The covariability between anomalous northeast monsoon rainfall in Malaysia and sea surface temperature in Indian-Pacific sector: a singular value decomposition analysis approach (original) (raw)
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Geographica Pannonica, 2020
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Sea Surface Temperature Anomaly Characteristics Affecting Rainfall in Western Java, Indonesia
Agromet
Western Java is densely populated with high socio-economic activity. Climate-related disasters can be mitigated with the support of an understanding of systems that produce reliable climate predictions. One of the climate variables included in hydrometeorological disasters is rainfall. The characteristics of rainfall in Western Java cannot be separated from the sea surface temperature (SST) around the area. This study compares the relationship between SST and rainfall with singular value decomposition (SVD) and compares it with Pearson's correlation. SVD Model performance was evaluated using square covariance fraction (SCF) and Pearson correlation. The results showed that rainfall has a higher correlation with SST Anomaly (SSTA) by using SVD, with a correlation of about 0.63 in 6 to 9 months without lag time. Rainfall in western Java was closely related to the positive SSTA anomaly in southern Indonesia, especially the waters south of Java Island, and negative anomalies in other...
International Journal of Climatology, 2008
This study examines the level and origin of seasonal forecast skills of rainfall anomalies in Malaysia. The forecast models are based on an empirical technique known as the canonical correlation analysis (CCA). The CCA technique searches for maximally correlated coupled patterns between sets of predictor and predictand matrices. The predictive skills of five predictor fields, namely station rainfall in preceding seasons (i.e. the predictand itself), local sea surface temperature (SST) over the western Pacific sector, quasi-global SST, sea level pressure, and northern hemisphere 700 hPa geopotential height, are investigated. The sequence of four consecutive 3-month predictor periods is used to capture evolutionary features in the predictor fields. The predictor-predictand setup is designed such that the predictor fields are followed by a lead-time ranging from 0 to 9 months and then by a single predictand period of 3 months' duration. The skills are estimated in hindcast mode using the one-year-out version of the cross-validation technique. Skill estimates are expressed as temporal correlation coefficients between forecasts and observed values. A series of experiments with different predictor combinations reveal that the model with quasi-global SST alone produces most favourable forecast skills. The forecast skills of this model generally outperform the persistence forecast. Moreover, the model also has higher forecast skills in the East Malaysian region compared to those in Peninsular Malaysia. The forecast skill peaks during the late Northern Hemisphere winter season (January-February-March (JFM)) with a secondary maximum during the early summer season (May-June-July, (MJJ)). The average forecast skills are between 0.3-0.5 for up to 5 months' lead-time in the East Malaysian region and this can be considered very useful for the appropriate users. In the Peninsular Malaysia region, the forecast skills are generally weak, although some stations registered modest skills even at a 5-month lead-time. For both prediction periods, the source of predictability originates from anomalous SST conditions associated with the El Niño-Southern Oscillation (ENSO) phenomenon. Generally, during a La Niña (an El Niño) event, regions in northern Borneo experience excess (deficit) rainfall during the JFM period. Similar conditions are experienced during the MJJ period except that the impact tends to be more widespread throughout the country. Interestingly, the origin of predictability during the JFM period can be traced to typical ENSO events, while ENSO events of longer duration are responsible for the MJJ period.
IEEE Geoscience and Remote Sensing Letters, 2016
This letter presents an alternative approach for investigating the teleconnections of summer monsoon rainfall in the state of Kerala, India, with four prominent climatic oscillations in multiple time scales based on multivariate empirical mode decomposition (EMD) (MEMD) and time-dependent intrinsic correlation (TDIC). First, the multivariate data set constituting summer monsoon rainfall time series and the climate oscillations such as the Quasi-Biennial Oscillation, El Niño Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation, and Equatorial Indian Ocean Oscillation are decomposed to several rotational modes by employing MEMD. To capture the associations in shorter time spells, the TDIC method is employed, in which the sliding window is adaptively fixed based on instantaneous periods obtained by the Hilbert transform of the obtained modes. From the analysis, it is found that the strength and nature of association between summer monsoon rainfall and the climate oscillations vary with time scales and time spells. The performance of MEMD-based TDIC analysis is compared with that of EMD-based TDIC, and it was found that the former one well captured the direct correlation between ENSO and summer monsoon rainfall in the 1997-1998 period and the opposing correlation in the 2002-2003 period, while the latter approach failed to capture such associations properly.
International Journal of Climatology, 2004
Singular value decomposition (SVD) techniques are used to deduce a relationship between rainfall over the Caribbean basin and oppositely signed sea-surface temperature anomalies in the Pacific and Atlantic. The analysis is done for four 3 month seasons. The first two seasons: November-January (NDJ) and February-April (FMA) encompass the Caribbean dry period, and the other two, May-July (MJJ) and August-October (ASO), include the early and late Caribbean rainy seasons. The first SVD mode for all seasons represents variability due to El Niño-southern oscillation (ENSO) and, with the exception of the later wet season, the second SVD mode represents variability due to tropical North Atlantic sea-surface temperatures. ENSO has the greatest impact during the late rainfall season (ASO) and the early dry season (NDJ), whereas the tropical Atlantic controls variability in the early rainfall season (MJJ). The configuration of concurrent but oppositely signed sea-surface temperature anomalies in the tropical Pacific and Atlantic basins is only associated with rainfall modification in the late Caribbean rainfall season (ASO) and the early Caribbean dry season (NDJ).
