Abstract

The amount of rainfall during June and July along the mei-yu front contributes about 45% to the total summer precipitation over the Yangtze River valley. How it will change under global warming is of great concern to the people of China because of its particular socioeconomic importance, but climate model projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) show large uncertainties. This paper examines model resolution sensitivity and reports large differences in projected future summer rainfall along the mei-yu front between a low-resolution (Gaussian N96 grid, ~1.5° latitude–longitude) and a high-resolution (N216, ~0.7°) version of the Hadley Centre’s latest climate model, the HadGEM3 Global Coupled Configuration 2.0 (HadGEM3-GC2). The high-resolution model projects large increases of summer rainfall under two representative concentration pathway scenarios (RCP8.5 and RCP4.5) whereas the low-resolution model shows a decrease. A larger increase of projected mei-yu rainfall in higher-resolution models is also observed across the CMIP5 ensemble. These differences can be explained in terms of enhanced moist static energy advection and moisture convergence by stationary eddies in the high-resolution model. A large-scale manifestation of the anomalous stationary eddies is the contrasting response to the same warming scenario by the western North Pacific subtropical high, which is almost unchanged in N216 but retreats evidently eastward in N96, reducing the southwesterly flow and consequently moisture supply to the mei-yu front. Further increases in model resolution to resolve parameterized processes and detailed orographic features will hopefully reduce the spread in future climate projections.

1. Introduction

During June–July, the rain belt of the East Asian summer monsoon usually marches northward from the southeastern coast of China to the Yangtze River valley. This rainy season is called mei-yu in China (and baiu and changma in Japan and Korea Peninsula, respectively). For China, the mei-yu rainfall provides nearly 45% of summer precipitation over the Yangtze River valley (Ding et al. 2007; Zhou et al. 2009a), a very important water resource for the highly dense population and economic centers in eastern China. Global warming is expected to alter the hydrological cycle with impacts on global and regional precipitation (Wu et al. 2013, 2015). Given its great impact on local agriculture and industrial activities, how the mei-yu rainfall will change in the warmer future has attracted increasing interest during the past decade (e.g., Chen and Sun 2009; Sun and Ding 2010; Kusunoki and Arakawa 2012; Chen and Sun 2013; Jiang and Tian 2013; Seo et al. 2013; Kitoh 2017).

Climate models are an indispensable tool for future climate projection including the mei-yu front rainfall, but an evident underestimation of the present-day rainfall intensity would impair the reliability of their projected results (Chen and Sun 2013; Song and Zhou 2014a). Such a model bias is long-lasting from CMIP3 to CMIP5 with limited improvements over decades of effort (Song and Zhou 2014b; Kusunoki and Arakawa 2015). One possible reason is that the coarse atmospheric resolution in most of the current coupled models cannot sufficiently resolve the mei-yu front since its meridional width is commonly around 500 km (Xu et al. 2009). Previous studies have shown that higher resolution of an atmospheric general circulation model (AGCM) can improve the spatial pattern of the mei-yu rainfall in the stand-alone atmosphere or coupled system, as well as rainfall frequency and intensity (Kitoh and Kusunoki 2008; Chen and Sun 2013; Kusunoki and Arakawa 2015). A recent study by Yao et al. (2017) has shown that rainfall amount in the mei-yu–baiu region is closer to observations in the higher-resolution CMIP5 AGCMs, owing to enhanced moisture convergence from an improvement in simulated stationary waves. High-resolution AGCMs can also improve the climatological simulation upstream of the mei-yu region, such as water transport over the Tibetan Plateau and spring rainfall in the southeastern China (Feng and Zhou 2015; Li et al. 2017).

High-resolution climate projections with fully coupled atmosphere–ocean general circulation models (AOGCMs) are still limited by computational challenges despite clear scientific benefits and ever increasing computer powers. A compromise is regional dynamical downscaling in order to realize the benefits of higher resolution within viable computational capability by projecting GCM projections to a targeted domain using a regional climate model (RCM) and/or for a specific period (Shi et al. 2009; Niu et al. 2015; Zou and Zhou 2016a,b). This is the so-called time-slice experiment. Previous attempts using RCM downscaling show that future rainfall changes over the mei-yu region are also highly uncertain and largely controlled by the driving GCM (Niu et al. 2015; Zou and Zhou 2016b).

The time-slice experiment is implemented by another way of exploring the benefits of high-resolution climate projections for a given period of time, usually with a high-resolution AGCM driven by prescribed sea surface temperatures (SSTs) from a coarse-resolution AOGCM projection (Kusunoki et al. 2006, 2011; Feng et al. 2011). This approach has revealed more robust future increase in the mei-yu rainfall across different SST boundary conditions (Kusunoki et al. 2011; Kitoh and Endo 2016) and convection parameterization schemes (Ose 2017). However, the lack of air–sea coupling limits the credibility of these results because strong negative feedback from the atmosphere plays an important role in climate variability in the western North Pacific (WNP) region (Zhou et al. 2009b) and excessive rainfall bias over the WNP is closely related to deficient bias in the mei-yu region in the CMIP5 AGCMs (Chen and Zhou 2014). Air–sea coupling in high-resolution models has strong impacts on the hydrological cycle (Roberts et al. 2016, 2018).

In this study, we compare future projections of mei-yu rainfall between a high- and low-resolution version of the fully coupled Met Office Unified Model (MetUM). There is a clear indication that coarse climate models may significantly underestimate projected changes of the mei-yu rainfall in the future, associated with a bias in simulating the future response of the WNP subtropical high (WNPSH) in the coarse resolution. Dynamical analysis suggests that the interaction between high-latitude wave activity and low-latitude convection plays an important role. To our knowledge, this is the first time in the monsoon community that a high-resolution global ocean–atmosphere coupled model is used to address the uncertainty in the projection of East Asian monsoon changes.

The remainder of this paper is organized as follows. Section 2 describes the model and data and three methods. The main results are presented in section 4, followed by conclusions and a brief discussion in section 5.

2. Model and data

In this study, we compare the simulated results at two atmospheric resolutions from the latest coupled configuration of MetUM, the HadGEM3 Global Coupled Configuration 2.0 (HadGEM3-GC2). The details of HadGEM3-GC2 are documented in Williams et al. (2015). Here only the most relevant information is provided. The two Gaussian grid resolutions are N96 and N216 with approximately 130- and approximately 60-km midlatitude grid spacing, respectively. Apart from the different horizontal resolutions, other aspects of the configurations including atmospheric vertical resolution (85 levels), physical schemes and ocean model (~0.25° latitude–longitude, 75 levels), and land and sea ice components are the same between N96 and N216. Hence differences found in the analysis of N216 from N96 can be attributed to the increased horizontal resolution.

The climatology of N216 and its variability across a range of time scales are evaluated in Williams et al. (2015), which shows significant improvement in mean and variability biases over the previous coupled version. On a global scale, increasing atmospheric resolution of HadGEM3-GC2 shows little impact on the future climate responses in idealized climate change simulations (Senior et al. 2016). Nevertheless, regional responses, especially for the East Asian summer monsoon, in HadGEM3-GC2 with different resolutions have not been well examined. In addition, the high-resolution MetUM has shown good performance in precipitation characteristics in eastern China (Zhang et al. 2016) and improvement in monsoon intraseasonal variability (Fang et al. 2017a,b).

To evaluate performance of HadGEM3-GC2 in simulating present-day mei-yu climatology, the observational monthly precipitation with 0.25° latitude–longitude resolution from the Tropical Rainfall Measuring Mission 3B43 dataset (TRMM 3B43, version 7) provided by National Aeronautics and Space Administration (Huffman et al. 2007) is used. The Japanese 55-year Reanalysis (JRA-55) from the Japan Meteorological Agency (Kobayashi et al. 2015; Harada et al. 2016) is also used to evaluate moisture transport related to the mei-yu rainfall in HadGEM3-GC2, including evaporation and three-dimensional moisture flux.

