Non-stationary and non-linear influence of ENSO and Indian Ocean Dipole on the variability of Indian monsoon rainfall and extreme rain events

Krishnaswamy, Jagdish; Vaidyanathan, Srinivas; Rajagopalan, Balaji; Bonell, M.; Sankaran, Mahesh; Bhalla, R. S.; Badiger, Shrinivas

doi:10.1007/s00382-014-2288-0

Cited by 132 publications

(67 citation statements)

References 60 publications

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“…A significant influence of El Niño events was also found for monsoonal precipitation amounts in eastern and southern Africa (Liebmann et al, 2014;Ratnam et al, 2014) and the Sahel region (Parhi et al, 2015). For the south Asian monsoon a negative response to El Niño events has been frequently perceived (Krishnaswamy et al, 2014;Lau and Wu, 2001;Surendran et al, 2015). For the semiarid regions of central Asia and for the Mediterranean region a positive relationship of winter and spring precipitation to El Niño events during previous autumn was found, e.g., by Barlow et al (2002), Hoell et al (2013), Roghani et al (2015), and Syed et al (2006).…”

Section: Introductionmentioning

confidence: 75%

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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Section: Introductionmentioning

confidence: 75%

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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“…Furthermore, they also do not account for intra-seasonal changes in rainfall and temperature patterns, or the effects of extreme rainfall events which have been reported to be increasing in frequency across large parts of the Indian subcontinent 78 . Future work that couples process-based DGVM models with high-temporal resolution regional climate models for India (e.g.…”

Section: Discussionmentioning

confidence: 99%

Modelling Biome Shifts in the Indian Subcontinent under Scenarios of Future Climate Change

Rasquinha¹,

Sankaran²

2016

Current Science

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The Indian subcontinent is expected to witness significant changes in climatic conditions in the future, but the implications of such changes for future spatial distribution of different biomes in the subcontinent are unclear. We sought to understand the potential shifts in the distribution of biomes in India by 2070 under different emission scenarios, identify biomes and regions of the country that are particularly at risk from future changes in climate, and quantify uncertainties associated with the predictions. We used an ensemble classifier (

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“…. .b 9 are estimated using a least squares adjustment (LSA, Koch 1999). Synthetic global TWS and SST time series are then generated using these estimated coefficients as (2018) …”

Section: Constructing the Synthetic Datamentioning

confidence: 99%

“…This is the case for many geophysical time series; for example, by looking at the global hydrological water fluxes, one can see the amplitude of seasonal cycles as well as their spread change in time . This can also be detected in longer time series such as precipitation, sea surface temperature, and sea surface pressure (Hannachi et al 2007;Timm et al 2005), which reflect the dynamic of spatially and temporally variable phenomena such as those related to the El Niño Southern Oscillation (ENSO, Trenberth 1990), the North Atlantic Oscillation (NAO, Feldstein 2003), and the Indian Ocean Dipole (IOD, Saji et al 1999;Krishnaswamy et al 2015). Generally speaking, based on how the statistical information is computed in (a) and (b), the techniques can potentially deal with stationary (A) and nonstationary (B) property of time series.…”

Section: Introductionmentioning

confidence: 99%

“…Each part of the model is then accounted for by introducing pre-defined base functions. Finally, the soughtfor parameters that are coefficients of the base functions are approximated using, e.g., a least squares adjustment (Koch 1999). Selecting appropriate base functions to meaningfully represent the behavior of observations is a difficult task in parametric techniques.…”

Section: Introductionmentioning

confidence: 99%

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Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

et al. 2017

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In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of realvalued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an

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Non-stationary and non-linear influence of ENSO and Indian Ocean Dipole on the variability of Indian monsoon rainfall and extreme rain events

Cited by 132 publications

References 60 publications

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

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

Modelling Biome Shifts in the Indian Subcontinent under Scenarios of Future Climate Change

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

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