Prediction of southern oscillation index using spectral components

The time series of SOI (Southern Oscillation Index, Tahiti minus Darwin sea-level atmospheric pressure difference) was spectrally analysed by a simple method MEM-MRA, where periodicities are detected by MEM (Maximum Entropy Method) and used in MRA (Multiple Regression Analysis) to get the estimates of their amplitudes and phases. From these, the three or four most prominent ones were used for reconstruction and prediction. Using data for 1935-80 as dependent data, the reconstructed values of SOI matched well with observed values and most of the El Niños (SOI minima) and La Niñas (SOI maxima) were located correctly. But for the independent data (1980 onwards), the matching was poor. Omitting earlier data, 1945- 80, 1955-80, 1965-80 as dependent data again gave poor matching for 1980 onwards. When data for 1980 onwards only were used as dependent data, the matching was better, indicating that the spectral characteristics have changed considerably with time and recent data were more appropriate for further predictions. The 1997 El Niño was reproduced only in data for 1985 onwards. For 1990 onwards, only a single wave of 3.5 years was appropriate and explained the 1997 and 1994 events but only one (1991) of the 3 complex and quick events that occurred during 1989-95. The UCLA group of Dr. Ghil has been using the SSA (Singular Spectrum Analysis)-MEM combination for SOI analysis. For the 1980s, they got very good matching, but the 1989-95 structures were not reproduced. For recent years, their SSA-filtered SOI (used for prediction) is a simple sinusoid of ~3.5 years. It predicted the El Niño of 1997 only at its peak and even after using data up to February 1997, the abrupt commencement of the event in March 1997 and its abrupt end in June 1998 could not be predicted. Using only a 3.5 years wave, an El Niño was expected for 2000-2001. However, a very long-lasting La Niña seems to be operative and there are no indications as yet (September of 2001) of any El Niño like conditions.

Drought Monitoring over Nepal for the Last Four Decades and Its Connection with Southern Oscillation Index

This study identified summer and annual drought events using the Standard Precipitation Index (SPI) for 107 stations across Nepal from 1977 to 2018. For this, frequency, duration, and severity of drought events were investigated. The SPI4 and SPI12 time scales were interpolated to illustrate the spatial patterns of major drought episodes and their severity. A total of 13 and 24 percent of stations over the country showed a significant decreasing trend for SPI4 and SPI12. Droughts were recorded during El Niño and non-El Niño years in Nepal. Among them, 1992 was the worst drought year, followed by the drought year, 2015. More than 44 percent of the locations in the country were occupied under drought conditions during these extreme drought events. Droughts have been recorded more frequently in Nepal since 2005. The areas of Nepal affected by extreme, severe, and moderate drought in summer were 8, 9, and 18 percent, while during annual events they were 7, 11, and 17 percent, respectively. Generally, during the drought years, the SPI and Southern Oscillation Index (SOI) have a strong phase relation compared to the average years.

Relation between southern oscillation index and Indian northeast monsoon as revealed in antecedent and concurrent modes

The relation between Southern Oscillation Index (SOI) and Indian northeast monsoon has been studied in antecedent and concurrent modes based on monthly /seasonal mean SOI and monthly/seasonal rainfall data of Tamil Nadu for the 104 year period, 1901 to 2004. It has been found that a good negative relationship exists between the SOI and Indian northeast monsoon in antecedent and concurrent modes, the former being stronger than the later. In the concurrent mode, a strong negative relationship exists during the beginning of the season which changes as the season advances and turns positive during the fag end of the season. Such a changing nature of relationship is explained through the variation of latitudinal positions of 200 hPa Sub Tropical Ridge (STR) / Equatorial Trough (ET) and the location of these with reference to the latitudinal location of the area benefited by the northeast monsoon. It has been shown that a positive (negative) SOI shifts the STR north(south)wards throughout the year. The relationship between latitudinal position of STR and the Indian northeast monsoon rainfall (NMR) is negative during the beginning of the season and turns positive during the fag end of the season, which is similar to the relationship between SOI and NMR. The relation between upper tropospheric wind/temperature anomalies and NMR also shows a similar change in relationship. Westerly wind and negative temperature anomalies in October changing to easterly wind and positive temperature anomalies in January are by and large associated with good northeast monsoon activity. The reversal in the relationship between latitudinal position of STR and NMR as the season advances has also been partly explained based on theoretical considerations by invoking the tilting term of the vorticity equation. Thus the SOI appears to manifest itself on Indian northeast monsoon rainfall by way of modulating the latitudinal positions of STR. An analysis based on Australian “Rainman” software on winter monsoon rainfall of some Sri Lankan and southeast Asian stations has substantiated the changing nature of relationship. A study of dates of onset and withdrawal of Indian northeast monsoon in relation to SOI has revealed that negative (positive) SOI in September is associated with early (late) onset. But, continuation of negative SOI throughout the season favours early and abrupt withdrawal. Positive SOI during the fag end of the season is frequently associated with extension of the monsoon into January of the next year.