In this study, we analyze the simulations of HadGEM3-GC2 in the historical period (1851–2005) and under two representative concentration pathway (RCP) scenarios (2006–2100), in which the radiative forcing is around 4.5 and 8.5 W m−2 in 2100 (i.e., RCP4.5 and RCP8.5; Moss et al. 2010). Since 30-yr climatology can sufficiently reduce most internal variability, only one realization of N96 and N216 is used. Projected changes are the climatological differences between the present (1976–2005) and future (2070–99), with a focus here on the difference between N216 and N96 in the projected change. Monthly output during June and July are used including precipitation P, evaporation E, longwave (LW) and shortwave (SW) radiation at the top of atmosphere (TOA) and the surface, upward sensible (SH) and latent heat (LH) at the surface, sea level pressure (SLP), tropospheric temperature T, zonal and meridional winds (u and υ), vertical pressure velocity ω, specific humidity q, and geopotential height z. For pattern comparison, the fields from N216 are remapped onto the N96 grid by bilinear interpolation.

To compare with the results of HadGEM3-GC2, precipitation data in historical and RCP8.5 simulations of 36 state-of-the-art climate models from the CMIP5 archive (Taylor et al. 2012) are also used. Each model is classified into a “high” or “low” resolution group if mean horizontal grid spacing is finer or coarser than 1.5°, respectively (Table 1). The resolutions of CMIP5 models are relatively low (only one higher than 1° in the 36 models), and they are not designed for studying the effect of increasing resolution since they may differ in many other aspects. Nonetheless, the simply classified high-resolution and low-resolution groups can help us address whether the result based on HadGEM3-GC2 is model dependent. The precipitation data from CMIP5 models are remapped onto a common 2.5° latitude–longitude grid by bilinear interpolation.

Table 1.

High- and low-resolution groups of 36 CMIP5 models. Mean grid spacing finer than 1.5° in latitude/longitude is grouped into high resolution and the others into low resolution. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

High- and low-resolution groups of 36 CMIP5 models. Mean grid spacing finer than 1.5° in latitude/longitude is grouped into high resolution and the others into low resolution. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)
High- and low-resolution groups of 36 CMIP5 models. Mean grid spacing finer than 1.5° in latitude/longitude is grouped into high resolution and the others into low resolution. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

3. Method

The moist static energy (MSE) and moisture budget analysis in local atmosphere column are useful tools to understand anomalous rainfall and upward motion in deep convective regions, widely used for explaining climatological change and bias in monsoon regions and intermodel spread (Chou and Neelin 2003; Chou and Lan 2012; Chen and Bordoni 2014; Yao et al. 2017; Wu et al. 2017a,b). All the budget analysis and regional averages are implemented on the original grid of each model to avoid introducing any artificial information from interpolation.

a. Moist static energy budget analysis

The MSE budget over a climatological period is

 
formula

where h = CpT + gz + Lυq is the MSE, which is the sum of internal energy, CpT; geopotential energy, gz; and latent energy, Lυq; and Cp is the specific heat at constant pressure, Lυ is the specific latent heat of vaporization, Fnet is the net energy flux into the atmosphere, V is the horizontal wind vector, and ω is the vertical p velocity, where p is pressure. The residual term denotes contribution from submonthly transient eddies and data or calculation errors. Also, 〈X〉 is a mass-weighted vertical integral (i.e., ) and is a long-term mean. For simplicity, Cp and Lυ will be omitted in the advection terms related to internal energy and latent energy.

Because (i) horizontal advection of geopotential energy is zero due to the geostrophic relation, (ii) the tendency of the MSE can be neglected for a long-term mean, and (iii) the upward gradient of the MSE is always positive in free troposphere (), Eq. (1) can be modified to diagnose upward motion as

 
formula
 
formula

where the subscripts s and t on the SW and LW radiation terms denote the surface and TOA, respectively. A positive value of indicates upward motion (negative ω), and vice versa. The residual term can be estimated by minus the horizontal energy advection and Fnet on the right-hand side to measure how accurate the diagnosis is.

The horizontal advection in Eq. (2) can be further decomposed into stationary and transient terms as

 
formula
 
formula
 
formula

where and are climatological zonal mean and stationary eddy, respectively; is transient variation. Then we can distinguish the contributions from different time and spatial scales to climatology.

b. Moisture budget analysis

The moisture budget deals with the mass conservation of water substance in an atmospheric column, as opposed to the MSE budget which is based on energy conservation. Considering the negligible moisture tendency in a long-term mean, climatological precipitation can be diagnosed as

 
formula

where P is precipitation, E is evaporation into atmosphere, and q is specific humidity. Total moisture flux convergence is decomposed into terms of circulation convergence and moisture advection.

There are two approaches widely used to further decompose the moisture convergence (e.g., Chen and Bordoni 2014; Endo and Kitoh 2014; Chen and Zhou 2015; Yao et al. 2017). One is following Eqs. (4)(6) to yield stationary and transient contributions. The other is to yield dynamic and thermodynamic contributions in a context of climate change. Based on Eq. (7), considering a climatological change in P, we have

 
formula
 
formula

where is the reference climatology, which here is the historical period (1976–2005). The new terms in Eq. (8) are dynamic (only change in circulation), thermodynamic (only change in moisture), and nonlinear (change in both circulation and moisture) components and the residual. Both decomposition approaches are used in this study to confirm the dominant physical processes responsible for differences in moisture supply between the N216 and N96 resolutions.

4. Results

a. Evaluation of present-day mei-yu rainfall simulation

Figure 1 shows the climatology of June–July rainfall over East Asia in N96 and N216 resolutions compared to the TRMM data during 1998–2005, the overlapping period of historical simulation and available TRMM observation. Both N96 and N216 can reproduce the rainfall along the mei-yu–baiu front stretching from the Yangtze River valley to the south of Japan. In the higher resolution (N216), the pattern of more rainfall in the eastern part of the mei-yu region (red box) than the western is evidently improved (Fig. 1c). The lower-resolution model (N96) overestimates the rainfall by about 1 mm day−1 averaged in the mei-yu region (Fig. 2a). The positive bias is largely reduced in N216 (Fig. 2c) except for underestimation in the northern part (Fig. 2b).

Fig. 1.

Climatology of June–July precipitation (mm day−1) in East Asia during 1998–2005 for (a) TRMM and HadGEM3-GC2 at (b) N96 and (c) N216 resolutions. Red box denotes the region of mei-yu front (27°–33°N, 108°–122°E). (d) Moisture budget in the mei-yu region based on Eqs. (6) and (7), including precipitation, evaporation, zonal and meridional moisture flux convergence, and residual (to the left of the dashed line) and major processes contributing to moisture flux convergence (to the right of the dashed line). Observational precipitation is from TRMM, and other observational terms are derived from JRA-55.

Fig. 1.

Climatology of June–July precipitation (mm day−1) in East Asia during 1998–2005 for (a) TRMM and HadGEM3-GC2 at (b) N96 and (c) N216 resolutions. Red box denotes the region of mei-yu front (27°–33°N, 108°–122°E). (d) Moisture budget in the mei-yu region based on Eqs. (6) and (7), including precipitation, evaporation, zonal and meridional moisture flux convergence, and residual (to the left of the dashed line) and major processes contributing to moisture flux convergence (to the right of the dashed line). Observational precipitation is from TRMM, and other observational terms are derived from JRA-55.

Fig. 2.

