rain belt are still controversial. Some studies emphasize the role of the subtropical westerly jet, which triggers the MB by transporting warm advection from the Tibetan Plateau to the MB domain (Sampe and Xie 2010). However, recent studies indicate that one of the essential factors affecting the EASM rain belt is the stationary eddy meridional velocity (Chen and Bordoni 2014a,b). The related meridional advection of atmospheric dry enthalpy and moisture flux convergence sustains and intensifies the MB rainfall system. Which mechanism dominates the improvement of MB simulation from low- to high-resolution climate models deserves study. The purposes of this study are to answer the following two questions: 1) Do the high-resolution models from

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phase 5 of the Coupled Model Intercomparison Project (CMIP5) better reproduce the MB rain belt compared to their low-resolution counterparts? 2) If so, based on dynamical diagnoses, do the large-scale circulation or the humidity overwhelm the rainfall improvements? 3) If increased model resolution does have impact on the large-scale circulation or the humidity, what are the related dynamical mechanisms? Are the related mechanisms merely related to the changes of horizontal resolutions? For the third scientific question, a set of sensitivity experiments with identical physical schemes, in order to avoid potential influences from CMIP5 model differences among physical schemes, would be included. The remainder of this study is organized as follows. Section 2 describes the reanalysis datasets, models, and analysis method used in this study. In section 3, we present our main results. Section 4 provides concluding remarks.

2. Data and methods a. Data description Monthly precipitation data were collected from the Global Precipitation Climatology Project (GPCP) version 2.2 Monthly Combined Precipitation Dataset (Adler et al. 2003; Huffman et al. 2009). Wind, temperature, specific humidity, heat flux, and radiation variables were collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011). The surface heat flux and radiation datasets are 3-h forecast fields obtained at 0000 and 1200 UTC. Also, the wind, temperature, and specific humidity variables were collected from the Japanese 55-year Reanalysis (JRA-55) for the comparison (Ebita et al. 2011). These observation and reanalysis datasets are chosen due to their relatively high resolution (around 18). In this study, in order to eliminate the sea surface temperature biases from coupled models (Song and Zhou 2014), monthly data of 16 AGCMs participating in the CMIP5 are examined. The AGCMs are divided into two groups by horizontal resolution: high horizontal resolution (higher than 18) models (HRMs) and low horizontal resolution (lower than 18) models (LRMs). Since AGCMs with resolution lower than and around 18 do not show improvements of rainfall as the highresolution models do, 18 is chosen as a threshold. Detailed information on the AGCMs is given in Table 1. Data from 1979 to 2004 are used in this study.

b. Sensitivity experiment Considering that the different physical schemes employed in the used CMIP5 models may obscure the

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TABLE 1. Information on the 16 CMIP5-AMIP models used in this study. The models in bold font are high-resolution models. (Expansions of acronyms are available online at http://www. ametsoc.org/PubsAcronymListin. No.

Model

Resolution (lon 3 lat)

Member

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

CanAM4 IPSL-CM5A-LR FGOALS-s2 NorESM1-M MPI-ESM-LR MPI-ESM-MR INM-CM4 HadGEM2-A ACCESS1.0 MIROC5 CCSM4 CESM1-CAM5 MRI-AGCM3-2H GFDL-HIRAM-C180 GFDL-HIRAM-C360 MRI-AGCM3-2S

2.8 3 2.8 3.75 3 1.875 2.8 3 1.65 2.5 3 1.875 1.875 3 1.875 1.875 3 1.875 2 3 1.5 1.875 3 1.25 1.875 3 1.25 1.4 3 1.4 1.25 3 1 1.25 3 1 0.55 3 0.55 0.6 3 0.5 0.3 3 0.25 0.18 3 0.18

1 6 2 3 3 3 1 5 3 2 6 1 1 3 2 1

results, we perform three sensitive experiments at 1.98 3 2.58 (CAM5–2deg), 0.98 3 1.258 (CAM5–1deg), and 0.478 3 0.638 (CAM5–0.5deg) by using CAM5.3, an AGCM developed by the National Center for Atmospheric Research that only differs in resolution and has the same framework and physical schemes. The model employs a default finite-volume dynamical core, Zhang and McFarlane’s deep convection scheme (Zhang and McFarlane 1995; Neale et al. 2008), Park and Bretherton’s (2009) shallow convection scheme, and the Morrison and Gettelman (MG) microphysics scheme (Morrison and Gettelman 2008; Gettelman et al. 2010). All configurations have 30 vertical levels. We integrate the model for eight years with the three kinds of horizontal resolution, forced by the same prescribed climatological sea surface temperatures (SSTs) with annual cycle. The climatological SSTs were chosen because the focus of this study is the impacts of horizontal resolutions on the EASM rainfall.

c. Methods 1) MOISTURE STATIC ENERGY BALANCE The moist static energy (MSE) budget is a useful tool for quantifying the roles of temperature, specific humidity, radiative processes, and large-scale circulation in the spatial distribution of rainfall due to deep convection (Chou and Neelin 2003; Neelin 2007; Chou et al. 2013; Chen and Bordoni 2014a,b; Sun et al. 2016). To understand the main factors that affect the spatial distribution of EASM precipitation, the MSE balance analysis is determined. Under a quasi-equilibrium assumption of deep convection,

a vertically integrated MSE budget averaged over a climatological period can be written as     ›M ›h 5 Fnet 2 hv  =Mi 2 v , ›t ›p

(1)

where M 5 cpT 1 Lyq is the moist enthalpy, h 5 cpT 1 Lyq 1 gz is the MSE, Fnet is the net energy entering the atmospheric column, and v is pressure velocity The T and q refer to air temperature and specific humidity, respectively; hXi is a vertical mass integral, and X denotes the temporal mean, which is a 2-month (June and July) mean. The quantity Fnet can be further decomposed as Fnet 5 SYt 2 S[t 2 SYs 1 S[s 2 R[t 1 R[s 2 RYs 1 SH 1 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.