Interannual variability of the Arabian Sea warm pool intensity and its association with monsoon onset over Kerala

In this paper the relationships between the Arabian Sea warm pool intensity, Southern Oscillation (SO) and the monsoon onset have been discussed. The results show that the peak intensity of the warm pool in the Lakshadweep Sea is significantly correlated with the monsoon onset date over Kerala. Warmer Sea Surface Temperature (SST) anomalies in the warm pool region during April-May are associated with delayed monsoon onset over Kerala though the cause-and-effect relationship is not known. The Southern Oscillation Index (SOI) of March can provide predictive indications of the peak intensity of the warm pool which, normally occurs during April.

Studi Analisa Kekeringan Metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI) di DAS Kemuning Kabupaten Sampang

Kekeringan ialah bencana alam yang terjadi secara perlahan dan berdampak buruk untuk kelangsungan hidup penduduk Kabupaten Sampang. Mengingat hal tersebut, perlu dilakukan analisa indeks kekeringan serta pemetaan sebarannya sebagai upaya mitigasi bencana kekeringan. Studi ini bertujuan untuk mengetahui tingkat keparahan kekeringan dengan metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI), serta kesesuaiannya dengan data Southern Oscillation Index (SOI) yang mampu mempresentasikan kejadian El Nino Southern Oscillation (ENSO). Setelah itu, Indeks kekeringan yang lebih sesuai dengan pola SOI dipetakan dengan metode Inverse Distance Weighting (IDW) untuk mengetahui sebaran kekeringan. Metode SPI menghasilkan indeks kekeringan terparah di bulan April 2004 sebesar -3,651 pada periode defisit 1 bulanan. Metode PDSI menghasilkan indeks kekeringan terparah di bulan September 2001 sebesar - 20,628. Berdasarkan hasil analisa rerata PDSI periode 1998-2017, diketahui bahwa bencana kekeringan umumnya bermula sejak bulan Juli dan berakhir di bulan Oktober, sedangkan puncak kekeringan terjadi pada bulan September. Metode PDSI juga memiliki kesesuaian sebesar 60% terhadap nilai SOI berdasarkan penggambaran grafik surplus dan defisit indeks rerata tahunan, lebih baik daripada metode SPI yang hanya bernilai 53%. Penggambaran peta sebaran kekeringan berdasarkan indeks kekeringan PDSI menunjukkan bahwa Kecamatan Sampang, Torjun, dan Camplong perlu diprioritaskan dalam upaya mitigasi bencana kekeringan di masa mendatang karena memiliki potensi bencana kekeringan lebih besar jika dibandingan kecamatan lainnya.

Studi Kekeringan Meteorologi dengan Menggunakan Metode Standardized Precipitation Index (SPI) dan China Z Index (CZI) di DAS Lekso Kabupaten Blitar

Kabupaten Blitar merupakan salah satu daerah yang rawan terjadi kekeringan, salah satunya yaitu DAS Lekso. Kekeringan yang terjadi di Kabupaten Blitar disebabkan oleh minimnya intensitas curah hujan yang turun, maka dari itu dibutuhkan upaya awal untuk memitigasi kekeringan meteorologi dengan cara memantau dan menganalisis kekeringan yang terjadi pada lokasi studi. Metode yang digunakan dalam menganalisis kekeringan yaitu metode Standardized Precipitation Index (SPI)) dan metode China Z Index (CZI) yang kemudian dibandingkan dengan data Southern Oscillation Index (SOI). Hasil indeks kekeringan kedua metode yang telah dikomparasi dengan data SOI akan digunakan sebagai penggambaran peta sebaran kekeringan menggunakan Sistem Informasi Geografis (SIG) dengan interpolasi Kriging. Pada hasil analisa perbandingan indeks kekeringan dengan data SOI bulanan didapatkan hasil persentase pendekatan metode CZI sebesar 57.45% dan metode SPI sebesar 42.55%. Pada perbandingan indeks kekeringan dengan SOI rerata tahunan didapatkan persentase metode CZI sebesar 63% dan metode SPI sebesar 60%. Pada hasil analisa korelasi indeks kekeringan yang dikomparasi dengan data hujan didapatkan nilai korelasi metode CZI memiliki tingkat hubungan korelasi mendekati positif sempurna dan metode SPI memiliki korelasi yang cukup. Sehingga metode CZI dipilih sebagai penggambaran peta sebaran kekeringan menggunakan interpolasi kriging yang kemudian didapatkan desa-desa yang terdampak kekeringan di Kabupaten Blitar khusunya di DAS Lekso.

Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006–2018

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

Monthly and Seasonal Drought Characterization Using GRACE-Based Groundwater Drought Index and Its Link to Teleconnections across South Indian River Basins

Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment; out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months; other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales; the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.