Instability of the East Asian Summer Monsoon-ENSO Relationship in a coupled global atmosphere-ocean GCM JIANG Dabang 1 WANG Huijun 1 DRANGE Helge 2 LANG Xianmei 1 1 State Key Laboratory of Numerical Modeling of Atmospheric Science and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Nansen Environmental and Remote Sensing Center, Bergen N-5059, Norway Abstract. A 300 years control integration of a coupled global atmosphere-ocean-sea ice general circulation model (GCM) has been used to study the relationship between the East Asian summer monsoon (EASM) and the ENSO (El Niño and Southern Oscillation) cycle by applying cross wavelet analysis. It follows that the correlation between the EASM and ESNO may break down on interdecadal time scales. Furthermore, the characteristics of summer atmospheric circulation during their high correlation periods (HCP) are quite different from those during the low correlation periods (LCP). Notably, the HCP is characterized by an anomalous low tropospheric cyclone coupled with an anticyclone circulation over the western North Pacific, and a board belt of strong low tropospheric easterly wind anomalies located from the Philippines to the Bay of Bengal. For HCP, large interannual variability is founded in the low tropospheric wind velocity field over Southeast Asia, and the tropospheric temperature and geopotential heights over the tropical western Pacific. In addition, the correlation pattern between summer rainfall in East Asia and simultaneous Niño3 region sea surface temperature (SST) anomalies are significantly different during the HCP and LCP periods. Key Words: East Asian summer monsoon, ENSO, Instability, Cross wavelet analysis
1. Introduction El Niño and La Niña (Philander 1990) are opposite phases of the ENSO cycle, and the strongest climatic signal in the tropics. The interaction between the tropical SST and mid-high latitude atmospheric circulation has been a key topic in meteorology and oceanography since the pioneering works of Bjerknes (1966, 1969). In these works, Bjerknes addressed the teleconnection between the equatorial central Pacific warming and the North Pacific extratropical circulation anomalies. Many studies (Huang and Wu 1989; Zhang et al. 1999; Wang et al. 2000; Wang 2000; Ailikun and Yasunari 2001; Wang et al. 2001) deal with the coupling between the ENSO cycle and the East Asian summer climate. All of them show, to various degrees, the possible linkage between SST anomalies in the tropical Pacific and the East Asian climate. Currently, the ENSO cycle has been an important factor for the extraseasonal short-term climatic forecast in China (Lin et al. 2000). However, a number of questions remain unresolved. For instance, is the relationship between the EASM and ENSO stable in their long-term march, and what controls the dynamic linkage between them? Webster and Yang (1992), Torrence and Webster (1998), and Webster et al. (1998) revealed that the relationship between the Niño3 region SST anomaly and the Indian monsoon is subject to changes over long (i.e., multi-decadal) time scales. Wang (2002) identified that there are periods where the correction between the EASM and ENSO is absent based on the reanalysis data and observed SST data from the National Centers for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project (Kalnay et al. 1996). However, observational based data with only 50 years duration are too short for proper analyses of multi-decadal processes. In this paper, a 300 years integration from a coupled global GCM are used to study the 1
relationship between the EASM and ENSO cycles, and the characteristics of the atmospheric circulation during their high and low correlation periods. Based on the analyses, two possible mechanisms are proposed for the bridge between the EASM and ENSO. 2. Data and analysis method 2.1 Coupled model outputs The analysis data are derived from a 300 years control integration with the Bergen Climate Model (BCM), a coupled global atmosphere-ocean-sea ice GCM (Furevik et al. 2003). The BCM consists of the atmospheric GCM ARPEGE (Deque et al. 1994) developed jointly by Météo-France and the European Center for Medium-range Weather Forecasts (ECMWF), and the ocean GCM MICOM (Bleck et al. 1992), the latter with a dynamic-thermo-dynamic sea ice module incorporated. The coupling between these two models is carried out with the coupler OASIS (Terray et al. 1998), taking care of the synchronization and the field transfer between the model components. Fields are exchanged at 24-hour intervals. The atmosphere model then receives SST, sea ice cover, and albedo from the ocean model, and whereas heat, fresh water and momentum fluxes are passed from the atmosphere to the ocean. Validation of the BCM model shows an encouraging agreement between simulated and observed global fields and quantities of the present climate system (Furevik et al. 2003). 2.2 Analysis method Cross Morlet-wavelet analysis method is applied in the work presented here. The analysis is based on the EASM index and the Niño3 region (90 o W~150 o W, 5 o S~5 o N) SST anomaly time series. Their relationship is then examined according to the cross wavelet power spectrum distribution. For 2
details of the wavelet analysis, including the tests for confidence level, see Torrence and Compo (1998). 3. Results The EASM index is defined as the deviation of the wind velocity from the climatological mean wind state at 850-hPa in the range 110 o E~125 o E, 20 o N~40 o N (Wang 2000). The Niño3 region SST anomaly is used as the indicator for the ENSO cycle. From the cross wavelet power spectrum distribution between these two time series (Fig.1), it follows that the EASM strength is related to the simultaneous Niño3 region SST anomaly with 95% confidence level for some periods. The observed relationship is, however, not persistent on inter-decadal time scale, which is clearly seen from Fig.1c. Based on Fig.1c, 30 year-length data representing the case for the HCP, and another one for the LCP, are selected. Consequently, we can compare the characteristics of atmospheric circulation during these two states. Intercomparison yields that the HCP is characterized by larger interannual variability of the tropospheric temperature over most part of the tropic Pacific, the subtropical western Pacific, most part of Southeast Asia, the northwestern Eurasia, the Tibetan Plateau and the southern China (Fig.2). Logically, atmospheric convective activity will become stronger over the above-mentioned regions. Given the results (Huang and Wu 1989; Ren and Huang 1999; Lu 2002) from observational based data, there are reasons to believe that the convective activity that takes place over the tropical Pacific may play an import in the interaction between the EASM and the ENSO cycle. During the HCP, the interannual variability of geopotential height increases in the mid-high troposphere over most of the tropical Pacific and the northwestern Eurasia, and in the overall troposphere over the Tibetan Plateau eastward to the subtropical western Pacific, the southern China 3
