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Simulations and Projections of the Western Pacific Subtropical High
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SIMULATIONS AND PROJECTIONS OF THE WESTERN PACIFIC SUBTROPICAL HIGH
10
10.1 INTRODUCTION As discussed in Chapter 5, Characteristics of the Western Pacific Subtropical High and June July August Rainfall Anomalies, the western Pacific subtropical high (WPSH) is one of the most important atmospheric circulation systems affecting the weather and climate over East Asia. Its variability in location and intensity has important impacts on the summer rainfall anomalies over China (Xu et al., 2001; Tao and Wei, 2006). Abundant moisture from the tropical oceans is transported to eastern China through the southerly flow from its western boundary, then converges with the cold air from the high latitude and conforms the front, causing rainfall at the northwestern flank of WPSH, and drought and high temperature in the area covered by WPSH (Han and Wang, 2007). Clearly, its interannual variability causes droughts and floods over East China (Wu et al., 2002; Liu et al., 2013), while its interdecadal variability modulates the drought and flood pattern over East Asia (Hu, 1997; Xiong, 2001). Liu et al. (2013) noted that the biennial component of the WPSH in intensity and zonal position also have the obvious interdecadal transition in the late-1970s, with larger amplitudes over the past 30 years. As a result, great attention has been given to the variation of the WPSH at the interannual and interdecadal timescales, since understanding the variation will help us to improve the forecast of summer climate anomalies in East Asia. To simulate and project climate change, climate models have become one of the main quantitative tools. Due to a lot of uncertainty and bias in the climate models and in order to improve model simulations, the coupled model intercomparison project (CMIP) has been proposed by the World Climate Research Program. The last phase of the CMIP (CMIP5) involved about 30 climate modeling groups around the world with the aim to advance our knowledge of climate variability and climate change and projection, and to provide simulations for evaluation in the IPCC’s fifth assessment report (Taylor et al., 2012). Compared with the models used in the previous phases of CMIP simulations (e.g., CMIP3), most models in CMIP5 have been improved in many aspects, including physical processes and coupled carbon cycles. Thus it will be interesting to assess the performance of CMIP5 models in the simulation of the WPSH mean state and variability. Also, it is meaningful to project the possible changes of the WPSH under different climate warming scenarios in the future. In this chapter, the capacities of simulating the WPSH from 26 CMIP5 models are evaluated from various aspects based on the set of WPSH indices discussed in Chapter 5, Characteristics of the Western Pacific Subtropical High and June July August Rainfall The Asian Summer Monsoon. DOI: https://doi.org/10.1016/B978-0-12-815881-4.00010-X © 2019 Elsevier Inc. All rights reserved.
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Anomalies (Liu et al., 2012). It is expected to provide some valuable implications for the model’s development and improvement in the future by comparisons between the simulations and observations, and among the models. Finally, the models with good performance are identified and selected to project the possible evolution of the WPSH in the future, which will provide implications for future trends of the East Asian summer climate over China. This chapter is organized as follows. Section 10.2 provides a description of the data, CMIP5 simulations, and approaches used in this chapter. Section 10.3 shows the climatological characteristics at 500 hPa geopotential height and zonal wind in CMIP5 models, the possible reason for systematic errors, and climatological calibrations. Section 10.4 examines the variability of the WPSH in CMIP5 models and quantitatively assesses the simulation capabilities. Section 10.5 investigates the possible future changes of the WPSH under three typical representation concentration pathway (RCP) scenarios. Summary and discussions are given in Section 10.6.
10.2 MODELS, DATA, AND APPROACHES Table 10.1 gives basic descriptions of the 26 coupled climate models used in the CMIP5. The simulation data used in this study include two types. First, historical runs, which are initiated from an arbitrary point of a quasi-equilibrium control run and integrating longer than 156 years (1850 2005). The historical run is forced by time-evolving greenhouse gases, ozone, aerosols, and a solar constant that are consistent with the observations, and for the first time, a time-evolving land cover/land use pattern was included (Taylor et al., 2012). Second, future climate change projection runs, which are forced by three typical RCPs, that is, RCP2.6, RCP4.5, and RCP8.5. Every emission scenario has a set of specified concentration of greenhouse gases, aerosols, and other chemical gases, and land cover/land use pattern (Moss et al., 2010; Xin et al., 2012). Atmospheric monthly reanalysis (Kalnay et al., 1996) provided by National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the Extended Reconstructed SST dataset (ERSSTv3b) (Smith et al., 2008) from National Oceanic and Atmospheric Administration (NOAA) are used in this chapter. Hereafter we refer to these observation-based datasets as observations. The analysis period of the historical runs and observations is from 1951 to 2005, and the period of the future projection runs is from 2006 to 2099. The model outputs in atmospheric circulation fields are interpolated to the regular grids with a horizontal resolution of 2.5 3 2.5 and SST fields are interpolated with the regular grids with a horizontal resolution of 2 3 2 for facilitating the comparison among CMIP5 models. Normally, the WPSH is represented in the weather chart as the region surrounded by the contour of 5880 gpm at the 500 hPa level within the range of 10 45 N and 110 E 180 . In order to quantitatively describe the variation of the WPSH in intensity and position, the monthly WPSH indices are computed based on NCEP/NCAR reanalysis, including the area, intensity, ridgeline, and westernmost point indices. It is well-known that the WPSH is strongest in summer and has a significant impact on the summer rainfall over East Asia (Nitta and Hu, 1996; Wu et al., 2002). Therefore we focus on the WPSH in summer (June, July, and August; JJA) for the aspects of spatial distribution, amplitude variation, and interdecadal and interannual variations. The entire time series of the monthly data is only used in the power spectrum analysis.
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Table 10.1 Description of 26 Coupled Climate Models Participating in the CMIP5 Model Name
Institute/Country
Resolution
ACCESS1-0 BCC-CSM1-1 CanESM2 CCSM4 CESM1-CAM5-1-FV2 CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-R HadCM3 HadGEM2-AO HadGEM2-CC HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR MIROC5 MIROC-ESM MPI-ESM-LR MPI-ESM-P MRI-CGCM3 NorESM1-M
CAWCR/Australia BCC/China CCCMA/Canada NCAR/USA NSF-DOE-NCAR/USA CNRM-CERFACS/France LASG-CESS/China FIO/China NOAA GFDL/USA NOAA GFDL/USA NOAA GFDL/USA NASA GISS/USA NASA GISS/USA MOHC/UK NIMR/Korea MOHC/UK MOHC/UK INM/Russia IPSL/France IPSL/France MIROC/Japan MIROC/Japan MPI-M/Germany MPI-M/Germany MRI/Japan NCC/Norway
1.875 3 1.25 2.8 3 2.8 2.8 3 2.8 1.25 3 0.94 2.5 3 1.9 1.4 3 1.4 2.8 3 2.8 2.8 3 2.8 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 3.75 3 2.5 1.875 3 1.25 1.875 3 1.25 1.875 3 1.25 2.0 3 1.5 3.75 3 1.875 2.5 3 1.25 1.4 3 1.4 2.8 3 2.8 1.9 3 1.9 1.9 3 1.9 1.1 3 1.1 2.5 3 1.875
To assess the ability in simulating current climate, a Taylor diagram (Taylor, 2001) is used to centralize multimodel related information. The correlation coefficient between the model simulations and observations represents the model’s capability to simulate climatic variability in the concerned region. The root-mean-square error (RMSE) indicates the model deviation from observations (the closer to zero the RMSE is, the higher the simulation capability of the model is). The standard deviation ratio of the model simulation relative to the observation indicates the model’s ability in simulating the amplitude of variability. These three assessment indicators displayed in a Taylor diagram can reflect the overall ability of model simulations. Therefore the Taylor diagram is used in this chapter to evaluate the 26 CMIP5 models, where both the self-correlation coefficient and standard deviation in observations are 1, and the RMSE is 0.
