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Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection
Quaternary International xxx (2016) 1e7
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Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection Yongjian Ren a, c, d, Botao Zhou b, c, *, Lianchun Song c, Ying Xiao d a
College of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China c National Climate Center, China Meteorological Administration, Beijing, China d Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Available online xxx
Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, this paper evaluated the performance of the state-of-the-art climate models in simulating the interannual variability of the western North Pacific subtropical high (WNPSH), East Asian jet (EAJ) and East Asian summer rainfall (EASR), and further projected their potential changes in a future warmer world. The results show that the multimodel ensemble mean (MME) simulation has good ability to model the interannual variability of the WNPSH, EAJ and EASR, although some discrepancy exists among the individual models. The MME simulation can also reasonably capture the observed relationship of the EASR with the WNPSH and the EAJ. Under the RCP4.5 and the RCP8.5 scenarios, the interannual variability in WNPSH, EAJ and EASR is projected by the MME to increase in the 21st century. In addition, the WNPSH and EAJ would still be the dominant systems influencing the East Asian summer precipitation under global warming scenarios. But the linkage of the EASR to the WNPSH may be slightly weaker and that to the EAJ may be slightly stronger in the 21st century as compared to the present. © 2016 Elsevier Ltd and INQUA. All rights reserved.
Keywords: Interannual variability Western North Pacific subtropical high East Asian jet East Asian summer precipitation Evaluation and projection
1. Introduction The East Asian summer monsoon (EASM) plays an important role in the occurrence of summer precipitation over Asia, thereby exerting significant impacts on the economic and social development of East Asian countries. The observations indicate that the EASM has experienced a significant weakening during the second half of the 20th century (Wang, 2001; Yu et al., 2004; Wang and Ding, 2006; Xu et al., 2006; Wang et al., 2015), which may be a response to anthropogenic forcing (Ueda et al., 2006; Zhu et al., 2012; Wang et al., 2013) and natural variability (Yang and Lau, 2004; Lei et al., 2014). The weakening of the EASM and resultant change of precipitation have large impacts on agriculture, water resources and society, particularly in eastern China with a dense population and concentrated industries and agricultures (Piao
* Corresponding author. National Climate Center, China Meteorological Administration, Beijing, 100081, China. E-mail address: [email protected] (B. Zhou).
et al., 2010). Therefore, how the EASM system and its related precipitation will change in the future is a key issue concerned to both science community and policy makers. Researches on it not only provide useful information for science community but also serve as the important scientific basis for policy-making in disaster prevention and mitigation. With better understanding of climate system and continuous improvement of climate models, the CMIP provides a great opportunity for projecting changes of the EASM and associated precipitation systematically. Many studies (e.g., Min et al., 2004; Kimoto, 2005; Sun and Ding, 2010; IPCC, 2013) have projected an increase in both East Asian monsoon circulation and precipitation under global warming scenarios. However, these studies mainly concentrated on the climate mean status and paid less attention to the interannual variability. As is known, besides changes in climate mean state, changes in variance that is used to represent interannual variability is another important indicator to measure climate change. In recent years, some researches have started to study future change of the interannual variability in East Asian summer
http://dx.doi.org/10.1016/j.quaint.2016.08.033 1040-6182/© 2016 Elsevier Ltd and INQUA. All rights reserved.
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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Y. Ren et al. / Quaternary International xxx (2016) 1e7
rainfall (EASR). For example, Kripalani et al. (2007) revealed a significant increase of the interannual variability of the EASR in response to doubled atmospheric CO2. Lu and Fu (2010) projected that the interannual variability of the EASR will be intensified by 12% and 19% during the 21st century under the A1B and A2 scenarios, respectively, with two abrupt changes occurring in the 2030s and the 2070s (Fu, 2012). The projected intensification of the interannual variability is much more prominent in comparison with the mean East Asian summer precipitation itself (Lu and Fu, 2010). In addition, the interannual variability of the EASR is closely associated with the western North Pacific subtropical high (WNPSH) and the East Asian westerly jet (EAJ), which are two key elements of the EASM system. The WNPSH and the EAJ affect the EASR respectively from the south in the lower troposphere and from the north in the upper troposphere. If the WNPSH extends westward (retreats eastward), more (less) precipitation tends to occur in East Asia (Chen and Wu, 1998; Lu, 2001). The summer EAJ, especially its location, plays a strong dynamical role on the precipitation variation. When the EAJ is located in the south (north) of the normal position, above-normal (below-normal) precipitation is inclined to appear in East Asia (Liang and Wang, 1998; Lau et al., 2000; Lu, 2004). Therefore, it is also crucial to project the interannual variability in WNPSH and EAJ and their relationships with the EASR under global warming scenarios. The results of Lu and Fu (2010) indicated that the relationships of the EASR with the WNPSH and the EAJ do not exhibit clear changes in the 21st century under the A1B and A2 scenarios, and there are great discrepancies among the individual CMIP3 models. It is worth noting that those results are based on the CMIP3 simulations. Compared with the CMIP3, the CMIP5 features substantial model improvements (Taylor et al., 2012) and adopts a new set of emission scenarios Representative Concentration Pathways (RCPs) (Moss et al., 2010) for future climate simulations. Then, how well do the CMIP5 models simulate the interannual variability of the WNPSH, EAJ and EASR? Can they capture the observed relationships of the EASR with the WNPSH and the EAJ? What about
their future changes under the RCP scenarios? This is the main motivation of the present study. The remainder of this paper is organized as follows. The data and methods used in this study are described in Section 2. Section 3 evaluates the performance of the CMIP5 models in simulating the interannual variability of the WNPSH, EAJ and EASR as well as the relationships of the EASR with the WNPSH and the EAJ. Their future changes under the RCP4.5 and RCP8.5 scenarios are projected in Section 4, followed by conclusions in Section 5.
2. Data and methods The results of 19 CMIP5 models (Table 1) for historical, RCP4.5 and RCP8.5 simulations are employed in this study. The historical experiment represents the simulations of the twentieth century climate. The RCP4.5 and RCP8.5, which have the radiative forcing peaking at 4.5 W/m2 and 8.5 W/m2 by 2100, represent a mediumlow and high radiative forcing scenario respectively. More details on the models and the forcings can be found at the CMIP5 website (http://cmippcmdi.llnl.gov/cmip5/availability.html). The time periods used for analysis are 1900e2005 for the historical simulation and 2006e2100 for the RCP scenarios. To validate the performance of the CMIP5 models, the monthly mean geopotential height and zonal wind for 1948e2010 from the National Centers for Environmental PredictioneNational Center for Atmospheric Research (NCEP/NCAR) (Kalnay et al., 1996) and the precipitation data for 1979e2010 from Global Precipitation Climatology Project (GPCP) (Huffman et al., 1995) are exploited and identified as the observation (OBS). The horizontal resolution for both the NCEP/ NCAR reanalysis and the GPCP precipitation data are 2.5 longitude by 2.5 latitude. These data can be downloaded from the website http://www.esrl.noaa.gov/psd/data/gridded/tables/monthly.html. Since the CMIP5 models have different spatial resolutions (see Table 1), data from the different models are all converted to the 2.5 2.5 grid using a bilinear interpolation scheme before analysis.