2) MOISTURE BUDGET ANALYSIS Moisture budget analyses are widely used to examine precipitation distributions and changes (Chou and Lan 2012). To understand the physical processes that modulate the EASM rainfall intensity and related model biases, a moisture budget analysis is conducted. Precipitation in the MB region should satisfy the moisture budget: P 5 2h›t qi 2 h=  (vq)i 2 h›p vqi 1 E.

(3)

Compared to the other terms, the tendency term 2h›t qi can be ignored because of its small climatological mean magnitude. The vertical term can also be neglected given the negligible magnitude of vertical motion at the bottom and top of the atmospheric column. The moisture flux term convergence 2h=  (vq)i can be further written as zonal moisture flux convergence 2h›x (uq)i and as meridional moisture flux convergence 2h›y (yq)i.

3. Results a. Comparison of EASM rainfall pattern between high- and low-resolution models Following previous studies (Ding and Chan 2005; Zhou et al. 2009b), the MB season is generally defined as the June–July average. As shown in Fig. 1a, the precipitation from GPCP shows an organized elongated rain belt spanning over East Asia and the northwestern Pacific. The maximum center locates in the Yangtze River valley of China and in the southwestern Japan. The

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TABLE 2. Pattern correlation coefficient (PCC) and root-meansquare difference (RMSE) between models and observations over the monsoon rain belt region. The models in bold font are highresolution models.

FIG. 1. Climatology of June–July precipitation (color shaded, mm day21) and vertical velocity (contours; Pa s21 with interval 20.02 Pa s21; thick lines denote a vertical velocity 5 20.01Pa s21) at 400 hPa derived from (a) GPCP and the mean of ERA-Interim and JRA-55, (b) the low-resolution models, and (c) the high-resolution models. The red rectangle marks the mei-yu–baiu (MB) region.

weighted average precipitation is 6.7 mm day21 over the southeast China region (SC; 258–358N, 112.58–122.58E) and 7.6 mm day21 over the southwestern Japan region (SJ; 278–378N, 1258–1458E). A wedge of deficit rainfall is located south of the baiu rain belt, marking the positioning of the northwestern Pacific subtropical high (;258N). The spatial distribution of ascending motion at 400 hPa matches the MB rainfall patterns well. The results of the ensemble mean of the LRMs are shown in Fig. 1b. The elongated rain belt is generally captured by LRMs with weaker magnitude over the SC and the SJ region. Rainfall amount simulated by LRMS over the SC and SJ are 5.6 and 4.8 mm day21, respectively. Compared to observations, the main biases of precipitation from the LRMs are the deficit rainfall between the eastern China and Japan over the ocean, which leads to a bowlike rainfall pattern. In addition, the rain belt shows

No.

Model

PCC

RMSE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

CanAM4 IPSL-CM5A-LR FGOALS-s2 NorESM1-M MPI-ESM-LR MPI-ESM-MR INM-CM4 HadGEM2-A ACCESS1.0 MIROC5 CCSM4 CESM1-CAM5 MRI-AGCM3-2H GFDL-HIRAM-C180 GFDL-HIRAM-C360 MRI-AGCM3-2S

0.17 0.24 20.13 0.01 0.41 0.48 20.07 0.58 0.66 0.12 20.01 0.10 0.78 0.56 0.69 0.82

2.61 2.31 2.19 2.2 1.90 1.81 2.41 1.58 1.49 2.16 2.43 2.05 1.23 1.63 1.24 1.32

poleward displacement that is related to the northward shifted ascending motion. In addition, the weakened ascending motion, especially between eastern China and Japan, results in the underestimation of precipitation in LRMs. The pattern correlation coefficient (PCC) over the MB region (box defined in Fig. 1) between the LRMs and observation is 0.31, and the root-mean-square difference (RMSE) is 2.1. As shown in Table 2, the PCCs between most of the LRMs and observation are generally below 0.50, and the RMSEs generally exceed 1.8, except for ACCESS1.0 and HadGEM2-A. The HRMs also reasonably capture the spatial pattern of the MB rain belt (Fig. 1c). Two rainfall centers over the Yangtze River valley in China and over southwestern Japan are also reproduced, although the rainfall intensities are slightly underestimated compared to the observation. In HRMs, precipitation over the SC and SJ is 6.0 and 6.3 mm day21, respectively. Benefiting from the enhanced rainfall amount over the SJ region, the rain belt between eastern China and Japan is more concentrated in the HRMs. However, there is not a significant enhancement in the rainfall over SC when the resolution improves. The ascending motion simulated by the HRMs is stronger than that by the LRMs, especially in eastern China, which leads to the enhancement of precipitation. The PCC between the simulated and observed precipitation over the MB region is 0.76, and the RMSE is 1.3. As shown in Table 2, the PCCs between the HRMs and observation are greater than 0.6, and the RMSEs generally below 1.3, except for GFDL-HIRAM-C180. The regionally averaged rainfall amounts over the red box (258–378N; 112.58–1458E) in Fig. 1 from all models