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10.3 SIMULATION OF THE WESTERN PACIFIC SUBTROPICAL HIGHS CLIMATOLOGY AND CALIBRATIONS 10.3.1 SPATIAL DISTRIBUTION According to the definition of the WPSH and to verify the ability of simulating the WPSH, the simulation of geopotential height (H500) and zonal wind fields at 500 hPa (u500) by CMIP5 are examined first. It is noted that the model biases present in the spatial pattern, coverage as well as intensity of WPSH relative to the observations (Fig. 10.1). The simulated H500 is stronger than the observations in CCSM4, CESM1-CM5, and FIO-ESM models, with a larger coverage area
FIGURE 10.1 Climatological mean of H500 in summer from the observation (NCEP/NCAR reanalysis) and CMIP5 model simulations (light and dark shaded areas denote H500 larger than 5840 and 5880 gpm, respectively). CMIP5, Coupled model intercomparison project phase 5.
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surrounded by the contour of 5880 gpm. In the other CMIP5 models, however, the simulated H500 is much weaker than the observations, without the 5880 gpm contour line over the subtropical western Pacific in summer when the WPSH is seasonally strongest in the observation, even no 5840 gpm contour line at H500 in HadGEM2-CC and IPSL-CM5A-LR models. Furthermore, the simulation of u500 5 0 m/s is also not good (not shown). Most of the models fail to capture the position of the WPSH ridgeline except for three models which reproduce the contour of 5880 gpm. It is also noted that the simulated easterly wind belt on the south of the WPSH is much weaker than the observation. This is consistent with the overall weak WPSH simulated by these models. The systematic errors usually exist in the simulation of the atmospheric circulation from the current global atmosphere ocean coupled models (also known as the model climate drift, Sun and Ding, 2008; Huang and Qu, 2009; Feng and Li, 2012). These biases in simulating the WPSH are common in most state-of-the-art GCMs, implying a challenge in simulating and predicting summer climate variability over East Asia (Zhang and Chen, 2011a,b). Considering that the intensity and spatial pattern of the WPSH are influenced by the thermal anomalies from the underlying surface, especially the sea surface temperature (SST) in the tropical Pacific and Indian oceans (Nitta, 1987; Huang and Sun, 1994; Nitta and Hu, 1996; Wu et al., 2002), Fig. 10.2 shows the climatological tropical SST in summer and the model biases relative to the observations. The observed results show that SST in the tropical Pacific warm pool and Indian Ocean is above 28 C in summer, which is favorable to the maintenance of the subtropical high system. However, compared to the observations, the simulated regions surrounded by the contour of SST 5 28 C in most CMIP5 models are much smaller, indicating cold biases in the tropical Indian Ocean and Pacific warm pool, and consisting with the underestimated H500 over the western Pacific in the simulations. Nevertheless, there are three models (CCSM4, CESM1-CAM5, and FIO-ESM) with realistic H500, which also reproduce SSTs in the tropical Indian and Pacific oceans with smaller cold biases. These results may suggest a connection in simulating the spatial distribution of tropical SST and in simulating the WPSH.
10.3.2 CLIMATOLOGY CALIBRATIONS The Taylor diagram of the climatological H500 over the western Pacific in summer compared to the observation (Fig. 10.3A) shows that the simulations in the spatial pattern of H500 are similar to the observation, with the correlation coefficient above 0.96, even 0.99 in five models. The RMSEs of all models are within 1 gpm. The high correlations and small RMSEs indicate the good simulation of the spatial distribution of H500 over the western Pacific. It is also noted that the amplitude of H500 is well captured by the models with a range of the standard deviation of models from 0.6 to 1.6 gpm. Fig. 10.3B is the Taylor diagram of the simulated climatological u500. All of the correlation coefficients with the observations are above 0.90, RMSEs are less than 0.5 m/s, and the standard deviations are from 0.5 to 1.5 m/s, which imply the good simulation of 26 CMIP5 models in the spatial distribution and amplitude of u500. We noted that although there are significant model biases in simulating H500 and u500 (Fig. 10.1), which may be associated with cold SST biases in the tropical Indian and Pacific oceans (Fig. 10.2), the spatial pattern and variability of H500 and u500 over the western Pacific are well reproduced in all 26 CMIP5 models (Fig. 10.3). To correct the climatology biases in the simulations, the simulated climatology of H500 and u500 from all of the models are replaced by that from the observations. In other words, all the model outputs are calibrated to the same climatic
FIGURE 10.2 Climatological mean of SST (contours, unit: C) in summer from the observation and CMIP5 model simulations, with the model biases (shading). SST, Sea surface temperature; CMIP5, coupled model intercomparison project phase 5.
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(A)
(B)
FIGURE 10.3 Taylor diagrams of the (A) simulated climatological H500 and (B) zonal wind over the western Pacific in summer compared to the observation. REF indicates the reference value of 1. The radial distance of the model code point from the origin is the standard deviation ratio of the models relative to the observations. The correlation coefficient of the spatial pattern between the model and observations is shown by the cosine of the azimuthal angle of the model code point, and their root-mean-square error is given by the distance of the model code point from the REF. REF, reference.
state, and then superimposed on each model’s own variability of H500 and u500. For example, the calibration for H500 for a model (the same in u500) is that: hmodel 5 hmodel 2 h model 1 h ncep :
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Here, hmodel represents H500 of one year from a specified model, and h model represents the model climatology of H500. h ncep denotes the climatology of H500 from the observation. In Section 10.4, the verification of the WPSH is based on the climatology corrected results.
10.4 THE PERFORMANCE OF MODELS IN SIMULATING THE WESTERN PACIFIC SUBTROPICAL HIGH’S VARIABILITY 10.4.1 WESTERN PACIFIC SUBTROPICAL HIGH INDICES To characterize the variability of the WPSH in intensity and position objectively, Fig. 10.4 shows the 9-year running mean time series of the WPSH indices, including the area, intensity, ridgeline, and westernmost point indices. It is noted from the observation that the area and intensity indices of the WPSH increased significantly and the westernmost point decreased after the late-1970s, which indicates that the WPSH has become stronger and more westward extending over the past 30 years (Fig. 10.4A and B). Such an interdecadal shift of the WPSH has been noted in previous works (Hu, 1997; Huang et al., 2006; Zhao et al., 2007; Liu and Ding, 2012). Interestingly, there is a slight downward trend in the observed ridgeline index (Fig. 10.4C), suggesting that the WPSH slightly shifted southward after the late-1970s and is consistent with the reports by Nitta and Hu (1996) and Hu (1997). Compared with the observations, all the calibrated CMIP5 model results capture the interdecadal shift of the WPSH in the late-1970s. In these model simulations, the observed greenhouse gases, ozone, aerosols and solar constant, and the variability of the land cover are taken into account in the models (Taylor et al., 2012), thus, it is suggested that these external forcing may play an important role in causing the interdecadal variability of the WPSH, although the internal variability of the atmospheric circulation over the western Pacific may be also influenced by the local atmosphere ocean interaction (Wang et al., 2005; Zhu and Shukla, 2013).
10.4.2 INTERDECADAL VARIABILITY OF THE WESTERN PACIFIC SUBTROPICAL HIGH IN THE SPATIAL PATTERN The comparison of the distribution of WPSH area and position between the period of 1951 60 and 1996 2005 are used to investigate the interdecadal variation of WPSH (Fig. 10.5). During the former period, the WPSH is relatively weaker and eastward, with the observed contour of 5880 gpm between 20 N and 30 N, and its westernmost point no more than 140 E (Fig. 10.5A). The observed ridgeline is near 25 N, in the northeastern southwestern direction (Fig. 10.5C). Most of the calibrated simulations are able to reproduce the WPSH, that is, the contour of 5880 gpm, much better than the model results before climatology correction (Fig. 10.1). Unfortunately, 10 models failed to capture the contours of 5880 gpm. Compared with the situation of simulating the contour line of 5880 gpm, the ridgelines of the WPSH are better simulated, except near the coastland of China at about 115 E.
FIGURE 10.4 Time series of the WPSH indices in summer from the observations (thick black line) and calibrated model simulations (thin colored lines) for the period from 1951 to 2005 (A, area index; B, intensity index; C, ridgeline index; D, westernmost point; the thick blue line is the 26 CMIP5 models’ ensemble mean). WPSH, Western Pacific subtropical high.
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FIGURE 10.4 (Continued.)
