Relative contributions of external SST forcing and internal atmospheric variability to July August heat waves over the Yangtze River valley

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1 Clim Dyn DOI /s y Relative contributions of external SST forcing and internal atmospheric variability to July August heat waves over the Yangtze River valley Xiaolong Chen 1 Tianjun Zhou 1,2 Received: 23 December 2016 / Accepted: 17 August 2017 Springer-Verlag GmbH Germany 2017 Abstract The Yangtze River valley (YRV), located in central-eastern China, has witnessed increased numbers of heat waves in the summer since Knowing what factors control and affect the interannual variability of heat waves, especially distinguishing the contributions of anomalous sea surface temperature (SST) forcings and those of internal modes of variability, is important to improving heat wave prediction. After evaluating 70 members of the atmospheric model intercomparison project (AMIP) experiments from the 25 models that participated in the coupled model intercomparison project phase 5 (CMIP5), 13 high-skill members (HSMs) are selected to estimate the SST-forced variability. The results show that approximately 2/3 of the total variability of the July August heat waves in the YRV during can be attributed to anomalous SST forcings, whereas the other 1/3 are due to internal variability. Within the SST-forced component, one-half of the influence is from the impact of the El Niño Southern Oscillation (ENSO) and the other half is from non-enso related SST forcings, specifically, the SST anomalies in the North Pacific and the North Atlantic. Both the decaying El Niño and developing This paper is a contribution to the special issue on East Asian Climate under Global Warming: Understanding and Projection, consisting of papers from the East Asian Climate (EAC) community and the 13th EAC International Workshop in Beijing, China on March 2016, and coordinated by Jianping Li, Huang-Hsiung Hsu, Wei-Chyung Wang, Kyung-Ja Ha, Tim Li, and Akio Kitoh. * Xiaolong Chen chenxl@lasg.iap.ac.cn 1 2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China University of Chinese Academy of Sciences, Beijing , China La Niña accompanied by a warm Indian Ocean and cold central Pacific, respectively, are favorable to hotter summers in the YRV because these patterns strengthen and extend the western North Pacific Subtropical High (WNPSH) westwards, for which the decaying ENSO plays a dominant role. The internal variability shows a circumglobal teleconnection in which Rossby waves propagate southeastwards over the Eurasian Continent and strengthen the WNPSH. Atmospheric model sensitivity experiments confirm that non-enso SST forcings can modulate the WNPSH and heat wave variability by projecting their influences onto the internal mode. Keywords Heat wave Yangtze River valley ENSO Internal variability AMIP 1 Introduction The Yangtze River valley (YRV), located in central-eastern China, is the nation s economic center with a large and highdensity population and has witnessed increased numbers of summer heat waves since 1951 (Zhou et al. 2014). For example, the 2013 boreal summer heatwave in the YRV region was unprecedented (Peng 2014). This heat wave, which was the strongest since 1951, severely harmed the economy and the health of the population (Zhou et al. 2014; Peng et al. 2016). In recent years, long-term changes in heat waves over China due to global warming have been extensively studied (Zhai and Pan 2003; Gong et al. 2004; Wei and Chen 2009; Ding et al. 2009; Qi and Wang 2012; Sun et al. 2014; Lu and Chen 2016). The previous studies mainly focused on changes in the mean states. However, less attention has been paid to the mechanisms that control the year-by-year Vol.:( )