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against the mean of horizontal resolutions are shown in Fig. 2. According to the observation, the regional averaged rainfall is 7.1 mm day21. The magnitude of rainfall over the MB region arises along with the increase of model horizontal resolution. MRI models show the nearest simulated rainfall amount to the observations, reaching a magnitude of nearly 6.5 mm day21. Notably, some of the models do not follow such a precipitation–resolution relationship (e.g., CCSM4, CESM1-CAM5, and GFDL-HIRAM), indicating that differences of model dynamical frame and physical schemes may obscure the results. The rainfall reproduced by sensitivity experiments increases from 5.1 to 6.3 mm day21 as resolution increases (Fig. 2, purple markers), which shows consistency with the precipitation– resolution relationship, confirming the influence of model horizontal resolution on the EASM rainfall. However, as the resolution increases, the precipitation improvement seems to have a limit. This implies that the HRM adjusting parameterization scheme may be necessary for better performance in simulating the EASM rainfall. Actually, the convection parameterization will be not needed in the convection-resolving scale. In brief, both the LRMs and HRMs reasonably reproduce the MB rain belt. In addition, the magnitude of MB rainfall is enhanced in the HRMs, especially over the ocean to the south of Japan, compared to the LRMs. However, the enhancement over the SC region is not significant. The enhanced rainfall intensity over the ocean to the south of Japan in the HRMs is related to the enhanced ascending motion. To show which physical processes are responsible for the enhancement of convection over the MB region, an MSE balance analysis will be performed in the following section.

b. Moist static energy balance analysis The EASM rainfall amount simulated by the HRMs is enhanced compared to that in the LRMs. To understand the processes that contribute to the enhancement, an MSE balance analysis is performed to reveal the relative roles of temperature, specific humidity, radiative processes, and large-scale circulation. The vertical integration of vertical MSE advection (hv›p hi) is susceptible to the relatively coarse vertical resolution of models. Similar to previous studies (Chou et al. 2013; Chen and Bordoni 2014a), we use a simple assumption that the residual is relatively small caused by coarse vertical resolution, so the vertical MSE advection is calculated as Fnet 2 hv  =Mi according to Eq. (1). The vertical MSE advection involves pressure velocity v and the MSE stratification h›p hi. Because the latter is generally negative in the stable troposphere, the region of positive hv›p hi represents ascending motion (v , 0), and vice versa. Thus, the vertical integration of vertical

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FIG. 2. Area averaged precipitation for the MB region for June– July (y axis) vs model resolutions. Black, brown, blue, and purple markers refer to GPCP, low-resolution models, high-resolution models, and sensitivity experiments, respectively.

MSE advection serves as a reasonable proxy for convection over the MB region, which has a spatial distribution similar to that of the EASM rain belt (Fig. 1). As shown in Fig. 3a, the spatial pattern of vertical MSE advection in the reanalysis data closely reflects the precipitation and ascending motion patterns (Fig. 1a) in the reanalysis data. Vertical MSE advection spans from the Yangtze River valley through Japan and into the northwestern Pacific with a center over the eastern mainland of China and east of Japan. As described in Eq. (1), vertical MSE advection involves vertically integrated horizontal moist enthalpy advection (2hv  =Mi)(Fig. 3b) and net energy flux into the atmosphere column (Fnet ) (Fig. 3c). The former overwhelms the latter and dominates the spatial patterns of vertical MSE advection, which is valid in both of the HRMs and LRMs (Figs. 3d–i). Thus, the energy triggering the convection over the EASM region is mainly contributed by the horizontal moist enthalpy advection. Consistent with the bias in simulated MB precipitation (Fig. 2), the vertical MSE advections along the rain belt in both the LRMs and HRMs are underestimated compared with the observations, which are mainly contributed by the horizontal advection term (Figs. 3b,e,h). The horizontal moist enthalpy advection center between eastern China and Japan is weakened and shifted to the north of Japan in the LRMs (Fig. 3e). This bias is considerably reduced in the HRMs (Fig. 3j). The most prominent improvement is located south of Japan, which is on average 38 W m22 (55%) larger than

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FIG. 3. Climatology of the June–July vertically integrated MSE budget. The difference between Fnet and hv  =Mi for (left) vertically integrated vertical MSE advection hv›p hi, (middle) vertically integrated horizontal moist enthalpy advection 2hv  =Mi, and (right) net energy flux into the atmospheric column Fnet for (top to third row) ERA-interim, the low-resolution multimodel ensemble mean, and the high-resolution multimodel ensemble mean. The bottom row shows the differences between the high- and low- resolution multimodel ensemble mean (color shaded, W m22); contours denote the differences of climatology of June–July precipitation between high- and lowresolution models (contours range from 0.5 to 2.5 with interval 0.5; mm day21).