In the latter period of 1996 2005, the WPSH becomes stronger and more westward, the area of the observed WPSH increases, with the southern and northern borders extending to 18 N and 32 N, and the westernmost point extends to 135 E (Fig. 10.5B). The calibrated model results well simulate the interdecadal shift of the WPSH. The area and intensity indices of the WPSH significantly increased relative to the former period in the model simulations (Fig. 10.5B and D).
10.4.3 LINEAR TRENDS AND STANDARD DEVIATION OF THE WESTERN PACIFIC SUBTROPICAL HIGH In order to quantitatively assess the simulation capabilities of the WPSH indices in the CMIP5 models, the linear trends of each WPSH index from all the CMIP5 models are calculated except for the ridgeline index due to its small trend (Fig. 10.6). The observed results show that the linear ascending trend of the WPSH area and intensity indices are more than 20% per decade, and the trend of the westernmost point is 2.7% per decade, within the significance level of 0.01. Compared to the observations, all the models well simulate the tendency of enhancing and westward extension of the WPSH during the period of 1951 to 2005, but with large quantitative differences. Some are larger than the observation in the intensity of the WPSH, such as IPSL-CM5A-LR and GFDL-ESM2G, while others are weaker than the observations, such as GFDL-CM3 and HadGEM2-ES. Taking the three WPSH indices into account, it is found that the simulations of nine models, CESM1-CAM5-1-FV2, CNRM-CM5, FGOALS-g2, FIO-ESM, HadCM3, HadGEM2CC, MIROC-ESM, MPI-ESM-P, and NorESM1-M, are better than others in the linear trend of the WPSH indices. Comparison of the standard deviation of WPSH indices can be used to measure the simulation capability of the models in the interannual variability of the WPSH. Fig. 10.7 shows the simulated standard deviation ratio of WPSH indices relative to the observations. The closer to 1 the ratio is,
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FIGURE 10.5 The distribution of (A and B) WPSH area (h500 5 5880 gpm) and (C and D) ridgeline (u500 5 0 m/s) in summer from the observations (thick black line) and calibrated model simulations (thin colored lines) averaged in two periods of 1951 1960 (A and C) and 1995 2005 (B and D). WPSH, Western Pacific subtropical high.
the better to simulate the interannual variability of WPSH index is. The gray bars in Fig. 10.7 denote the accumulated distance between the ratios of these four WPSH indices, with the reference value of 1. The smaller the accumulated distance is, that is, closer to observations, the better to simulate all the WPSH indices is, which provides a measurement for the models in simulating the overall features of the WPSH. It is noted that most of the standard deviation ratios of the calibrated simulations to the observations are larger than the reference value, indicating an overestimation of the amplitudes of the WPSH indices in the models. GFDL-ESM2M, MIROC5, and INMCM4 models have relatively poor
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FIGURE 10.5 (Continued.)
simulations in the interannual variability of the WPSH, because of the too large accumulated ratios in the GFDL-ESM2M and MIROC5, and the too small one in INMCM4. According to the simulation of four WPSH indices, it is noted that six models (ACCESS1-0, CanESM2, CNRM-CM5, FGOALSg2, IPSL-CM5A-MR, MIROC-ESM) have relatively better performance.
10.4 WESTERN PACIFIC SUBTROPICAL HIGH’S VARIABILITY
(A)
(B)
NorESM1-M MRI-CGCM3 MPI-ESM-P MPI-ESM-LR MIROC-ESM MIROC5 IPSL-CM5A-MR IPSL-CM5A-LR INMCM4 HadGEM2-ES HadGEM2-CC HadGEM2-AO HadCM3 GISS-E2-R GISS-E2-H GFDL-ESM2M GFDL-ESM2G GFDL-CM3 FIO-ESM FGOALS-g2 CNRM-CM5 CESM1-CAM5-1-FV2 CCSM4 CanESM2 BCC-CSM1-1 ACCESS1-0 NCEP
0
(C)
191
10
20
30
NorESM1-M MRI-CGCM3 MPI-ESM-P MPI-ESM-LR MIROC-ESM MIROC5 IPSL-CM5A-MR IPSL-CM5A-LR INMCM4 HadGEM2-ES HadGEM2-CC HadGEM2-AO HadCM3 GISS-E2-R GISS-E2-H GFDL-ESM2M GFDL-ESM2G GFDL-CM3 FIO-ESM FGOALS-g2 CNRM-CM5 CESM1-CAM5-1-FV2 CCSM4 CanESM2 BCC-CSM1-1 ACCESS1-0 NCEP 40 0
10
20
30
40
50
NorESM1-M MRI-CGCM3 MPI-ESM-P MPI-ESM-LR MIROC-ESM MIROC5 IPSL-CM5A-MR IPSL-CM5A-LR INMCM4 HadGEM2-ES HadGEM2-CC HadGEM2-AO HadCM3 GISS-E2-R GISS-E2-H GFDL-ESM2M GFDL-ESM2G GFDL-CM3 FIO-ESM FGOALS-g2 CNRM-CM5 CESM1-CAM5-1-FV2 CCSM4 CanESM2 BCC-CSM1-1 ACCESS1-0 NCEP –5
–4
–3
–2
–1
0
FIGURE 10.6 Linear trend coefficients of the WPSH indices in summer from the observation (black bar) and CMIP5 model simulations (gray bars) for the period from 1951 to 2005 (A, area index; B, intensity index; C, westernmost point). WPSH, western Pacific subtropical high; CMIP5, coupled model intercomparison project phase 5.
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Accumulted distance Area index Intensity index Ridgeline index Westernmost point
2
1.5
1
0.5
NorESM1-M
MPI-ESM-P
MRI-CGCM3
MPI-ESM-LR
MIROC5
MIROC-ESM
IPSL-CM5A-MR
INMCM4
IPSL-CM5A-LR
HadGEM2-ES
HadGEM2-CC
HadCM3
HadGEM2-AO
GISS-E2-R
GISS-E2-H
GFDL-ESM2G
GFDL-ESM2M
FIO-ESM
GFDL-CM3
FGOALS-g2
CNRM-CM5
CCSM4
CESM1-CAM5
CanESM2
ACCESS1-0
BCC-CSM1-1
0
FIGURE 10.7 Standard deviation ratios of the simulated WPSH indices relative to the observation. The gray bars are the accumulated distance of the standard deviation ratio of the four WPSH indices from the reference value of 1. The smaller the gray bar is, the closer to the observation the simulated result is. WPSH, Western Pacific subtropical high.
10.4.4 INTERANNUAL PERIODS OF THE WESTERN PACIFIC SUBTROPICAL HIGH The spectrum of variability is another important feature to describe the characteristics of the WPSH. Fig. 10.8 shows the power spectrum of the monthly WPSH intensity index in the entire analysis period from the observations and 26 CMIP5 models. Considering that the seasonal variability of the WPSH is more significant than other timescales, the low-pass filter is first used to remove the seasonal variability (the cycle of 11 months) from the raw data to highlight the interannual signals. It has been known that there is a significant period of tropospheric biennial oscillation (TBO) over the East Asian monsoon region at the interannual timescales (Nitta and Hu, 1996). The WPSH, as one of the dominant members of the East Asian monsoon system, has a TBO component at interannual timescales (Liu et al., 2013). The observation shows that there are two interannual periods, that is, the quasi-four-year (36 60 months) and the quasi-biennial year (24 36 months) periods, and both pass the 0.05 significance level of the red-noise test. The period of the quasibiennial year is consistent with the TBO component of the WPSH and the monsoon rainfall in China (Chang and Li, 2000; Chang et al., 2000; Ding, 2007). The power spectra in the simulations of the CMIP5 models have obvious differences at the interannual timescales (Fig. 10.8). To some extent, only ACCESS1-0, GFDL-CM3, HadGEM2-CC, and HadGEM2-ES models can capture the quasi-four-year period and the TBO period.
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FIGURE 10.8 Power spectrums (red solid line) of the monthly WPSH intensity index from the observation and CMIP5 model simulations. Blue dashed lines indicate the 0.05 significance level of the red-noise test; the x-axis is the period (month), and the y-axis is the spectrum density. WPSH, Western Pacific subtropical high; CMIP5, coupled model intercomparison project phase 5.
