On the 60-month cycle of multivariate ENSO index

Mazzarella, A.; Giuliacci, Andrea; Liritzis, Ioannis

doi:10.1007/s00704-009-0159-0

Cited by 29 publications

(15 citation statements)

References 13 publications

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“…Pattern of PC1 correlation with SST shows correlations in the eastern Indian Ocean and western Pacific (Figure 6a), however the correlation is not significant after detrend. PC2 shows a positive yet insignificant correlation with the El Niño-Southern Oscillation (ENSO) pattern, similar to that found by Mazzarella et al [53], albeit weaker (Figure 6b). PC3 is related to reduced precipitation over the east coast of Tanzania (Figure 5c,g) in association with increased SST in the Arabian Sea and the western Pacific Ocean (Figure 6c).…”

Section: Variabilitysupporting

confidence: 84%

On Tanzania’s Precipitation Climatology, Variability, and Future Projection

Borhara

Pokharel

Bean

et al. 2020

Climate

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We investigate historical and projected precipitation in Tanzania using observational and climate model data. Precipitation in Tanzania is highly variable in both space and time due to topographical variations, coastal influences, and the presence of lakes. Annual and seasonal precipitation trend analyses from 1961 to 2016 show maximum rainfall decline in Tanzania during the long rainy season in the fall (March–May), and an increasing precipitation trend in northwestern Tanzania during the short rainy season in the spring (September–November). Empirical orthogonal function (EOF) analysis applied to Tanzania’s precipitation patterns shows a stronger correlation with warmer temperatures in the western Indian Ocean than with the eastern-central Pacific Ocean. Years with decreasing precipitation in Tanzania appear to correspond with increasing sea surface temperatures (SST) in the Indian Ocean, suggesting that the Indian Ocean Dipole (IOD) may have a greater effect on rainfall variability in Tanzania than the El Niño-Southern Oscillation (ENSO) does. Overall, the climate model ensemble projects increasing precipitation trend in Tanzania that is opposite with the historical decrease in precipitation. This observed drying trend also contradicts a slightly increasing precipitation trend from climate models for the same historical time period, reflecting challenges faced by modern climate models in representing Tanzania’s precipitation.

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Section: Variabilitysupporting

confidence: 84%

On Tanzania’s Precipitation Climatology, Variability, and Future Projection

Borhara

Pokharel

Bean

et al. 2020

Climate

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“…We found that the short periods (2-8 years) and medium periods (9-14 years) were mainly distributed in north Xinjiang. Mazzarella et al [50] found that multivariate El Niño Southern Oscillation (ENSO) index had a short period of 5 years. Their result was similar to a short period of snow depth.…”

Section: Spatiotemporal Variability Of Snow Depths In Xinjiangmentioning

confidence: 99%

Spatiotemporal Variability of Monthly and Annual Snow Depths in Xinjiang, China over 1961–2015 and the Potential Effects

Liu

et al. 2019

Water

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The spatiotemporal variability of snow depth supplies important information for snow disaster prevention. The monthly and annual snow depths and weather data (from Xinjiang Meteorological Observatory) at 102 meteorological stations in Xinjiang, China over 1961-2015 were used to analyze the spatiotemporal characteristics of snow depths from different aspects. The empirical orthogonal function (EOF), the modified Mann-Kendall method, Morlet wavelet, Daubechies wavelet decomposition and cross wavelet transform were applied to investigate the trend and significance, spatial structure, periods, decomposed series and coherence of monthly and annual snow depths. The results indicated that: (1) The value of EOF first spatial mode (EOF1) of the monthly and annual snow depths in north Xinjiang were larger than south Xinjiang, indicating greater variability of snow depths in north Xinjiang. (2) The change points of annual snow depth mainly occurred during 1969-1979 and 1980-1990. The annual snow depth of most sites showed increasing trends, but with different slope magnitudes. (3) The sites that had main periods of 2-8 and 9-14 years of monthly and annual snow depths (detected by the Morlet wavelet) mainly distributed in northern Xinjiang. The sites that had main periods of 15-20 years of monthly and annual snow depths mainly distributed in southwestern Xinjiang. (4) By using the Daubechies wavelet, the decomposed annual snow depth in entire Xinjiang tended to increase. (5) Through the cross wavelet transform, annual snow depths in entire Xinjiang had good correlations with annual precipitation or relative humidity, and showed a low negative correlation with minimum temperature or sunshine hours. In conclusion, the monthly and annual snow depths had comprehensive spatiotemporal variability but had overall increasing trend during 1961-2015.There have been several research projects on spatial and temporal variation analysis, simulation and remote sensing analysis of snow depth, conducted at daily, monthly, or annual scales and in different parts of the world [7,8]. Liu et al. [9] used wavelet analysis to analyze the periodic characteristics of maximum snow depth in northern Xinjiang over 1961-2008. They found that the maximum snow depth had a main period of 7-11 years in northern Xinjiang. Hu et al. [10] analyzed the correlation coefficient between the annual snow depth and the annual precipitation (or average temperature) in Xinjiang over 1960-2011. The annual snow depth had a good correlation with the annual precipitation, but a low correlation with the average temperature. Xu et al. [11] used a linear model to investigate the spatiotemporal characteristics of daily snow depth and the snow-cover days in the Qinghai-Tibetan Plateau over 1961-2010. They revealed that snow cover was controlled by multiple climate factors rather than a single variable. Ding et al. [6] used the modified Mann-Kendall test to analyze trends and significance of daily snow depth at 105 stations (spread in the whole area) in Xinjiang, China over 19...

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“…The values of A/vpe ratio (Table 1) show that the 28-month harmonic is much more statistically significant inside the bin enclosed between 15 and 40 hPa levels in respect to that enclosed between 40 and 70 hPa. Similar statistical analyses [21] applied to monthly series of Multivariate Enso Index (MEI) have allowed the identification of a quasi biennial harmonic at a level of confidence higher than 99%. Since the highest values of MEI represent El Niño events, the obtained results would suggest the strict relationship between El Niño and the equatorial stratospheric winds.…”

Section: Discussionmentioning

confidence: 99%

On The Dominance of 28-Month Harmonic in the Equatorial- Stratospheric-Wind Quasi Biennial Oscillation

Mazzarella

Giuliacci²

2010

TOASCJ

Self Cite

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Abstract:The Quasi Biennial Oscillation, the downward propagating easterly and westerly wind regimes in the equatorial stratosphere, has been investigated according to robust statistics that utilizes the amplitude/vector probable error ratio to provide the confidence level of the investigated harmonic. The amplitude-to-noise ratio is found to be the highest in correspondence of the 28-month harmonic for each examined height; the relative amplitude and phase values are found, respectively, to decrease and increase with the height and to take about a year to descend from 15 to 70 hPa with a progressive lag of about 1 month/km. At the top of the stratosphere, easterlies dominate, while at the bottom, westerlies are more likely to be found.

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On the 60-month cycle of multivariate ENSO index

Cited by 29 publications

References 13 publications

On Tanzania’s Precipitation Climatology, Variability, and Future Projection

On Tanzania’s Precipitation Climatology, Variability, and Future Projection

Spatiotemporal Variability of Monthly and Annual Snow Depths in Xinjiang, China over 1961–2015 and the Potential Effects

On The Dominance of 28-Month Harmonic in the Equatorial- Stratospheric-Wind Quasi Biennial Oscillation

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