Present-day climatology bias (shading) of HadGEM3-GC2 at (left) N96 and (center) N216 resolutions relative to observations and (right) differences between HadGEM3-GC2 N216 and N96 resolutions. Contours are observational climatology. Shown are (a),(b) precipitation bias (mm day−1) relative to TRMM during 1998–2005, with values averaged over the mei-yu region (red box) shown at the top-right corner, and biases of (d),(e) SLP (hPa), (g),(h) meridional wind at 850 hPa (m s−1), and (j),(k) zonal wind at 200 hPa (m s−1) relative to JRA-55 during 1976–2005. Dotted areas exceed the 5% significance level of the Student’s t test.

Fig. 2.

Present-day climatology bias (shading) of HadGEM3-GC2 at (left) N96 and (center) N216 resolutions relative to observations and (right) differences between HadGEM3-GC2 N216 and N96 resolutions. Contours are observational climatology. Shown are (a),(b) precipitation bias (mm day−1) relative to TRMM during 1998–2005, with values averaged over the mei-yu region (red box) shown at the top-right corner, and biases of (d),(e) SLP (hPa), (g),(h) meridional wind at 850 hPa (m s−1), and (j),(k) zonal wind at 200 hPa (m s−1) relative to JRA-55 during 1976–2005. Dotted areas exceed the 5% significance level of the Student’s t test.

Here the moisture budget diagnosis [Eq. (7)] is used to show contributions from different physical processes to the observed and modeled climatology. As shown in Fig. 1d, mei-yu rainfall is mainly contributed by local evaporation and meridional moisture flux convergence [] partly offset by zonal divergence []. The convergence and divergence of stationary eddies and , respectively, play a dominant role. The negligible residual terms indicate robustness of the diagnosis using monthly data in the mei-yu region (Fig. 1d) owing to minor contribution from submonthly transient eddies (Chen and Bordoni 2014; Li et al. 2015; Yao et al. 2017). In contrast to the improvement in total rainfall amount and the pattern in N216, the higher-resolution model underestimates both the meridional moisture convergence and zonal divergence, and overestimates evaporation, whereas those in N96 are closer to JRA-55 (Fig. 1d). The underestimation in N216 relates to a weaker WNPSH (Fig. 2e) associated with southward shift of westerly jet in the upper troposphere (Fig. 2k). The weaker WNPSH moves eastward and reduces (enhances) the southerlies over southern China (the western North Pacific) (Fig. 2h), leading to weaker moisture convergence in the mei-yu region (Fig. 1d). However, similar circulation biases already exist in N96 (Figs. 2d,g,j). Simply increasing resolution without tuning other aspects does not necessarily lead to an all-sided improvement. A recent study has shown that improvement in the mei-yu rainfall in high-resolution AGCMs usually accompanies with some degradation in simulating circulation (Yao et al. 2017).

The differences in present climate simulation between the high- and low-resolution models may propagate to future projection, which was reported in previous studies by comparing RCMs and their driving low-resolution GCMs (Liang et al. 2008; Liu et al. 2013). Since the bias in mei-yu precipitation is closely related to key circulation systems, such as the WNPSH and East Asian jet (Liang and Wang 1998; Liang et al. 2001), the simulated weaker WNPSH and southward shifted East Asian jet in present climate with N216 resolution may imply greater decreasing rainfall in the warmer future than that with N96 resolution. The following analysis will show if the bias propagation is still valid for the fully coupled HadGEM3-GC2.

b. Comparison of mei-yu rainfall projection in different resolution models

The projected June–July rainfall over East Asia in N96 and N216 resolutions is shown in Fig. 3. Under both the warming scenarios, RCP4.5 and RCP8.5, rainfall in N96 decreases along the mei-yu front over the Yangtze River valley, accompanied by descending anomalies indicating weakened upward motion (Figs. 3a,b). The anomalous dryness center in RCP8.5 is located slightly northward compared to that in RCP4.5. In contrast to the low resolution, the projected mei-yu rainfall in N216 shows a significant increase at the end of the twenty-first century (Figs. 3e,f), which is opposite to the pattern in present climate simulation (Fig. 2c). In addition, negative rainfall anomalies in the difference between the N216- and N96-resolution projections are seen near the coast of southern China and over the WNP (Figs. 3e,f) whereas it is positive in present climate simulation (Fig. 2c). Hence, the bias propagation does not happen everywhere in HadGEM3-GC2 expect in northern China under the RCP8.5 scenario (Figs. 2c and 3f).

Fig. 3.

Projected rainfall (shading; mm day−1) and vertical p velocity (contours; 10−3 Pa s−1) changes under the (a),(c),(e) RCP4.5 and (b),(d),(f) RCP8.5 scenarios in HadGEM3-GC2 at (a),(b) N96 and (c),(d) N216 resolutions and (e),(f) the differences between N216 and N96 resolutions. Contours are drawn for ±35, ±21, and ±7 × 10−3 Pa s−1 (dashed contours are negative values). Dotted areas exceed the 5% significance level of the Student’s t test. Red box in (e),(f) denotes the mei-yu region as in Fig. 1.

Fig. 3.

Projected rainfall (shading; mm day−1) and vertical p velocity (contours; 10−3 Pa s−1) changes under the (a),(c),(e) RCP4.5 and (b),(d),(f) RCP8.5 scenarios in HadGEM3-GC2 at (a),(b) N96 and (c),(d) N216 resolutions and (e),(f) the differences between N216 and N96 resolutions. Contours are drawn for ±35, ±21, and ±7 × 10−3 Pa s−1 (dashed contours are negative values). Dotted areas exceed the 5% significance level of the Student’s t test. Red box in (e),(f) denotes the mei-yu region as in Fig. 1.

The above result shows a substantial impact of increased resolution on the mei-yu rainfall projection in HadGEM3-GC2, but how much does this depend on the single model? A similar result is found in an early study using time-slice experiments of an AGCM from the Meteorological Research Institute in Japan (Kusunoki et al. 2006), in which the mei-yu rainfall decreases in the lowest resolution (~270 km) under warming conditions whereas it increases in higher-resolution simulations. An intensified rain belt in the projection of another high-resolution coupled model [MIROC3.2 (hires)], more evident than other CMIP3 AOGCMs, was also reported (Min and Jhun 2010; Seo and Ok 2013). Moreover, here we show the projected rainfall changes from the high- and low-resolution groups of 36 CMIP5 models (Fig. 4). For the low-resolution group (with mean grid spacing larger than 1.5° in latitude/longitude), a minimum center of rainfall increase associated with weakening of upward motion is seen in the mei-yu region (Fig. 4a) although no evident drying occurs as in the N96 resolution. Projected decreasing rainfall over the lower reaches of the Yangtze River basin is also observed in the ensemble mean of some selected CMIP5 models (Seo et al. 2013). In contrast, in the high-resolution group (with mean grid spacing smaller than 1.5° in latitude/longitude), the mei-yu rainfall increases more under the RCP8.5 scenario (Fig. 4b). Hence, the effect of higher resolution on more projected mei-yu rainfall and stronger upwelling is also observed in the CMIP5 models (Fig. 4c), consistent with the result of HadGEM3-GC2 (Figs. 3e,f).

Fig. 4.

Projected rainfall (shading; mm day−1) and vertical p velocity (contours; 10−3 Pa s−1) changes under RCP8.5 scenarios in CMIP5 (a) low- and (b) high-resolution ensembles and (c) differences between the high- and low-resolution ensembles. High- and low-resolution groups are classified by mean grid spacing smaller and larger than 1.5°, respectively (see Table 1). Contours are drawn for ±14, ±10, ±6 and ±2 × 10−3 Pa s−1 (dashed contours are negative values). Dotted areas exceed the 5% significance level of the Student’s t test. Red box in (c) denotes the mei-yu region as in Fig. 1.

Fig. 4.