Table 1 Information of the 19 CMIP5 models used in the present analysis. Name
Modeling group
Atm. Resolution (lon lat)
ACCESS1-0
Common wealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia Common wealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia Beijing Climate Center, China Meteorological Administration, China Beijing Normal University/China Canadian Centre for Climate Modeling and Analysis, Canada National Center for Atmosphere Research, United States Centro Euro-Mediterraneo per I Cambiamenti Climatici, Italy Centre National de Recherches Meteorologiques and Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique, France State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, China First Institute of Oceanography, China NOAA Geophysical Fluid Dynamics Laboratory, United States NOAA Geophysical Fluid Dynamics Laboratory, United States NOAA Geophysical Fluid Dynamics Laboratory, United States Met Office Hadley Centre, United Kingdom Met Office Hadley Centre, United Kingdom Met Office Hadley Centre, United Kingdom Institute Pierre-Simon Laplace, France Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan Norwegian Climate Centre/Norway
192 145
ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M
192 145 320 128 128 288 192 256
160 64 64 192 96 128
128 60 128 144 144 144 192 192 192 144 128
64 90 90 90 144 144 144 143 64
144 96
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
Y. Ren et al. / Quaternary International xxx (2016) 1e7
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The intensity of interannual variability is measured by standard deviation. The regression and correlation are used to depict the relationship between two variables. Before calculating standard deviation, regression and correlation, the time series were filtered by a nine-year Gaussian filtering on the detrended data. 3. Fidelity of the CMIP5 models 3.1. Interannual variability First of all, we evaluate the capacity of the CMIP5 models in simulating the interannual variability of the WNPSH, EAJ and EASR. Figs. 1 and 2 show the standard deviations of summer geopotential height at 850 hPa and zonal winds at 200 hPa in the observation and the historical simulation of the MME, respectively. The MME was calculated as the arithmetic mean of the 19 models. As shown in the observations, the standard deviation of the low-level geopotential height is the largest over the North Pacific and extends to the subtropical western Pacific (Fig. 1a). For the 200 hPa zonal wind, its strong variability appears in the region where the EAJ is situated (Fig. 2a). The MME simulated spatial distribution of the interannual variability in 850 hPa geopotential height (Fig. 1b) and 200 hPa zonal winds (Fig. 2b) bears general resemblance with the observations, but the magnitude of the maximum in geopotential height and zonal wind speed is significantly over-estimated over the North Pacific. This indicates that the MME simulation can capture the feature of the interannual variability of the low-level geopotential height and the upper-level zonal winds. These features can also be reproduced by the individual models (figure not shown), although the quantitative amplitudes and patterns are somewhat different among the models. To quantify the interannual variability of the WNPSH and EAJ, we define the summer 850 hPa geopotential height anomalies averaged over the region (110 e150 E, 10 e30 N) as the WNPSH index (WNPSHI) (Lu, 2001) and the difference of 200 hPa zonal winds over the region (30 e40 N, 120 e150 E) and (40 e50 N, 120 e150 E) as the EAJ index (EAJI) (Lu, 2004). The positive
Fig. 2. Interannual standard deviations of summer zonal winds (m/s) at 200 hPa in (a) the observation and (b) the historical simulation of the MME. Areas with the value larger than 2.7 are shaded.
(negative) WNPSHI is corresponding to the westward extension (eastward retreat) of the WNPSH, and the positive (negative) EAJI is corresponding to the southward (northward) distribution of the EAJ. The interannual standard deviations of the WNPSHI and EAJI for the observations and the historical simulations are listed in Table 2. In the observations, the standard deviation is 6.0 gpm for the WNPSHI and 4.8 m/s for the EAJI. The counterpart simulated by the MME is 6.7 gpm and 4.9 m/s respectively, which is very close to the observation. For the individual models, the simulated interannual standard deviations for the WNPSHI range from 4.2 (MIROCESM) to 8.8 (GFDL-ESM2M) gpm, and that for the EAJI is in the range of 4.0 m/s (IPSL-CM5A-MR and MIROC-ESM) to 5.8 m/s (CMCC-CMS).
Table 2 Interannual standard deviations of WNPSHI and EAJI.
Fig. 1. Interannual standard deviations of summer geopotential height (gpm) at 850 hPa in (a) the observation and (b) the historical simulation of the MME. Areas with the value larger than 6 are shaded.
Model
WNPSHI (gpm)
EAJI (m/s)
Historical
RCP4.5
RCP8.5
Historical
RCP4.5
RCP8.5
ACCESS1-0 ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M MME OBS
6.9 6.6 6.8 7.9 5.9 8.1 7.5 6.5 6.3 7.6 4.9 6.0 8.8 7.3 7.4 7.3 4.3 4.2 7.8 6.7 6.0
7.9 7.7 7.4 8.3 5.8 8.2 9.1 6.4 5.3 7.9 7.2 7.4 8.9 7.3 6.3 6.7 3.9 4.5 7.9 7.0
7.1 9.1 7.2 9.5 6.1 8.6 8.8 6.3 6.4 8.0 5.8 6.6 9.5 7.9 7.3 6.4 4.1 4.2 7.7 7.2
5.6 5.0 5.2 5.4 5.7 5.5 5.8 4.6 4.8 4.9 4.7 4.2 4.8 4.7 5.1 4.9 4.0 4.0 5.4 4.9 4.8
5.8 5.1 5.0 6.5 4.6 5.7 6.4 5.0 4.7 5.4 4.8 4.9 4.7 5.4 5.0 5.3 4.4 3.8 5.1 5.1
5.1 5.3 5.8 5.9 4.6 6.0 6.5 4.6 4.2 6.0 4.8 4.7 5.0 5.3 5.4 5.0 4.5 3.6 5.3 5.2
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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Fig. 3 displays the observed and the MME simulated standard deviations of summer precipitation. It is of interest to find that the spatial distribution in the MME simulation and the observation generally resembles each other, but the models tend to overestimate the interannual variability in the tropical western Pacific and somewhat under-estimate it over East Asia. Such a resemblance suggests that the observed interannual variability of the East Asian precipitation can be captured by the MME simulation. Following Lu and Fu (2010), we define the summer precipitation averaged over the parallelogram region determined by the points (25 N, 100 E), (35 N, 100 E), (30 N, 160 E) and (40 N, 160 E) as the EASR index (EASRI). As presented in Table 3, the MME simulated interannual standard deviation is 1.19 mm/d, also close to the observed value of 1.22 mm/d. The variance of the CMIP5 simulation relative to the observation is 2.5% and less than that in the CMIP3 simulation (Lu and Fu, 2010), indicating the improvement of the CMIP5 models on the simulation of the interannual variability in EASR. For the individual models, the standard deviations range from 0.91 (CanESM2) to 1.64 (ACCESS1-0). Table 3 Interannual standard deviations of EASRI. Model
ACCESS1-0 ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M MME OBS
EASRI (mm/d) Historical
RCP4.5
RCP8.5
1.64 1.53 1.01 1.12 0.91 1.16 1.23 1.07 0.93 1.16 0.96 0.97 1.07 1.56 1.48 1.56 0.98 1.03 1.22 1.19 1.22
1.88 1.67 1.11 1.31 1.00 1.35 1.36 1.16 0.95 1.21 1.05 1.13 1.19 1.77 1.64 1.74 1.05 1.15 1.36 1.32
1.84 1.75 1.18 1.37 1.20 1.40 1.43 1.14 0.92 1.30 1.06 1.16 1.24 1.83 1.77 1.83 1.12 1.16 1.38 1.37
Fig. 3. Interannual standard deviations of summer precipitation (mm/d) in (a) the observation and (b) the historical simulation of the MME. Areas with the value larger than 1.5 are shaded.