The comparison of the 26 CMIP5 model simulations with the observations shows that most of the simulated H500 in the subtropical western Pacific region are weaker than observation, and even the calibrated results of some models still fail to capture the spatial and temporal characteristics of the WPSH. According to the overall simulations of the distributions of SST, H500, and u500, and the quantitative assessment of the WPSH indices, it is noted that the CNRM-CM5,
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FGOALS-g2, FIO-ESM, MIROC-ESM, and MPI-ESM-P models are better than others. Due to unavailable simulations of the RCP scenarios in the MPI-ESM-P model and based on the assessment, the ensemble mean results of four models (CNRM-CM5, FGOALS-g2, FIO-ESM, and MIROC-ESM) are selected to project the evolution of the WPSH in the future under different RCP scenarios.
10.5 FUTURE CHANGES OF THE WESTERN PACIFIC SUBTROPICAL HIGH UNDER DIFFERENT REPRESENTATION CONCENTRATION PATHWAY SCENARIOS To investigate the possible changes of the WPSH under three typical RCP scenarios in the 21st century, long-term integration (2006 99) of H500 and u500 from the four selected models are first averaged as the climatological state, and then the anomaly of each model is added to calculate the time series of the WPSH indices, based on a similar approach in Section 10.3.2. Then the 9-year running mean of the WPSH indices from 2006 to 2099 in the different RCP scenarios from these four CMIP5 models’ ensemble mean is used to project the possible changes of the WPSH in the future. It is noted that in the simulated area, intensity, and westernmost point indices of the WPSH emerge significantly interdecadal variation in the different RCP scenarios (Fig. 10.9). Under the RCP2.6 scenario, the WPSH area and intensity increase and extend westward obviously. After 2050, the linear trends of the WPSH area, intensity, and westernmost point indices gradually approach zero. The linear ascending trends of the WPSH under the RCP4.5 scenario are similar to that under the RCP2.6, but the period of significant growth lasts until about 2070, and then the growth trend weakens. The similar long-term variations of the WPSH present with faster growth in the former period and relatively smaller trend in the latter period under the RCP8.5 scenario. Interestingly, the westernmost point index of the WPSH maintained at 90 E since the late 2050s. In fact, the most westernmost point of the contour of 5880 gpm extends westward to the west of 90 E after the late 2050s. In that case, the definition of the westernmost point of the WPSH is limited as 90 E (Liu et al., 2012). Overall, the WPSH area enlarges, intensity strengthens, and the position extends westward under different RCP scenarios, with the largest linear growth trend in RCP8.5, the weakest in RCP2.6, and in between in RCP4.5. The ridgeline of the WPSH has no obvious long-term trend in the three RCP scenarios. These results have implications for the attribution and projection of climate changes in East Asia by using the CMIP5 model output.
10.6 SUMMARY AND DISCUSSION The performance of 26 coupled climate models used in the CMIP5 for the simulation of the present-day temporal variability and spatial pattern of the WPSH are assessed in this chapter. Then
FIGURE 10.9 Nine-year running mean of the WPSH indices in summer from 2006 to 2099 under different RCP scenarios from four CMIP5 models ensemble mean (A, area index; B, intensity index; C, ridgeline index; D, westernmost point; shaded areas represent one standard deviation from the multimodel mean). WPSH, Western Pacific subtropical high; CMIP5, coupled model intercomparison project phase 5.
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FIGURE 10.9 (Continued.)
the ensemble mean of the selected models is used to project the future possible changes of the WPSH under three typical RCPs. The results are as follows: 1. The intensity of H500 in the subtropical western Pacific is underestimated in most CMIP5 models, which may be associated with colder SST biases in the tropical Indian and western Pacific oceans in the models. Nevertheless, the spatial distribution and variability of H500 and u500 are captured. To eliminate the impact of the model climatology biases in the assessment, the climatology data is replaced by that of NCEP/NCAR reanalysis, which allow the model results to be more utilized, and without any change in temporal variability. 2. The calibrated model results reproduce the observed interdecadal shift of the WPSH (enhancement and westward extension after the late-1970s). According to an overall assessment of the WPSH indices, it is identified that CNRM-CM5, FGOALS-g2, FIO-ESM, MIROC-ESM, and MPI-ESM-P have better performance than the other models in simulating the WPSH. 3. The selected models’ simulations suggest that the WPSH area enlarges, intensity strengthens, and the position extends westward under RCP scenarios, with the highest linear growth trend in RCP8.5, in between in RCP4.5, and weakest in RCP2.6. Interestingly, the simulated ridgeline of the WPSH has no obvious long-term trend in the scenarios. It is easily noticed that the model defaults, such as the systemic biases in the western subtropical regions, may affect the credibility of these projections. Thus it is necessary to explore the reasons resulting in the biases as well as their connection with the cold biases in the tropical Indian and western Pacific oceans. It is also an interesting topic to examine possible changes in the East Asian summer rain belt under the projection of strengthening and westward extending of the WPSH with the increasing concentration of greenhouse gases in the future. Last, the mechanism for the projection of enhancing and westward extending of WPSH under the global warming scenarios requires further analysis.
REFERENCES
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REFERENCES Chang, C.P., Li, T., 2000. A theory of the tropical tropospheric biennial oscillation. J. Atmos. Sci. 57, 2209 2224. Chang, C.P., Zhang, Y.S., Li, T., 2000. Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part I. Roles of the subtropical ridge. J. Clim. 13, 4310 4340. Ding, Y.H., 2007. The variability of the Asian summer monsoon. J. Meteorol. Soc. Jpn 85B, 21 54. Feng, J., Li, J.P., 2012. Evaluation of IPCC AMIP models in simulating monsoon-like southwest Australian circulation. Clim. Environ. Res 17, 409 421 (in Chinese). Han, J.P., Wang, H.J., 2007. Features of interdecadal changes of the East Asian summer monsoon and similarity and discrepancy in ERA-40 and NCEP/NCAR reanalysis. Chin. J. Geophys. 50, 1666 1676 (in Chinese). Hu, Z.-Z., 1997. Interdecadal variability of summer climate over East Asia and its association with 500 hPa height and global sea surface temperature. J. Geophys. Res. 102 (D16), 19403 19412. Huang, G., Qu, X., 2009. Meridional location of west Pacific subtropical high in summer in IPCC AR4 simulation. Trans. Atmos. Sci. 32, 351 359 (in Chinese). Huang, R.H., Sun, F.Y., 1994. Impacts of the thermal state and the convective activities in the tropical western warm pool on the summer climate anomalies in East Asia. Chin. J. Atmos. Sci. 36, 673 685 (in Chinese). Huang, R.H., Cai, R.S., Chen, J.L., et al., 2006. Interdecadal variations of drought and flooding disasters in China and their association with the East Asian climate system. Chin. J. Atmos. Sci. 30, 730 743 (in Chinese). Kalnay, E., Kanamitsu, M., Kistler, R., et al., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteorool. Soc. 77, 437 472. Liu, Y.Y., Ding, Y.H., 2012. Analysis of the leading modes of the Asian-Pacific summer monsoon system. Chin. J. Atmos. Sci. 2012 (36), 673 685 (in Chinese). Liu, Y.Y., Li, W.J., Ai, W.X., et al., 2012. Reconstruction and application of the monthly western Pacific subtropical high indices. J. Appl. Meteorol. Sci. 23, 414 423 (in Chinese). Liu, Y.Y., Ding, Y.H., Gao, H., et al., 2013. Tropospheric biennial oscillation of the western Pacific subtropical high and its relationship with the tropical SST and atmospheric circulation anomalies. Chin. Sci. Bull. 58, 3664 3672. Moss, R.H., Edmonds, J.A., Hibbard, K.A., et al., 2010. The next generation of scenarios for climate change research and assessment. Nature 46, 747 756. Nitta, T., 1987. Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. J. Meteorol. Soc. Jpn. 65, 373 390. Nitta, T., Hu, Z.-Z., 1996. Summer climate variability in China and its association with 500 hPa height and tropical convection. J. Meteorol. Soc. Jpn 74, 425 445. Smith, T.M., Reynolds, R.W., Peterson, T.C., Lawrimore, J., 2008. Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880 2006). J. Clim. 21, 2283 2296. Sun, Y., Ding, Y.H., 2008. Validation of IPCC AR4 climate models in simulating interdecadal change of East Asian summer monsoon. Acta Meteorol. Sin. 66, 765 780 (in Chinese). Tao, S.Y., Wei, J., 2006. The westward, northward advance of the subtropical high over the west Pacific in summer. J. Appl. Meteorol. Sci. 17, 513 525 (in Chinese). Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106 (D7), 7183 7192. Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An overview of CMIP5 and the experiment design. Bull. Amer. Meteorool. Soc. 93, 485 498.
