# Representation and Predictability of the East Asia–Pacific Teleconnection in the Beijing Climate Center and UK Met Office Subseasonal Prediction Systems

• Corresponding author: Peiqun ZHANG, zhangpq@cma.gov.cn
• Funds:

Supported by the National Key Research and Development Program of China (2018YFC1505906), National Natural Science Foundation of China (41905067 and 41775066), National (Key) Basic Research and Development (973) Program of China (2015CB453203), and UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund

• doi: 10.1007/s13351-020-0040-8
• Based on the empirical orthogonal function (EOF) analysis, the East Asia–Pacific (EAP) teleconnection is extracted as the leading mode of the subseasonal variability over East Asia in summer, with a meridional tripole structure and significant periods of 10–30 and 50–70 days. A two-dimensional phase–space diagram is established for the EAP index and its time tendency so as to monitor the real-time state of EAP events. Based on the phase composite analy-sis, the general circulation anomalies first occur over the high-latitude area of Europe centered near Novaya Zemlya at the beginning of EAP events. These general circulation anomalies then influence rainfall over Northeast China, North China, and the region south of the Yangtze River valley (YRV) as the phases of EAP event progress. The representation, predictability, and prediction skill of the EAP teleconnection are examined in the two fully coupled subseasonal prediction systems of the Beijing Climate Center (BCC) and UK Met Office (UKMO GloSea5). Both models are able to simulate the EAP meridional tripole over East Asia as the leading mode and its characteristics of evolution as well, except for the weaker precursors over Novaya Zemlya and an inconspicuous influence on precipitation over Northeast China. The actual prediction skill of the EAP teleconnection during May–September (MJJAS) is about 10 days in the BCC model and 15 days in the UKMO model based on correlation measures, but is higher when initialized from the EAP peak phases or when targeted on strong EAP scenarios. However, both of the ensemble prediction systems are under-dispersive and the predictable signals extend to 18 and 30 days in BCC and UKMO models based on signal-to-error metrics, indicating that there may be further scope for enhancing the capability of these models for the EAP teleconnection prediction and the associated impacts studies.
• Fig. 1.  (a) The first eigenvector of the normalized 5-day running mean 500-hPa geopotential height (H500) anomalies in JJA 1981–2018, the value of which is multiplied by the square of eigenvalue to represent correlation coefficients (CORs). The geopotential height anomalies in the boxed areas are chosen to define the EAP index (IEAP). (b) The normalized time series of the first eigenvector (PC1; blue lines) and IEAP (red lines). (c) The mean seasonal cycle of the standard deviation of IEAP obtained via a 21-day smooth running window during the time period of 1981–2010. VAR denotes variance in (a) and ACC denotes anomaly correlation coefficient in (b).

Fig. 2.  (a) The averaged wavelet power spectrum (contours) of EAP indices during May–September (MJJAS) of 1981–2018. (b) The averaged wavelet spectrum during MJJAS. The shading in (a) and the red dotted line (values > 0) in (b) represent the 95% confidence level for the wavelet spectrum.

Fig. 3.  Lead–lag correlation coefficients between (a) the EAP index with RMM1, RMM2, and itself; (b) the EAP index with BSISO1-1, BSISO1-2, BSISO2-1, and BSISO2-2; and (c) the EAP index and its time tendency, during MJJAS of 1981–2018. The dashed line is the 95% confidence level for the correlations in (a) and (b).

Fig. 4.  Time series of the normalized 5-day running mean EAP index and its time tendency in MJJAS of (a) 2016 and (b) 2018. (c) The phase–space diagram of [−IEAP′, −IEAP] in MJJAS 2016, with different months in different colors.

Fig. 5.  The composite of anomalous OLR (color shading; W m−2), H500 (contour; gpm), and WAF (vector; m2 s−2) in the phase–space diagram of [−IEAP′, −IEAP]. The composited OLR anomalies significant at the 95% confidence level and geopotential height anomalies > 5 gpm are shown. The number of days falling within each phase category is given on the top right of each panel.

Fig. 6.  As in Fig. 5, but for the anomalous precipitation rate in China (color shading; mm day−1) and vertically integrated water vapor flux (vector; 104 g m−1 s−1). Stipples for precipitation and thick black vectors for water vapor flux mark the results that are significant at the 95% confidence level.

Fig. 7.  The first eigenvector of the normalized five-day running mean H500 anomalies in JJA in the BCC (upper panels) and UKMO (lower panels) subseasonal prediction systems. The left, middle, and right columns represent LDs of 10, 20, and 30 days, respectively. The explained VAR and spatial ACC between the model and observations (as in Fig. 1a) are given in the upper left and upper right corners of each panel.

Fig. 8.  As in Fig. 5, but for the lead time of 10 days in the BCC model.

Fig. 9.  As in Fig. 5, but for the lead time of 11–15 days in the UKMO model.

Fig. 10.  As in Fig. 6, but for the lead time of 10 days in the BCC model.

