Modulation of wet‐season rainfall over Iran by the Madden–Julian Oscillation, Indian Ocean Dipole and El Niño–Southern Oscillation

2019 ◽  
Vol 39 (10) ◽  
pp. 4029-4040 ◽  
Author(s):  
Farnaz Pourasghar ◽  
Eric C. J. Oliver ◽  
Neil J. Holbrook
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Indian Ocean Dipole ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Madden Julian Oscillation ◽  
Wet Season

scholarly journals Faktor Osean – Atmosfer untuk Memprediksi Titik Panas (Hostspot) di Wilayah Asia Tenggara Bagian Selatan

2021 ◽  
pp. 116-130
Author(s):  
Santriwati Santriwati ◽  
Halmar Halide ◽  
Hasanuddin Hasanuddin
Keyword(s):  
Indian Ocean ◽  
Multiple Regression ◽  
El Niño ◽  
Indian Ocean Dipole ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Madden Julian Oscillation

Penelitian ini bertujuan untuk membuat pemodelan prediksi titik panas (hotspot) di wilayah Asia Tenggara bagian Selatan dengan sejumlah prediktor signifikan menggunakan Model Multiple Regression (MR) dan untuk melakukan verifikasi prediksi model tersebut. Data yang digunakan dalam penelitian ini yaitu data observasi titik panas (hotspot) di Wilayah Indonesia yakni di Pulau Kalimantan dan Sumatera dan di Wilayah Semenanjung Malaysia serta Sabah-Sarawak. Kemudian data indeks El Nino Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), Indian Ocean Dipole (IOD) dan Monsun selama 6 tahun mulai dari tahun 2013 hingga 2018 sebagai data prediktor. Metode yang digunakan yaitu Model Multiple Regression dengan Metode Regresi Stepwise dan verifikasi skill model prediksi yang digunakan yaitu Korelasi Pearson dan RMSE. Berdasarkan hasil pemodelan dan verifikasi prediksi terbaiknya, diperoleh nilai Korelasi Pearson sebesar 0,698 dan nilai RMSE-nya sebanyak 908 hotspot. Untuk model prediksi di wilayah Sumatera oleh 7 prediktor signifikan yang terkait dengan kejadian hotspot yaitu, IOD 0 (IOD pada bulan munculnya hotspot), MJO 0, MJO 9, MJO 10, Mons 1, MJO 8, dan MJO 5. Untuk wilayah Kalimantan nilai Korelasi Pearson sebesar 0,795 dan nilai RMSE-nya sebanyak 1150 hotspot oleh 4 prediktor signifikan, MJO 9 (MJO pada 9 bulan sebelum munculnya hotspot), Mons 1, Mons 0, dan ENSO 3. Untuk wilayah Semenanjung Malaysia diperoleh nilai Korelasi Pearson sebesar 0,145 dan nilai RMSE-nya sebanyak 135 hotspot oleh 2 prediktor signifikan, Mons 2 (Mons pada 2 bulan sebelum munculnya hotspot) dan MJO 0. Kemudian untuk wilayah Sabah dan Sarawak diperoleh nilai Korelasi Pearson sebesar 0,242 dan nilai RMSE-nya sebanyak 113 hotspot oleh 2 prediktor signifikan, IOD 2 (IOD pada 2 bulan sebelum munculnya hotspot) dan MJO 0. Untuk wilayah Sumatera prediktor yang paling berpengaruh yaitu IOD 0, yakni fenomena IOD khususnya fenomena IOD (+) penyebab terjadinya musim kering ini beberapa kali terjadi di wilayah Pulau Sumatera karena letaknya berdekatan langsung dengan Samudera Hindia sehingga iklimnya juga dipengaruhi oleh lautan di dekatnya. Untuk fenomena MJO dan Monsun yang paling berpengaruh di Wilayah Kalimantan (MJO 9), Semenanjung Malaysia (Mons 2) serta Sabah - Sarawak (MJO 0). Kedua fenomena tersebut secara periodik selalu melintas di ketiga wilayah tadi khususnya berkontribusi pada bulan-bulan terjadinya musim kering, sehingga diindikasikan dapat mempengaruhi munculnya hotspot.