We also examined the performance of the CMIP5 models in simulating the climatology of WNPSH, EAJ and EASR, although the focus of this study is on their interannual variability. The MME simulation shows that it can reasonably reproduce spatial distribution of the WNPSH and EAJ. The summer rain belt in a northeastsouthwest direction over East Asia can be roughly captured by the CMIP5 MME simulation, although the simulated rain belt is southward and weaker compared to the observation (Figure not shown). The under-estimation of the MME simulation on the East Asian rain belt in climate mean is consistent with that in variance. 3.2. Relationship of the EASR with the WNPSH and the EAJ As pointed out in the introduction, changes of the summer precipitation over East Asia are tightly connected to the variation of the WNPSH and EAJ. The East Asian summer precipitation is influenced by the WNPSH from the south in the lower troposphere and by the EAJ from the north in the upper troposphere. Such relationships can be well demonstrated in Fig. 4a and Fig. 5a, which respectively indicate the regressions of the summer precipitation against the WNPSHI and the EAJI in the observation. It is impressive
Fig. 4. Summer precipitation regressed onto the standardized WNPSHI in (a) the observation and (b) the historical simulation of the MME. Areas above the 95% significance level are shaded.
in Fig. 4a that the positive anomalies are dominated in East Asia oriented from the Yangtze River valley to Japan, and the negative anomalies are predominated over the western Pacific. This pattern
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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models (CanESM2, CCSM4, FIO-ESM, MIROC-ESM and NorESM1-M) simulating an out-of-phase EASRI-WNPSHI relationship that is in contrast to the observation. If these models are excluded from the MME result, the simulated correlation coefficient is 0.33 (significant at the 99.9% level). For the EASRI-EAJI relationship, all the models can capture the positive correlation. The correlation coefficients simulated by the individual models range from 0.27 (HadGEM2-CC) to 0.76 (NorESM1-M), higher above the 99% significance level.
Table 4 Correlation coefficients of the EASRI with the WNPSHI and the EAJI. Number in the parentheses indicates the MME result excluding five models with negative EASRIWNPSHI correlation in the historical simulation. Model
Fig. 5. Summer precipitation regressed onto the standardized EAJI in (a) the observation and (b) the historical simulation of the MME. Areas above the 95% significance level are shaded.
hints that more (less) precipitation in East Asia concurrent with less (more) precipitation in the western Pacific tends to occur if the WNPSH extends westward (retreats eastward). Similarly, in association with the positive EAJI (Fig. 5a), positive anomalies from the Yangtze River valley to Japan and negative anomalies in the western Pacific can be observed, implying that the southward (northward) located EAJ may result in wetter (drier) conditions in the East Asian region from the Yangtze River valley to Japan and drier (wetter) conditions in the western Pacific. The WNPSHI- and EAJIrelated precipitation pattern simulated by the MME is in general in agreement with the observation. Corresponding to the western extension of the WNPSH (Fig. 4b) or southward distribution of the EAJ (Fig. 5b), the summer precipitation is inclined to increase in East Asia and decrease in the western Pacific, despite that the precipitation anomalies are somewhat weaker than the observation. Table 4 further delineates the correlation coefficients of the EASRI with the WNPSHI and EAJI. The EASRI-WNPSHI and the EASRI-EAJI correlation coefficients are 0.70 and 0.75 respectively for the observation, both significant at the 99.9% level. The corresponding correlation coefficients in the historical simulation of the MME are 0.21 (significant at the 95% level) and 0.53 (significant at the 99.9% level), respectively. Although the correlation coefficient in the MME simulation is lower than the observed result, especially for WNPSH (note that the length of the samples used for the historical simulation is much longer than that for the observation), the significance tests indicate that the MME simulation can capture the observed relations. For the individual models, 14 out of 19 (74%) models produce the observed in-phase EASRI-WNPSHI relationship, with the positive correlation coefficient ranging from 0.14 (HadGEM2-ES) to 0.57 (GFDL-ESM2M). Note that there are 5
ACCESS1-0 ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M MME OBS
EASRI-WNPSHI
EASRI-EAJI
Historical
RCP4.5
RCP8.5
Historical RCP4.5 RCP8.5
0.38 0.24 0.28 0.21 0.23 0.03 0.51 0.17 0.27 0.02 0.42 0.46 0.57 0.28 0.38 0.14 0.32 0.19 0.1 0.21 (0.33) 0.70
0.19 0.21 0.24 0.05 0.19 0.05 0.7 0.25 0.21 0.27 0.6 0.42 0.63 0.4 0.26 0.08 0.35 0.4 0.05 0.19 (0.29)
0.28 0.13 0.24 0.09 0.06 0.02 0.5 0.29 0.22 0.19 0.4 0.44 0.57 0.28 0.15 0.02 0.1 0.49 0.01 0.17 (0.25)
0.66 0.54 0.48 0.61 0.58 0.65 0.56 0.54 0.68 0.54 0.28 0.55 0.64 0.29 0.29 0.55 0.27 0.73 0.76 0.53 0.75
0.54 0.41 0.3 0.8 0.45 0.73 0.56 0.66 0.61 0.48 0.31 0.75 0.75 0.48 0.54 0.43 0.12 0.65 0.72 0.54
0.56 0.65 0.54 0.83 0.52 0.69 0.59 0.54 0.56 0.51 0.53 0.55 0.67 0.5 0.49 0.39 0.37 0.62 0.69 0.57
In summary, the MME simulation can capture the interannual variability of the WNPSH, EAJ and EASR. It can also reproduce the observed relationship of the EASRI with the WNPSHI and the EAJI. The simulation on the variance of EAJI is better than of WNPSHI. This provides justification of the use of the MME for the future projection. 4. Projection of the CMIP5 models 4.1. Interannual variability Fig. 6 exhibits the MME projected changes of the interannual standard deviations of summer precipitation, 850 hPa geopotential height and 200 hPa zonal winds in the 21st century under the RCP4.5 and RCP8.5 scenarios. For the precipitation, the MME projects a similar distribution of the interannual variability change under both RCPs, with an increase along the East Asian summer rain belt and the western Pacific (Fig. 6a and b). It reveals that the regions with large variability (Fig. 3) will experience greater precipitation variability during the 21st century, that is, stronger becomes stronger. In addition, the enhanced variability in the two areas is in general greater under the RCP8.5 scenario than under the RCP4.5 scenario, reflecting the influence of the external forcing on the projections. Compared to 1900e2005, the MME projected interannual standard deviation of the EASRI will increase by 9.2% under the RCP4.5 scenario and 15.1% under the RCP8.5 scenario during 2006e2100 (Table 3), which are significant at the 95% level. The increase in amplitude is lower than the
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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Fig. 6. MME projected changes in interannual standard deviation of (a, b) precipitation (mm/d), (c, d) 850 hPa geopotential height (gpm), and (e, f) 200 hPa zonal winds (m/s) in the 21st century under the RCP 4.5 (left panel) and the 8.5 (right panel) scenarios. Areas above the 95% significance level are shaded.