the LRMs, to which the horizontal moist enthalpy advection contributes 29 W m22 (76%). Meanwhile, the horizontal moist enthalpy advection is suppressed in the north of the East Asian region, leading to a more

realistic moist enthalpy advection around the front in the HRMs (Fig. 3k). However, the horizontal moist 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 differences of MSE advection with precipitation (Figs. 3j–l). The enhancements of precipitation, accompanied with the MSE advection enhancement, are mainly located in the SJ region in the HRMs. The two patterns coincidently match each other. Hence, the improvement of the EASM rain belt over the SJ region can be explained by the enhanced horizontal moist enthalpy advection (Fig. 3k).

c. Decomposition of horizontal moist enthalpy advection The above evidence shows that the moist enthalpy advection plays a critical role in improving the EASM rainfall in the HRM. To clearly identify the relative importance of mean advection, eddy advection, and transient advection to the moist enthalpy advection, we decompose the advection term into five terms following Eq. (A1) (see detailed information in the appendix). Because of the small magnitude of the pure zonal mean moist enthalpy advection (2hv  =Mi), it can be ignored in the following analysis. The remaining four terms are the advection of stationary eddy energy by zonal-mean flow 2h[v]  =M*i, the advection of zonal-mean energy by eddy flow 2hv*  [=M]i, the advection of stationary eddy energy by the stationary eddy flow, or pure eddy advection, 2hv*  [=M*]i, and the advection of transient eddy energy by the transient eddy flow, or transient advection, 2hv0  =M0 i. Along the EASM rain belt, in both the HRMs and LRMs the contribution from the advection of zonalmean energy by the eddy flow 2hv*  [=M]i and the pure eddy advection 2hv*  [=M*]i overwhelms that from the advection of stationary eddy energy by the zonal-mean flow h[v]  =M*i and the transient advection 2hv0  =M0 i in the reanalysis data (Fig. 4). As shown in Fig. 4, both the LRMs and HRMs reasonably reproduce the spatial pattern of four terms. In terms of the advection of stationary eddy energy by zonal-mean flow h[v]  =M*i (Figs. 4a–c) and transient advection 2hv0  =M0 i (Figs. 4j–l), the HRMs (LRMs) simulated them by 15.1 W m22 (17.5 W m22) and 219.5 W m22 (215.6 W m22) over the MB region, compared to 24.1 and 23.5 W m22 in the reanalysis data results. The LRMs underestimate their magnitudes, which are further underestimated in the HRMs. Thus, the enhancement of the total moist enthalpy advection along the EASM rain belt in the HRMs is contributed by the advection of zonal-mean energy by the eddy flow 2hv*  [=M]i and the pure eddy advection 2hv*  [=M*]i2hv*  =M*i, which averaged are 11.8 and 19.8 W m22 higher than in the LRMs. Although the magnitudes of these two terms are enhanced in the HRMs, there is no significant added value in their spatial

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distribution from the LRMs, especially the advection of the zonal-mean energy by eddy flow 2hv*  [=M]i. This term is underestimated and northward shifted in the LRMs over the SJ region. However, it is overestimated on the south of Japan and underestimated over the east mainland of China in the HRMs. In terms of the pure eddy advection 2hv*  [=M*]i, the HRMs well capture its spatial distribution, while the LRMs show insufficient skill around the SJ region. To clearly identify the relative importance of zonal influence, meridional influence, dry enthalpy, and moist enthalpy to the advection of the zonal-mean energy by eddy flow 2hv*  [=M]i and the pure eddy advection 2hv*  [=M*]i, we decompose the total horizontal moist enthalpy advection term into 20 terms [Eq. (A2)]. Two terms consist of eight terms [the 9th to 16th terms in the right-hand side of Eq. (A2)]. The results of the decomposition averaged over enhanced rainfall area (258–348N, 1208–1558E) are shown in Fig. 5. The results indicate that the enhanced energy advection in the HRMs is mainly contributed by two terms, which are the advection of eddy moist energy by eddy meridional flows (2hy* 3 ›y q*i, referred to as the moist term) and the advection of zonal-mean dry energy by eddy meridional flows (2hy* 3 ›y Ti, referred to as the dry term). The moist (dry) term in the HRMs enhances the energy advection by 18.5 W m22 (7.1 W m22) compared to the LRMs. In addition, they explain 88% of the enhancement from the horizontal moist advection. Thus, we focus on them in the following analysis. In terms of the advection of eddy dry energy by eddy zonal flows (2hu* 3 ›x T*i) and the advection of eddy moist energy by eddy zonal flows (2hu* 3 ›x q*i), the underestimation in the HRMs is associated with the weakened eddy zonal velocity compared to the LRMs (figure not shown here). The results of dry and moist terms are shown in Fig. 6. The spatial patterns of the dry and moist terms indicate that the warm air and moisture transported from the tropical ocean by eddy meridional velocity are sustaining the convection along the MB rain belt in the reanalysis data (Figs. 6a and 5d). The dry term is mainly affected by the vertically integrated stationary eddy meridional velocity hy*i (Fig. 5a, contours), which is valid in the models (Figs. 6b,c, contours). The deficiency in simulating the dry term, especially over the SJ region, mainly results from the weak and northward shifted vertically integrated stationary eddy meridional velocity hy*i in the LRMs (Fig. 6b). Furthermore, compared to that in the LRMs, the vertically integrated stationary eddy meridional velocity hy*i in the HRMs exhibits enhanced northerly wind over the central northern area of China and enhanced southerly wind over the south of Japan. Such a