2 X. Chen, T. Zhou variations of heat waves in the YRV, which are more important for the seasonal-to-annual climate predictions. Heat waves over China are often accompanied by anticyclonic circulations that result in decreased rainfall and increased solar incidence on the surface (Wei and Sun 2007; Hu et al. 2012; Liu et al. 2015). The western North Pacific Subtropical High (WNPSH) is the main anticyclone system affecting the heat waves in eastern China (Shi et al. 2009; Hu et al. 2012; Liu et al. 2015). For instance, the anomalous westward and temporal extension of the WNPSH has been identified as responsible for the extreme heat wave over the YRV in 2013 (Peng 2014; Li et al. 2015). A previous study found that the frequency of hot days and heat waves in eastern China increases, whereas rainy days decrease, with enhanced WNPSH at interannual time scales (Ding et al. 2010). The anomalous land sea northwesterly flow associated with the anomalous anticyclone can dry the atmosphere over land by reducing sea-land moisture transport (Luo and Lau 2017). Adiabatic heating due to the presence of the anomalous anticyclone and moisture divergence in the south also play an important role in the extreme heat events in the YRV region (Chen and Lu 2015). Based on previous studies, the WNPSH variability is mainly modulated by tropical forcing from sea surface temperature (SST) anomalies and their related convective heating (e.g., Wang et al. 2000; Wu et al. 2009; Chen and Zhou 2014; He et al. 2015) as well as internal modulation from wave activity propagating along the steering westerlies in the middle-high latitudes (e.g., Kosaka et al. 2012; Lin 2014; Liu et al. 2015). The El Niño Southern Oscillation (ENSO) is the most important source of tropical forcing. The Indian Ocean basin warming, lasting from the peak of El Niño in the winter to the next summer, can induce an anomalous anticyclone over the western North Pacific (WNP) by exciting Kelvin waves eastward (Yang et al. 2007; Xie et al. 2009; Wu et al. 2009; Du et al. 2009) and taking over the role of local cold SST anomalies in the WNP (Wu et al. 2010). The Indian Ocean variability has been suggested to impact the high temperature extremes across the southern YRV in the late summer though Kelvin-wave mechanisms (Hu et al. 2012). The influence of a decaying ENSO on heat waves in the YRV can change at the decadal time scale due to varied responses of the Indian Ocean SST anomalies (Hu et al. 2013). Additionally, the La Niña developing phase in the concurrent summer favors a strengthened WNPSH forced by the warm maritime continent and cold central Pacific (Wu et al. 2009; Wang et al. 2013; Chen and Zhou 2014). Thus, a developing ENSO could also impact heat wave variability in the YRV by modulating the WNPSH, which has been less considered in previous studies. In addition to the tropical forcing, midlatitude teleconnections associated with circumglobal Rossby waves may also influence the strength and location of the WNPSH in the boreal summer (Kosaka et al. 2012; Lin 2014; Liu et al. 2015). A European blocking system and the related triggering of a silk road pattern shifts the WNPSH northward, which led to the 2010 heat wave over East Asia (Kosaka et al. 2012). Such teleconnection patterns are regarded as modes of atmospheric internal variability. The extratropical SST anomalies, such as those in the North Pacific and North Atlantic, may also trigger similar wave trains (Peng et al. 2002; Liu et al. 2006; Deser et al. 2007), even at multidecadal time scales (Wu et al. 2016a, b; Lin et al. 2016), indicating that the extratropical SSTs may modulate heat wave variability in the YRV through their projection onto internal modes. Hence, both the strength and location of the WNPSH can be affected by SST forcing and atmospheric internal variability, which can eventually modulate the variability of heat waves in eastern China, including the YRV region. Nonetheless, the relative importance of anomalous SST forcing, including that from ENSO and extratropical SST anomalies, and internal atmospheric processes to heat wave variability have not been clarified by previous studies. Therefore, by focusing on the interannual variability of heat waves over the YRV in July and August, we aim to address this question and attempt to estimate the variance explained by each factor. The remainder of the paper is organized as follows. Section 2 describes the data from the observations and model simulations used in this study. Section 3 clarifies the definition of the heat wave index, criteria of model selection and method of variability decomposition. The main results are shown in Sect. 4. Several discussions are presented in Sect. 5, followed by conclusions in Sect Data The observed daily maximum surface air temperature (Tmax) from 756 stations, provided by the China Meteorological Administration, is used to analyze the interannual variability of heat waves in the YRV. To examine the associated circulations, precipitation and SST anomalies, the following reanalysis datasets and observations are used: 1. The ERA-Interim reanalysis during from the European Center for Medium-Range Weather Forecasts (Dee et al. 2011), including the daily Tmax, monthly winds and geopotential heights, all at a resolution. To validate the ability of the ERA-Interim reanalysis data to represent the heat wave variability, the daily Tmax values from the NCEP/NCAR reanalysis (Kalnay et al. 1996) and NCEP/DOE reanalysis II (Kanamitsu et al. 2002) are also used to calculate the heat wave index and compare these with the ERA-Interim.

3 Relative contributions of external SST forcing and internal atmospheric variability to July 2. The monthly SST data from the Hadley Centre sea ice and sea surface temperature (HadISSTv1) data set on a grid of 1 1 during (Rayner et al. 2003). 3. Global Precipitation Climatology Project (version 2.2) monthly data provided by NOAA/OAR/ESRL PSD with a resolution during (Adler et al. 2003). The multimodel AMIP simulations, including the 25 models and their 70 members (Table 1), as derived from the coupled model intercomparison project phase 5 (CMIP5; Taylor et al. 2012), are used to distinguish SSTforced and internal variabilities. In the AMIP simulation, the atmospheric general circulation models (AGCMs) were forced by historical global SST and sea ice data during The interannual variabilities in AMIP runs are mainly from the observed SST variabilities, and the internally produced ones are caused by atmospheric and air land non-linear processes. The AMIP outputs, including the daily Tmax values, monthly winds, precipitation, and geopotential heights are used for the analysis. All the monthly fields in both the observations and model data are interpolated to a common resolution of Method 3.1 Definition of heat wave index Based on the daily Tmax values in July and August, the heat wave index (HWI) for the YRV is calculated for the domain of N, E, which is consistent with the defined central-eastern China region where an extremely hot summer occurred in 2013 (Zhou et al. 2014). First, we calculate the 90% percentile of the daily Tmax throughout July and August at each station during Second, we sum up the July August Tmax values above the percentile value for each station and each year and then average the values from the all stations to yield the time series of HWI (Fig. 1a). In contrast to the commonly used TX90p (warm days index; Hartmann et al. 2013), which only counts the number of hot days, the HWI defined here involves both the intensity and frequency of the heat wave. We apply a 9-year Table 1 Description of CMIP5 models used Model Institute/Country Atmosphere res. (lat lon, level) Realization number ACCESS1-0 CSIRO-BOM/Australia , L38 1 ACCESS1-3 CSIRO-BOM/Australia , L38 1 BCC-CSM1-1 BCC/China , L26 3 BCC-CSM1-1-m BCC/China , L26 3 BNU-ESM BNU/China , L26 1 CanAM4 CCCma/Canada , L35 4 CCSM4 NCAR/USA , L27 5 CMCC-CM CMCC/Italy , L27 3 CNRM-CM5 CNRM-CERFACS/France , L31 1 CSIRO-Mk3-6-0 CSIRO-QCCCE/Australia , L18 10 EC-EARTH ICHEC/Netherlands, Ireland , L62 1 FGOALS-g2 IAP-THU/China , L26 1 FGOALS-s2 IAP-LASG/China , L26 3 GFDL-CM3 NOAA-GFDL/USA , L48 1 GISS-E2-R NASA-GISS/USA , L40 2 HadGEM2-A KMA-NIMR/South Korea , L38 6 INMCM4 INM/Russia , L21 1 IPSL-CM5A-LR IPSL/France 96 96, L39 6 IPSL-CM5A-MR IPSL/France 96 96, L39 3 IPSL-CM5B-LR IPSL/France 96 96, L39 1 MIROC5 MIROC/Japan , L40 2 MPI-ESM-LR MPI-M/Germany , L47 3 MPI-ESM-MR MPI-M/Germany , L96 3 MRI-CGCM3 MRI/Japan , L48 2 NorESM1-M NCC/Norway , L26 3