Fig. 11.  As in Fig. 6, but for the lead time of 11–15 days in the UKMO model.

Fig. 12.  The predictability (correlation coefficient; dashed) and prediction skill (correlation coefficient; solid) of the EAP index as a function of the forecast LD for MJJAS in (a, c) BCC and (b, d) UKMO models. In (a, b), the solid and dashed lines represent the prediction skill and predictability of individual members (blue) and ensemble mean (red), respectively. In (c, d), the solid and dashed lines represent the prediction skill and predictability of the EAP teleconnection (red), WP (blue), EA (green), and OK (brown), respectively.

Fig. 13.  As in Fig. 12, but for (a, b) RMSE (solid lines) and ensemble spread (dashed line) as well as (c, d) predictability (signal-to-error metric). In (a, b), the blue and red solid lines represent RMSE of individual members and ensemble mean respectively, and the red dashed line represents the ensemble spread. In (c, d), the saturation of blue solid error growth curve (single-member estimate) with respect to the signal (red line) marks the EAP predictability for individual forecasts (denoted by the left black vertical line) and the saturation of green solid error growth curve (ensemble-mean estimate) with respect to the signal marks the potential predictability of the EAP teleconnection for the ensemble-mean forecasts (right black vertical line).

Fig. 14.  The ensemble prediction skill (shading) and predictability (contour) of the EAP index as a function of the forecast LD and the (a, b) initial phase and (c, d) calendar year for (a, c) BCC and (b, d) UKMO models. The prediction skill is computed via correlation coefficient for the period of MJJAS, while the predictability is represented by the ratio of signal to the ensemble-mean estimate error. The red line indicates that the ratio of signal-to-error equals one.

Fig. 15.  The predictability (COR; dashed lines) and prediction skill (COR; solid lines) of the EAP index for the (a, b) initial and (c, d) target weak scenarios (observed standard deviation of IEAP < 1; blue lines) and strong scenarios (observed standard deviation of IEAP > 1; red lines) as a function of the forecast LD in (a, c) BCC and (b, d) UKMO models.

Fig. 16.  Composite phase–space diagrams of the EAP teleconnection for observations (solid lines) and forecasts (dashed lines) of (a) BCC and (b) UKMO models. The dots denote the states of each day from the start date of forecast. Only the EAP scenarios with an initial combined amplitude > 1 in the observational dataset are used in the composite diagrams.

Fig. 17.  The sketch of time evolution of a sinusoidal index [$I = \sin (\omega t)$] and its time tendency [$I' = \omega \cos (\omega t)$]. The phase division corresponds to that of Fig. 4c. Here, $\omega = 2\pi /T$, T = 40, and I′ is multiplied by $1/\omega$.

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Representation and Predictability of the East Asia–Pacific Teleconnection in the Beijing Climate Center and UK Met Office Subseasonal Prediction Systems

###### Corresponding author: Peiqun ZHANG, zhangpq@cma.gov.cn;
• 1. Laboratory for Climate Studies & China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
• 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
• 3. Met Office Hadley Centre, Exeter EX1 3PB, UK
• 4. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Devon EX4 4QF, UK
• 5. Secretariat of Jiangxi Meteorological Society, Nanchang 330096, China
• 6. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
Funds: Supported by the National Key Research and Development Program of China (2018YFC1505906), National Natural Science Foundation of China (41905067 and 41775066), National (Key) Basic Research and Development (973) Program of China (2015CB453203), and UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund

Abstract: Based on the empirical orthogonal function (EOF) analysis, the East Asia–Pacific (EAP) teleconnection is extracted as the leading mode of the subseasonal variability over East Asia in summer, with a meridional tripole structure and significant periods of 10–30 and 50–70 days. A two-dimensional phase–space diagram is established for the EAP index and its time tendency so as to monitor the real-time state of EAP events. Based on the phase composite analy-sis, the general circulation anomalies first occur over the high-latitude area of Europe centered near Novaya Zemlya at the beginning of EAP events. These general circulation anomalies then influence rainfall over Northeast China, North China, and the region south of the Yangtze River valley (YRV) as the phases of EAP event progress. The representation, predictability, and prediction skill of the EAP teleconnection are examined in the two fully coupled subseasonal prediction systems of the Beijing Climate Center (BCC) and UK Met Office (UKMO GloSea5). Both models are able to simulate the EAP meridional tripole over East Asia as the leading mode and its characteristics of evolution as well, except for the weaker precursors over Novaya Zemlya and an inconspicuous influence on precipitation over Northeast China. The actual prediction skill of the EAP teleconnection during May–September (MJJAS) is about 10 days in the BCC model and 15 days in the UKMO model based on correlation measures, but is higher when initialized from the EAP peak phases or when targeted on strong EAP scenarios. However, both of the ensemble prediction systems are under-dispersive and the predictable signals extend to 18 and 30 days in BCC and UKMO models based on signal-to-error metrics, indicating that there may be further scope for enhancing the capability of these models for the EAP teleconnection prediction and the associated impacts studies.

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