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FIG. 4. Decomposition of the vertically integrated horizontal moist enthalpy advection 2hv  =Mi for (left) ERA-Interim, (middle) the low-resolution multimodel ensemble mean, and (right) the high-resolution multimodel ensemble mean. Shown from top to bottom are the advection of stationary eddy energy by the zonal-mean flow 2h[v]  =M*i, the advection of zonal-mean energy by eddy flow 2hv*  [=M]i, the advection of stationary eddy energy by the stationary eddy flow 2hv*  [=M*]i, and the advection of transient eddy energy by the transient eddy flow 2hv0  =M0 i. (Color shading is in W m22).

spatial pattern results in stronger meridional convergence along the MB rain belt (Figs. 6h,i, contours). For the moist term, in the SJ region, the LRMs underestimate its magnitude compared to that in the reanalysis data while the HRMs show opposite results (Fig. 6e). The moist term is mainly affected by the

vertically integrated eddy meridional moisture gradient 2h›y q*i, for which spatial distribution well explains the moist term (Figs. 6g–i). Previous studies (e.g., Zhou et al. 2010) indicate that such a moisture gradient over the EASM region is highly related to the circulation convergence. As shown in Figs. 6g–i, the convergence of

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FIG. 5. Values of main variables (along x axis) derived from decomposed horizontal moist enthalpy advection averaged over the enhanced rainfall region [;(258–348N, 1208–1558E; W m22)]. Each variable is vertically integrated. The LRM results are in red and the HRM in blue; the observations in gray is the mean of ERA-Interim and JRA-55.

the vertically integrated eddy meridional moisture gradient 2h›y q*i along the EASM rain belt is mainly due to the stationary eddy meridional velocity convergence ›y y*. Thus, the enhancement of the moist term on the south of Japan in the HRMs is also related to the enhanced northerly over the central northern areas of China and the enhanced southerly to the south of Japan. Such enhanced northerlies and southerlies lead to stronger stationary eddy meridional velocity convergence ›y y* along the rain belt, especially over the SJ, in the HRMs. Based on the analysis above, the stationary eddy meridional velocity convergence ›y y* shows its importance in improving the EASM rain belt in the HRMs. Its PCC between the HRMs and the reanalysis data is 0.78 compared to 0.64 for the LRMs, whereas the PCC of vertically integrated stationary eddy meridional velocity hy*i is 0.75 and 0.73 for the HRMs and LRMs, respectively, indicating more importance of ›y y* than of hy*i in the improved EASM rainfall due to high resolution. Moreover, the intensified northerly over the central northern area of China and the intensified southerly wind over the south of Japan in the HRMs play an important role in improving the simulation of ›y y*, despite its PCC showing negligible improvement compared to that from the LRMs. Such a spatial distribution of hy*i creates stronger gradients over the SC and SJ region in the HRMs, exactly where the MB rain belt locates. Thus, the MB rain belt simulation is improved in the HRMs.

d. Moisture budget analysis 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.

The area-averaged (MB domain) moisture budget results based on reanalysis data and models are shown in Fig. 7a. The results indicate that the precipitation over MB domain is mainly contributed by the local surface evaporation (E) and the meridional moisture flux convergence (2h›y (yq)i) in both reanalysis data and AGCMs. The improvement from HRMs of the evaporation (0.34 mm day21) is not as large as the meridional moisture flux convergence (3.3 mm day21). Thus, the improvement of rainfall amount over the MB domain is attributed to intensified meridional moisture flux convergence in the HRMs. To clearly identify the effects of meridional flow convergence in moisture transport, the same decomposition method is applied to the meridional moisture flux convergence (detailed information is available in the appendix). As shown in Fig. 7b, four terms contributed to the improvement of the meridional moisture flux convergence 2h›y (yq)i. Of these, the three main contributors to the improvements of the meridional moisture flux convergence from HRMs are the stationary eddy meridional moisture transported via meridional flow convergence (2hq* 3 ›y y*i), the zonal-mean moisture transported via stationary eddy meridional flow convergence (2h[q] 3 ›y y*i), and the pure stationary eddy meridional moisture advection (2hy* 3 ›y q*i). The third term is discussed in section 3c. Thus, we focus on the first two terms in the following analysis. The 2hq* 3 ›y y*i term (the 2h[q] 3 ›y y*i term) contributes 63% (21%) of the enhancement to the meridional moisture flux convergence in the HRMs. Moreover, both terms are affected by the stationary

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FIG. 6. (top) The advection of zonal mean dry enthalpy by the stationary eddy meridional velocity 2hy* 3 [›y T]i (color shaded, W m22), (middle) the advection of eddy moist enthalpy by meridional eddy meridional velocity 2hy* 3 [›y q*]i (color shaded, W m22), and (bottom) the vertically integrated eddy meridional moisture gradient 2h›y q*i [color shaded, 105 kg (m s22) 21] for (left) ERA-Interim, (middle) the LRM ensemble mean, and (right) the HRM ensemble mean. Contours in top row denote vertically integrated stationary eddy meridional velocity hy*i [they range from 220.0 to 20.0 with interval 4.0; solid and dashed contours denote positive and negative values, respectively; 106 kg (m s21)21], and contours in bottom row denote stationary eddy meridional velocity convergence ›y y* at 700 hPa (they range from 24.0 to 21.0 with interval of 21.0; 1025 s21).