4 X. Chen, T. Zhou Fig. 1 a Normalized heat wave index (units: C) based on July August daily Tmax in centraleastern China based on station observation and three reanalysis data (ERA-Interim, NCEP1 and NCEP2). The numbers in legend are the correlation coefficients between observation and corresponding reanalysis. b Spatial distribution of Tmax anomalies associated with the heat wave index in observation and ERA-Interim reanalysis. The black dots indicate the anomalies are statistically signifcant at the 5% level (c) high-pass filter to the HWI time series to extract the interannual variability. For gridded data (reanalysis and model output), the above method is applied to each grid in the same way. 3.2 Model member selection The correlation coefficients (CCs) between the HWIs calculated using the AMIP simulations and observations show a large spread across the models and members (Fig. 2a). To reasonably estimate the contribution of SST-forced variability, not all the model members are used to calculate the ensemble mean, since the members with low CCs (49 members with CC < 0.2) fail to reproduce the SST-forced variability reasonably, especially in terms of the ENSO impact (Table 2). Therefore, the 13 members for which the CCs exceed 0.36 (the 5% significance level), hereinafter called the high-skill members (HSMs), are averaged to derive the SST-forced signal due to the canceling out of the internal variability in different models. The mean of HSM shows the highest CC with the observed HWI, which far exceeds that of the individual members (Fig. 2a), indicating that internal variability is largely reduced. 3.3 Decomposition of observed variability 1. Total variability: variance of the defined HWI time series and the related circulation anomalies based on Fig. 2 a Correlation coefficients between observed and simulated HWI from 70 AMIP runs of 25 CMIP5 models during (crosses). Red dashed line indicates the correlation coefficient statistically significant at the 5% level. 13 members above the level is selected as high-skill member (HSM). The skill of HSM mean is marked by triangle, highest among all the members. b Time series of observed (megenta) and simulated HWI (units: C) in HSM (gray lines for each member and thick black line for the mean). Corrlation coefficient between observed and the mean of HSM is labeled on the top-right corner

5 Relative contributions of external SST forcing and internal atmospheric variability to July Table 2 Correlation coefficients of Nino3.4 index (SST anomalies averaged in 5 S 5 N, W) in previous September October (SO0) and in current July August (JA1) against the JA1 heat wave index during OBS HSM LSM Nino3.4(SO0) 0.54 (29%) 0.62 (38%) 0.24 Nino3.4(JA1) 0.25 (6%) 0.25 (6%) 0.01 NinoC 0.59 (35%) 0.66 (44%) 0.25 NinoC index is defined in Eq. 1. Percentages in the parentheses, the squared correlation coefficients, are variance of the heat wave variability explained by corresponding Nino index. The HSM (LSM) is high-skill (low-skill) members of which the correlation coefficients of heat wave index between simulation and observation is above (below) 0.36 (0.2) the observational or reanalysis data. The term interannual variability hereinafter denotes the yearly variations whereas the variance is the square of the standard deviation, a statistical concept used to measure the intensity of the variability. 2. SST-forced variability: variance of the defined HWI time series based on the ensemble mean of HSM. The percentage of the variance explained is the squared CC of the HWI between the HSM and the observation. The related circulation pattern is obtained by regressing onto the SST-forced HWI. 3. Internal variability: the residual percentage of the variance found by deducting the SST-forced variability from the total. For the observations, the internal HWI is obtained by linearly removing the SST-forced HWI from the original series; the associated circulation is estimated by regressing the observational/reanalysis data against the internal HWI. For the models, the departure of the individual members from the mean of the HSM is regarded as the internal variability; the associated circulation is obtained by regressing the departure fields against the departure HWI of each member, and the internal mode is estimated by averaging the departure patterns of all the members. 4. Forced variability from the ENSO-related SST anomalies: the HWI variance and the associated circulation that can be explained by the ENSO variability represented by the Nino3.4 index, considering both the decaying and developing ENSO signals (details seen in Sect. 4.1 and Eq. 1). 5. Forced variability from non-enso SST anomalies: residual percentage of the HWI variance after the ENSO-related variability is linearly removed from the SST-forced variability. Linearly removing ENSO from the total SST-forced HWI yields the non-enso-forced HWI, onto which the ENSO-signal-removed fields are regressed to derive the associated patterns. The contributions of the above decomposed variabilities are estimated for only the period of due to the limited time coverage of most of the AMIP simulations in CMIP5. 4 Results 4.1 SST forced and internal variability contributions to the observed HWI The time series of HWI for the YRV region is derived from station-based observations and is shown in Fig. 1a, along with the series calculated using NCEP1, NCEP2 and ERA- Interim reanalysis. The ERA-Interim series has the highest CC with the station-based index. Hence, further analysis of the associated circulations is based on the ERA-Interim dataset. Both the HWIs from the stations and reanalysis data show evident interannual variability during , with an especially strengthened amplitude after 2000 and the hottest summer in The associated anomalies of Tmax show a warm center along the YRV and some cooling in northeastern and northwestern China (Fig. 1b). In addition to the similar patterns to the station-based results in the China domain, the ERA-Interim data reveals a larger picture of the two significant cooling centers to the west of Lake Baikal and in the northwestern Indian subcontinent (Fig. 1c). Is such a large interannual variability of the HWI and its related anomalous patterns caused by SST forcing, especially ENSO variability? If so, how much variance can be explained? To answer these questions, we analyze the results of the AMIP simulations. We use the ensemble mean of the AMIP simulations from the selected HSM to estimate the SST-forced variability. The CC between the observed and simulated HWI in HSM is as high as 0.82, which means that approximately 2/3 (67%) of the variance of HWI during can be explained by the SST forcing. Thus, the residual variance (approximately 1/3) of the HWI can be attributed to internal variability. In addition to SST anomalies, other forcings, such as snow cover on the Tibetan Plateau (Wu et al. 2016a, b), are not considered in the present study, which may cause an overestimation of the contribution of internal variability. To show the effect of ENSO evolution on the summer heat wave in July August (JA1), the lead-lag relations between the Nino3.4 index from January February (JF) in the previous year (year 0) to November December (ND) in the current year (year 1) and HWI in JA1 are examined (Fig. 3). In the observations, the peak CC occurs in the previous autumn (September October, SO0), and then decreases rapidly to a negative value in the simultaneous JA1. Hence, the decaying ENSO has the most prominent impact on the HWI of the whole ENSO cycle, accounting