Projected rainfall (shading; mm day−1) and vertical p velocity (contours; 10−3 Pa s−1) changes under RCP8.5 scenarios in CMIP5 (a) low- and (b) high-resolution ensembles and (c) differences between the high- and low-resolution ensembles. High- and low-resolution groups are classified by mean grid spacing smaller and larger than 1.5°, respectively (see Table 1). Contours are drawn for ±14, ±10, ±6 and ±2 × 10−3 Pa s−1 (dashed contours are negative values). Dotted areas exceed the 5% significance level of the Student’s t test. Red box in (c) denotes the mei-yu region as in Fig. 1.

To understand why the projected upward motions over the mei-yu region become stronger and then lead to more rainfall in the higher-resolution model, we analyze energy and moisture budgets in the local atmospheric column based on N96 and N216 versions of HadGEM3-GC2 in the next section.

c. Moist static energy budget diagnostics

Analysis of the MSE budget can reveal the drivers of upward motion in a certain region. Based on Eq. (2), the diagnosed vertical MSE advection is shown in Fig. 5. The stronger in the N216 resolution (Figs. 5e,f) is consistent with the enhanced upward motion and rainfall (Figs. 3e,f) under both of the scenarios. For RCP4.5, the difference between N216 and N96 is mainly contributed by enhanced upward motion in N216 (Fig. 5c) whereas for RCP8.5 the contribution is mainly from strong downward motion in N96 (Fig. 5b). Since vertical motion is determined by horizontal advection of internal and latent energy [Eq. (2)], further decomposition (Fig. 6) is implemented following Eqs. (4)(6) to show which process enhances mei-yu rainfall projected in N216.

Fig. 5.

Diagnosed vertical MSE advection (; W m−2) by MSE budget analysis () under (a),(c),(e) RCP4.5 and (b),(d),(f) RCP8.5 scenarios in HadGEM3-GC2 at (a),(b) N96 and (c),(d) N216 resolutions and (e),(f) difference between N216 and N96 resolutions. Dotted areas exceed the 5% significance level of the Student’s t test. Black box in (e),(f) denotes the mei-yu region as in Fig. 1.

Fig. 5.

Diagnosed vertical MSE advection (; W m−2) by MSE budget analysis () under (a),(c),(e) RCP4.5 and (b),(d),(f) RCP8.5 scenarios in HadGEM3-GC2 at (a),(b) N96 and (c),(d) N216 resolutions and (e),(f) difference between N216 and N96 resolutions. Dotted areas exceed the 5% significance level of the Student’s t test. Black box in (e),(f) denotes the mei-yu region as in Fig. 1.

Fig. 6.

Decomposed differences between N216 and N96 resolutions in horizontal MSE advection (W m−2) averaged in the mei-yu region (red box in Figs. 3e,f) under RCP4.5 (blue bar) and RCP8.5 (magenta bar) scenarios. Decomposition of the MSE budget into (a) total radiation and heat into atmospheric column Fnet, zonal and meridional advection of internal (T-related terms) and latent energy (q-related terms), and residual. (b) Decomposition of MSE advection into transient terms and stationary terms . (c) Decomposition of stationary MSE advection into contributions from zonal mean and stationary eddy terms and their interactions. The last two terms to the right of the dashed line in (a) are meridional advection of internal and latent energy by the stationary eddy.

Fig. 6.

Decomposed differences between N216 and N96 resolutions in horizontal MSE advection (W m−2) averaged in the mei-yu region (red box in Figs. 3e,f) under RCP4.5 (blue bar) and RCP8.5 (magenta bar) scenarios. Decomposition of the MSE budget into (a) total radiation and heat into atmospheric column Fnet, zonal and meridional advection of internal (T-related terms) and latent energy (q-related terms), and residual. (b) Decomposition of MSE advection into transient terms and stationary terms . (c) Decomposition of stationary MSE advection into contributions from zonal mean and stationary eddy terms and their interactions. The last two terms to the right of the dashed line in (a) are meridional advection of internal and latent energy by the stationary eddy.

It is noted that all the MSE budget terms in Fig. 6 are differences of projected changes between N216 and N96 resolutions. As shown in Fig. 6a, meridional advection of internal and latent energy ( and , respectively) are the major contributors to stronger MSE advection in N216 under both of the RCP4.5 and RCP8.5 scenarios, except for additional contributions from zonal advection of internal energy () for RCP4.5. The residual term is small relative to the major advection terms, showing robustness of the MSE diagnosis in the mei-yu region. Contributions from net radiation and heat flux into the atmosphere are also negligible. Decomposition based on Eq. (6) shows that stationary terms ( and ) play a more important role than transient terms ( and ) in meridional MSE advection except for a large contribution from transient latent energy advection for RCP8.5 (Fig. 6b). For the stationary terms, meridional advection (, , , and ) associated with the meridional wind eddy contribute the most (Fig. 6c). Thus, we sum the terms for internal and latent energy advection and show them as the last two terms in Fig. 6a (to the right of the dashed line). Obviously, these two terms ( and ) can explain most of the meridional MSE advection, which means that the processes of stationary meridional wind eddy advecting climatological temperature and moisture mainly lead to the stronger convection over the mei-yu region in higher-resolution projection.

To make it clearer, the spatial patterns of meridional-eddy-related advection terms in Fig. 6a (right of the dashed line) are shown in Fig. 7. The sum of internal and latent energy advection (Figs. 7e,f) is consistent with the diagnosed vertical MSE advection (Figs. 5e,f) and explains the more mei-yu rainfall projected in the N216 resolution (Figs. 3e,f). The latent energy advection plays a larger role in the enhanced upward motion along the mei-yu front than that of internal energy, especially under the RCP4.5 scenario (Fig. 7c).

Fig. 7.

Differences of projected changes between N216 and N96 resolutions in vertically integrated meridional advection (W m−2) of (a),(b) internal and (c),(d) latent energy by stationary wind eddy and (e),(f) the sum of them under (a),(c),(e) RCP4.5 and (b),(d),(f) RCP8.5 scenarios. Dotted areas exceed the 5% significance level of the Student’s t test. The black box in (e),(f) denotes the mei-yu region as in Fig. 1.

Fig. 7.

Differences of projected changes between N216 and N96 resolutions in vertically integrated meridional advection (W m−2) of (a),(b) internal and (c),(d) latent energy by stationary wind eddy and (e),(f) the sum of them under (a),(c),(e) RCP4.5 and (b),(d),(f) RCP8.5 scenarios. Dotted areas exceed the 5% significance level of the Student’s t test. The black box in (e),(f) denotes the mei-yu region as in Fig. 1.

The above analysis demonstrates the important role of in triggering upward motion by MSE advection, but what is its role in the enhanced rainfall in higher-resolution projection from the perspective of moisture supply?

d. Moisture budget analysis

By decomposing moisture budget into dynamic and thermodynamic contributions [Eq. (8)], we find that most of the increased mei-yu rainfall in the N216-resolution projection relative to N96 can be explained by the dynamic term [; Fig. 8a], while the difference in evaporation is negligible. For the dynamic term, meridional wind convergence () is the largest contributor, partly cancelled by the zonal term (; Fig. 8b), which is consistent with the negative effect of zonal moisture convergence in the present-day climate (Fig. 1d). Using the same decomposition approach as the MSE budget diagnostics [Eq. (6)], it consistently shows that meridional moisture flux convergence [] dominates, partly offset by the zonal term [; Fig. 8a]. Further decomposition shows that a major contribution is from meridional convergence of wind eddy ( and in Fig. 8b). The above results are similar between the RCP4.5 and RCP8.5 scenarios. Hence, dynamic factors, specifically the meridional convergence of , also enhance moisture supply to the mei-yu region in the N216-resolution projection.

Fig. 8.