eddy meridional flow convergence, confirming that it plays an important role in enhanced rainfall amount in the HRMs. The distribution of stationary eddy meridional velocity in HRMs largely enhances the meridional convergence over the MB domain. As a result, the moisture transport along the rain belt is intensified, thereby leading to the enhancement of rainfall intensity of the EASM rainfall in the HRMs.

e. Results of sensitivity experiments To avoid the potential influence from model physics and further investigate the resolution impact on the stationary eddy flow and its convergence, we performed a series of sensitivity experiments using the CAM5 model

with the same dynamic core and physics scheme at three different resolutions (details seen in section 2). During the EASM season, all the experiments using CAM5 models at different resolutions can reasonably reproduce the EASM rain belts. However, the EASM rainfall intensity in CAM5–2deg is significantly underestimated (Fig. 8a), especially over the southwest of Japan, compared to observation. The rain belt over eastern China and southern Japan is separated by an artificial minimum center (Fig. 7a), similar to the pattern observed in the CMIP5 LRMs (Fig. 1b). Such a bowlike spatial distribution of precipitation also exists in CAM5–1deg. When the resolution of CAM5 increases to 0.58 (CAM5–0.5deg), the precipitation intensity is further enhanced, closer to the observation. The precipitation over the East China Sea

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FIG. 7. (a) Results of the moisture budget and (b) values of the main variables (along x axis) decomposed from the meridional moisture flux averaged over the MB region. Each variable is vertically integrated. The LRM results are in red and the HRM in blue; the observations in gray is the mean of ERA-Interim and JRA-55.

and south of Japan, which is underestimated in the lowresolution simulations (CAM5–2deg and CAM5–1deg), is significantly enhanced in CAM5–0.5deg. Consequently, the bowlike pattern over East Asia in low-resolution simulations is improved in CAM5–0.5deg. The PCCs of the monsoon rain belt between CAM5–2deg, CAM5–1deg, and CAM5–0.5deg, respectively, and observation are 0.26, 0.39, and 0.52, increasing with resolution. For the RMSE, the values are 1.81, 1.62, and 1.60 respectively, decreasing with resolution. The enhancement of precipitation, especially over the SJ, in CAM5 with increased resolution, are similar to those of CMIP5 AGCMs, indicating that model resolution does have an impact on simulating EASM rainfall. As mentioned in the previous sections, the precipitation is actually dominated by the stationary eddy meridional velocity convergence ›y y*. As described in previous section, the HRMs reproduce stronger northerly wind over the central northern areas of China and stronger southerly

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FIG. 8. Climatology of June–July precipitation (color shaded; mm day21), vertically integrated meridional eddy velocity hy*i [black contours; range from 220.0 to 20.0 with interval of 4.0, solid and dashed contours denote positive and negative value, respectively; 106 kg (m s21) 21] and stationary eddy meridional velocity convergence ›y y* at 700 hPa (red contours; theybrange from 24.0 to 21.0 with interval of 21.0; 1025 s21) from (a) CAM5–2deg, (b) CAM5–1deg, and (c) CAM5–0.5deg, respectively. The Tibetan Plateau is represented by the gray shaded area.

wind over the south of Japan, thereby intensifying the meridional convergence along the MB rain belt, compared to that in the LRMs. Such phenomenon would be verified by the CAM5 experiments. As shown in Fig. 8, the vertically integrated stationary eddy meridional velocity hy*i decreased over the central northern areas of China and increased over the south of Japan, with resolution increasing (Fig. 8, black contours), which is similar with the CMIP5 AGCMs. Furthermore, such northerly (southerly) flow over the central northern areas of China (south of Japan) gradually enhanced from CAM5–2deg???to CAM5–0.5deg??, leading to stronger meridional convergence over the MB rain belt region (Fig. 8, red contours). The regional weighted averaged

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stationary eddy meridional velocity convergences ›y y* over the MB rain belt region in CAM5–2deg, CAM5– 1deg, and CAM5–0.5deg are 21.12 3 1025 s21, 21.33 3 1025 s21, and 21.45 3 1025 s21, respectively, approaching 21.63 3 1025 s21 in the reanalysis data. Such enhancements in meridional convergence are also similar to those of the CMIP5 AGCMs due to increasing resolution. Thus, the sensitivity experiments confirm the resolution impact on meridional flow convergence, and further enhance the EASM rainfall over the SJ region. To further investigate the meridional circulation change resulting in stronger meridional convergence due to resolution impact, the differences of eddy geopotential height and meridional eddy flow at 500 hPa between the low- and high-resolution simulations from the CMIP5 AGCMs and sensitivity experiments are shown in Fig. 9. A Rossby wave train is organized from mainland of China into the western North Pacific. To depict the propagation direction of the wave train, the wave activity flux (WAF) at 500 hPa is calculated according to Takaya and Nakamura (2001) with the differences between the high- and low-resolution models (Fig. 9). As shown, in CMIP5 HRMs, CAM5– 1degree, and CAM5–0.5degree, the WAFs downstream of the Tibetan Plateau and to the north of the South China Sea converge over the eastern mainland of China around 1208E, and propagate farther east into the western North Pacific (Fig. 9a). Such a wave pattern corresponds to enhanced northerly anomalies over the central northern areas of China and southerly anomalies in the south of Japan, inducing the enhanced meridional flow convergence along the EASM rain belts. The wave patterns of meridional eddy flow and eddy geopotential height are consistent between AGCMs and CAM5 sensitivity experiments. The vertical structure of the wave train is quasibarotropic is shown in Fig. 10, which shows the differences of eddy meridional eddy flow averaged from 258 to 358N between low- and high-resolution simulations from all models. Such a well-organized barotropic Rossby wave structure in the troposphere indicates that it does not likely result from the enhanced EASM rainfall, since the Rossby wave driven by diabatic heating is usually organized with a baroclinic structure. Notably, such a barotropic Rossby wave is responsible for the enhanced northerly anomalies over the central northern areas of China [;(1108–1208E)] and southerly anomalies in the south of Japan [;(1308– 1408E)]. Moreover, the results of the CMIP5 AGCMs are consistent with the sensitivity experiments, indicating that such a barotropic Rossby wave is due to the increasing model resolution rather than to potential influences from diversities of model dynamic core and physics scheme. The quasi-barotropic wave is downstream of the Tibetan Plateau and shows some similarity to that due to orographic forcing (Held and Ting 1990; Cook and Held 1992;

