Different prediction skill for the East Asian winter monsoon in the early and late winter season

Tian, Binghui; Fan, Ke

doi:10.1007/s00382-019-05068-6

Cited by 26 publications

(16 citation statements)

References 65 publications

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“…Some recent studies have shown that the relationships between the EAWM and other modes of variability (e.g., ENSO) vary between early and late winter (Yu and Sun, 2018; Tian and Fan, 2019). Here, we also examined the linkages between the QBO and EAWM during early (ND) and late winter (JF).…”

Section: Relationship Between the Qbo And Eawm In The Nh Wintermentioning

confidence: 99%

The observed influence of the Quasi‐Biennial Oscillation in the lower equatorial stratosphere on the East Asian winter monsoon during early boreal winter

Chen

Huangfu

et al. 2021

Intl Journal of Climatology

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This study examines the relationship between the Quasi‐Biennial Oscillation (QBO) and the variability of the East Asian winter monsoon (EAWM) in the Northern Hemisphere (NH) winter. Here, we consider the effects of QBO winds in the lower stratosphere at 70 hPa. It is found that during the period of 1958–2019, the EAWM in the early winter months (November–December) is weaker in the easterly phase of the QBO (EQBO) than in the westerly phase of the QBO (WQBO). During EQBO, the northerly monsoon flow is weakened, and anomalous warm temperatures occur over East Asia. Moreover, components of the EAWM circulation system, including the Siberian High, Aleutian Low, East Asian trough, and East Asian jet stream, are all weakened. Our further analysis suggests that the QBO may influence the EAWM directly via a subtropical pathway. The EQBO tends to weaken the East Asian jet stream and shift it poleward, and the changes in the East Asian jet stream lead to a weakened EAWM. The activities of planetary waves are also changed in association with the QBO. Examinations of individual zonal wavenumbers (WNs) of planetary waves in the SLP field reveal that WN 1 is strengthened while WNs 2 and 3 are weakened during EQBO. Specifically, the anomalous WN 2 leads to weakening of the Siberian High, Aleutian Low, and East Asian trough, and the anomalous WN 3 reduces the pressure gradient between the East Asia landmass and the western Pacific. The changes in WNs 2 and 3 associated with the QBO play a dominant role in anomalous EAWM.

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Section: Relationship Between the Qbo And Eawm In The Nh Wintermentioning

confidence: 99%

The observed influence of the Quasi‐Biennial Oscillation in the lower equatorial stratosphere on the East Asian winter monsoon during early boreal winter

Chen

Huangfu

et al. 2021

Intl Journal of Climatology

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“…Besides, the connections between El Niño-Southern Oscillation (ENSO) and the EAWM can be found on seasonal and intraseasonal time scales (Li, 1990;Zhang et al, 1996;Webster et al, 1998;Li et al, 2021). Tian and Fan (2020) pointed out that the prediction skill for the EAWM in early winter (November-December) is higher than in late winter (January-February), due to the weakening relationship between ENSO and the EAWM in late winter. As the Arctic warming has reached rates that are double that of the global average, the influence of the Arctic on the mid-to-high latitude climate has been strengthened (Francis and Vavrus, 2012;Kug et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Strengthened Impacts of November Snow Cover Over Siberia on the Out-of-phase Change in the Siberian High Between December and January Since 2000 and Implication for Intraseasonal Climate Prediction

Yang

Fan

2021

Front. Earth Sci.