Decomposed differences between N216 and N96 resolutions in moisture budget (mm day−1) averaged in the mei-yu region (red box in Figs. 3e,f) under RCP4.5 (blue bar) and RCP8.5 (magenta bar) scenarios. (a) Left of the dashed line: moisture flux convergence decomposed into dynamic , thermodynamic , and nonlinear terms based on Eq. (8); and right of the dashed line: moisture flux convergence decomposed into zonal and meridional directions. (b) Left of the dashed line: decomposition of dynamic terms in (a); and right to the dashed line: decomposition of meridional convergence into transient and stationary terms, the latter are further decomposed into contributions from zonal mean and stationary eddy and their interactions based on Eq. (6).

Fig. 8.

Decomposed differences between N216 and N96 resolutions in moisture budget (mm day−1) averaged in the mei-yu region (red box in Figs. 3e,f) under RCP4.5 (blue bar) and RCP8.5 (magenta bar) scenarios. (a) Left of the dashed line: moisture flux convergence decomposed into dynamic , thermodynamic , and nonlinear terms based on Eq. (8); and right of the dashed line: moisture flux convergence decomposed into zonal and meridional directions. (b) Left of the dashed line: decomposition of dynamic terms in (a); and right to the dashed line: decomposition of meridional convergence into transient and stationary terms, the latter are further decomposed into contributions from zonal mean and stationary eddy and their interactions based on Eq. (6).

e. Large-scale mechanisms of increased mei-yu rainfall in higher-resolution projection

The meridional wind eddy is associated with an anomalous anticyclone over the WNP shown in the difference of projected circulation changes between N216 and N96 resolutions (Fig. 9). The western edge of the anticyclone extends over southern China and leads to positive temperature and moisture advection over the mei-yu region (Fig. 9). Moist and warm flows in the middle and lower troposphere provide unstable energy and trigger upward motion. Meridional wind convergence at the northwestern edge of the anticyclone further supplies plenty of moisture into the convective region. For RCP4.5, the anticyclone at 500 hPa is located around 25°N, nearly 5° latitude southward shift relative to that in RCP8.5 (Figs. 9a,b), which results in a strong zonal temperature advection over southern China. That explains why zonal advection of internal energy also contributes a large part of the MSE budget under the RCP4.5 scenario (Fig. 6a).

Fig. 9.

Advective effects of differences between N216 and N96 resolutions from the stationary eddy at typical levels under (a),(c) RCP4.5 and (b),(d) RCP8.5 scenarios. (a),(b) Climatological temperature during 1976–2005 (shading; K) and anomalous stationary eddy (vectors; m s−1) at 500 hPa during 2070–99. (c),(d) Climatological specific humidity during 1976–2005 (shading; g kg−1) and anomalous stationary eddy (vectors; m s−1) at 850 hPa during 2070–99. Winds less than 0.5 m s−1 are omitted. Gray shading in (c),(d) denotes orography above 850 hPa.

Fig. 9.

Advective effects of differences between N216 and N96 resolutions from the stationary eddy at typical levels under (a),(c) RCP4.5 and (b),(d) RCP8.5 scenarios. (a),(b) Climatological temperature during 1976–2005 (shading; K) and anomalous stationary eddy (vectors; m s−1) at 500 hPa during 2070–99. (c),(d) Climatological specific humidity during 1976–2005 (shading; g kg−1) and anomalous stationary eddy (vectors; m s−1) at 850 hPa during 2070–99. Winds less than 0.5 m s−1 are omitted. Gray shading in (c),(d) denotes orography above 850 hPa.

The anticyclonic anomaly means an apparently strengthened WNPSH in the N216-resolution projection, reflected by the increased SLP over the WNP (Fig. 10a). Moreover, the SLP anomalies extending eastward to the central North Pacific indicates that the changes of the WNPSH are associated with the main body of North Pacific subtropical high. We use the extreme west longitude of the zero isoline of stationary eddy geopotential height z* at 850 hPa to represent the location of the WNPSH (He et al. 2018). As shown in Fig. 8a, the WNPSH is nearly unchanged at N216 resolution under the RCP8.5 scenario (from 151.2° to 151.8°E), whereas it retreats eastward about 4.5° longitude at N96 resolution (from 147.1° to 151.6°E). Therefore, the decreased mei-yu rainfall under warming in N96 (Figs. 3a,b) can be attributed to the eastward retreat of the WNPSH. The retreat of the WNPSH in N96 weakens the southwesterly over southern China and reduces moisture flux convergence to the mei-yu region. The mechanism is identical with that related to the N216 bias of underestimating the present-day mei-yu rainfall (Figs. 2b,e,h). Similar to the N96-resolution projection, the WNPSH in CMIP5 models are suggested to retreat eastward as response to global warming in the recently published studies (He et al. 2015, 2017). However, the results from N216 imply that the WNPSH may not retreat very much in higher-resolution projections. Considering that most of the CMIP5 models are even coarser than N96, the effect of increasing model resolution on mei-yu rainfall projection is significant enough (Fig. 4c) to overwhelm the large uncertainty across CMIP5 models (Sperber et al. 2013; Kitoh et al. 2013).

Fig. 10.

Differences between N216 and N96 resolutions in projected circulation changes and associated wave activity flux under RCP8.5 scenario, (a) SLP (shading; hPa), wave activity flux at 850 hPa (vectors; m2 s−2), and zero isoline of geopotential height eddy at 850 hPa (m). Solid (dashed) lines denote N216 (N96), and black (red) lines denote historical (RCP8.5) simulation. (b) Geopotential height eddy (shading; m) and wave activity flux at 200 hPa (vectors; m2 s−2). Wave activity flux is calculated following the formula in Takaya and Nakamura (2001). (c) Precipitation (solid shading) and sea surface temperature (SST; hatching). Light green (orange) shading denotes rainfall anomalies larger (less) than 1 mm day−1. Red (blue) hatching denotes SST anomalies larger (less) than 0.2 (−0.2) K. Dotted areas in (a),(b) exceed the 5% significance level of the Student’s t test.

Fig. 10.

Differences between N216 and N96 resolutions in projected circulation changes and associated wave activity flux under RCP8.5 scenario, (a) SLP (shading; hPa), wave activity flux at 850 hPa (vectors; m2 s−2), and zero isoline of geopotential height eddy at 850 hPa (m). Solid (dashed) lines denote N216 (N96), and black (red) lines denote historical (RCP8.5) simulation. (b) Geopotential height eddy (shading; m) and wave activity flux at 200 hPa (vectors; m2 s−2). Wave activity flux is calculated following the formula in Takaya and Nakamura (2001). (c) Precipitation (solid shading) and sea surface temperature (SST; hatching). Light green (orange) shading denotes rainfall anomalies larger (less) than 1 mm day−1. Red (blue) hatching denotes SST anomalies larger (less) than 0.2 (−0.2) K. Dotted areas in (a),(b) exceed the 5% significance level of the Student’s t test.

The shift of the WNPSH is associated with the stationary wave activity in the subpolar region (Fig. 10a). There is a low–high dipole over the subpolar and subtropical North Pacific near the surface in the differences between the N216- and N96-resolution projections (Fig. 10a). At the upper troposphere (200 hPa), the dipole shifts 1/4 phase northward and is associated with a wave train propagating from the subpolar (~60°N) to tropical Pacific (~20°N; Fig. 10b). The cyclonic anomalies in the upper tropical troposphere could induce convergence and downwelling, eventually causing suppression and deficient rainfall around 20°N (Fig. 10c).

As a result, high pressure anomalies over the ocean surface enhance the circulation related to the WNPSH and then the mei-yu rainfall (Figs. 10a,c). On the latitude–pressure profile over East Asia, corresponding to baroclinic anomalies within 10°–40°N (Fig. 11), the suppression over the WNP (~20°N) and convection over the mei-yu region (~30°N) can reinforce each other through the local Hadley circulation. Negative heating from the western to central Pacific could perturb the atmosphere and propagate northward in the lower troposphere (Fig. 10a), similar to the feature of energy dispersion in the Pacific–Japan teleconnection pattern (Kosaka and Nakamura 2010). Therefore, at N216 compared with N96, interactions between extratropical stationary wave activity and convection over the western North Pacific keep the WNPSH steady. With unchanged circulation in the higher-resolution model, more moisture in warmer future will naturally increase the projected mei-yu rainfall.