6 X. Chen, T. Zhou Fig. 3 Lead-lag correlation coefficients of Nino3.4 (solid; 5 S 5 N, W), Indian Ocean Basin index (dashed; 20 S 20 N, E; Yang et al. 2007) from January Faburaty in previous year (labeled by 0) to November December in current year (labeled by 1) against July August heat wave index (JA1) for both in the observation (megenta) and HSM (blue). Dashed black lines denote the 5% significance level for approximately 29% of the variance of HWI (CC = 0.54; Table 2), which suggests that an extremely hot summer may occur in the next year following an El Niño that develops in the autumn. Although the impact of a simultaneous ENSO in JA1 is much smaller than that of the decaying ENSO, it can independently contribute approximately 6% of the variance of HWI (CC = 0.25; Table 2) because of the uncorrelated Nino3.4 index between SO0 and JA1 (CC = 0.06). Using the statistical independence of the decaying and developing ENSOs, we define a single index to represent the ENSO impact on the July August heat wave over a whole ENSO cycle. The index is defined as NinoC = CC[Nino3.4(SO0) vs HWI(JA1)] Nino3.4(SO0) + CC[Nino3.4(JA1) vs HWI(JA1)] Nino3.4(JA1), which combines the decaying and developing ENSO signals, weighted by their CCs with the July August HWI. The NinoC index can explain 35% of the variance (CC = 0.59) of the observed HWI during , which demonstrates the additivity of the independently contributed variances of decaying (29%) and developing (6%) ENSO signals to the summer heat wave. Because of the approximately 67% of explained variance from the total SST forcing estimated by the HSM, the non-enso SST forcing contributes approximately 32% of the HWI variance. Thus, the contribution of non-enso SST forcing to the HWI variability is almost equal to that of the ENSO effect. (1) The HSM can excellently reproduce the observed leadlag relation between ENSO and the July August HWI (Fig. 3). The lead-lag CC curve in the HSM across the whole ENSO cycle is located above that of the observation (Fig. 3), indicating that the effect of ENSO on the heat wave in the HSM is more evident due to most of the internal variability being removed from the averaging of the HSM ensemble. In contrast, the mean of the lowskill members (LSMs) cannot reasonably reproduce the ENSO-HWI relation (Table 2). The ENSO-related SST forcing can explain approximately 44% of the variance of HWI in the HSM (CC = 0.66; Table 2). Considering the 67% variance explained by the total SST forcing, ENSO contributes approximately 30% (i.e., 67% 44%) of the total variance of the observed HWI, based on the model result, which is close to the 35% of the variance calculated based on the observations. The 5% bias may come from the imperfect representation of the influence of ENSO in the AMIP models. Based on the analysis above, we conclude that the ENSO-related SST forcing, non-enso SST forcing and internal variability respectively contribute approximately 1/3 of the variance of the observed July August HWI in the YRV region. The decomposed time series of the HWI from the different contributors are shown in Fig. 4. The excellent additivity of the variance ratio explained in each component shows the independent contributions from the ENSO-related forcing, non-enso SST forcing and internal variability. The contributions of the SST forcing and internal variability vary annually (Fig. 4). SST forcing is mainly responsible for the three strongest heat waves in 1988, 1998 and 2003 (Fig. 4). The 3 years correspond to El Niño decay summers characterized by warm SST anomalies in the Indian Ocean (Fig. 5). Larger contributions of the ENSOrelated forcings of the heat wave intensities were observed in 1988 and 1998 (Fig. 4) and may be due to the evident La Niña developing signals in these 2 years (Fig. 5a, b). Nevertheless, the heat wave in 2003 is the strongest among the 3 years due to the larger contributions from non-enso SST forcings and internal variability (Fig. 4). Although the midlatitude ocean is regarded as having a passive role in air sea interactions, the midlatitude SST anomalies can also exert feedbacks on the atmosphere (Peng and Whitaker 1999). Hence, the evident SST anomalies in the North Pacific and North Atlantic (Fig. 5c) may be effective non-enso SST forcing sources, which will be clarified in Sect In the following section, we show how SST forcing and internal variability affect the heat waves over the YRV by analyzing the related SST and circulation anomalies.