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This study investigates the out-of-phase change in the Siberian High (SH) between December and January (stronger than normal in December and weaker than normal in January, and vice versa). The results show that the monthly reversal frequency of the SH between December and January increases significantly after 2000 from 30% (1981–2000) to 63% (2001-2019). Correspondingly, the influence of November snow cover over Siberia on the phase reversal of the SH has intensified after 2000. The reasons may be as follows. Higher snow depth over Siberia (SSD) in November corresponds to stronger diabatic cooling and increased snow accumulation over Siberia in November and December, which may strengthen the SH in December via the positive feedback of snow albedo. The dynamic mechanisms between the higher SSD in November and weaker SH in January are further investigated from the perspective of troposphere–stratosphere interaction. Such anomalously higher SSD with strong upward heat flux induces the upward-propagating wave activity flux in November and December over the Urals and Siberia, leading to a weaker and warmer stratospheric polar vortex in January. Subsequently, the anomalies of the stratospheric polar vortex signal propagate downwards, giving rise to a negative Arctic Oscillation–like structure in the troposphere and a weakening of the SH in January. This mechanism can be partly reproduced in CMIP6. Additionally, the variability of the September–October Arctic sea ice mainly leads to coherent variations of the SH in December and January via the eddy–mean flow interaction before 2000. Furthermore, the preceding November snow cover over Siberia enhances the intraseasonal prediction skill for the winter SH after 2000. Meanwhile, considering the previous November SSD, the prediction accuracy for the out-of-phase change in the SH between December and January increases from 16% (outputs of the NCEP’s Climate Forecast System, version 2) to 75%.

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“…In this work, the abbreviation ‘DY’ refers to the year‐to‐year increment of a climate variable. This method has been proven to have some advantages in predicting the East Asian monsoon, summer and spring precipitation, typhoons, and extreme heavy precipitation in China (Fan et al ., 2009; Fan et al ., 2012; Liu and Fan, 2012; Liu and Fan, 2014; Tian and Fan, 2020). In order to ensure the independence between the physical processes of the predictors, the partial correlation analysis method is used to explore the impact of each predictor on spring drought in China.…”

Section: Methodsmentioning

confidence: 99%

New downscaling prediction models for spring drought in China

Tian

Fan

2022

Intl Journal of Climatology

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Accurate prediction of spring drought in China is helpful toward reducing associated agricultural losses. In this study, spring drought is defined as the three‐month Standardized Precipitation Evapotranspiration Index (SPEI) ending in May. Based on the year‐to‐year increment and downscaling method, three single‐predictor prediction models (P1 model, P2 model, and P3 model) and two multi‐predictor models for spring drought at 677 stations in China are developed for the period 1983–2020. As physical processes affecting spring drought in the China region, the tropical Pacific–Indian Ocean sea surface temperature (SST) in winter, the Davis Strait–Barents sea‐ice concentration (SIC) in winter, and the 500‐hPa spring vertical velocity, predicted by the Climate Forecast System, version 2 (CFSv2), are considered in the prediction models. The prediction skill of the downscaling models for the spring SPEI is measured by cross‐validation for the period 1983–2020. The CFSv2 model only shows convincing prediction skill for the spring SPEI at 35 of 677 stations. However, among the 677 stations, the temporal correlation coefficient between the observed and predicted spring SPEI at 621 stations for P1 model, 598 stations for P2 model, 545 stations for P3 model, 674 stations for the statistical downscaling model (SD model), and 675 stations for the hybrid downscaling dynamical–statistical prediction model (HD model) exceeds the 95% confidence level. Therefore, compared to the CFSv2 model, the prediction skill for spring drought is improved by the single‐predictor and multi‐predictor models. The prediction skill of HD model for spring drought, which combines preceding observational predictors and the simultaneous predictor of the CFSv2 model, is higher than that of SD model. In addition, the severe drought that occurred in Northeast and North China in spring 2017 can be successfully predicted by HD model.

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Different prediction skill for the East Asian winter monsoon in the early and late winter season

Cited by 26 publications

References 65 publications

The observed influence of the Quasi‐Biennial Oscillation in the lower equatorial stratosphere on the East Asian winter monsoon during early boreal winter

The observed influence of the Quasi‐Biennial Oscillation in the lower equatorial stratosphere on the East Asian winter monsoon during early boreal winter

Strengthened Impacts of November Snow Cover Over Siberia on the Out-of-phase Change in the Siberian High Between December and January Since 2000 and Implication for Intraseasonal Climate Prediction

New downscaling prediction models for spring drought in China

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