Fig. 11.

Pressure–latitude profile of differences between N216 and N96 resolutions in projected circulation changes averaged within 110°–130°E under RCP8.5 scenario. Meridional wind eddy (m s−1) and inverted vertical p velocity anomalies (vectors; 10−3 Pa s−1) and geopotential height eddy anomalies (shading; m). Dotted areas exceed the 5% significance level of the Student’s t test. Domain between the green dashed lines denotes the mei-yu region.

Fig. 11.

Pressure–latitude profile of differences between N216 and N96 resolutions in projected circulation changes averaged within 110°–130°E under RCP8.5 scenario. Meridional wind eddy (m s−1) and inverted vertical p velocity anomalies (vectors; 10−3 Pa s−1) and geopotential height eddy anomalies (shading; m). Dotted areas exceed the 5% significance level of the Student’s t test. Domain between the green dashed lines denotes the mei-yu region.

5. Conclusions and discussion

Uncertainties in regional precipitation projections remain persistently large within the latest Intergovernmental Panel on Climate Change (IPCC) report. One of the contributing factors is likely that model resolution prevents sufficient moisture transport from evaporative regions to precipitating regions. Reducing the size of model grid spacing is hopefully one of the solutions for improving hydrological predictions. By comparing low- and high-resolution versions of HadGEM-GC2 (at Gaussian N96 and N216 resolutions), this study investigates the resolution sensitivity of the future mei-yu rainfall projections under the IPCC’s medium-emission (RCP4.5) and high-emission (RCP8.5) scenarios. It is found that the mei-yu rainfall decreases in N96 but increases in N216 at the end of the twenty-first century under both scenarios, consistent with the result from simply grouped high and low resolutions of CMIP5 models. Both the MSE and moisture budget analysis indicate that stationary meridional wind plays the most important role in the more mei-yu rainfall projected in N216 by increasing energy advection and moisture convergence over the Yangtze River valley. Large-scale circulation associated with manifests as an anomalous anticyclone over the WNP, indicating an apparent enhancement of the WNPSH in the N216-resolution projection relative to N96, which is, however, caused by evidently eastward retreat of the WNPSH under warming in N96 while nearly unchanged in N216.

Interactions between stationary wave activity over the subpolar region and tropical convection over the WNP keep the WNPSH steady under warming in N216. Compared with N96, cyclonic anomalies in the tropical upper troposphere around 20°N (one phase in a wave train propagating from high latitudes with large absolute vorticity) can induce high pressure at the ocean surface by suppressing convection over the WNP and then strengthen the WNPSH. Perturbations from the negative heating in turn propagate to the high latitudes in the lower troposphere to reinforce the subpolar stationary wave. The opposite phase of wave activity can explain the eastward retreat of the WNPSH and decreased mei-yu rainfall in the N96-resolution projection.

Since there are no significant atmospheric signals forced by SST anomalies in the tropics (Fig. 10c), the suppressed convection over the WNP may be more forced by circulation anomalies from high latitudes than those from lower latitudes. However, how the stationary waves change with increasing model resolution is still an open question, although perturbations from more realistic orography are a possible reason suggested in some previous studies (Lutsko and Held 2016; Yao et al. 2017). Since the future change of the mei-yu rainfall may be potentially underestimated in coarse-resolution models, similar influences from resolution could occur worldwide. For better understanding regional climate change, high-resolution models are encouraged to be applied in future projections. It must be noted that this investigation is based on using one particular model. The robustness of the conclusions has yet to be verified with multimodel ensembles such as the CMIP6 High Resolution Model Intercomparison Project (HighResMIP; Haarsma et al. 2016).

Acknowledgments

This work was jointly supported by National Natural Science Foundation of China under Grants 41420104006, 41605057, and 41330423, International Partnership Program of the Chinese Academy of Sciences under Grant 134111KYSB20160031, China Postdoctoral Science Foundation under Grant 2015M581152, the Jiangsu Collaborative Innovation Center for Climate Change, and the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund.