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Wang, B., Ding, Q.H., Fu, X., et al., 2005. Fundamental challenges in simulation and prediction of summer monsoon rainfall. Geophys. Res. Lett. 32, L15711. Available from: https://doi.org/10.1029/2005GL022734. Wu, G.X., Chou, J.F., Liu, Y.M., et al., 2002. Dynamics of the Formation and Variation of Subtropical Anticyclones. Science Press, Beijing, pp. 1 20 (in Chinese). Xin, X.G., Wu, T.W., Zhang, J., 2012. Introduction of CMIP5 experiments carried out by climate system model. Progressus Inquisitiones De Mutatione Climatis 8, 378 382 (in Chinese). Xiong, A.Y., 2001. Analysis on background of climatic variation for extremely rainy over the middle reaches of the Yangtze River in the 1990s. J. Appl. Meteorol. Sci. 12, 113 117 (in Chinese). Xu, H.M., He, J.H., Zhou, B., 2001. The features of atmospheric circulation during Meiyu onset and possible mechanisms for westward extension (northward shift) of Pacific subtropical high. J. Appl. Meteorol. Sci. 12, 150 158 (in Chinese). Zhang, H.F., Chen, H.S., 2011a. Evaluation of summer circulation simulation over East Asia by 21 climate models. Part I. Climatology. Sci. Meteorol. Sin. 31, 119 128 (in Chinese). Zhang, H.F., Chen, H.S., 2011b. Evaluation of summer circulation simulation over East Asia by 21 climate models. Part II. Interannual variability. Sci. Meteorol. Sin. 31, 247 257 (in Chinese). Zhao, P., Zhu, Y.N., Zhang, R.H., 2007. An Asian-Pacific teleconnection in summer tropospheric temperature and associated Asian climate variability. Clim. Dyn. 29, 293 303. Zhu, J.S., Shukla, J., 2013. The role of air-sea coupling in seasonal prediction of Asian-Pacific summer monsoon rainfall. J. Clim. 26, 5689 5697.
FURTHER READING IPCC, 2007. In: Solomon, S., Qin, D., Manning, M., et al.,Climate Change 2007: The Physical Scientific Basis. Cambridge University Press, Cambridge and New York, NY, 996 pp. Zhou, T.J., Yu, R.C., Zhang, J., et al., 2009. Why the western Pacific subtropical high has extended westward since the late 1970s. J. Clim. 22, 2199 2215.
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10.1 INTRODUCTION As discussed in Chapter 5, Characteristics of the Western Pacific Subtropical High and June July August Rainfall Anomalies, the western Pacific subtropical high (WPSH) is one of the most important atmospheric circulation systems affecting the weather and climate over East Asia. Its variability in location and intensity has important impacts on the summer rainfall anomalies over China (Xu et al., 2001; Tao and Wei, 2006). Abundant moisture from the tropical oceans is transported to eastern China through the southerly flow from its western boundary, then converges with the cold air from the high latitude and conforms the front, causing rainfall at the northwestern flank of WPSH, and drought and high temperature in the area covered by WPSH (Han and Wang, 2007). Clearly, its interannual variability causes droughts and floods over East China (Wu et al., 2002; Liu et al., 2013), while its interdecadal variability modulates the drought and flood pattern over East Asia (Hu, 1997; Xiong, 2001). Liu et al. (2013) noted that the biennial component of the WPSH in intensity and zonal position also have the obvious interdecadal transition in the late-1970s, with larger amplitudes over the past 30 years. As a result, great attention has been given to the variation of the WPSH at the interannual and interdecadal timescales, since understanding the variation will help us to improve the forecast of summer climate anomalies in East Asia. To simulate and project climate change, climate models have become one of the main quantitative tools. Due to a lot of uncertainty and bias in the climate models and in order to improve model simulations, the coupled model intercomparison project (CMIP) has been proposed by the World Climate Research Program. The last phase of the CMIP (CMIP5) involved about 30 climate modeling groups around the world with the aim to advance our knowledge of climate variability and climate change and projection, and to provide simulations for evaluation in the IPCC’s fifth assessment report (Taylor et al., 2012). Compared with the models used in the previous phases of CMIP simulations (e.g., CMIP3), most models in CMIP5 have been improved in many aspects, including physical processes and coupled carbon cycles. Thus it will be interesting to assess the performance of CMIP5 models in the simulation of the WPSH mean state and variability. Also, it is meaningful to project the possible changes of the WPSH under different climate warming scenarios in the future. In this chapter, the capacities of simulating the WPSH from 26 CMIP5 models are evaluated from various aspects based on the set of WPSH indices discussed in Chapter 5, Characteristics of the Western Pacific Subtropical High and June July August Rainfall The Asian Summer Monsoon. DOI: https://doi.org/10.1016/B978-0-12-815881-4.00010-X © 2019 Elsevier Inc. All rights reserved.
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Anomalies (Liu et al., 2012). It is expected to provide some valuable implications for the model’s development and improvement in the future by comparisons between the simulations and observations, and among the models. Finally, the models with good performance are identified and selected to project the possible evolution of the WPSH in the future, which will provide implications for future trends of the East Asian summer climate over China. This chapter is organized as follows. Section 10.2 provides a description of the data, CMIP5 simulations, and approaches used in this chapter. Section 10.3 shows the climatological characteristics at 500 hPa geopotential height and zonal wind in CMIP5 models, the possible reason for systematic errors, and climatological calibrations. Section 10.4 examines the variability of the WPSH in CMIP5 models and quantitatively assesses the simulation capabilities. Section 10.5 investigates the possible future changes of the WPSH under three typical representation concentration pathway (RCP) scenarios. Summary and discussions are given in Section 10.6.
10.2 MODELS, DATA, AND APPROACHES Table 10.1 gives basic descriptions of the 26 coupled climate models used in the CMIP5. The simulation data used in this study include two types. First, historical runs, which are initiated from an arbitrary point of a quasi-equilibrium control run and integrating longer than 156 years (1850 2005). The historical run is forced by time-evolving greenhouse gases, ozone, aerosols, and a solar constant that are consistent with the observations, and for the first time, a time-evolving land cover/land use pattern was included (Taylor et al., 2012). Second, future climate change projection runs, which are forced by three typical RCPs, that is, RCP2.6, RCP4.5, and RCP8.5. Every emission scenario has a set of specified concentration of greenhouse gases, aerosols, and other chemical gases, and land cover/land use pattern (Moss et al., 2010; Xin et al., 2012). Atmospheric monthly reanalysis (Kalnay et al., 1996) provided by National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the Extended Reconstructed SST dataset (ERSSTv3b) (Smith et al., 2008) from National Oceanic and Atmospheric Administration (NOAA) are used in this chapter. Hereafter we refer to these observation-based datasets as observations. The analysis period of the historical runs and observations is from 1951 to 2005, and the period of the future projection runs is from 2006 to 2099. The model outputs in atmospheric circulation fields are interpolated to the regular grids with a horizontal resolution of 2.5 3 2.5 and SST fields are interpolated with the regular grids with a horizontal resolution of 2 3 2 for facilitating the comparison among CMIP5 models. Normally, the WPSH is represented in the weather chart as the region surrounded by the contour of 5880 gpm at the 500 hPa level within the range of 10 45 N and 110 E 180 . In order to quantitatively describe the variation of the WPSH in intensity and position, the monthly WPSH indices are computed based on NCEP/NCAR reanalysis, including the area, intensity, ridgeline, and westernmost point indices. It is well-known that the WPSH is strongest in summer and has a significant impact on the summer rainfall over East Asia (Nitta and Hu, 1996; Wu et al., 2002). Therefore we focus on the WPSH in summer (June, July, and August; JJA) for the aspects of spatial distribution, amplitude variation, and interdecadal and interannual variations. The entire time series of the monthly data is only used in the power spectrum analysis.