7 Relative contributions of external SST forcing and internal atmospheric variability to July Fig. 4 Decomposition of observed HWI time series (units: C) in the YRV (black line) into contributions of total SST forcing (magenta line) and internal variability (blue line). Total SST forcing is futher decomposed into ENSO-related (red line) and non-enso (pink line) SST forcing. Sum of non- ENSO SST forcing and internal varibility is also shown (orange line). Correlation coefficient (r) between each contributor and the observation is labeled at the top-right of each line, along with the square of r used to estimate explained percentage of the observed variance by each contributor. The excellent additivity of variance ratio shows independent contributions of each other 4.2 Circulation anomalies associated with the total SST forced and internal variability To understand how the SST forcing and internal atmospheric processes affect heat wave variability in the YRV, the associated circulation is examined by regressing onto the SST-forced and internal HWI time series, respectively (details seen in Sect. 3.3). The ratio of the Tmax variance in the mean of the HSM to that in the reanalysis over the YRV region (less than 30%; Fig. 6) is lower than the explained percentage (approximately 67%) estimated by the squared CC in the previous section, indicating internal variability is overestimated in each individual member. Hence the forced anomalies associated with the HWI are expected to be smaller in the model results. When comparing the models with the observations/reanalysis data, similarities of patterns are more important than those of magnitude Total SST forced variability Figures 6 and 7 show the anomalies of the temperature and circulation associated with the SST-forced HWI. In the observations, evidently warm SST anomalies are seen in the Indian Ocean and around the maritime continent, whereas cold anomalies are found in the central Pacific. On the one hand, the excessive rainfall over the warm Indian Ocean is favorable for a strengthened WNPSH due to an eastward Kelvin wave (Yang et al. 2007; Xie et al. 2009; Wu et al. 2009), which then leads to an increased heat wave in central-eastern China (Fig. 7a). On the other hand, the dipole rainfall pattern over the maritime continent and western-central Pacific due to the enhanced Walker circulation can also lead to an anticyclonic anomaly in the WNP (Fig. 7a) via the local Hadley circulation and Gill-style Rossby waves, respectively (Wu et al. 2009; Chen and Zhou 2014).

8 X. Chen, T. Zhou Fig. 5 SST (shading in ocean with color bar at the bottom; units: C) and Tmax (shading on land with color bar on the left; units: C) anomalies during July August in a 1988, b 1998 and c 2003 with the three strongest heat wave in the YRV region during (see Fig. 4) (c) The forced anomalies in the HSM are similar those observed (Fig. 7b), regardless of their weaker amplitudes. The heat wave center in the YRV is associated with the westward extension of the WNPSH, which results from the forcing of similar tropical SST patterns to those in the observations and their related convective heating anomalies. In contrast to the observation, in which the heat wave center is confined to eastern China, the pattern in the HSM spreads more westward, consistent with the WNPSH which extends westward but weakens over the east coast of China (Fig. 7). The concurrent Tmax anomalies around East Asia, such as those in the Iran-Tibetan plateau and northern Australia, as seen in the observations (Fig. 7a), are also well reproduced by the HSM (Fig. 7b), indicating that the large-scale circulations causing heat waves forced by the observed SSTs are captured by the HSM. In addition to the tropical forcing, anomalous circulations in the middle-high latitudes associated with the HWI are as strong as those in the tropics (Fig. 8a). Quasi-stationary 13 Rossby waves with a wavenumber of four propagate circumglobally from west to the east and lead to Tmax anomalies, such as the heat wave centers over Europe, North Asia and western North America. In East Asia, the westward extension of WNPSH is associated with a wave train propagating southeastward from Europe (Fig. 8a), creating a tripolar pattern similar to the so-called Scandinavia teleconnection or Eurasian teleconnection (Lin 2014; Wang and Zhang 2015) and to the dominant interannual mode of heat wave frequency over Eurasia (Zhou and Wu 2016). The highlow-high pattern of the 500 hpa geopotential anomalies over Europe, Siberia, Mongolia and East Asia, coupled with the warm-cold-warm Tmax pattern, is also reproduced by the HSM (Fig. 8b). Some differences between the HSM and the observations are also obvious. The cold anomalies of the tripolar pattern are weaker in the HSM, shifting northward into Siberia and splitting the warm polarity to the north into two parts, one in northern-eastern Europe and the other in eastern Siberia. Despite the bias, the circumglobal waves

9 Relative contributions of external SST forcing and internal atmospheric variability to July are generally captured by the HSM (Fig. 8b). Therefore, the observed wave activities in the middle-high latitudes that could affect the WNPSH and the heat wave in the YRV region are also supposed to be forced by the observed SST anomalies. Evident SST anomalies in the extratropical oceans (30 N northward) associated with the HWI are seen in the North Atlantic and North Pacific, both of which show warm centers between 30 N and 45 N (Fig. 8). Are these extratropical SST anomalies independent of the tropical signal or related to ENSO? Can they force the heat wave variability in the YRV by modulating the circumglobal wave propagation? We will answer these questions in Sect Internal variability Fig. 6 Variance ratio of Tmax in the mean of HSM to observation. Ratio below 0.3 is evident in most of the land including the YRV region (black box) Fig. 7 Forced anomalies estimated by regressing onto the HWI in the mean of HSM for both a observation and b HSM. Tmax (shading on land, units: C), SST (shading in ocean; units: C), precipitation (contour drawn for ±0.2, ±0.6,, ±1.4; solid green contours denote positive anomalies and dashed purple the negative; units: mm d ay 1) and 500 hpa geopotential height (black contour drawn for ±1.5, ±4.5,, ±13.5 in and ±0.5, ±1.5,, ±9.5 in ; dashed black contours denote negative anomalies; units: m). Dotted regions exceed the 5% significance level Based on Sect. 3, the internal atmospheric processes contribute approximately 1/3 of the variability of the HWI. How does the internal variability impact the heat wave in centraleastern China? Here, we show the anomalous pattern of the internal mode by regressing onto the departure of HWI from the forced component (Fig. 9). In the observations, the SST anomalies associated with the internal HWI are weak, but the amplitudes of the wave activity and consequential surface temperature anomalies are comparable to the forced 13