REFERENCES

REFERENCES
Chen
,
H.
, and
J.
Sun
,
2009
:
How the “best” models project the future precipitation change in China
.
Adv. Atmos. Sci.
,
26
,
773
782
, https://doi.org/10.1007/s00376-009-8211-7.
Chen
,
H.
, and
J.
Sun
,
2013
:
Projected change in East Asian summer monsoon precipitation under RCP scenario
.
Meteor. Atmos. Phys.
,
121
,
55
77
, https://doi.org/10.1007/s00703-013-0257-5.
Chen
,
J.
, and
S.
Bordoni
,
2014
:
Intermodel spread of East Asian summer monsoon simulations in CMIP5
.
Geophys. Res. Lett.
,
41
,
1314
1321
, https://doi.org/10.1002/2013GL058981.
Chen
,
X.
, and
T.
Zhou
,
2014
:
Relative role of tropical SST forcing in the 1990s periodicity change of the Pacific–Japan pattern interannual variability
.
J. Geophys. Res. Atmos.
,
119
,
13 043
13 066
, https://doi.org/10.1002/2014JD022064.
Chen
,
X.
, and
T.
Zhou
,
2015
:
Distinct effects of global mean warming and regional sea surface warming pattern on projected uncertainty in the South Asian summer monsoon
.
Geophys. Res. Lett.
,
42
,
9433
9439
, https://doi.org/10.1002/2015GL066384.
Chou
,
C.
, and
J. D.
Neelin
,
2003
:
Mechanisms limiting the northward extent of the northern summer monsoons over North America, Asia, and Africa
.
J. Climate
,
16
,
406
425
, https://doi.org/10.1175/1520-0442(2003)016<0406:MLTNEO>2.0.CO;2.
Chou
,
C.
, and
C.-W.
Lan
,
2012
:
Changes in the annual range of precipitation under global warming
.
J. Climate
,
25
,
222
235
, https://doi.org/10.1175/JCLI-D-11-00097.1.
Ding
,
Y.
,
J.
Liu
,
Y.
Sun
,
Y.
Liu
,
J.
He
, and
Y.
Song
,
2007
:
A study of the synoptic-climatology of the meiyu system in East Asia (in Chinese)
.
Chin. J. Atmos. Sci.
,
31
,
1082
1101
.
Endo
,
H.
, and
A.
Kitoh
,
2014
:
Thermodynamic and dynamic effects on regional monsoon rainfall changes in a warmer climate
.
Geophys. Res. Lett.
,
41
,
1704
1711
, https://doi.org/10.1002/2013GL059158.
Fang
,
Y.
, and Coauthors
,
2017a
:
High-resolution simulation of the boreal summer intraseasonal oscillation in Met Office Unified Model
.
Quart. J. Roy. Meteor. Soc.
,
143
,
362
373
, https://doi.org/10.1002/qj.2927.
Fang
,
Y.
,
P.
Wu
,
M. S.
Mizielinski
,
M. J.
Roberts
,
B.
Li
,
X.
Xin
, and
X.
Liu
,
2017b
:
Monsoon intra-seasonal variability in a high-resolution version of Met Office Global Coupled Model
.
Tellus
,
69A
,
1354661
, https://doi.org/10.1080/16000870.2017.1354661.
Feng
,
L.
, and
T.
Zhou
,
2015
:
Simulation of summer precipitation and associated water vapor transport over the Tibetan Plateau by Meteorological Research Institute model (in Chinese)
.
Chin. J. Atmos. Sci.
,
39
,
385
396
.
Feng
,
L.
,
T.
Zhou
,
B.
Wu
,
T.
Li
, and
J.-J.
Luo
,
2011
:
Projection of future precipitation change over China with a high-resolution global atmospheric model
.
Adv. Atmos. Sci.
,
28
,
464
476
, https://doi.org/10.1007/s00376-010-0016-1.
Haarsma
,
R. J.
, and Coauthors
,
2016
:
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6
.
Geosci. Model Dev.
,
9
,
4185
4208
, https://doi.org/10.5194/gmd-9-4185-2016.
Harada
,
Y.
, and Coauthors
,
2016
:
The JRA-55 Reanalysis: Representation of atmospheric circulation and climate variability
.
J. Meteor. Soc. Japan
,
94
,
269
302
, https://doi.org/10.2151/jmsj.2016-015.
He
,
C.
,
T.
Zhou
,
A.
Lin
,
B.
Wu
,
D.
Gu
,
C.
Li
, and
B.
Zheng
,
2015
:
Enhanced or weakened western North Pacific subtropical high under global warming?
Sci. Rep.
,
5
,
16771
, https://doi.org/10.1038/srep16771.
He
,
C.
,
B.
Wu
,
L.
Zou
, and
T.
Zhou
,
2017
:
Responses of the summertime subtropical anticyclones to global warming
.
J. Climate
,
30
,
6465
6479
, https://doi.org/10.1175/JCLI-D-16-0529.1.
He
,
C.
,
A.
Lin
,
D.
Gu
,
C.
Li
,
B.
Zheng
,
B.
Wu
, and
T.
Zhou
,
2018
:
Using eddy geopotential height to measure the western North Pacific subtropical high in a warming climate
.
Theor. Appl. Climatol.
,
131
,
681
691
, https://doi.org/10.1007/s00704-016-2001-9.
Huffman
,
G. J.
, and Coauthors
,
2007
:
The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales
.
J. Hydrometeor.
,
8
,
38
55
, https://doi.org/10.1175/JHM560.1.
Jiang
,
D.
, and
Z.
Tian
,
2013
:
East Asian monsoon change for the 21st century: Results of CMIP3 and CMIP5 models
.
Chin. Sci. Bull.
,
58
,
1427
1435
, https://doi.org/10.1007/s11434-012-5533-0.
Kitoh
,
A.
,
2017
:
The Asian monsoon and its future change in climate models: A review
.
J. Meteor. Soc. Japan
,
95
,
7
33
, https://doi.org/10.2151/jmsj.2017-002.
Kitoh
,
A.
, and
S.
Kusunoki
,
2008
:
East Asian summer monsoon simulation by a 20-km mesh AGCM
.
Climate Dyn.
,
31
,
389
401
, https://doi.org/10.1007/s00382-007-0285-2.
Kitoh
,
A.
, and
H.
Endo
,
2016
:
Changes in precipitation extremes projected by a 20-km mesh global atmospheric model
.
Wea. Climate Extremes
,
11
,
41
52
, https://doi.org/10.1016/j.wace.2015.09.001.
Kitoh
,
A.
,
H.
Endo
,
K.
Krishna Kumar
,
I. F. A.
Cavalcanti
,
P.
Goswami
, and
T.
Zhou
,
2013
:
Monsoons in a changing world: A regional perspective in a global context
.
J. Geophys. Res. Atmos.
,
118
,
3053
3065
, https://doi.org/10.1002/jgrd.50258.
Kobayashi
,
S.
, and Coauthors
,
2015
:
The JRA-55 Reanalysis: General specifications and basic characteristics
.
J. Meteor. Soc. Japan
,
93
,
5
48
, https://doi.org/10.2151/jmsj.2015-001.
Kosaka
,
Y.
, and
H.
Nakamura
,
2010
:
Mechanisms of meridional teleconnection observed between a summer monsoon system and a subtropical anticyclone. Part I: The Pacific–Japan pattern
.
J. Climate
,
23
,
5085
5108
, https://doi.org/10.1175/2010JCLI3413.1.
Kusunoki
,
S.
, and
O.
Arakawa
,
2012
:
Change in the precipitation intensity of the East Asian summer monsoon projected by CMIP3 models
.
Climate Dyn.
,
38
,
2055
2072
, https://doi.org/10.1007/s00382-011-1234-7.
Kusunoki
,
S.
, and
O.
Arakawa
,
2015
:
Are CMIP5 models better than CMIP3 models in simulating precipitation over East Asia?
J. Climate
,
28
,
5601
5621
, https://doi.org/10.1175/JCLI-D-14-00585.1.
Kusunoki
,
S.
,
J.
Yoshimura
,
H.
Yoshimura
,
A.
Noda
,
K.
Oouchi
, and
R.
Mizuta
,
2006
:
Change of baiu rain band in global warming projection by an atmospheric general circulation model with a 20-km grid size
.
J. Meteor. Soc. Japan
,
84
,
581
611
, https://doi.org/10.2151/jmsj.84.581.
Kusunoki
,
S.
,
R.
Mizuta
, and
M.
Matsueda
,
2011
:
Future changes in the East Asian rain band projected by global atmospheric models with 20-km and 60-km grid size
.
Climate Dyn.
,
37
,
2481
2493
, https://doi.org/10.1007/s00382-011-1000-x.
Li
,
P.
,
T.
Zhou
,
L.
Zou
,
X.
Chen
,
W.
Zhang
, and
Z.
Guo
,
2017
:
Simulation of climatology and interannual variability of spring persistent rains by MRI model: Comparison of different horizontal resolutions (in Chinese)
.
Chin. J. Atmos. Sci.
,
41
,
515
532
.
Li
,
X.
,
M.
Ting
,
C.
Li
, and
N.
Henderson
,
2015
:
Mechanisms of Asian summer monsoon changes in response to anthropogenic forcing in CMIP5 models
.
J. Climate
,
28
,
4107
4125
, https://doi.org/10.1175/JCLI-D-14-00559.1.
Liang
,
X.-Z.
, and
W.-C.
Wang
,
1998
:
Associations between China monsoon rainfall and tropospheric jets
.
Quart. J. Roy. Meteor. Soc.
,
124
,
2597
2623
, https://doi.org/10.1002/qj.49712455204.
Liang
,
X.-Z.
,
W.-C.
Wang
, and
A. N.
Samel
,
2001
:
Biases in AMIP model simulations of the east China monsoon system
.
Climate Dyn.
,
17
,
291
304
, https://doi.org/10.1007/s003820000136.
Liang
,
X.-Z.
,
K. E.
Kunkel
,
G. A.
Meehl
,
R. G.
Jones
, and
J. X. L.
Wang
,
2008
:
Regional climate models downscaling analysis of general circulation models present climate biases propagation into future change projections
.
Geophys. Res. Lett.
,
35
,
L08709
, https://doi.org/10.1029/2007GL032849.
Liu
,
S.
,
W.
Gao
, and
X.-Z.
Liang
,
2013
:
A regional climate model downscaling projection of China future climate change
.
Climate Dyn.
,
41
,
1871
1884
, https://doi.org/10.1007/s00382-012-1632-5.
Lutsko
,
N. J.
, and
I. M.
Held
,
2016
:
The response of an idealized atmosphere to orographic forcing: Zonal versus meridional propagation
.
J. Atmos. Sci.
,
73
,
3701
3718
, https://doi.org/10.1175/JAS-D-16-0021.1.
Min
,
H.-J.
, and
J.-G.
Jhun
,
2010
:
The change in the East Asian summer monsoon simulated by the MIROC3.2 high-resolution coupled model under global warming scenarios
.
Asia-Pac. J. Atmos. Sci.
,
46
,
73
88
, https://doi.org/10.1007/s13143-010-0008-1.
Moss
,
R.
, and Coauthors
,
2010
:
The next generation of scenarios for climate change research and assessment
.
Nature
,
463
,
747
756
, https://doi.org/10.1038/nature08823.
Niu
,
X.
, and Coauthors
,
2015
:
Multimodel ensemble projection of precipitation in eastern China under A1B emission scenario
.
J. Geophys. Res. Atmos.
,
120
,
9965
9980
, https://doi.org/10.1002/2015JD023853.
Ose
,
T.
,
2017
:
Future precipitation changes during summer in East Asia and model dependence in high-resolution MRI-AGCM experiments
.
Hydrol. Res. Lett.
,
11
,
168
174
, https://doi.org/10.3178/hrl.11.168.
Roberts
,
M. J.
,
H. T.
Hewitt
,
P.
Hyder
,
D.
Ferreira
,
S. A.
Josey
,
M.
Mizielinski
, and
A.
Shelly
,
2016
:
Impact of ocean resolution on coupled air–sea fluxes and large-scale climate
.
Geophys. Res. Lett.
,
43
,
10 430
10 438
, https://doi.org/10.1002/2016GL070559.
Roberts
,
M. J.
, and Coauthors
,
2018
:
The benefits of high resolution for climate simulation: Process understanding and the enabling of stakeholder decisions at the regional scale
.
Bull. Amer. Meteor. Soc.
, https://doi.org/10.1175/BAMS-D-15-00320.1
, in press
.
Senior
,
C. A.
, and Coauthors
,
2016
:
Idealized climate change simulations with a high-resolution physical model: HadGEM3-GC2
.
J. Adv. Model. Earth Syst.
,
8
,
813
830
, https://doi.org/10.1002/2015MS000614.
Seo
,
K.-H.
, and
J.
Ok
,
2013
:
Assessing future changes in the East Asian summer monsoon using CMIP3 models: Results from the best model ensemble
.
J. Climate
,
26
,
1807
1817
, https://doi.org/10.1175/JCLI-D-12-00109.1.
Seo
,
K.-H.
,
J.
Ok
,
J.-H.
Son
, and
D.-H.
Cha
,
2013
:
Assessing future changes in the East Asian summer monsoon using CMIP5 coupled models
.
J. Climate
,
26
,
7662
7675
, https://doi.org/10.1175/JCLI-D-12-00694.1.
Shi
,
Y.
,
X.-J.
Gao
,
Y.-G.
Wang
, and
F.
Giorgi
,
2009
:
Simulation and projection of monsoon rainfall and rain patterns over eastern China under global warming by RegCM3
.
Atmos. Oceanic Sci. Lett.
,
2
,
308
313
, https://doi.org/10.1080/16742834.2009.11446816.
Song
,
F.
, and
T.
Zhou
,
2014a
:
The climatology and interannual variability of East Asian summer monsoon in CMIP5 coupled models: Does air–sea coupling improve the simulation?
J. Climate
,
27
,
8761
8777
, https://doi.org/10.1175/JCLI-D-14-00396.1.
Song
,
F.
, and
T.
Zhou
,
2014b
:
Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean–western Pacific anticyclone teleconnection
.
J. Climate
,
27
,
1679
1697
, https://doi.org/10.1175/JCLI-D-13-00248.1.
Sperber
,
K. R.
,
H.
Annamalai
,
I.-S.
Kang
,
A.
Kitoh
,
A.
Moise
,
A.
Turner
,
B.
Wang
, and
T.
Zhou
,
2013
:
The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century
.
Climate Dyn.
,
41
,
2711
2744
, https://doi.org/10.1007/s00382-012-1607-6.
Sun
,
Y.
, and
Y.
Ding
,
2010
:
A projection of future changes in summer precipitation and monsoon in East Asia
.
Sci. China Earth Sci.
,
53
,
284
300
, https://doi.org/10.1007/s11430-009-0123-y.
Takaya
,
K.
, and
H.
Nakamura
,
2001
:
A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow
.
J. Atmos. Sci.
,
58
,
608
627
, https://doi.org/10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2.
Taylor
,
K. E.
,
R. J.
Stouffer
, and
G. A.
Meehl
,
2012
:
An overview of CMIP5 and the experiment design
.
Bull. Amer. Meteor. Soc.
,
93
,
485
498
, https://doi.org/10.1175/BAMS-D-11-00094.1.
Williams
,
K. D.
, and Coauthors
,
2015
:
The Met Office Global Coupled Model 2.0 (GC2) configuration
.
Geosci. Model Dev.
,
8
,
1509
1524
, https://doi.org/10.5194/gmd-8-1509-2015.
Wu
,
B.
,
T.
Zhou
, and
T.
Li
,
2017a
:
Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part I: Maintenance mechanisms
.
J. Climate
,
30
,
9621
9635
, https://doi.org/10.1175/JCLI-D-16-0489.1.
Wu
,
B.
,
T.
Zhou
, and
T.
Li
,
2017b
:
Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part II: Formation processes
.
J. Climate
,
30
,
9637
9650
, https://doi.org/10.1175/JCLI-D-16-0495.1.
Wu
,
P.
,
N.
Christidis
, and
P.
Stott
,
2013
:
Anthropogenic impact on Earth’s hydrological cycle
.
Nat. Climate Change
,
3
,
807
810
, https://doi.org/10.1038/nclimate1932.
Wu
,
P.
,
J.
Ridley
,
A.
Pardaens
,
R.
Levine
, and
J.
Lowe
,
2015
:
The reversibility of CO2 induced climate change
.
Climate Dyn.
,
45
,
745
754
, https://doi.org/10.1007/s00382-014-2302-6.
Xu
,
W.
,
E. J.
Zipser
, and
C.
Liu
,
2009
:
Rainfall characteristics and convective properties of mei-yu precipitation systems over south China, Taiwan, and the South China Sea. Part I: TRMM observations
.
Mon. Wea. Rev.
,
137
,
4261
4275
, https://doi.org/10.1175/2009MWR2982.1.
Yao
,
J.
,
T.
Zhou
,
Z.
Guo
,
X.
Chen
,
L.
Zou
, and
Y.
Sun
,
2017
:
Improved performance of high-resolution atmospheric models in simulating the East Asian summer monsoon rain belt
.
J. Climate
,
30
,
8825
8840
, https://doi.org/10.1175/JCLI-D-16-0372.1.
Zhang
,
L.
,
P.
Wu
,
T.
Zhou
,
M. J.
Roberts
, and
R.
Schiemann
,
2016
:
Added value of high resolution models in simulating global precipitation characteristics
.
Atmos. Sci. Lett.
,
17
,
646
657
, https://doi.org/10.1002/asl.715.
Zhou
,
T.
,
D.
Gong
,
J.
Li
, and
B.
Li
,
2009a
:
Detecting and understanding the multi-decadal variability of the East Asian summer monsoon—Recent progress and state of affairs
.
Meteor. Z.
,
18
,
455
467
, https://doi.org/10.1127/0941-2948/2009/0396.
Zhou
,
T.
,
B.
Wu
, and
B.
Wang
,
2009b
:
How well do atmospheric general circulation models capture the leading modes of the interannual variability of the Asian–Australian monsoon?
J. Climate
,
22
,
1159
1173
, https://doi.org/10.1175/2008JCLI2245.1.
Zou
,
L.
, and
T.
Zhou
,
2016a
:
A regional ocean–atmosphere coupled model developed for CORDEX East Asia: Assessment of Asian summer monsoon simulation
.
Climate Dyn.
,
47
,
3627
3640
, https://doi.org/10.1007/s00382-016-3032-8.
Zou
,
L.
, and
T.
Zhou
,
2016b
:
Future summer precipitation changes over CORDEX-East Asia domain downscaled by a regional ocean-atmosphere coupled model: A comparison to the stand-alone RCM
.
J. Geophys. Res. Atmos.
,
121
,
2691
2704
, https://doi.org/10.1002/2015JD024519.

Footnotes

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