Weakening relationship between East Asian winter monsoon and ENSO after mid-1970s

Wang, Huijun; He, Shengping

doi:10.1007/s11434-012-5285-x

Cited by 166 publications

(140 citation statements)

References 18 publications

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“…In observation, the EAWMIs have robust positive (negative) correlations with SH (EA-T2m) (Jhun and Lee 2004;Wang and He 2012;Wang and Chen 2014). I WC shows a weak (strong) negative correlation with NP (Niño-3.4), whereas I JL and I WH are strongly (weakly) related with NP (Niño-3.4).…”

Section: Evaluation Of Historical Performancementioning

confidence: 87%

“…2. They generally exhibit the decadal weakening of EAWM after the mid-1980s in observation (Jhun and Lee 2004;Wang and He 2012;Lee et al 2013;Wang and Chen 2014). Three to six and four to eight strong and weak monsoon years, respectively, exceed positive and negative of one standard deviation (SD) of each index.…”

Section: Evaluation Of Historical Performancementioning

confidence: 99%

“…According to Wang and Chen (2010), EAWMIs can be classified into four types: (1) east-west pressure gradient indices (e.g., Wu and Wang 2002;Chan and Li 2004;Wang et al 2009b;Wang and Chen 2014), (2) low-level meridional wind indices (e.g., Ji et al 1997;Lu and Chan 1999;Yang et al 2002), (3) upper-level zonal wind shear indices (e.g., Jhun and Lee 2004;Zhu 2008;Li and Yang 2010), and (4) EA trough indices (e.g., Wang et al 2009a;Wang and He 2012). The EAWM-related circulation and air temperature anomalies are well delineated by these indices (Ji et al 1997;Jhun and Lee 2004;Chen 2010, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices

2016

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that resemble the composite strong winter monsoon pattern are simulated more or less weakly in CMIP5 compared to the observation. However, the regressed fields of sea level pressure, surface air temperature, 500-hPa geopotential height, and 300-hPa zonal wind are well established with pattern correlations above 0.83 between CMIP5 and observation data. The differences between RCPs and Historical indicate strong warming, which increases with latitude, ranging from 1 to 5 °C under RCP4.5 and from 3 to 7 °C under RCP8.5 in the East Asian region. The anomalous southerly winds generally become stronger, implying weaker EAWMs in both scenarios. These features are also identified with fields regressed onto the indices in RCPs. The future projections reveal that the interannual variability of the indices will be maintained with an intensity similar to that of the present. The correlation between monsoon indices and Arctic Oscillation increases over time. On the other hand, the correlation between monsoon indices and North Atlantic Oscillation decreases.

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Section: Evaluation Of Historical Performancementioning

confidence: 87%

Section: Evaluation Of Historical Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices

2016

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“…In addition to increasing temperature, precipitation and extreme precipitation have also increased, and these three variables consistently demonstrate an abrupt decadal change around the mid-1980s. Previous studies have shown that the East Asian winter monsoon (EAWM) experienced an abrupt decadal change around the mid1980s [19,20], after which it became weaker, The weakening of the EAWM during the past several decades could be related to climate warming, because simulations of coupled climate models show that the EASM will decrease under the warming background [21]. The weakened EAWM further weakens the control of its related cold-dry air over East Asia, favouring the northward flow of warm-moist air from low latitudes and the ocean.…”

Section: Summary and Discussionmentioning

confidence: 99%

Changes in precipitation and extreme precipitation in a warming environment in China

Sun

2012

Chin. Sci. Bull.

118

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This study analyses the decadal changes in winter precipitation and extreme precipitation in a warming environment in China. The results show that, together with a trend of winter warming in China, winter precipitation and extreme precipitation in the region are also increasing. In addition, concurrent with the decadal warming shift that occurred in the mid-1980s, precipitation and extreme precipitation both increased significantly. Quantitative analysis shows that precipitation and extreme precipitation increased at rates of 9.7% and 22.6% per 1°C of surface warming in China. This rate of precipitation increase is greater than the global mean, which indicates that precipitation in China is highly sensitive to climate warming and further highlights the importance of studying regional responses to climate warming. The fact that extreme precipitation is increasing at a higher rate than precipitation implies that winter precipitation in China will increasingly be of more extreme type in the context of global warming, which could partly explain why there have recently been a number of record-breaking extreme snowfall events in China.winter precipitation, extreme precipitation, global warming, sensitivity, regional response, climate change Citation:Sun

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“…Ashok et al (2001) further identified the Indian Ocean dipole as an important predictor for the Indian summer monsoon. Some studies illustrated that the correlation of the Southern Oscillation index (SOI) and the Indian summer monsoon precipitation is nonstationary and weakened during recent decades (Kumar et al, 1999;Wang and He, 2012). However, Yim et al (2013) detected a recovery of the negative ENSO-monsoon relationship during the 1990s.…”

Section: Pluviometric Regimes and Precipitation Variability Over Centmentioning

confidence: 99%

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

Gerlitz

Vorogushyn

Apel

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The study presents a statistically based seasonal precipitation forecast model, which automatically identifies suitable predictors from globally gridded sea surface temperature (SST) and climate variables by means of an extensive data-mining procedure and explicitly avoids the utilization of typical large-scale climate indices. This leads to an enhanced flexibility of the model and enables its automatic calibration for any target area without any prior assumption concerning adequate predictor variables. Potential predictor variables are derived by means of a cell-wise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability-based cluster analysis. Finally, for every month and lead time, an individual randomforest-based forecast model is constructed, by means of the preliminary generated predictor variables. Monthly predictions are aggregated to running 3-month periods in order to generate a seasonal precipitation forecast.The model is applied and evaluated for selected target regions in central and south Asia. Particularly for winter and spring in westerly-dominated central Asia, correlation coefficients between forecasted and observed precipitation reach values up to 0.48, although the variability of precipitation rates is strongly underestimated. Likewise, for the monsoonal precipitation amounts in the south Asian target area, correlations of up to 0.5 were detected. The skill of the model for the dry winter season over south Asia is found to be low.A sensitivity analysis with well-known climate indices, such as the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East Atlantic (EA) pattern, reveals the major large-scale controlling mechanisms of the seasonal precipitation climate for each target area. For the central Asian target areas, both ENSO and NAO are identified as important controlling factors for precipitation totals during moist winter and spring seasons. Drought conditions are found to be triggered by a cold ENSO phase in combination with a positive state of NAO in northern central Asia, and by cold ENSO conditions in combination with a negative NAO phase in southern central Asia. For the monsoonal summer precipitation amounts over southern Asia, the model suggests a distinct negative response to El Niño events.

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Weakening relationship between East Asian winter monsoon and ENSO after mid-1970s

Cited by 166 publications

References 18 publications

Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices

Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices

Changes in precipitation and extreme precipitation in a warming environment in China

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

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