World Scientific News, 2020
Rainfall is one of the climate variables that have a significant influence, especially in supporting the activities of various sectors in tropical countries. Climate change is causing rainfall variability in Indonesia. However, the analysis of climate variable patterns is difficult because of the formation of a large matrix. Empirical Orthogonal Function (EOF) analysis can be used to reduce the dimensions of large data by maintaining as much variation as possible from the original data set. The method used in this study is through the Singular Value Decomposition (SVD) approach. The analysis shows that 98.50% of the total rainfall variance can be represented by four EOF modes. Analysis of the spatial pattern of EOF1 shows that rainfall is below average, while the other EOF modes show variations in rainfall.
Heterogeneous Correlation Map Between Estimated ENSO And IOD From ERA5 And Hotspot In Indonesia
Jambura Geoscience Review
El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can reduce the amount of rainfall in Indonesia. The previous study found that ENSO and IOD derived from the OISST dataset have an association with hotspots in Indonesia, especially in southern Sumatra dan Kalimantan. But the correlation results are still too small, and the correlation strength between regions has not been analyzed. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. We use a singular value decomposition method to quantify this HCM. Besides OISST, ERA5 is an estimation data often used for weather forecast analysis. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the...
Journal of Climate, 2003
The interaction of the Indian Ocean dynamics and the tropospheric biennial oscillation (TBO) is analyzed in the 300-yr control run of the National Center for Atmospheric Research (NCAR) Climate System Model (CSM). Sea surface temperature (SST) anomalies and equatorial ocean dynamics in the Indian Ocean are associated with the TBO and interannual variability of Asian-Australian monsoons in observations. The air-sea interactions involved in these processes in the coupled ocean-atmosphere model are analyzed, so as to diagnose the causes of the SST anomalies and their role in the development of a biennial cycle in the Indian-Pacific Ocean region. By using singular value decomposition (SVD) analysis, it is found that the model reproduces the dominant mechanisms that are involved in the development of the TBO's influence on the south Asian monsoon: largescale forcing from the tropical Pacific and regional forcing associated with both the meridional temperature gradient between the Asian continent and the Indian Ocean, as well as Indian Ocean SST anomalies. Using cumulative anomaly pattern correlation, the strength of each of these processes in affecting the interannual variability of both Asian and Australian monsoon rainfall is assessed. In analyzing the role of the Indian Ocean dynamics in the TBO, it is found that the Indian Ocean zonal mode (IOZM) is an inherent feature of the Asian summer monsoon and the TBO. The IOZM is thus a part of the biennial nature of the Indian-Pacific Ocean region. The coupled ocean-atmosphere dynamics and cross-equatorial heat transport contribute to the interannual variability and biennial nature of the ENSO-monsoon system, by affecting the heat content of the Indian Ocean and resulting SST anomalies over multiple seasons, which is a key factor in the TBO.
Lagged relationships between ENSO and the Asian Summer Monsoon in the CSIRO coupled model
Geophysical Research Letters, 2004
1] The lagged relationship between the El Nino-Southern Oscillation (ENSO) and Asian Summer Monsoon (ASM) variability is investigated using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark3 coupled model. Composite analyses of 17 warm and 16 cold events from an extended unforced run reveal that the model captures the asymmetric structures of rainfall and SST anomalies over the tropical Indian Ocean during northern hemisphere spring which is one of the major precursory signals of anomalous ASM variability. As a remote forcing mechanism prior to the ASM onset, simulated anomalous convection over the northern Indian Ocean strongly influences the land surface hydrologic conditions over central and southwest Asia. Simulated landsurface processes result in anomalous temperatures over the land, which in turn produce a change in the land-ocean thermal contrast over the domain. The results are consistent with observed features, which show a delayed and indirect impact of ENSO on the ASM variability during the early summer monsoon period, but not during the rest of the monsoon season.
2014
Various earlier studies have demonstrated that rainfall in the Maritime Continent–Indonesia region is strongly related to the El Niño–Southern Oscillation (ENSO) during the dry half of the year but has a very weak association with ENSO during the summer–wet season months. This relationship is investigated over a wider domain through the use of outgoing longwave radiation (OLR) data as a proxy for rainfall. Consistent with the hypothesis of Haylock and McBride, it is found that the large-scale structure of the low-order empirical orthogonal functions (EOFs) of OLR have a strong resemblance to the patterns of correlation between OLR and the Southern Oscillation index (SOI). This supports the hypothesis that the predictable component of rainfall is determined by the component that is spatially coherent, as quantified through EOF analysis. As was found earlier with rainfall, the region of largest correlation between interannual OLR anomalies and the SOI lies in the winter hemisphere. T...