CMIP3 projections (about 12% and 19% under the A1B and A2 scenarios, respectively) (Lu and Fu, 2010). Almost all the individual models project enhancement of the interannual variability in precipitation over the region where the East Asian summer rain belt is located. The standard deviations of the low-level geopotential height over the western Pacific during the 21st century are also projected to increase under both RCP scenarios (Fig. 6c and d). Thus, the interannual variability of the WNPSH is expected to increase under the warming scenarios. The increase is relatively larger under the RCP8.5 scenario as compared to the RCP4.5 scenario, which is also associated with stronger external forcing imposed on the models. The MME projected interannual standard deviation of the WNPSHI increases from 6.7 gpm during 1900e2005 to 7.2 (7.0) gpm during 2006e2100 under the RCP8.5 (RCP4.5) scenario, with the enhancement of 8.3% (4.5%) (significant at the 95% level). For the individual models, 12 models (63%) and 13 models (68%) project the increase of the WNPSHI variability under the RCP4.5 and RCP8.5 scenarios, respectively (Table 2). As for the standard deviations of 200 hPa zonal winds, significant positive anomalies appear mainly over the Asian-North Pacific region north of 40 N (Fig. 6e and f). Under the RCP8.5 (RCP4.5) scenario, the EAJI standard deviation projected by the MME reaches 5.2 (5.1) m/s. Relative to 1990e2005, it will increase by 6.1% (4.1%) in the 21st century. For the individual models, 13 (12) out of 19 models project the increase of the EAJI variability under the RCP8.5 (RCP4.5 scenario (Table 2)). Additionally, for the mean state, the MME projects an increase in summer precipitation over East Asia, an increase (a decrease) in 850 hPa geopotential height over the North (western) Pacific, and a northward displaced EAJ with the 200 hPa zonal wind increasing (decreasing) to the north (south) of 40 N during the 21st century under the RCP4.5 and RCP8.5 (Figure not shown).
4.2. Relationship of the EASR with the WNPSH and the EAJ Table 4 presents the correlation coefficients of the EASRI with the WNPSHI and the EAJI for the time period 2006e2100 under the RCP4.5 and the RCP8.5 scenarios. As seen in this table, the MME (including all the models) projected EASRI-WNPSHI correlation coefficient is 0.19 (significant at 95% level) for the RCP4.5 scenario and 0.17 (significant at 90% level) for the RCP8.5 scenario, slightly weaker than that in the historical simulation. If the five models aforementioned are excluded from the MME result, the MME projected EASRI-WNPSHI correlation coefficients under the RCP4.5 and RCP8.5 scenarios are 0.29 and 0.27 respectively (both significant at the 99% level), also slightly lower than the counterpart in the historical simulation. For the individual models, 11 models project positive correlations significant at the 95% level under the RCP4.5 scenario, ranging from 0.19 (ACCESS1-0) to 0.57 (GFDL-ESM2M). 10 models project positive correlations in the range of 0.19 (FIO-ESM) to 0.70 (CMCC-CMS) that is significant at the 95% level under the RCP8.5 scenario. For the EASRI-EAJI relationship, the MME projects a correlation coefficient of 0.54 under the RCP4.5 scenario and 0.57 under the RCP8.5 scenario, higher about the 99.9% significance level and slightly larger than the counterpart in the historical simulation. Such a significant positive correlation is projected by all the individual models under both RCP4.5 and RCP8.5 scenarios, except IPSL-CM5A-MR (0.12, not passing the 95% significance level) under the RCP4.5 scenario. In general, the projected changes reflect the historical correlations, both for the MME and for individual models. 5. Conclusions In the present study, we examined the fidelity of the CMIP5 models in simulating the interannual variability of the WNPSH, EAJ and EASR and the association of the EASR with the WNPSH and the
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
Y. Ren et al. / Quaternary International xxx (2016) 1e7
EAJ. Their future changes under the RCP4.5 and RCP8.5 scenarios were further projected. The main results are summarized below: 1) Despite some inter-model spreads for the individual models, the MME simulation of the CMIP5 models can capture the features of the present-day interannual variability of the WNPSH, EAJ and EASR. It can also reproduce the observed linkage of the EASR to the WNPSH and the EAJ. 2) The CMIP5 MME projects that the interannual variability of the WNPSH, EAJ and EASR tends to increase in a future warmer world. Relative to the period 1900e2005, the interannual standard deviation of the WNPSHI, EAJI and EASRI would increase by 4.5%, 4.1%, and 9.2% under the RCP4.5 scenario and by 8.3%, 6.1% and 15.1% under the RCP8.5 scenario during the 21st century, respectively. 3) The present relationships of the East Asian summer precipitation with the WNPSH and the EAJ are projected by the CMIP5 MME to still exist under the warming scenarios. However, the linkage of the EASR to the WNPSH may become slightly weaker and that to the EAJ may become slightly stronger in the 21st century in the context of future warming, although there are appreciable discrepancies among the models. The increasing tendency of the interannual variability in WNPSH, EAJ and EASR as projected in this study is consistent with the previous result but the increase in amplitude is lower than the CMIP3 projection (Lu and Fu, 2010). The CMIP5 projected relationship of the East Asian summer precipitation with the WNPSH and the EAJ are, to some extent, also different from the CMIP3 projection for which Lu and Fu (2010) found no change in the relationships between the EASR and the WNPSH and the EAJ in the 21st century. This discrepancy might be related to climate model difference between the CMIP3 and CMIP5. Due to better understanding of climate system and improvement of the horizontal resolution of climate models, the CMIP5 shows substantial improvements compared with the CMIP3 (Taylor et al., 2012; IPCC, 2013; Jiang et al., 2016). The difference of the forcing scenario between the CMIP3 and CMIP5 (IPCC, 2013) may be another reason for the difference of the projection results. In-depth analysis is needed in future work. Acknowledgments We acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for CMIP, and thank climate modeling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was jointly supported by the National Natural Science Foundation of China (41275078, 41405077), the National Key Research and Development Program of China (2016YFA0600701), and the National Basic Research Program of China (2012CB955900). References Chen, L.T., Wu, R.G., 1998. Relationship between summer rainbelt patterns in eastern China and 500 hPa circulation anomalies over the Northern Hemisphere. Scientia Atmospheric Sinica 22 (6), 849e857. Fu, Y.H., 2012. The projected temporal evolution in the interannual variability of East Asian summer rainfall by CMIP3 coupled models. Science China Earth Science 42, 1937e1950. Huffman, G.J., Adler, R.F., Rudolf, B., Schneider, U., Keehn, P.R., 1995. Global precipitation estimates based on a technique for combining satellite-based estimates,
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Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection Yongjian Ren a, c, d, Botao Zhou b, c, *, Lianchun Song c, Ying Xiao d a
College of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China c National Climate Center, China Meteorological Administration, Beijing, China d Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Available online xxx
Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, this paper evaluated the performance of the state-of-the-art climate models in simulating the interannual variability of the western North Pacific subtropical high (WNPSH), East Asian jet (EAJ) and East Asian summer rainfall (EASR), and further projected their potential changes in a future warmer world. The results show that the multimodel ensemble mean (MME) simulation has good ability to model the interannual variability of the WNPSH, EAJ and EASR, although some discrepancy exists among the individual models. The MME simulation can also reasonably capture the observed relationship of the EASR with the WNPSH and the EAJ. Under the RCP4.5 and the RCP8.5 scenarios, the interannual variability in WNPSH, EAJ and EASR is projected by the MME to increase in the 21st century. In addition, the WNPSH and EAJ would still be the dominant systems influencing the East Asian summer precipitation under global warming scenarios. But the linkage of the EASR to the WNPSH may be slightly weaker and that to the EAJ may be slightly stronger in the 21st century as compared to the present. © 2016 Elsevier Ltd and INQUA. All rights reserved.