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Table 10.1 Description of 26 Coupled Climate Models Participating in the CMIP5 Model Name
Institute/Country
Resolution
ACCESS1-0 BCC-CSM1-1 CanESM2 CCSM4 CESM1-CAM5-1-FV2 CNRM-CM5 FGOALS-g2 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-R HadCM3 HadGEM2-AO HadGEM2-CC HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR MIROC5 MIROC-ESM MPI-ESM-LR MPI-ESM-P MRI-CGCM3 NorESM1-M
CAWCR/Australia BCC/China CCCMA/Canada NCAR/USA NSF-DOE-NCAR/USA CNRM-CERFACS/France LASG-CESS/China FIO/China NOAA GFDL/USA NOAA GFDL/USA NOAA GFDL/USA NASA GISS/USA NASA GISS/USA MOHC/UK NIMR/Korea MOHC/UK MOHC/UK INM/Russia IPSL/France IPSL/France MIROC/Japan MIROC/Japan MPI-M/Germany MPI-M/Germany MRI/Japan NCC/Norway
1.875 3 1.25 2.8 3 2.8 2.8 3 2.8 1.25 3 0.94 2.5 3 1.9 1.4 3 1.4 2.8 3 2.8 2.8 3 2.8 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 3.75 3 2.5 1.875 3 1.25 1.875 3 1.25 1.875 3 1.25 2.0 3 1.5 3.75 3 1.875 2.5 3 1.25 1.4 3 1.4 2.8 3 2.8 1.9 3 1.9 1.9 3 1.9 1.1 3 1.1 2.5 3 1.875
To assess the ability in simulating current climate, a Taylor diagram (Taylor, 2001) is used to centralize multimodel related information. The correlation coefficient between the model simulations and observations represents the model’s capability to simulate climatic variability in the concerned region. The root-mean-square error (RMSE) indicates the model deviation from observations (the closer to zero the RMSE is, the higher the simulation capability of the model is). The standard deviation ratio of the model simulation relative to the observation indicates the model’s ability in simulating the amplitude of variability. These three assessment indicators displayed in a Taylor diagram can reflect the overall ability of model simulations. Therefore the Taylor diagram is used in this chapter to evaluate the 26 CMIP5 models, where both the self-correlation coefficient and standard deviation in observations are 1, and the RMSE is 0.
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10.3 SIMULATION OF THE WESTERN PACIFIC SUBTROPICAL HIGHS CLIMATOLOGY AND CALIBRATIONS 10.3.1 SPATIAL DISTRIBUTION According to the definition of the WPSH and to verify the ability of simulating the WPSH, the simulation of geopotential height (H500) and zonal wind fields at 500 hPa (u500) by CMIP5 are examined first. It is noted that the model biases present in the spatial pattern, coverage as well as intensity of WPSH relative to the observations (Fig. 10.1). The simulated H500 is stronger than the observations in CCSM4, CESM1-CM5, and FIO-ESM models, with a larger coverage area
FIGURE 10.1 Climatological mean of H500 in summer from the observation (NCEP/NCAR reanalysis) and CMIP5 model simulations (light and dark shaded areas denote H500 larger than 5840 and 5880 gpm, respectively). CMIP5, Coupled model intercomparison project phase 5.
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surrounded by the contour of 5880 gpm. In the other CMIP5 models, however, the simulated H500 is much weaker than the observations, without the 5880 gpm contour line over the subtropical western Pacific in summer when the WPSH is seasonally strongest in the observation, even no 5840 gpm contour line at H500 in HadGEM2-CC and IPSL-CM5A-LR models. Furthermore, the simulation of u500 5 0 m/s is also not good (not shown). Most of the models fail to capture the position of the WPSH ridgeline except for three models which reproduce the contour of 5880 gpm. It is also noted that the simulated easterly wind belt on the south of the WPSH is much weaker than the observation. This is consistent with the overall weak WPSH simulated by these models. The systematic errors usually exist in the simulation of the atmospheric circulation from the current global atmosphere ocean coupled models (also known as the model climate drift, Sun and Ding, 2008; Huang and Qu, 2009; Feng and Li, 2012). These biases in simulating the WPSH are common in most state-of-the-art GCMs, implying a challenge in simulating and predicting summer climate variability over East Asia (Zhang and Chen, 2011a,b). Considering that the intensity and spatial pattern of the WPSH are influenced by the thermal anomalies from the underlying surface, especially the sea surface temperature (SST) in the tropical Pacific and Indian oceans (Nitta, 1987; Huang and Sun, 1994; Nitta and Hu, 1996; Wu et al., 2002), Fig. 10.2 shows the climatological tropical SST in summer and the model biases relative to the observations. The observed results show that SST in the tropical Pacific warm pool and Indian Ocean is above 28 C in summer, which is favorable to the maintenance of the subtropical high system. However, compared to the observations, the simulated regions surrounded by the contour of SST 5 28 C in most CMIP5 models are much smaller, indicating cold biases in the tropical Indian Ocean and Pacific warm pool, and consisting with the underestimated H500 over the western Pacific in the simulations. Nevertheless, there are three models (CCSM4, CESM1-CAM5, and FIO-ESM) with realistic H500, which also reproduce SSTs in the tropical Indian and Pacific oceans with smaller cold biases. These results may suggest a connection in simulating the spatial distribution of tropical SST and in simulating the WPSH.
10.3.2 CLIMATOLOGY CALIBRATIONS The Taylor diagram of the climatological H500 over the western Pacific in summer compared to the observation (Fig. 10.3A) shows that the simulations in the spatial pattern of H500 are similar to the observation, with the correlation coefficient above 0.96, even 0.99 in five models. The RMSEs of all models are within 1 gpm. The high correlations and small RMSEs indicate the good simulation of the spatial distribution of H500 over the western Pacific. It is also noted that the amplitude of H500 is well captured by the models with a range of the standard deviation of models from 0.6 to 1.6 gpm. Fig. 10.3B is the Taylor diagram of the simulated climatological u500. All of the correlation coefficients with the observations are above 0.90, RMSEs are less than 0.5 m/s, and the standard deviations are from 0.5 to 1.5 m/s, which imply the good simulation of 26 CMIP5 models in the spatial distribution and amplitude of u500. We noted that although there are significant model biases in simulating H500 and u500 (Fig. 10.1), which may be associated with cold SST biases in the tropical Indian and Pacific oceans (Fig. 10.2), the spatial pattern and variability of H500 and u500 over the western Pacific are well reproduced in all 26 CMIP5 models (Fig. 10.3). To correct the climatology biases in the simulations, the simulated climatology of H500 and u500 from all of the models are replaced by that from the observations. In other words, all the model outputs are calibrated to the same climatic
FIGURE 10.2 Climatological mean of SST (contours, unit: C) in summer from the observation and CMIP5 model simulations, with the model biases (shading). SST, Sea surface temperature; CMIP5, coupled model intercomparison project phase 5.
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(A)
(B)
FIGURE 10.3 Taylor diagrams of the (A) simulated climatological H500 and (B) zonal wind over the western Pacific in summer compared to the observation. REF indicates the reference value of 1. The radial distance of the model code point from the origin is the standard deviation ratio of the models relative to the observations. The correlation coefficient of the spatial pattern between the model and observations is shown by the cosine of the azimuthal angle of the model code point, and their root-mean-square error is given by the distance of the model code point from the REF. REF, reference.
state, and then superimposed on each model’s own variability of H500 and u500. For example, the calibration for H500 for a model (the same in u500) is that: hmodel 5 hmodel 2 h model 1 h ncep :
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Here, hmodel represents H500 of one year from a specified model, and h model represents the model climatology of H500. h ncep denotes the climatology of H500 from the observation. In Section 10.4, the verification of the WPSH is based on the climatology corrected results.