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FIG. 9. Differences of eddy (zonal mean removed) geopotential height (color shaded; m) and meridional eddy (zonal-mean removed) flow [contours; purple dashed lines denote negative values, black lines denote positive values, and the zero contour is omitted; contour intervals for (a)–(c) are 0.1, 0.2, 0.2 respectively; m s21] and wave activity flux (vectors; m2 s22) between (a) AMIP high- and low-resolution models, (b) CAM5–1deg and 2deg, and (c) CAM5–0.5deg and CAM5–2deg.

Lutsko and Held 2016). Thus, more realistic topography in the high-resolution simulations may responsible for this wave train pattern. However, determining whether the barotropic Rossby wave in higher-resolution simulations is induced by finer topography may require a set of idealized experiments for further investigation. In addition, the coarse-resolution models average the precipitation at the given location (e.g., over a 2.0-degree box compared to a 0.5-degree box) leading to reduced precipitation intensity and increased precipitation frequency (Chen and Dai 2017). Whether such phenomena influence the enhancement convergence of the meridional eddy flow in the HRMs still needs further investigation.

4. Summary Climate models with higher resolutions generally exhibit enhanced performance in terms of simulating the

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FIG. 10. Pressure–longitude cross sections of differences of meridional eddy (zonal mean removed) flow (color shaded; m s21) averaged from 258 and 358N between (a) AMIP high- and lowresolution models, (b) CAM5–1deg and 2deg, and (c) CAM5– 0.5deg and CAM5–2deg.

EASM rain belt, especially on the south of Japan. To understand the underlying physical mechanisms, we conducted an MSE balance analysis and moisture budget analysis to examine the spatial distribution and rainfall levels of the EASM rain belt reproduced by low- and high-resolution AGCMs. Sensitivity experiments using CAM5 with identical dynamic core and physics scheme but different resolutions are also performed to validate the robustness of rainfall improvements and related mechanisms apart from the potential influences from different physical schemes employed in CMIP5 models. The main conclusions are summarized as follows. 1) The EASM rain belt simulated by LRMs is underestimated compared to observations, especially for the south of Japan. Such biases are reduced in the HRMs. The rainfall intensity is generally enhanced by 1.0 mm day21 in HRMs compared to LRMs, to which the enhancement over the SJ region contributes 1.5 mm day21. The increase of PCC (0.76 for HRMs and 0.31 for LRMs) and the decrease of RMSE (1.3 for HRMs and 2.1 for LRMs) indicate that such enhancement due to increased model resolution is robust. The results of sensitivity experiments further confirm such rainfall intensity enhancements. 2) By conducting an MSE balance analysis, the factor dominating improvements of EASM rainfall simulation

in the HRMs was found to be the horizontal MSE advection. The decomposition results show that the enhanced horizontal moist enthalpy advection in the HRMs results from the intensified northerly flow over the central northern areas of China and southerly flow on the south of Japan. Such meridional eddy stationary flow enhancements lead to stronger stationary eddy meridional flow convergence along the MB rain belt in the HRMs. The intensified convergence along the MB front region in the HRMs strengthens the moist energy transport from the tropics, and thereby the improved MB rain belt. 3) A moisture budget analysis confirms that the enhanced rainfall intensity is mainly contributed by enhanced stationary eddy meridional flow convergence in the HRMs. Such enhanced convergence intensifies the moisture transport into the MB rain belt in the HRMs and thereby the enhanced rainfall intensity. 4) The above improvements of MB rain belt and stationary eddy meridional flow convergences due to increased resolution are verified by sensitivity experiments apart from potential influences from different physical schemes employed in CMIP5 AGCMs. Further analysis indicates that the stationary eddy meridional flow convergence along the MB rain belt is intensified as model resolution increases. The convergence enhancements are related to the intensified northerly (southerly) flow over the central northern areas of China (the south of Japan). Such meridional flow changes result from the barotropic Rossby wave due to increased model resolution. The barotropic wave downstream of the Tibetan Plateau propagates from eastern China to the western North Pacific, creating stronger meridional convergence along the MB rain belt. However, whether such a quasibarotropic wave is forced by the finer topography of the Tibetan Plateau needs further investigation. Acknowledgments. This work was jointly supported by the R&D Special Fund for Public Welfare Industry (meteorology) (GYHY201506012), NSFC project GOTHAM (41661144009), and the National Natural Science Foundation of China (NFSC) under Grants 41420104006, 41405103, and 41125017.