Keywords: Interannual variability Western North Pacific subtropical high East Asian jet East Asian summer precipitation Evaluation and projection
1. Introduction The East Asian summer monsoon (EASM) plays an important role in the occurrence of summer precipitation over Asia, thereby exerting significant impacts on the economic and social development of East Asian countries. The observations indicate that the EASM has experienced a significant weakening during the second half of the 20th century (Wang, 2001; Yu et al., 2004; Wang and Ding, 2006; Xu et al., 2006; Wang et al., 2015), which may be a response to anthropogenic forcing (Ueda et al., 2006; Zhu et al., 2012; Wang et al., 2013) and natural variability (Yang and Lau, 2004; Lei et al., 2014). The weakening of the EASM and resultant change of precipitation have large impacts on agriculture, water resources and society, particularly in eastern China with a dense population and concentrated industries and agricultures (Piao
* Corresponding author. National Climate Center, China Meteorological Administration, Beijing, 100081, China. E-mail address: [email protected] (B. Zhou).
et al., 2010). Therefore, how the EASM system and its related precipitation will change in the future is a key issue concerned to both science community and policy makers. Researches on it not only provide useful information for science community but also serve as the important scientific basis for policy-making in disaster prevention and mitigation. With better understanding of climate system and continuous improvement of climate models, the CMIP provides a great opportunity for projecting changes of the EASM and associated precipitation systematically. Many studies (e.g., Min et al., 2004; Kimoto, 2005; Sun and Ding, 2010; IPCC, 2013) have projected an increase in both East Asian monsoon circulation and precipitation under global warming scenarios. However, these studies mainly concentrated on the climate mean status and paid less attention to the interannual variability. As is known, besides changes in climate mean state, changes in variance that is used to represent interannual variability is another important indicator to measure climate change. In recent years, some researches have started to study future change of the interannual variability in East Asian summer
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Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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rainfall (EASR). For example, Kripalani et al. (2007) revealed a significant increase of the interannual variability of the EASR in response to doubled atmospheric CO2. Lu and Fu (2010) projected that the interannual variability of the EASR will be intensified by 12% and 19% during the 21st century under the A1B and A2 scenarios, respectively, with two abrupt changes occurring in the 2030s and the 2070s (Fu, 2012). The projected intensification of the interannual variability is much more prominent in comparison with the mean East Asian summer precipitation itself (Lu and Fu, 2010). In addition, the interannual variability of the EASR is closely associated with the western North Pacific subtropical high (WNPSH) and the East Asian westerly jet (EAJ), which are two key elements of the EASM system. The WNPSH and the EAJ affect the EASR respectively from the south in the lower troposphere and from the north in the upper troposphere. If the WNPSH extends westward (retreats eastward), more (less) precipitation tends to occur in East Asia (Chen and Wu, 1998; Lu, 2001). The summer EAJ, especially its location, plays a strong dynamical role on the precipitation variation. When the EAJ is located in the south (north) of the normal position, above-normal (below-normal) precipitation is inclined to appear in East Asia (Liang and Wang, 1998; Lau et al., 2000; Lu, 2004). Therefore, it is also crucial to project the interannual variability in WNPSH and EAJ and their relationships with the EASR under global warming scenarios. The results of Lu and Fu (2010) indicated that the relationships of the EASR with the WNPSH and the EAJ do not exhibit clear changes in the 21st century under the A1B and A2 scenarios, and there are great discrepancies among the individual CMIP3 models. It is worth noting that those results are based on the CMIP3 simulations. Compared with the CMIP3, the CMIP5 features substantial model improvements (Taylor et al., 2012) and adopts a new set of emission scenarios Representative Concentration Pathways (RCPs) (Moss et al., 2010) for future climate simulations. Then, how well do the CMIP5 models simulate the interannual variability of the WNPSH, EAJ and EASR? Can they capture the observed relationships of the EASR with the WNPSH and the EAJ? What about
their future changes under the RCP scenarios? This is the main motivation of the present study. The remainder of this paper is organized as follows. The data and methods used in this study are described in Section 2. Section 3 evaluates the performance of the CMIP5 models in simulating the interannual variability of the WNPSH, EAJ and EASR as well as the relationships of the EASR with the WNPSH and the EAJ. Their future changes under the RCP4.5 and RCP8.5 scenarios are projected in Section 4, followed by conclusions in Section 5.
2. Data and methods The results of 19 CMIP5 models (Table 1) for historical, RCP4.5 and RCP8.5 simulations are employed in this study. The historical experiment represents the simulations of the twentieth century climate. The RCP4.5 and RCP8.5, which have the radiative forcing peaking at 4.5 W/m2 and 8.5 W/m2 by 2100, represent a mediumlow and high radiative forcing scenario respectively. More details on the models and the forcings can be found at the CMIP5 website (http://cmippcmdi.llnl.gov/cmip5/availability.html). The time periods used for analysis are 1900e2005 for the historical simulation and 2006e2100 for the RCP scenarios. To validate the performance of the CMIP5 models, the monthly mean geopotential height and zonal wind for 1948e2010 from the National Centers for Environmental PredictioneNational Center for Atmospheric Research (NCEP/NCAR) (Kalnay et al., 1996) and the precipitation data for 1979e2010 from Global Precipitation Climatology Project (GPCP) (Huffman et al., 1995) are exploited and identified as the observation (OBS). The horizontal resolution for both the NCEP/ NCAR reanalysis and the GPCP precipitation data are 2.5 longitude by 2.5 latitude. These data can be downloaded from the website http://www.esrl.noaa.gov/psd/data/gridded/tables/monthly.html. Since the CMIP5 models have different spatial resolutions (see Table 1), data from the different models are all converted to the 2.5 2.5 grid using a bilinear interpolation scheme before analysis.