10.4 THE PERFORMANCE OF MODELS IN SIMULATING THE WESTERN PACIFIC SUBTROPICAL HIGH’S VARIABILITY 10.4.1 WESTERN PACIFIC SUBTROPICAL HIGH INDICES To characterize the variability of the WPSH in intensity and position objectively, Fig. 10.4 shows the 9-year running mean time series of the WPSH indices, including the area, intensity, ridgeline, and westernmost point indices. It is noted from the observation that the area and intensity indices of the WPSH increased significantly and the westernmost point decreased after the late-1970s, which indicates that the WPSH has become stronger and more westward extending over the past 30 years (Fig. 10.4A and B). Such an interdecadal shift of the WPSH has been noted in previous works (Hu, 1997; Huang et al., 2006; Zhao et al., 2007; Liu and Ding, 2012). Interestingly, there is a slight downward trend in the observed ridgeline index (Fig. 10.4C), suggesting that the WPSH slightly shifted southward after the late-1970s and is consistent with the reports by Nitta and Hu (1996) and Hu (1997). Compared with the observations, all the calibrated CMIP5 model results capture the interdecadal shift of the WPSH in the late-1970s. In these model simulations, the observed greenhouse gases, ozone, aerosols and solar constant, and the variability of the land cover are taken into account in the models (Taylor et al., 2012), thus, it is suggested that these external forcing may play an important role in causing the interdecadal variability of the WPSH, although the internal variability of the atmospheric circulation over the western Pacific may be also influenced by the local atmosphere ocean interaction (Wang et al., 2005; Zhu and Shukla, 2013).
10.4.2 INTERDECADAL VARIABILITY OF THE WESTERN PACIFIC SUBTROPICAL HIGH IN THE SPATIAL PATTERN The comparison of the distribution of WPSH area and position between the period of 1951 60 and 1996 2005 are used to investigate the interdecadal variation of WPSH (Fig. 10.5). During the former period, the WPSH is relatively weaker and eastward, with the observed contour of 5880 gpm between 20 N and 30 N, and its westernmost point no more than 140 E (Fig. 10.5A). The observed ridgeline is near 25 N, in the northeastern southwestern direction (Fig. 10.5C). Most of the calibrated simulations are able to reproduce the WPSH, that is, the contour of 5880 gpm, much better than the model results before climatology correction (Fig. 10.1). Unfortunately, 10 models failed to capture the contours of 5880 gpm. Compared with the situation of simulating the contour line of 5880 gpm, the ridgelines of the WPSH are better simulated, except near the coastland of China at about 115 E.
FIGURE 10.4 Time series of the WPSH indices in summer from the observations (thick black line) and calibrated model simulations (thin colored lines) for the period from 1951 to 2005 (A, area index; B, intensity index; C, ridgeline index; D, westernmost point; the thick blue line is the 26 CMIP5 models’ ensemble mean). WPSH, Western Pacific subtropical high.
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FIGURE 10.4 (Continued.)
In the latter period of 1996 2005, the WPSH becomes stronger and more westward, the area of the observed WPSH increases, with the southern and northern borders extending to 18 N and 32 N, and the westernmost point extends to 135 E (Fig. 10.5B). The calibrated model results well simulate the interdecadal shift of the WPSH. The area and intensity indices of the WPSH significantly increased relative to the former period in the model simulations (Fig. 10.5B and D).
10.4.3 LINEAR TRENDS AND STANDARD DEVIATION OF THE WESTERN PACIFIC SUBTROPICAL HIGH In order to quantitatively assess the simulation capabilities of the WPSH indices in the CMIP5 models, the linear trends of each WPSH index from all the CMIP5 models are calculated except for the ridgeline index due to its small trend (Fig. 10.6). The observed results show that the linear ascending trend of the WPSH area and intensity indices are more than 20% per decade, and the trend of the westernmost point is 2.7% per decade, within the significance level of 0.01. Compared to the observations, all the models well simulate the tendency of enhancing and westward extension of the WPSH during the period of 1951 to 2005, but with large quantitative differences. Some are larger than the observation in the intensity of the WPSH, such as IPSL-CM5A-LR and GFDL-ESM2G, while others are weaker than the observations, such as GFDL-CM3 and HadGEM2-ES. Taking the three WPSH indices into account, it is found that the simulations of nine models, CESM1-CAM5-1-FV2, CNRM-CM5, FGOALS-g2, FIO-ESM, HadCM3, HadGEM2CC, MIROC-ESM, MPI-ESM-P, and NorESM1-M, are better than others in the linear trend of the WPSH indices. Comparison of the standard deviation of WPSH indices can be used to measure the simulation capability of the models in the interannual variability of the WPSH. Fig. 10.7 shows the simulated standard deviation ratio of WPSH indices relative to the observations. The closer to 1 the ratio is,
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FIGURE 10.5 The distribution of (A and B) WPSH area (h500 5 5880 gpm) and (C and D) ridgeline (u500 5 0 m/s) in summer from the observations (thick black line) and calibrated model simulations (thin colored lines) averaged in two periods of 1951 1960 (A and C) and 1995 2005 (B and D). WPSH, Western Pacific subtropical high.
the better to simulate the interannual variability of WPSH index is. The gray bars in Fig. 10.7 denote the accumulated distance between the ratios of these four WPSH indices, with the reference value of 1. The smaller the accumulated distance is, that is, closer to observations, the better to simulate all the WPSH indices is, which provides a measurement for the models in simulating the overall features of the WPSH. It is noted that most of the standard deviation ratios of the calibrated simulations to the observations are larger than the reference value, indicating an overestimation of the amplitudes of the WPSH indices in the models. GFDL-ESM2M, MIROC5, and INMCM4 models have relatively poor
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FIGURE 10.5 (Continued.)
simulations in the interannual variability of the WPSH, because of the too large accumulated ratios in the GFDL-ESM2M and MIROC5, and the too small one in INMCM4. According to the simulation of four WPSH indices, it is noted that six models (ACCESS1-0, CanESM2, CNRM-CM5, FGOALSg2, IPSL-CM5A-MR, MIROC-ESM) have relatively better performance.
10.4 WESTERN PACIFIC SUBTROPICAL HIGH’S VARIABILITY
(A)
(B)
NorESM1-M MRI-CGCM3 MPI-ESM-P MPI-ESM-LR MIROC-ESM MIROC5 IPSL-CM5A-MR IPSL-CM5A-LR INMCM4 HadGEM2-ES HadGEM2-CC HadGEM2-AO HadCM3 GISS-E2-R GISS-E2-H GFDL-ESM2M GFDL-ESM2G GFDL-CM3 FIO-ESM FGOALS-g2 CNRM-CM5 CESM1-CAM5-1-FV2 CCSM4 CanESM2 BCC-CSM1-1 ACCESS1-0 NCEP
0
(C)
191
10
20
30
NorESM1-M MRI-CGCM3 MPI-ESM-P MPI-ESM-LR MIROC-ESM MIROC5 IPSL-CM5A-MR IPSL-CM5A-LR INMCM4 HadGEM2-ES HadGEM2-CC HadGEM2-AO HadCM3 GISS-E2-R GISS-E2-H GFDL-ESM2M GFDL-ESM2G GFDL-CM3 FIO-ESM FGOALS-g2 CNRM-CM5 CESM1-CAM5-1-FV2 CCSM4 CanESM2 BCC-CSM1-1 ACCESS1-0 NCEP 40 0
10
20
30
40
50
NorESM1-M MRI-CGCM3 MPI-ESM-P MPI-ESM-LR MIROC-ESM MIROC5 IPSL-CM5A-MR IPSL-CM5A-LR INMCM4 HadGEM2-ES HadGEM2-CC HadGEM2-AO HadCM3 GISS-E2-R GISS-E2-H GFDL-ESM2M GFDL-ESM2G GFDL-CM3 FIO-ESM FGOALS-g2 CNRM-CM5 CESM1-CAM5-1-FV2 CCSM4 CanESM2 BCC-CSM1-1 ACCESS1-0 NCEP –5
–4
–3
–2
–1
0
FIGURE 10.6 Linear trend coefficients of the WPSH indices in summer from the observation (black bar) and CMIP5 model simulations (gray bars) for the period from 1951 to 2005 (A, area index; B, intensity index; C, westernmost point). WPSH, western Pacific subtropical high; CMIP5, coupled model intercomparison project phase 5.
