Oscillations & Dipoles

  • Ocean-atmosphere oscillations create El Niño conditions.
    Ocean-atmosphere oscillations create El Niño conditions, bringing rain and mudslides to some regions.
  • Severe droughts caused by El Niño can impact the food supply chain.
    In other areas, severe droughts caused by El Niño events can impact the food supply chain.
  • The IOD has sparked drought and wildfires in Australia.
    El Niño’s "cousin," the Indian Ocean Dipole, has sparked drought and wildfires in Australia.
  • The IOD can strengthen monsoons, leading to massive floods.
    The Indian Ocean Dipole can also strengthen monsoons, leading to massive floods in India.
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"The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present." - Paul Saffo

Earth's ocean and atmosphere interact in countless ways. A striking example is the El Niño Southern Oscillation (ENSO). "El Niño" is widely recognized but what is the "Southern Oscillation"? It's the coupled system where neither the ocean nor the atmosphere is clearly the dominant driving force.

ENSO is just one of many such oscillations that occur naturally over different times and regions. Each varies among three phases; for example, a neutral ENSO means normal conditions, while El Niño and La Niña are warming and cooling phases, respectively.

El Niño conditions were first documented in the year 1525. The Indian Ocean Dipole (IOD), however, has only been recognized for about two decades. Better understanding the IOD's impact on weather – including the monsoon of South Asia - is crucial. This is a challenge because ocean-atmosphere oscillations and dipoles are erratic in strength, timing, and notoriously difficult to predict.