Table 1 Information of the 19 CMIP5 models used in the present analysis. Name
Modeling group
Atm. Resolution (lon lat)
ACCESS1-0
Common wealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia Common wealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia Beijing Climate Center, China Meteorological Administration, China Beijing Normal University/China Canadian Centre for Climate Modeling and Analysis, Canada National Center for Atmosphere Research, United States Centro Euro-Mediterraneo per I Cambiamenti Climatici, Italy Centre National de Recherches Meteorologiques and Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique, France State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, China First Institute of Oceanography, China NOAA Geophysical Fluid Dynamics Laboratory, United States NOAA Geophysical Fluid Dynamics Laboratory, United States NOAA Geophysical Fluid Dynamics Laboratory, United States Met Office Hadley Centre, United Kingdom Met Office Hadley Centre, United Kingdom Met Office Hadley Centre, United Kingdom Institute Pierre-Simon Laplace, France Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan Norwegian Climate Centre/Norway
192 145
ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M
192 145 320 128 128 288 192 256
160 64 64 192 96 128
128 60 128 144 144 144 192 192 192 144 128
64 90 90 90 144 144 144 143 64
144 96
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3
The intensity of interannual variability is measured by standard deviation. The regression and correlation are used to depict the relationship between two variables. Before calculating standard deviation, regression and correlation, the time series were filtered by a nine-year Gaussian filtering on the detrended data. 3. Fidelity of the CMIP5 models 3.1. Interannual variability First of all, we evaluate the capacity of the CMIP5 models in simulating the interannual variability of the WNPSH, EAJ and EASR. Figs. 1 and 2 show the standard deviations of summer geopotential height at 850 hPa and zonal winds at 200 hPa in the observation and the historical simulation of the MME, respectively. The MME was calculated as the arithmetic mean of the 19 models. As shown in the observations, the standard deviation of the low-level geopotential height is the largest over the North Pacific and extends to the subtropical western Pacific (Fig. 1a). For the 200 hPa zonal wind, its strong variability appears in the region where the EAJ is situated (Fig. 2a). The MME simulated spatial distribution of the interannual variability in 850 hPa geopotential height (Fig. 1b) and 200 hPa zonal winds (Fig. 2b) bears general resemblance with the observations, but the magnitude of the maximum in geopotential height and zonal wind speed is significantly over-estimated over the North Pacific. This indicates that the MME simulation can capture the feature of the interannual variability of the low-level geopotential height and the upper-level zonal winds. These features can also be reproduced by the individual models (figure not shown), although the quantitative amplitudes and patterns are somewhat different among the models. To quantify the interannual variability of the WNPSH and EAJ, we define the summer 850 hPa geopotential height anomalies averaged over the region (110 e150 E, 10 e30 N) as the WNPSH index (WNPSHI) (Lu, 2001) and the difference of 200 hPa zonal winds over the region (30 e40 N, 120 e150 E) and (40 e50 N, 120 e150 E) as the EAJ index (EAJI) (Lu, 2004). The positive
Fig. 2. Interannual standard deviations of summer zonal winds (m/s) at 200 hPa in (a) the observation and (b) the historical simulation of the MME. Areas with the value larger than 2.7 are shaded.
(negative) WNPSHI is corresponding to the westward extension (eastward retreat) of the WNPSH, and the positive (negative) EAJI is corresponding to the southward (northward) distribution of the EAJ. The interannual standard deviations of the WNPSHI and EAJI for the observations and the historical simulations are listed in Table 2. In the observations, the standard deviation is 6.0 gpm for the WNPSHI and 4.8 m/s for the EAJI. The counterpart simulated by the MME is 6.7 gpm and 4.9 m/s respectively, which is very close to the observation. For the individual models, the simulated interannual standard deviations for the WNPSHI range from 4.2 (MIROCESM) to 8.8 (GFDL-ESM2M) gpm, and that for the EAJI is in the range of 4.0 m/s (IPSL-CM5A-MR and MIROC-ESM) to 5.8 m/s (CMCC-CMS).
Table 2 Interannual standard deviations of WNPSHI and EAJI.
Fig. 1. Interannual standard deviations of summer geopotential height (gpm) at 850 hPa in (a) the observation and (b) the historical simulation of the MME. Areas with the value larger than 6 are shaded.
Model
WNPSHI (gpm)
EAJI (m/s)
Historical
RCP4.5
RCP8.5
Historical
RCP4.5
RCP8.5
ACCESS1-0 ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M MME OBS
6.9 6.6 6.8 7.9 5.9 8.1 7.5 6.5 6.3 7.6 4.9 6.0 8.8 7.3 7.4 7.3 4.3 4.2 7.8 6.7 6.0
7.9 7.7 7.4 8.3 5.8 8.2 9.1 6.4 5.3 7.9 7.2 7.4 8.9 7.3 6.3 6.7 3.9 4.5 7.9 7.0
7.1 9.1 7.2 9.5 6.1 8.6 8.8 6.3 6.4 8.0 5.8 6.6 9.5 7.9 7.3 6.4 4.1 4.2 7.7 7.2
5.6 5.0 5.2 5.4 5.7 5.5 5.8 4.6 4.8 4.9 4.7 4.2 4.8 4.7 5.1 4.9 4.0 4.0 5.4 4.9 4.8
5.8 5.1 5.0 6.5 4.6 5.7 6.4 5.0 4.7 5.4 4.8 4.9 4.7 5.4 5.0 5.3 4.4 3.8 5.1 5.1
5.1 5.3 5.8 5.9 4.6 6.0 6.5 4.6 4.2 6.0 4.8 4.7 5.0 5.3 5.4 5.0 4.5 3.6 5.3 5.2
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Fig. 3 displays the observed and the MME simulated standard deviations of summer precipitation. It is of interest to find that the spatial distribution in the MME simulation and the observation generally resembles each other, but the models tend to overestimate the interannual variability in the tropical western Pacific and somewhat under-estimate it over East Asia. Such a resemblance suggests that the observed interannual variability of the East Asian precipitation can be captured by the MME simulation. Following Lu and Fu (2010), we define the summer precipitation averaged over the parallelogram region determined by the points (25 N, 100 E), (35 N, 100 E), (30 N, 160 E) and (40 N, 160 E) as the EASR index (EASRI). As presented in Table 3, the MME simulated interannual standard deviation is 1.19 mm/d, also close to the observed value of 1.22 mm/d. The variance of the CMIP5 simulation relative to the observation is 2.5% and less than that in the CMIP3 simulation (Lu and Fu, 2010), indicating the improvement of the CMIP5 models on the simulation of the interannual variability in EASR. For the individual models, the standard deviations range from 0.91 (CanESM2) to 1.64 (ACCESS1-0). Table 3 Interannual standard deviations of EASRI. Model
ACCESS1-0 ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M MME OBS
EASRI (mm/d) Historical
RCP4.5
RCP8.5
1.64 1.53 1.01 1.12 0.91 1.16 1.23 1.07 0.93 1.16 0.96 0.97 1.07 1.56 1.48 1.56 0.98 1.03 1.22 1.19 1.22
1.88 1.67 1.11 1.31 1.00 1.35 1.36 1.16 0.95 1.21 1.05 1.13 1.19 1.77 1.64 1.74 1.05 1.15 1.36 1.32
1.84 1.75 1.18 1.37 1.20 1.40 1.43 1.14 0.92 1.30 1.06 1.16 1.24 1.83 1.77 1.83 1.12 1.16 1.38 1.37
Fig. 3. Interannual standard deviations of summer precipitation (mm/d) in (a) the observation and (b) the historical simulation of the MME. Areas with the value larger than 1.5 are shaded.