10 X. Chen, T. Zhou Fig. 8 Forced anomalies as in Fig. 7 but for Tmax, SST, 500 hpa geopotential height and 200 hpa wave-activity flux (vectors; units: m2 s 2) formulated by Takaya and Nakamura (2001). Wave-activity fluxes exceeding the 5% significance level are drawn component (Fig. 8a), indicating that purely atmospheric processes dominate. Compared with the SST-forced component, the anomalous anticyclone over the WNP moves northward by approximately 5 but becomes stronger (Fig. 9a). Larger areas of heat waves in the YRV are affected by the internal processes extending westward inland (Fig. 9a), which is consistent with the pattern of full variability (Fig. 1b). The circumglobal Rossby waves propagating eastward are also evident in Fig. 9a and show similar features to the forced signal (Fig. 8a). For example, there are warm centers over northwestern North America, Europe and 13 Fig. 9 Same as Fig. 7, but for internally induced pattern estimated by linearly removing the SST-forced signal of HWI from the total variability and regressing onto the residual part for both observation and HSM. The internal variability of HWI in the HSM is the departure from the mean in each member central-eastern Asia, and cold centers in Mongolia and the Indian subcontinent. The meridional tripolar pattern over the Eurasian Continent is also prominent in the observed internal variability, but has slight deformations. The internal mode of the observations may contain too much noise due to only one sample being based in reality. By averaging the anomalous patterns of each member in the HSM ensemble, we show the most relevant internal mode to the heat waves in the YRV (Fig. 9b). A clear picture of the tripolar pattern over the Eurasian Continent shows that the wave train

11 Relative contributions of external SST forcing and internal atmospheric variability to July propagates southeastward from Europe to East Asia, eventually strengthening the WNPSH over East Asia and leading to heat waves over a large domain, covering the YRV. The similarities of the extratropical wave patterns of the forced and internal components imply that the SST-forced signal may affect the heat wave in the YRV region partly through projecting its influence onto the internal mode. In the following section, we aim to understand the mechanism of the SST-forced variability of the HWI by decomposing the variability into ENSO-related and non-enso SST forcings and exploring the possible connection between the non- ENSO SST-forced component and the internal mode. 4.3 Mechanisms of ENSO related and non ENSO SST forcing Based on the analysis in Sect. 4.1, the ENSO-related and non-enso SST forcing have equivalent contributions to the total SST-forced HWI variability. Although the SST patterns in the tropical and extratropical regions shown in Figs. 7 and 8 may provide some evidence of different sources of forcing, the relative roles of the ENSO and non-enso SST forcings on the observed circulation anomalies remain unclear. Therefore, we examine the effects of ENSO by regressing onto the NinoC index, which combines both the decaying and developing ENSO signals and examine the effect of non- ENSO SST forcing by regressing onto the forced HWI in the HSM with the NinoC variability removed (Fig. 4) ENSO related variability The tropical rainfall and circulation anomalies related to ENSO variability (Fig. 10) are similar to the total SSTforced patterns (Fig. 7) but with larger amplitudes. In the Indian Ocean basin, there are stronger convection patterns over the warmer SSTs, which is a typical feature of the El Niño decaying summers (Fig. 10a). The Indian Ocean basin warming index is persistently and positively correlated with the HWI in JA1 from SO0 to JA1 and lags Nino3.4 by 2 6 months (Fig. 3), indicating the important role of the capacitor effect of the Indian Ocean (Yang et al. 2007; Xie et al. 2009). Based on the Gill response theory, a pair of cyclonic Rossby waves located on both sides of the equator over the western Indian Ocean result from equatorial heating Fig. 10 ENSO-related anomalies by regressing onto a combined Nino3.4 index (NinoC seen Eq. 1) that including both the effects of decaying and developing ENSO. Tmax (shading on land; units: C), SST (shading in ocean; units: C), 500 hpa geopotential height (black contour; units: m), precipitation (contour; solid green for positive and dashed purple for negative; units: mm day 1 ) and 850 hpa winds (arrow; units: m s 1 ). Wind anomalies exceeding the 5% significance level are drawn. Dotted regions exceed the 5% significance level

12 X. Chen, T. Zhou and sequentially enhance the rainfall in the Arabian Sea and southwestern Indian Ocean. The basin-scale heating causes divergence over the Iran-Tibetan plateau and the high Tmax anomalies there. Meanwhile, the Kelvin waves over the eastern Indian Ocean, which manifest as anomalous easterlies and a wedge-shaped pattern at a 500 hpa geopotential height (Fig. 10a), enhance the WNPSH via Ekman pumping in the boundary layer when propagating into the maritime continent based on previous studies (Wu et al. 2009). In addition to the El Niño decay signal in the Indian Ocean, the La Niña developing signal is also significant, which is characterized by the bipolar pattern of warm-cold SST anomalies in the warm pool and central Pacific along with the enhanced Walker circulation (Fig. 10a). The corresponding positive heating over the maritime continent can directly drive a descending motion over the WNP region via the local Hadley circulation (Chen and Zhou 2014). The Gill response to the negative heating over the western-central Pacific generates a pair of anticyclones over the WNP and western South Pacific (Fig. 10a), which favors the observed heat wave anomalies in central-eastern China and northern Australia. The results of the HSM well support the observed impacts of the ENSO-related SST forcing (Figs. 3, 10b), including the connection of the heat waves occurring over centraleastern China, the Iran-Tibetan plateau and northern Australia. Similar to the total SST-forced Tmax anomalies in the HSM (Fig. 7b), the ENSO-related heat wave pattern in the YRV extends further westward than that in the observations. The bias may result from the incorrectly modeled response of rainfall over the WNP to the warm local SSTs in AGCMs due to the lack of air sea interaction (Figs. 7b, 10b; Zhou et al. 2009). High-pressure anomalies in the upper level of the atmosphere are induced by the increased rainfall over the WNP, which spreads inland and expands the WNPSH westward. In summary, the enhanced and westward extension of the WNPSH and its consequential heat wave in central-eastern China can be explained physically by the SST forcing related to both the decaying El Niño and developing La Niña phases. Most of the heat wave anomalies over the Iran-Tibetan plateau and northern Australia that associated with the HWI can also be explained by the ENSO impact Non ENSO SST forcing After the ENSO signal is removed, significant SST anomalies are seen in the North Atlantic and North Pacific, characterized by warm centers between 30 N and 45 N (Fig. 11a). The circumglobal wave activities over the extratropical region are similar to those of the total SST-forced pattern, showing four wave nodes located over northern Europe, western North Pacific, western North America and Fig. 11 Non-ENSO signal related anomalies by linearly removing the ENSO signal represented by NinoC index from SST-forced HWI (mean of HSM) and regressing onto the residual part. Tmax (shading on land; units: C), SST (shading in ocean; units: C), 500 hpa geopotential height (black contour; units: m). Dotted regions exceed the 5% significance level the western North Atlantic. The meridional tripolar wave pattern over the Eurasia Continent is also prominent from Europe to East Asia, leading to high-pressure and temperature anomalies in eastern China (Fig. 11a). The anomalous circulation and Tmax patterns in the HSM are consistent with those observed (Fig. 11b), confirming the key role of extratropical wave activities under non-enso SST forcing on the heat waves in central-eastern China. As mentioned in Sect , the similar circumglobal wave patterns associated with the non-enso SSTs to