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Accumulted distance Area index Intensity index Ridgeline index Westernmost point
2
1.5
1
0.5
NorESM1-M
MPI-ESM-P
MRI-CGCM3
MPI-ESM-LR
MIROC5
MIROC-ESM
IPSL-CM5A-MR
INMCM4
IPSL-CM5A-LR
HadGEM2-ES
HadGEM2-CC
HadCM3
HadGEM2-AO
GISS-E2-R
GISS-E2-H
GFDL-ESM2G
GFDL-ESM2M
FIO-ESM
GFDL-CM3
FGOALS-g2
CNRM-CM5
CCSM4
CESM1-CAM5
CanESM2
ACCESS1-0
BCC-CSM1-1
0
FIGURE 10.7 Standard deviation ratios of the simulated WPSH indices relative to the observation. The gray bars are the accumulated distance of the standard deviation ratio of the four WPSH indices from the reference value of 1. The smaller the gray bar is, the closer to the observation the simulated result is. WPSH, Western Pacific subtropical high.
10.4.4 INTERANNUAL PERIODS OF THE WESTERN PACIFIC SUBTROPICAL HIGH The spectrum of variability is another important feature to describe the characteristics of the WPSH. Fig. 10.8 shows the power spectrum of the monthly WPSH intensity index in the entire analysis period from the observations and 26 CMIP5 models. Considering that the seasonal variability of the WPSH is more significant than other timescales, the low-pass filter is first used to remove the seasonal variability (the cycle of 11 months) from the raw data to highlight the interannual signals. It has been known that there is a significant period of tropospheric biennial oscillation (TBO) over the East Asian monsoon region at the interannual timescales (Nitta and Hu, 1996). The WPSH, as one of the dominant members of the East Asian monsoon system, has a TBO component at interannual timescales (Liu et al., 2013). The observation shows that there are two interannual periods, that is, the quasi-four-year (36 60 months) and the quasi-biennial year (24 36 months) periods, and both pass the 0.05 significance level of the red-noise test. The period of the quasibiennial year is consistent with the TBO component of the WPSH and the monsoon rainfall in China (Chang and Li, 2000; Chang et al., 2000; Ding, 2007). The power spectra in the simulations of the CMIP5 models have obvious differences at the interannual timescales (Fig. 10.8). To some extent, only ACCESS1-0, GFDL-CM3, HadGEM2-CC, and HadGEM2-ES models can capture the quasi-four-year period and the TBO period.
10.4 WESTERN PACIFIC SUBTROPICAL HIGH’S VARIABILITY
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FIGURE 10.8 Power spectrums (red solid line) of the monthly WPSH intensity index from the observation and CMIP5 model simulations. Blue dashed lines indicate the 0.05 significance level of the red-noise test; the x-axis is the period (month), and the y-axis is the spectrum density. WPSH, Western Pacific subtropical high; CMIP5, coupled model intercomparison project phase 5.
The comparison of the 26 CMIP5 model simulations with the observations shows that most of the simulated H500 in the subtropical western Pacific region are weaker than observation, and even the calibrated results of some models still fail to capture the spatial and temporal characteristics of the WPSH. According to the overall simulations of the distributions of SST, H500, and u500, and the quantitative assessment of the WPSH indices, it is noted that the CNRM-CM5,
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FGOALS-g2, FIO-ESM, MIROC-ESM, and MPI-ESM-P models are better than others. Due to unavailable simulations of the RCP scenarios in the MPI-ESM-P model and based on the assessment, the ensemble mean results of four models (CNRM-CM5, FGOALS-g2, FIO-ESM, and MIROC-ESM) are selected to project the evolution of the WPSH in the future under different RCP scenarios.
10.5 FUTURE CHANGES OF THE WESTERN PACIFIC SUBTROPICAL HIGH UNDER DIFFERENT REPRESENTATION CONCENTRATION PATHWAY SCENARIOS To investigate the possible changes of the WPSH under three typical RCP scenarios in the 21st century, long-term integration (2006 99) of H500 and u500 from the four selected models are first averaged as the climatological state, and then the anomaly of each model is added to calculate the time series of the WPSH indices, based on a similar approach in Section 10.3.2. Then the 9-year running mean of the WPSH indices from 2006 to 2099 in the different RCP scenarios from these four CMIP5 models’ ensemble mean is used to project the possible changes of the WPSH in the future. It is noted that in the simulated area, intensity, and westernmost point indices of the WPSH emerge significantly interdecadal variation in the different RCP scenarios (Fig. 10.9). Under the RCP2.6 scenario, the WPSH area and intensity increase and extend westward obviously. After 2050, the linear trends of the WPSH area, intensity, and westernmost point indices gradually approach zero. The linear ascending trends of the WPSH under the RCP4.5 scenario are similar to that under the RCP2.6, but the period of significant growth lasts until about 2070, and then the growth trend weakens. The similar long-term variations of the WPSH present with faster growth in the former period and relatively smaller trend in the latter period under the RCP8.5 scenario. Interestingly, the westernmost point index of the WPSH maintained at 90 E since the late 2050s. In fact, the most westernmost point of the contour of 5880 gpm extends westward to the west of 90 E after the late 2050s. In that case, the definition of the westernmost point of the WPSH is limited as 90 E (Liu et al., 2012). Overall, the WPSH area enlarges, intensity strengthens, and the position extends westward under different RCP scenarios, with the largest linear growth trend in RCP8.5, the weakest in RCP2.6, and in between in RCP4.5. The ridgeline of the WPSH has no obvious long-term trend in the three RCP scenarios. These results have implications for the attribution and projection of climate changes in East Asia by using the CMIP5 model output.
10.6 SUMMARY AND DISCUSSION The performance of 26 coupled climate models used in the CMIP5 for the simulation of the present-day temporal variability and spatial pattern of the WPSH are assessed in this chapter. Then
FIGURE 10.9 Nine-year running mean of the WPSH indices in summer from 2006 to 2099 under different RCP scenarios from four CMIP5 models ensemble mean (A, area index; B, intensity index; C, ridgeline index; D, westernmost point; shaded areas represent one standard deviation from the multimodel mean). WPSH, Western Pacific subtropical high; CMIP5, coupled model intercomparison project phase 5.
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FIGURE 10.9 (Continued.)
the ensemble mean of the selected models is used to project the future possible changes of the WPSH under three typical RCPs. The results are as follows: 1. The intensity of H500 in the subtropical western Pacific is underestimated in most CMIP5 models, which may be associated with colder SST biases in the tropical Indian and western Pacific oceans in the models. Nevertheless, the spatial distribution and variability of H500 and u500 are captured. To eliminate the impact of the model climatology biases in the assessment, the climatology data is replaced by that of NCEP/NCAR reanalysis, which allow the model results to be more utilized, and without any change in temporal variability. 2. The calibrated model results reproduce the observed interdecadal shift of the WPSH (enhancement and westward extension after the late-1970s). According to an overall assessment of the WPSH indices, it is identified that CNRM-CM5, FGOALS-g2, FIO-ESM, MIROC-ESM, and MPI-ESM-P have better performance than the other models in simulating the WPSH. 3. The selected models’ simulations suggest that the WPSH area enlarges, intensity strengthens, and the position extends westward under RCP scenarios, with the highest linear growth trend in RCP8.5, in between in RCP4.5, and weakest in RCP2.6. Interestingly, the simulated ridgeline of the WPSH has no obvious long-term trend in the scenarios. It is easily noticed that the model defaults, such as the systemic biases in the western subtropical regions, may affect the credibility of these projections. Thus it is necessary to explore the reasons resulting in the biases as well as their connection with the cold biases in the tropical Indian and western Pacific oceans. It is also an interesting topic to examine possible changes in the East Asian summer rain belt under the projection of strengthening and westward extending of the WPSH with the increasing concentration of greenhouse gases in the future. Last, the mechanism for the projection of enhancing and westward extending of WPSH under the global warming scenarios requires further analysis.
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FURTHER READING IPCC, 2007. In: Solomon, S., Qin, D., Manning, M., et al.,Climate Change 2007: The Physical Scientific Basis. Cambridge University Press, Cambridge and New York, NY, 996 pp. Zhou, T.J., Yu, R.C., Zhang, J., et al., 2009. Why the western Pacific subtropical high has extended westward since the late 1970s. J. Clim. 22, 2199 2215.