We also examined the performance of the CMIP5 models in simulating the climatology of WNPSH, EAJ and EASR, although the focus of this study is on their interannual variability. The MME simulation shows that it can reasonably reproduce spatial distribution of the WNPSH and EAJ. The summer rain belt in a northeastsouthwest direction over East Asia can be roughly captured by the CMIP5 MME simulation, although the simulated rain belt is southward and weaker compared to the observation (Figure not shown). The under-estimation of the MME simulation on the East Asian rain belt in climate mean is consistent with that in variance. 3.2. Relationship of the EASR with the WNPSH and the EAJ As pointed out in the introduction, changes of the summer precipitation over East Asia are tightly connected to the variation of the WNPSH and EAJ. The East Asian summer precipitation is influenced by the WNPSH from the south in the lower troposphere and by the EAJ from the north in the upper troposphere. Such relationships can be well demonstrated in Fig. 4a and Fig. 5a, which respectively indicate the regressions of the summer precipitation against the WNPSHI and the EAJI in the observation. It is impressive
Fig. 4. Summer precipitation regressed onto the standardized WNPSHI in (a) the observation and (b) the historical simulation of the MME. Areas above the 95% significance level are shaded.
in Fig. 4a that the positive anomalies are dominated in East Asia oriented from the Yangtze River valley to Japan, and the negative anomalies are predominated over the western Pacific. This pattern
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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models (CanESM2, CCSM4, FIO-ESM, MIROC-ESM and NorESM1-M) simulating an out-of-phase EASRI-WNPSHI relationship that is in contrast to the observation. If these models are excluded from the MME result, the simulated correlation coefficient is 0.33 (significant at the 99.9% level). For the EASRI-EAJI relationship, all the models can capture the positive correlation. The correlation coefficients simulated by the individual models range from 0.27 (HadGEM2-CC) to 0.76 (NorESM1-M), higher above the 99% significance level.
Table 4 Correlation coefficients of the EASRI with the WNPSHI and the EAJI. Number in the parentheses indicates the MME result excluding five models with negative EASRIWNPSHI correlation in the historical simulation. Model
Fig. 5. Summer precipitation regressed onto the standardized EAJI in (a) the observation and (b) the historical simulation of the MME. Areas above the 95% significance level are shaded.
hints that more (less) precipitation in East Asia concurrent with less (more) precipitation in the western Pacific tends to occur if the WNPSH extends westward (retreats eastward). Similarly, in association with the positive EAJI (Fig. 5a), positive anomalies from the Yangtze River valley to Japan and negative anomalies in the western Pacific can be observed, implying that the southward (northward) located EAJ may result in wetter (drier) conditions in the East Asian region from the Yangtze River valley to Japan and drier (wetter) conditions in the western Pacific. The WNPSHI- and EAJIrelated precipitation pattern simulated by the MME is in general in agreement with the observation. Corresponding to the western extension of the WNPSH (Fig. 4b) or southward distribution of the EAJ (Fig. 5b), the summer precipitation is inclined to increase in East Asia and decrease in the western Pacific, despite that the precipitation anomalies are somewhat weaker than the observation. Table 4 further delineates the correlation coefficients of the EASRI with the WNPSHI and EAJI. The EASRI-WNPSHI and the EASRI-EAJI correlation coefficients are 0.70 and 0.75 respectively for the observation, both significant at the 99.9% level. The corresponding correlation coefficients in the historical simulation of the MME are 0.21 (significant at the 95% level) and 0.53 (significant at the 99.9% level), respectively. Although the correlation coefficient in the MME simulation is lower than the observed result, especially for WNPSH (note that the length of the samples used for the historical simulation is much longer than that for the observation), the significance tests indicate that the MME simulation can capture the observed relations. For the individual models, 14 out of 19 (74%) models produce the observed in-phase EASRI-WNPSHI relationship, with the positive correlation coefficient ranging from 0.14 (HadGEM2-ES) to 0.57 (GFDL-ESM2M). Note that there are 5
ACCESS1-0 ACCESS1-3 BCC-CSM1-1-m BNU-ESM CanESM2 CCSM4 CMCC-CMS CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO HadGEM2-CC HadGEM2-ES IPSL-CM5A-MR MIROC-ESM NorESM1-M MME OBS
EASRI-WNPSHI
EASRI-EAJI
Historical
RCP4.5
RCP8.5
Historical RCP4.5 RCP8.5
0.38 0.24 0.28 0.21 0.23 0.03 0.51 0.17 0.27 0.02 0.42 0.46 0.57 0.28 0.38 0.14 0.32 0.19 0.1 0.21 (0.33) 0.70
0.19 0.21 0.24 0.05 0.19 0.05 0.7 0.25 0.21 0.27 0.6 0.42 0.63 0.4 0.26 0.08 0.35 0.4 0.05 0.19 (0.29)
0.28 0.13 0.24 0.09 0.06 0.02 0.5 0.29 0.22 0.19 0.4 0.44 0.57 0.28 0.15 0.02 0.1 0.49 0.01 0.17 (0.25)
0.66 0.54 0.48 0.61 0.58 0.65 0.56 0.54 0.68 0.54 0.28 0.55 0.64 0.29 0.29 0.55 0.27 0.73 0.76 0.53 0.75
0.54 0.41 0.3 0.8 0.45 0.73 0.56 0.66 0.61 0.48 0.31 0.75 0.75 0.48 0.54 0.43 0.12 0.65 0.72 0.54
0.56 0.65 0.54 0.83 0.52 0.69 0.59 0.54 0.56 0.51 0.53 0.55 0.67 0.5 0.49 0.39 0.37 0.62 0.69 0.57
In summary, the MME simulation can capture the interannual variability of the WNPSH, EAJ and EASR. It can also reproduce the observed relationship of the EASRI with the WNPSHI and the EAJI. The simulation on the variance of EAJI is better than of WNPSHI. This provides justification of the use of the MME for the future projection. 4. Projection of the CMIP5 models 4.1. Interannual variability Fig. 6 exhibits the MME projected changes of the interannual standard deviations of summer precipitation, 850 hPa geopotential height and 200 hPa zonal winds in the 21st century under the RCP4.5 and RCP8.5 scenarios. For the precipitation, the MME projects a similar distribution of the interannual variability change under both RCPs, with an increase along the East Asian summer rain belt and the western Pacific (Fig. 6a and b). It reveals that the regions with large variability (Fig. 3) will experience greater precipitation variability during the 21st century, that is, stronger becomes stronger. In addition, the enhanced variability in the two areas is in general greater under the RCP8.5 scenario than under the RCP4.5 scenario, reflecting the influence of the external forcing on the projections. Compared to 1900e2005, the MME projected interannual standard deviation of the EASRI will increase by 9.2% under the RCP4.5 scenario and 15.1% under the RCP8.5 scenario during 2006e2100 (Table 3), which are significant at the 95% level. The increase in amplitude is lower than the
Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033
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Fig. 6. MME projected changes in interannual standard deviation of (a, b) precipitation (mm/d), (c, d) 850 hPa geopotential height (gpm), and (e, f) 200 hPa zonal winds (m/s) in the 21st century under the RCP 4.5 (left panel) and the 8.5 (right panel) scenarios. Areas above the 95% significance level are shaded.