13 Relative contributions of external SST forcing and internal atmospheric variability to July those associated with the internal modes may imply the effects of SST forcing on the circulation and temperature anomalies through projections onto the internal mode. In fact, the non-enso SST forcing should result from extratropical air sea interaction which can be regarded as internal variability in a fully coupled system. Whether the statistically significant SST anomalies in the North Atlantic and North Pacific force the internally similar variabilities remains unknown. Therefore, we perform a series of sensitivity experiments to verify the above hypothesis and evaluate the relative roles of forcings from the North Atlantic and North Pacific. Three groups of AMIP-type sensitivity experiments are carried out using CAM4, a widely used AGCM from the National Center for Atmospheric Research (Neale et al. 2011). The model is driven by the climatologically observed SST with a seasonal cycle, but the non-enso SST patterns in the North Pacific and/or North Atlantic between 30 N and 60 N (Fig. 10) are added into the climatology (details seen in Table 3). To amplify the forcing signal, each group includes a pair of experiments forced by positive and negative SST patterns with two-fold magnitudes, respectively. Each experiment is run for 30 years. The differences of the averages in the last 20 years between the positive- and negative-pattern-forced experiments are analyzed. The results of the sensitivity experiments are shown in Fig. 12. When both the North Atlantic and North Pacific SST patterns are prescribed to drive the model in the Exp_ NPNA run, the heat wave in central-eastern China is well reproduced. The circulation anomalies show a circumglobal wave train with a tripolar pattern over the Eurasian Continent (Fig. 12a), which is similar to the internal mode derived from the HSM ensemble (Fig. 9b) and the pattern related to the non-enso forced HWI (Fig. 11). Thus, the warm SST anomalies in the North Pacific and those in the North Atlantic can contribute to the YRV heat wave by projecting their signals onto the internal mode. To clarify whether the observed circulations and Tmax anomalies related to the non-enso SST anomalies are forced by the pattern in the North Atlantic, the North Pacific, or the both, we analyzed the Exp_NP and Exp_NA runs, which are forced by the SST patterns in the North Pacific and North Atlantic, respectively (Table 3). In the Exp_NP run (Fig. 12b), the anomalous Rossby-wave and Tmax patterns can be reproduced to some extent, but the amplitudes are much weaker than those of the results of Exp_NPNA (Fig. 12a). In contrast, in the Exp_NA run, the Rossby-wave pattern is different from that in the Exp_NPNA and cold Tmax anomalies are observed instead of heat waves in centraleastern China (Fig. 12c). The result of linearly adding the Exp_NA and Exp_NP runs (Fig. 12d) is also much different from that in the Exp_NPNA run (Fig. 12a), indicating the importance of the non-linear interactions between the effects of the non-enso SST forcings in the two ocean basins. The SST anomalies in the North Pacific may mainly force the large-scale anomalous patterns of circulation and Tmax through their projection onto the internal mode, while the North Atlantic SST anomalies can modulate and amplify the responses to the North Pacific forcing. 5 Discussion The heat wave in the YRV is significantly related to a cold center in the northwestern Indian subcontinent (Fig. 1c), which accompanies enhanced rainfall (Fig. 7a). A recent study suggested that the rainfall over the northwestern India and Arabian Seas can adjust the WNPSH by exciting a circumglobal teleconnection and eventually reinforcing the heat wave in the YRV region (Liu et al. 2015). However, the present study suggests that the Indian anomalies are partly due to the teleconnection instead of being the cause. Most of the cold anomalies over northwestern India are components Table 3 Sensitivity experiments for non-enso SST forcing Exp. name SST boundary condition Integrated length (years) Exp_NPNA Exp_pos_NPNA Climatological SST with seasonal cycle is used. Then add SST anomalies (ENSO signal removed) within N to the climatology during June September Exp_neg_NPNA Same as Exp_pos_NPNA, except that the sign of SST anomalies added changes to the opposite 30 Exp_NP Exp_pos_NP Same as Exp_pos_NPNA, except that only SST anomalies in North Pacific are used 30 Exp_neg_NP Same as Exp_pos_NP, except that SST anomalies are opposite 30 Exp_NA Exp_pos_NA Same as Exp_pos_NPNA, except that only SST anomalies in North Atlantic are used 30 Exp_neg_NA Same as Exp_pos_NA, except that SST anomalies are opposite 30 30

