A New Upper-Level Circulation Index for the East Asian Summer Monsoon Variability

The prediction skill and source of the predictability of the East Asian summer monsoon (EASM) system are examined in this work based on four state‐of‐the‐art seasonal climate forecast models including BCC_CSM1.1, ECMWF_SYS4, NCEP_CFS2 and TCC_CPS2. The prediction of the climatology and interannual EASM pattern and the impact on the prediction are further investigated. It is noted that the four models have some skill in predicting summer rainfall in the East Asia, however, the skill is low on average and also largely regional dependence. The interannual variation of EASM measured by monsoon circulation index is well reproduced, implying that the broad‐scale feature/pattern of EASM has higher predictability than the detailed spatial variation of EASM rainfall. The possible sources of predictability of the interannual variability of EASM are associated with the El Niño‐Southern Oscillation (ENSO) and the north Indian Ocean (NIO) sea surface temperature (SST) anomalies. The correlation pattern of rainfall with the NIO SST is characterized by a tripole pattern from south to north of East Asia, which is different from the correlation distribution of the southern‐northern dipole with ENSO, suggesting that NIO SST may exert influence on the EASM independently. The major biases in climatology of EASM in the models are the northward shift of the western Pacific subtropical high (WPSH) and weak monsoonal southerly over the coast of East Asia, which leads to the prediction bias of the Meiyu/Baiu/Changma (MBC) rainfall belt. The prediction of the interannual EASM pattern presents two deficiencies: too weak rainfall variability and northward shift of the dipole rainfall pattern (opposite variation between MBC and the northwestern Pacific), that may be caused by the biases of WPSH in the models.

Section: Models and Observational Datasupporting

confidence: 93%

Predictability of East Asian summer monsoon in seasonal climate forecast models

Liu

Ding

2019

“…The ENSO index has a significantly positive correlation with the EASMI from the preceding December to May, and the NIOWNP index has a significantly positive correlation from May to July. These results are consistent with the previous finding from Zhao et al ().…”

Section: Forecast Skills At Different Leadssupporting

confidence: 94%

“…The WNPSMI largely reflects the features in low latitudes and is well‐studied (Wang et al , ; Cherchi and Navarra, ; Sooraj et al , ). The other is the EASM index (EASMI), defined (Zhao et al , ) as a normalized tripole of 200 hPa zonal wind anomaly, that is, u (2.5°–10°N, 105°–140°E) − u (17.5°–22.5°N, 105°–140°E) + u (30°–37.5°N, 105°–140°E). The EASMI reflects the circulation feature over WNP and the related features over the mid‐latitudes and tropics.…”

Section: ‐Month Lead Forecastmentioning

confidence: 99%

An evaluation of East Asian summer monsoon forecast with the North American Multimodel Ensemble hindcast data

Nie

Guo

2019

The abilities of three models (Climate Forecast System version 2 [CFSv2], Canadian Coupled Climate Model version 3 [CanCM3] and Canadian Coupled Climate Model version 4 [CanCM4]) in the North American Multimodel Ensemble for the East Asian summer monsoon (EASM) forecast were evaluated with their 29‐year hindcast data (1982–2010). Many EASM features including monsoon precipitation centres, large‐scale monsoon circulations and monsoon onset and retreat are generally captured by the three models and their ensemble mean, and the multimodel ensemble has the best performance. Since the East Asian domain includes the tropical western North Pacific summer monsoon (WNPSM) and the subtropical continent monsoon, two well‐known monsoon indices, the WNPSM index (WNPSMI) and EASM index (EASMI), and their associated low‐level winds and precipitation anomalies are well forecasted by the models. However, the forecast performance generally decreases as the leads increase, and the performance of EASMI is not as good as that of WNPSMI. CFSv2 forecasts well at leads up to 6 months whereas the skill of CanCM3 (CanCM4) decreases rapidly when the lead increases to 2 months (3 months). The failure of CanCM3 is mainly attributed to the poor forecast of the relationship of EASMI with the El Niño‐Southern Oscillation and Northern Indian Ocean‐western North Pacific (WNP) sea surface temperature anomaly. However, the causes of the poor forecast of CanCM4 for EASMI require further investigation. Sources of the forecast error (FE), which is the difference between model and observation for monsoon precipitation, are more significant than those of the predictability error (PE), which originates from the initial condition error, indicating that model deficiency plays a dominant role in limiting the EASM precipitation forecast. However, the PE cannot be neglected over the tropical western Pacific in CFSv2, over the WNP in CanCM3 and over the Tibetan Plateau in CanCM4. As the lead time increases, the FE does not remarkably change whereas the PE decreases significantly.