CMIP3 projections (about 12% and 19% under the A1B and A2 scenarios, respectively) (Lu and Fu, 2010). Almost all the individual models project enhancement of the interannual variability in precipitation over the region where the East Asian summer rain belt is located. The standard deviations of the low-level geopotential height over the western Pacific during the 21st century are also projected to increase under both RCP scenarios (Fig. 6c and d). Thus, the interannual variability of the WNPSH is expected to increase under the warming scenarios. The increase is relatively larger under the RCP8.5 scenario as compared to the RCP4.5 scenario, which is also associated with stronger external forcing imposed on the models. The MME projected interannual standard deviation of the WNPSHI increases from 6.7 gpm during 1900e2005 to 7.2 (7.0) gpm during 2006e2100 under the RCP8.5 (RCP4.5) scenario, with the enhancement of 8.3% (4.5%) (significant at the 95% level). For the individual models, 12 models (63%) and 13 models (68%) project the increase of the WNPSHI variability under the RCP4.5 and RCP8.5 scenarios, respectively (Table 2). As for the standard deviations of 200 hPa zonal winds, significant positive anomalies appear mainly over the Asian-North Pacific region north of 40 N (Fig. 6e and f). Under the RCP8.5 (RCP4.5) scenario, the EAJI standard deviation projected by the MME reaches 5.2 (5.1) m/s. Relative to 1990e2005, it will increase by 6.1% (4.1%) in the 21st century. For the individual models, 13 (12) out of 19 models project the increase of the EAJI variability under the RCP8.5 (RCP4.5 scenario (Table 2)). Additionally, for the mean state, the MME projects an increase in summer precipitation over East Asia, an increase (a decrease) in 850 hPa geopotential height over the North (western) Pacific, and a northward displaced EAJ with the 200 hPa zonal wind increasing (decreasing) to the north (south) of 40 N during the 21st century under the RCP4.5 and RCP8.5 (Figure not shown).
4.2. Relationship of the EASR with the WNPSH and the EAJ Table 4 presents the correlation coefficients of the EASRI with the WNPSHI and the EAJI for the time period 2006e2100 under the RCP4.5 and the RCP8.5 scenarios. As seen in this table, the MME (including all the models) projected EASRI-WNPSHI correlation coefficient is 0.19 (significant at 95% level) for the RCP4.5 scenario and 0.17 (significant at 90% level) for the RCP8.5 scenario, slightly weaker than that in the historical simulation. If the five models aforementioned are excluded from the MME result, the MME projected EASRI-WNPSHI correlation coefficients under the RCP4.5 and RCP8.5 scenarios are 0.29 and 0.27 respectively (both significant at the 99% level), also slightly lower than the counterpart in the historical simulation. For the individual models, 11 models project positive correlations significant at the 95% level under the RCP4.5 scenario, ranging from 0.19 (ACCESS1-0) to 0.57 (GFDL-ESM2M). 10 models project positive correlations in the range of 0.19 (FIO-ESM) to 0.70 (CMCC-CMS) that is significant at the 95% level under the RCP8.5 scenario. For the EASRI-EAJI relationship, the MME projects a correlation coefficient of 0.54 under the RCP4.5 scenario and 0.57 under the RCP8.5 scenario, higher about the 99.9% significance level and slightly larger than the counterpart in the historical simulation. Such a significant positive correlation is projected by all the individual models under both RCP4.5 and RCP8.5 scenarios, except IPSL-CM5A-MR (0.12, not passing the 95% significance level) under the RCP4.5 scenario. In general, the projected changes reflect the historical correlations, both for the MME and for individual models. 5. Conclusions In the present study, we examined the fidelity of the CMIP5 models in simulating the interannual variability of the WNPSH, EAJ and EASR and the association of the EASR with the WNPSH and the
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EAJ. Their future changes under the RCP4.5 and RCP8.5 scenarios were further projected. The main results are summarized below: 1) Despite some inter-model spreads for the individual models, the MME simulation of the CMIP5 models can capture the features of the present-day interannual variability of the WNPSH, EAJ and EASR. It can also reproduce the observed linkage of the EASR to the WNPSH and the EAJ. 2) The CMIP5 MME projects that the interannual variability of the WNPSH, EAJ and EASR tends to increase in a future warmer world. Relative to the period 1900e2005, the interannual standard deviation of the WNPSHI, EAJI and EASRI would increase by 4.5%, 4.1%, and 9.2% under the RCP4.5 scenario and by 8.3%, 6.1% and 15.1% under the RCP8.5 scenario during the 21st century, respectively. 3) The present relationships of the East Asian summer precipitation with the WNPSH and the EAJ are projected by the CMIP5 MME to still exist under the warming scenarios. However, the linkage of the EASR to the WNPSH may become slightly weaker and that to the EAJ may become slightly stronger in the 21st century in the context of future warming, although there are appreciable discrepancies among the models. The increasing tendency of the interannual variability in WNPSH, EAJ and EASR as projected in this study is consistent with the previous result but the increase in amplitude is lower than the CMIP3 projection (Lu and Fu, 2010). The CMIP5 projected relationship of the East Asian summer precipitation with the WNPSH and the EAJ are, to some extent, also different from the CMIP3 projection for which Lu and Fu (2010) found no change in the relationships between the EASR and the WNPSH and the EAJ in the 21st century. This discrepancy might be related to climate model difference between the CMIP3 and CMIP5. Due to better understanding of climate system and improvement of the horizontal resolution of climate models, the CMIP5 shows substantial improvements compared with the CMIP3 (Taylor et al., 2012; IPCC, 2013; Jiang et al., 2016). The difference of the forcing scenario between the CMIP3 and CMIP5 (IPCC, 2013) may be another reason for the difference of the projection results. In-depth analysis is needed in future work. Acknowledgments We acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for CMIP, and thank climate modeling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was jointly supported by the National Natural Science Foundation of China (41275078, 41405077), the National Key Research and Development Program of China (2016YFA0600701), and the National Basic Research Program of China (2012CB955900). References Chen, L.T., Wu, R.G., 1998. Relationship between summer rainbelt patterns in eastern China and 500 hPa circulation anomalies over the Northern Hemisphere. Scientia Atmospheric Sinica 22 (6), 849e857. Fu, Y.H., 2012. The projected temporal evolution in the interannual variability of East Asian summer rainfall by CMIP3 coupled models. Science China Earth Science 42, 1937e1950. Huffman, G.J., Adler, R.F., Rudolf, B., Schneider, U., Keehn, P.R., 1995. Global precipitation estimates based on a technique for combining satellite-based estimates,
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Please cite this article in press as: Ren, Y., et al., Interannual variability of western North Pacific subtropical high, East Asian jet and East Asian summer precipitation: CMIP5 simulation and projection, Quaternary International (2016), http://dx.doi.org/10.1016/j.quaint.2016.08.033