Adding a pinch of salt improves el nino models

Related Publications

  • Jarugula, S., and McPhaden, M. (2023). Indian Ocean Dipole Affects Eastern Tropical Atlantic Salinity Through Congo River Basin Hydrology, Commun. Earth Environ., 4, 366, doi: 10.1038/s43247-023-01027-6.
  • Bahiyah, A., Wirasatriya, A., Mardiansyah, W., and Iskandar, I. (2023). Massive SST-Front Anomaly in the Tip of Sumatra Waters Triggered by Extreme Positive IOD 2019 Event, Int. J. Remote Sens., doi: 10.1080/01431161.2023.2268821.
  • Sherin, V., Girishkumar, M., Shivaprasad, S., Sureshkumar, N., Farrar, J., Athulya, K., Ashin, K., Pattabhi Rama Rao, E., Sengupta, D., Venkatesan, R., and Ravichandran, M. (2023). Importance of Seasonally Evolving Near-Surface Salinity Stratification on Mixed Layer Heat Budget During Summer Monsoon Intraseasonal Oscillation in the Northern Bay of Bengal in 2019, JGR Oceans, 128(11), e2023JC019800, doi: 10.1029/2023JC019800.
  • Hackert, E., Akella, S., Ren, L., Nakada, K., Carton, J., and Molod, A. (2023). Impact of the TAO/TRITON Array on Reanalyses and Predictions of the 2015 El Niño, JGR Oceans, 128(11), e2023JC020039, doi: 10.1029/2023JC020039.
  • Shee, A., Sil, S., and Gangopadhyay, A. (2023). Recent Changes in the Upper Oceanic Water Masses over the Indian Ocean using Argo data, Sci. Rep., 13, 20252, doi: 10.1038/s41598-023-47658-9.
  • Paul, N., Sukhatme, J., Gayen, B., and Sengupta, D. (2023). Eddy-Freshwater Interaction Using Regional Ocean Modeling System in the Bay of Bengal, J. Geophys. Res. Oceans, 128(4), e2022JC019439, doi: 10.1029/2022JC019439.
  • Trott, C. and Subrahmanyam, B. (2023). Eddy Characteristics and Vertical Structure in the Bay of Bengal during Different Monsoon Regimes, Remote Sens., 15(4), 1079, doi: 10.3390/rs15041079.
  • Anoopa Prasad, C., Jossia Joseph, K., Navaneeth, K., Mathew, M., Papa, F., Rohith, B., Venkatesan, R., and Latha, G. (2023). Characterizing near-surface salinity variability in the northern Bay of Bengal and its potential drivers during extreme freshening years of the 2011–2019 period, Dyn. Atmospheres Oceans, 102, 101357, doi: 10.1016/j.dynatmoce.2023.101357.
  • Priyanka, K., Sarangi, R., Shanthi, R., Poornima, D. and Saravanakumar, A. (2022). Inter-annual and Seasonal Cycle of Satellite Derived Sea Surface Salinity in the Western Bay of Bengal, Arab. J. Geosci., 15, 1670, doi: 10.1007/s12517-022-10945-2.
  • Sun, Q., Zhang, Y., Du, Y. and Jiang, X. (2022). Asymmetric Response of Sea Surface Salinity to Extreme Positive and Negative Indian Ocean Dipole in the Southern Tropical Indian Ocean, J. Geophys. Res. Oceans, 127 (11), e2022JC018986, doi: 10.1029/2022JC018986.
  • Johnson, G. and Lumpkin, R. (Eds.) (2022). State of the Climate in 2021: Global Oceans, Global Oceans [in “State of the Climate in 2021”]. Bull. Amer. Meteor. Soc., 103 (8), S143–S191, https://doi.org/10.1175/BAMS-D-22-0072.1.
  • Nayak, A., Vinayachandran, P. and George, J. (2022). Turbulent dissipation rates across the Summer Monsoon Current, Ocean Dyn., 72, 695-714, doi: 10.1007/s10236-022-01524-w.
  • Anutaliya, A., Send, U., McClean, J., Sprintall, J., Lankhorst, M., Lee, C., Rainville, L., Priyadarshani, W. and Jinadasa, S. (2022). Seasonal and Year-To-Year Variability of Boundary Currents and Eddy Salt Flux along the Eastern and Southern Coasts of Sri Lanka Observed by PIES and Satellite Measurements, J. Phys. Oceanogr., 52 (12), 3015-3031, doi: 10.1175/JPO-D-22-0030.1.
  • Liu, J., Wang, D., Zu, T., Huang, K. and Zhang, O. (2022). Either IOD Leading or ENSO Leading Triggers Extreme Thermohaline Events in the Tropical Central Indian Ocean, Clim. Dyn., doi: 10.1007/s00382-022-06413-y.
  • Rainville, L., Lee, C., Arulananthan, K., Jinadasa, S., Fernando, H., Priyadarshani, W. and Wijesekera, H. (2022). Water Mass Exchanges between the Bay of Bengal and Arabian Sea from Multiyear Sampling with Autonomous Gliders, J. Phys. Oceanogr., 52(10), 2377-2396, doi: 10.1175/JPO-D-21-0279.1.
  • Qi, J., Du, Y., Chi, J., Yi, D., Li, D., and Yin, B. (2022). Impacts of El Niño on the South China Sea Surface Salinity as seen from Satellites, Environ. Res. Lett., 17, 054040, doi: 10.1088/1748-9326/ac6a6a.