and Southeast Asia (Fig.3). The stronger atmospheric circulation activities over these regions must change the EASM strength greatly because the EASM is mainly composed of the western Pacific subtropical high, the low tropospheric cross-equatorial flow over South China Sea, the monsoon trough in Southeast Asia and Meiyu front (Tao and Chen 1987). Just as shown in Fig.4a, the interannual variability of low tropospheric wind speed increases over most part of the tropical Pacific, the southern and eastern China, and Japan eastward to the dateline during the HCP. The differences of atmospheric circulation between the HCP and the LCP indicate two remarkable low tropospheric anomalous circulation systems (Fig.4b): A strong easterly wind anomaly belt extending from the Philippines to the Bay of Bengal, and an anomalous cyclone centered near Sakhalin, coupled with an anticyclone over the western North Pacific. Most important is that both of them have played an essential influence on the East Asian atmospheric circulation. We regard these two circulation features as key components in the interaction between the EASM and ENSO. Based on observational evidence, Wang et al. (2000) put forward Pacific-East Asia teleconnections, and concluded that the key system that bridges the warm (cold) events in the eastern Pacific and the weak (strong) East Asian winter monsoons is an anomalous low tropospheric anticyclone (cyclone) located in the western North Pacific. Therefore, both observed data and coupled model outputs highlight the importance of low tropospheric circulation anomaly over the western North Pacific as the linkage between the EASM and ENSO even if the seasonal difference. In addition, the spatial correlation between the summer rainfall in East Asia and the Niño3 region SST anomaly during the HCP is also quite different from that during the LCP (see Fig.5). During the HCP, significant negative correlations exist over southern China and in the He-Tao region, and positive correlations are found in part of southern Northeast China. Except for limited 4
correlation over southwestern China, there is hardly any relationship between the EASM and ENSO during the LCP. Concluding Remarks: (1) The EASM is, to a certain degree, related to the simultaneous Niño3 region SST anomaly. The relationship is, however, unstable on the inter-decadal time scales. (2) Anomalous low tropospheric atmospheric circulation, i.e. a cyclone coupled with an anticyclone confined to the western North Pacific, along with a broad belt of strong easterly wind anomalies located from the Philippines to the Bay of Bengal make up of the bridge between the EASM and ENSO during the HCP. Atmospheric convective activity over most part of the tropical Pacific may play an important role during their interaction. Care must be exercised when tropical SST anomalies are used to predict the EASM, and even the summer precipitation in East Asia. The reason for this is that both observational data and coupled BCM outputs show that the EASM-ENSO relationship may break down on long time scales. Given the studies done by Ju and Slingo (1995), Ailikun and Yasunari (2001), Li et al. (2001), and Wang et al. (2001), asynchronous correlation between the EASM and ENSO will be examined based on both the NCEP/NCAR reanalysis data and the BCM outputs in our following work. It is expected that accurate understanding of the interrelation between the EASM and ENSO will lead to an improvement of the extraseasonal climate predictability in East Asia. Acknowledgements: This study is mainly finished in Nansen center of Norway, and jointly supported by the 5
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Figure captions Fig.1 (a) EASM index series. (b) Niño3 region SST anomaly index series. (c) Cross wavelet power spectrum between them. In (c), ordinate is the Fourier period in year; abscissa is time in year. The shaded regions have a confidence level 95% for a red-noise process with a lag-1 coefficient of 0.72. The parabola indicates the cone of influence. Fig.2 Ratio of the interannual variation (represented by the standard deviation) of the JJA temperature during the HCP to that during the LCP at 200-hPa (a), 500-hPa (b) and 850-hPa (c), repectively. Areas with ratios larger than 1.2 or smaller than 0.8 are shaded Fig.3 As Fig.2, but for the geopotential height Fig.4 (a): as Fig.2, but for the wind velocity at 850-hPa; (b): the differences of JJA average wind at 850-hPa between HCP and LCP Fig.5 Correlation coefficients between JJA rainfall and simultaneous Niño3 region SST anomaly during the HCP (a) and the LCP (b) The areas with confidence level greater than 95% are shaded. 8
(a) (b) (c) Fig.1 (a) EASM index series. (b) Niño3 region SST anomaly index series. (c) Cross wavelet power spectrum between them. In (c), ordinate is the Fourier period in year; abscissa is time in year. The shaded regions have a confidence level 95% for a red-noise process with a lag-1 coefficient of 0.72. The parabola indicates the cone of influence. 9
(a) (b) (c) Fig.2 Ratio of the interannual variation (represented by the standard deviation) of the JJA temperature during the HCP to that during the LCP at 200-hPa (a), 500-hPa (b) and 850-hPa (c), repectively. Areas with ratios larger than 1.2 or smaller than 0.8 are shaded 10
(a) (b) (c) Fig.3 As Fig.2, but for the geopotential height 11
(a) (m/s) (b) Fig.4 (a): as Fig.2, but for the wind velocity at 850-hPa; (b): the differences of JJA average wind at 850-hPa between HCP and LCP (a) (b) Fig.5 Correlation coefficients between JJA rainfall and simultaneous Niño3 region SST anomaly during the HCP (a) and the LCP (b) The areas with confidence level greater than 95% are shaded. 12