APPENDIX Decomposition of the Advection Term As explained in section 3, during the EASM season both net energy flux and horizontal moist enthalpy advection play essential roles in sustaining the monsoon rainfall. However, because of the significant improvements of

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moist enthalpy advection term and moisture reproduced by high-resolution models, a method clearly differentiating the contribution from stationary eddy fluxes and zonal mean fluxes should be introduced in order to seek out the improved fluxes influenced by model horizontal resolution. Hence, the time mean advection term hv  =Xi can be decomposed as hv  =Xi 5 h[v]  [=X]i 1 h[v]  =X*i 1 hv*  [=X]i 1 hv*  =X*i 1 hv0  =X 0 i . (A1) 0

Here, (X) denotes the deviation from time X (twomonth June and July mean for each individual year) and (X)* denotes the deviation from the global zonal mean [X], where X is as in section 2. As explained in section 3c, the moist enthalpy advection term hv  =Mi plays an essential role in improvements of monsoon rainfall from high-resolution models. Thus, by using the decomposition method, the moist enthalpy advection term can be decomposed as hv  =Mi 5 h[u] 3 [›x T]i 1 h[y] 3 [›y T]i 1 h[u] 3 [›x q]i 1 h[y] 3 [›y q]i 1 hu* 3 [›x T]i 1 hy* 3 [›y T]i 1 hu* 3 [›x q]i 1 hy* 3 [›y q]i 1 h[u] 3 ›x T*i 1 h[y] 3 ›y T*i 1 h[u] 3 ›x q*i 1 h[y] 3 ›y q*i 1 hu* 3 ›x T*i 1 hy* 3 ›y T*i 1 hu* 3 ›x q*i 1 hy* 3 ›y q*i 1 hu0 3 ›x T 0 i 1 hy 0 3 ›y T 0 i 1 hu0 3 ›x q0 i 1 hy0 3 ›y q0 i . (A2) Here, the first four terms on the right-hand side are the zonal-mean energy advection by the zonal-mean flow (i.e., h[u] 3 [›x T]i, h[u] 3 [›x q]i, h[y] 3 [›y q]i); the second four terms on the right-hand side denote the advection of the zonal-mean energy by the stationary eddy flow (i.e., hu* 3 [›x T]i, hy* 3 [›y T]i,hu* 3 [›x q]i, hy* 3 [›y q]i); the third four terms on the right-hand side denote the advection of the stationary eddy energy by the zonal-mean flow (i.e. h(u) 3 ›x T*i, h(y) 3 ›y T*i, h[y] 3 [›y q*]i); the fourth four terms on the right-hand side denote the advection of stationary eddy energy by the stationary eddy flow (i.e., hu* 3 ›x T*i, hy* 3 ›y T*i, hu* 3 ›x q*i, hy* 3 ›y q*i); and the last four terms on the right-hand side denote the advection of transient eddy energy by transient eddies (i.e., hu0 ›x T 0 i, hy 0 ›y T 0 i, hu0 ›x q0 i, hy0 ›y q0 i). Note that T and q are considered in energy units in (A2). The zonal-mean terms (i.e., h[u] 3 [›x T]i, h[y] 3 [›y T]i, h[u] 3 [›x q]i, h[y] 3 [›y q]i) are generally small

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compared to the other terms; the transient terms (i.e., hu0 ›x T 0 i, hy 0 ›y T 0 i, hu0 ›x q0 i, hy0 ›y q0 i) are also very small due to the long-temporal mean, and they can all thus be neglected. Besides, the contributions from h[u] 3 ›x q*i, h[y] 3 ›y q*i, h[y] 3 ›y T*i, hu* 3 [›x T]i, hu* 3 [›x q]i and hy* 3 ›y T*i to the total advection are much smaller compared to other terms, and they are also neglected in section 3c. As explained in section 3d, the meridional moisture convergence flux h›y (yq)i is another essential factor affected improvements from high-resolution models. Like moist enthalpy advection, it can be decomposed as h›y (yq)i 5 hy* 3 ›y q*i 1 h[y] 3 ›y q*i 1 hy* 3 [›y q]i 1 h[y] 3 [›y q]i 1 hq* 3 ›y y*i 1 h[q] 3 ›y y*i 1 hq* 3 [›y y]i 1 h[q] 3 [›y y]i 1 hy 0 3 ›y q0 i 1 hq0 3 ›y y 0 i . (A3) The mean terms (h[y] 3 [›y q]i, h[q] 3 [›y y]i) are associated with planetary-scale circulation and humidity and thus are generally small and neglected. The stationary eddy terms (hy* 3 ›y q*i, h[y] 3 ›y q*i, hq* 3 [›y y]i) are associated with meridional stationary eddy flows and meridional humidity distributions. A previous study (Chen and Bordoni 2014a) indicated that the transient terms (hy 0 ›y q0 i, hq0 ›y y 0 i) play a minor role compared to the other terms due to the long-temporal mean, and they are neglected in section 3d. In addition, the contribution from these eddy terms (hy* 3 ›y q*i, hq* 3 [›y y]i) to the total meridional moisture convergence flux is gradually less than 0.1 mm day21 and thus can be neglected.

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