scholarly journals El Niño-Southern Oscillation and Indian Ocean Dipole Modes: Their Effects on South American Rainfall during Austral Spring

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1437
Author(s):  
Mary T. Kayano ◽  
Wilmar L. Cerón ◽  
Rita V. Andreoli ◽  
Rodrigo A. F. Souza ◽  
Itamara P. Souza ◽  
...  
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Indian Ocean Dipole ◽  
El Nino ◽  
Southern Oscillation ◽  
Tropical Indian Ocean ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Austral Spring ◽  
Precipitation Anomalies

This paper examines the effects of the tropical Pacific Ocean (TPO) and Indian Ocean Dipole (IOD) modes in the interannual variations of austral spring rainfall over South America (SA). The TPO mode refers to the El Niño-Southern Oscillation (ENSO). The isolated effects between IOD and TPO were estimated, events were chosen from the residual TPO (R-TPO) or residual IOD (R-IOD), and the IOD (TPO) effects for the R-TPO (R-IOD) composites were removed from the variables. One relevant result was the nonlinear precipitation response to R-TPO and R-IOD. This feature was accentuated for the R-IOD composites. The positive R-IOD composite showed significant negative precipitation anomalies along equatorial SA east of 55° W and in subtropical western SA, and showed positive anomalies in northwestern SA and central Brazil. The negative R-IOD composite indicated significant positive precipitation anomalies in northwestern Amazon, central–eastern Brazil north of 20° S, and western subtropical SA, and negative anomalies were found in western SA south of 30° S. This nonlinearity was likely due to the distinct atmospheric circulation responses to the anomalous heating sources located in longitudinally distinct regions: the western tropical Indian Ocean and areas neighboring Indonesia. The results obtained in this study might be relevant for climate monitoring and modeling studies.

scholarly journals Indian Ocean Dipole and El Niño/Southern Oscillation impacts on regional chlorophyll anomalies in the Indian Ocean

2013 ◽  
Vol 10 (10) ◽  
pp. 6677-6698 ◽  
Author(s):  
J. C. Currie ◽  
M. Lengaigne ◽  
J. Vialard ◽  
D. M. Kaplan ◽  
O. Aumont ◽  
...  
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Indian Ocean Dipole ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Near Surface ◽  
Climate Modes ◽  
The Indian Ocean

Abstract. The Indian Ocean Dipole (IOD) and the El Niño/Southern Oscillation (ENSO) are independent climate modes, which frequently co-occur, driving significant interannual changes within the Indian Ocean. We use a four-decade hindcast from a coupled biophysical ocean general circulation model, to disentangle patterns of chlorophyll anomalies driven by these two climate modes. Comparisons with remotely sensed records show that the simulation competently reproduces the chlorophyll seasonal cycle, as well as open-ocean anomalies during the 1997/1998 ENSO and IOD event. Results suggest that anomalous surface and euphotic-layer chlorophyll blooms in the eastern equatorial Indian Ocean in fall, and southern Bay of Bengal in winter, are primarily related to IOD forcing. A negative influence of IOD on chlorophyll concentrations is shown in a region around the southern tip of India in fall. IOD also depresses depth-integrated chlorophyll in the 5–10° S thermocline ridge region, yet the signal is negligible in surface chlorophyll. The only investigated region where ENSO has a greater influence on chlorophyll than does IOD, is in the Somalia upwelling region, where it causes a decrease in fall and winter chlorophyll by reducing local upwelling winds. Yet unlike most other regions examined, the combined explanatory power of IOD and ENSO in predicting depth-integrated chlorophyll anomalies is relatively low in this region, suggestive that other drivers are important there. We show that the chlorophyll impact of climate indices is frequently asymmetric, with a general tendency for larger positive than negative chlorophyll anomalies. Our results suggest that ENSO and IOD cause significant and predictable regional re-organisation of chlorophyll via their influence on near-surface oceanography. Resolving the details of these effects should improve our understanding, and eventually gain predictability, of interannual changes in Indian Ocean productivity, fisheries, ecosystems and carbon budgets.

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