“…Term D has the same physical meaning with Term C because precipitation can release condensation, which will reinforce ascending motions and then produce more precipitation in turn. Thus, in the following, we diagnose Term B and Term C, by separating each variable into a time‐averaged basic state and its departure (Peixóto and Oort, ; Wei et al ., ; Zhao et al ., ). Then Term B and Term C in Equation can be written as:

\begin{matrix} {right left}true \\ f_{0} \frac{\partial}{\partial p} [\overset{true\to}{V_{g}} \cdot \nabla (ζ_{g} + f)] = f_{0} \frac{\partial}{\partial p} [(\overset{true¯}{\overset{V_{g}}{true}} + \overset{true\to}{{V_{g}}^{'}}) \cdot [\nabla (\overset{true‾}{ζ_{g}} + {ζ_{g}}^{'}) + \frac{\partial f}{\partial y}]] \\ = f_{0} \frac{\partial}{\partial p} [((, \overset{u_{g}}{true} \frac{\partial \overset{ζ_{g}}{true}}{\partial x} + u_{g}^{'} \frac{\partial \overset{ζ_{g}}{true}}{\partial x} + \overset{u_{g}}{true} \frac{\partial ζ_{g}^{'}}{\partial x} + u_{g}^{'} \frac{\partial ζ_{g}^{'}}{\partial x} + \overset{v_{g}}{true} \frac{\partial \overset{ζ_{g}}{true}}{\partial y} + v_{g}^{'} \frac{\partial \overset{ζ_{g}}{true}}{\partial y}) \\ (), + \overset{v_{g}}{true} \frac{\partial ζ_{g}^{'}}{\partial y} + v_{g}^{'} \frac{\partial ζ_{g}^{'}}{\partial y})] + (\overset{true‾}{v_{g}} \frac{\partial f}{\partial y} + v_{g}^{'}) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Interannual variation of precipitation over the Hengduan Mountains during rainy season

Dong

Huang

et al. 2017

Self Cite

The present study investigates the interannual variability of precipitation over the Hengduan Mountains (HMs) during rainy season based on daily precipitation dataset of 151 meteorological stations. More HMs precipitation is associated with an anomalous lower level cyclonic circulation over the northern South China Sea (SCS) and the upper level Silk Road pattern (SRP)‐like geopotential height anomalies. Along the north flank of the anomalous cyclonic circulation, easterly wind anomalies weaken climatology winds and prevent further movement of southwest moisture transport, leading to the enhancement of moisture convergence over HMs. Diagnosis shows that anomalous vertical motions are owing to the advection of anomalous temperature by basic zonal winds. The anomalous lower level cyclonic circulation over the northern SCS is related to La Niña‐like sea surface cooling in the equatorial central and eastern Pacific. This cooling adjusts the Walker Circulation, causing positive precipitation anomalies near the SCS, which trigger the anomalous cyclonic circulation as a warm Rossby wave response. The distribution of upper level geopotential height anomalies over the mid‐latitude Asian land resembles the SRP. Due to its barotropic structure, the SRP leads to warm and cold anomalies over the central Asia and the central China, respectively. The advection of anomalous temperature gradient by mean westerlies leads to anomalous ascending motions and more precipitation over HMs.