14 X. Chen, T. Zhou Fig. 12 Sensitivity experiment of non-enso SST forcing. Tmax on land (shadings; units: C) and 500 hpa geopotential height (contours drawn for ±3, ±9,, ±27; units: m). a Both SST anomalies in the North Pacific and North Atlantic are prescribed (shading in ocean; units: C). b Only North Pacific SST anomalies are prescribed. c Only North Atlantic SST anomalies are prescribed. d Linear addition of b and c (c) (d) of the internal variability (Fig. 9) and non-enso SST forcing (Fig. 11). The high-pressure anomalies over the Iran- Tibetan plateau associated with the teleconnection may induce divergence in the upper level of the atmosphere over northwestern Indian and trigger anomalous convection. An earlier study noted the intensity of Indian summer monsoon (ISM) can be affected by wave train excited in the jet-stream exit region of the North Atlantic and a supposed interaction between a global wave train and the ISM heat source (Ding and Wang 2005). This hypothesis is partly supported by the present study. In addition to the ENSO-related forcing mechanisms presented in Sect , ENSO may also play a role in the heat wave variability of the YRV by modulating Indian rainfall. Part of the enhanced rainfall and cold anomalies over northwestern India is associated with the NinoC index (Fig. 10), which can be explained by the developing La Niña (Ashok and Saji 2007). The ENSO variability could indirectly affect the heat wave in the YRV via the path of the wave train that interacts with the Indian monsoon rainfall. The amplitude of the heat wave variability has been strengthened over the past three decades, especially after the late 1980s and late 1990s (Fig. 1a), consistent with the dominant interannual modes of heat wave frequencies over Eurasia (Zhou and Wu 2016). The variations of the observed HWI are within the envelope of the HSM members (Fig. 2b), indicating that the decadal-scale changes may result from internal variability. To make it more clear, 11-year running standard deviations of the observed HWI and corresponding components are calculated (Fig. 13). The sum of non- ENSO SST-forced and internal components, both sharing the similar mechanism, shows synchronously intensifying variability as the observation, indicating the important role of strengthened circumglobal teleconnection in the extratropics during the recent three decades. Nevertheless, the underlying mechanism deserves further study. 6 Conclusion To understand the relative contributions of anomalous SST forcings and internal atmospheric processes to the interannual variability of the July August heat waves in the YRV region, we decompose the observed heat wave variability

15 Relative contributions of external SST forcing and internal atmospheric variability to July Fig year running standard deviation (units: C) of the observed HWI (black line) and components from total SST forcing (magenta line) and internal variability (blue line). Total SST forcing is further decomposed into ENSO-related (red line) and non-enso forcing (pink line). The result of internal variability plus non-enso forcing (orange line), both of which share similar mechanism, is also shown. The time series correspond to those in Fig. 4 into three components: the ENSO-related SST forcing, non- ENSO SST forcing and internal variability. The total SST forcing is derived from the ensemble mean of the selected high-skill AMIP runs in the CMIP5 archive. The residual is regarded as the internal variability. The contribution of ENSO is estimated from a reconstructed Nino index that combines both the ENSO decaying and developing signals, whereas the non-enso SST-forced variability is the residual of removing the ENSO contribution from the total SST forcing. The mechanisms related to the three components of heat wave variability are depicted by the schematics in Fig. 14. The main conclusions are summarized as follows. 1. The ENSO-related SST forcing, non-enso SST forcing and internal variability are each estimated to contribute approximately 1/3 of the variance of the observed heat- Internal Non-ENSO SST forcing ENSO-related forcing El Nino decaying signal La Nina developing signal Fig. 14 Schematics of three major sources for interannual variability of heat wave in the YRV region: ENSO-related, non-enso SST forcing and internal variability. Orange and blue shadings represent warm and cold SST anomalies, respectively. Black solid arrows denote lowlevel wind in tropics. Red wedge-like curve denotes eastward Kelvin wave excited by heating over the warm Indian Ocean from El Niño decaying effect. Dark red and green shadings represent heat waves and cold anomalies on land, along with anticyclone and cyclone at 500 hpa, respectively. The black hollow arrows denote the Rossby wave propagation. A blue dome-like arrow at the top denotes the effect of non-enso SST forcing on the YRV heat wave via projecting onto the internal mode

16 X. Chen, T. Zhou wave interannual variability in the YRV region during The heatwave variability over the YRV is driven by different sources of forcing that modulate the strength and location of the WNPSH. The enhanced and northwestward extension of the WNPSH is favorable to heatwaves over the YRV. The contribution of ENSO is reflected by the forcing of the Indian Ocean basin warming via the Kelvin-wave mechanism in the El Niño decaying summer and the forcing of a warm-cold pattern between the maritime continent and central Pacific via a Rossbywave response to the cooling over the western Pacific during the La Niña developing summer. The ENSO decay signal dominates the total ENSO forcing. 3. The non-enso SST forcing is related to a warm pattern in the North Pacific and North Atlantic between 30 N and 45 N and a slightly cold pattern north at 45 N in the North Atlantic. The non-enso SST anomalies can force a circumglobal teleconnection marked with a tripolar pattern across the Eurasian continent. The internal variability also shows a similar pattern to that related to the non-enso SST-forced component. A series of AGCM sensitivity experiments in which the non-enso SST anomalies are prescribed confirms the extratropical SST variability in the North Pacific and North Atlantic can affect the heatwave in the YRV region by projecting its influence onto the internal mode. The nonlinearly combined effect of the North Pacific and North Atlantic is important to the non-enso SST forcing, whereas the North Pacific may play a basic role in forcing the teleconnection pattern. Acknowledgements This work was supported by R&D Special Fund for Public Welfare Industry (meteorology) (GYHY ), the National Natural Science Foundation of China (NSFC) under Grant Nos , and , China Postdoctoral Science Foundation (Grant No. 2015M581152), and the Jiangsu Collaborative Innovation Center for Climate Change. References Adler RF et al (2003) The Version 2 Global precipitation climatology project (GPCP) monthly precipitation analysis (1979 present). J Hydrometeorol 4: Ashok K, Saji NH (2007) On the impacts of ENSO and Indian Ocean dipole events on sub-regional Indian summer monsoon rainfall. Nat Hazards 42: Chen R, Lu R (2015) Comparisons of the circulation anomalies associated with extreme heat in different regions of eastern China. J Clim 28: Chen X, Zhou T (2014) Relative role of tropical SST forcing in the 1990s periodicity change of the Pacific-Japan pattern interannual variability. 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