  • Zhu, J., Zhang, Y., Cheng, X., Wang, X., Sun, Q., and Du, Y. (2022). Effect of Mesoscale Eddies on the Transport of Low-salinity Water from the Bay of Bengal into the Arabian Sea during Winter, Geosci. Lett., doi: 10.21203/rs.3.rs-1606473/v1.
  • Essink, S., Hormann, V., Centurioni, L., and Mahadevan, A. (2022). On Characterizing Ocean Kinematics from Surface Drifters, J. Atmos. Ocean. Tech., 39 (8), 1183-1198, doi: https://doi.org/10.1175/JTECH-D-21-0068.1.
  • Akhter, S., Qiao, F., Wu, K., Yin, X., Azam Chowdhury, M., Kawser Ahmed, M., and Maksud Kamal, A. (2022). Spatiotemporal Variations of the Thermohaline Structure and Cyclonic Response in the Northern Bay of Bengal: The Evaluation of a Global Ocean Forecasting System, J. Sea Res., 182, 102188, doi: 10.1016/j.seares.2022.102188.
  • Weldeab, S., Rühlemann, C., Ding, Q., Khon, V., Schneider, B., and Gray, W. (2022). Impact of Indian Ocean Surface Temperature Gradient Reversals on the Indian Summer Monsoon, Earth Planet. Sci. Lett., 578, 117327, doi: 10.1016/j.epsl.2021.117327.
  • Chacko, N. and Jayaram, C. (2022). Response of the Bay of Bengal to super cyclone Amphan examined using synergistic satellite and in-situ observations, Oceanologia, 64(1), 131-144, doi: 10.1016/j.oceano.2021.09.006.
  • Gadi, R., Vinayachandran, P., and Subramani, D. (2021). Data-driven feature-oriented modeling of Southwest Monsoon Current, Ocean Model., 168, 101912, doi: 10.1016/j.ocemod.2021.101912.
  • Mandal, A., Chaudhary, A., Agarwal, N., and Sharma, R. (2021). Sub-Surface Ocean Structure from Satellite Surface Observations in the North Indian Ocean, Marine Geodesy, doi: 10.1080/01490419.2021.1974132.
  • Nyadjro, E. (2021). Impacts of the 2019 Strong IOD and Monsoon Events on Indian Ocean Sea Surface Salinity, Remote Sens. Earth Syst. Sci., doi: 10.1007/s41976-021-00054-1.
  • Chi, J., Qu, T., Du, Y., Qi, J., and Shi, P. (2021). Ocean Salinity Indices of Interannual Modes in the Tropical Pacific, Clim. Dyn., doi: 10.1007/s00382-021-05911-9.
  • Khan, S. Piao, S., Khan, I., Xu, B., Khan, S., Ismail, M., and Song, Y. (2021). Variability of SST and ILD in the Arabian Sea and Sea of Oman in Association with the Monsoon Cycle, Math. Probl. Eng., 9958257, doi: 10.1155/2021/9958257.
  • Ashafahani, A.A., Wirasatriya, A., Pranowo, W.S., Sugianto, D.N., and Maslukah, L. (2021). The Dynamic of Convergence Zone Displacement in Western Pacific Ocean on 2015 Super El Niño Event, IOP Conf. Ser.: Earth Environ. Sci., 750, 012015, doi: 10.1088/1755-1315/750/1/012015.
  • Lekha, J.S., Lucas, A., Sukhatme, J., Joseph, J., Ravichandran, M., Kumar, N. S., Farrar, J.T., and Sengupta, D. (2020). Quasi-Biweekly Mode of the Asian Summer Monsoon Revealed in Bay of Bengal Surface Observations, J. Geophys. Res. Oceans, 125(12), e2020JC016271, doi: 10.1029/2020JC016271.
  • Yi, D., Melnichenko, O., Hacker, P., and Potemra, J. (2020). Remote Sensing of Sea Surface Salinity Variability in the South China Sea, J. Geophys. Res. Oceans, 125(12), e2020JC016827, doi: 10.1029/2020JC016827.
To view all salinity publications, visit the publications page.

Interview: Drs. Heather Roman-Stork and Subrahmanyam Bulusu

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Featured Publications

Sea surface temperature anomaly

El Niño/Southern Oscillation (ENSO) has far reaching global climatic impacts and extending useful ENSO forecasts would have great societal benefit. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near‐surface ocean salinity. Satellite sea surface salinity (SSS), combined with temperature, help to improve estimates of ocean density changes and associated near‐surface mixing. In this study, the authors assess the impact of satellite SSS observations for improving near‐surface dynamics within ocean reanalyses and how these initializations impact dynamical ENSO forecasts using NASA's coupled forecast system.


Hackert, E., Kovach, R.M., Molod, A., Vernieres, G., Borovikov, A., Marshak, J., and Chang, Y. (2020). Read the full paper.

Deseasonalized SMAP‐CAP) anomaly composite of the 30‐90‐day intraseasonal oscillation in the Indian Ocean

Intraseasonal oscillations (ISOs) in the Indian Ocean play a significant role in determining the active (wet) and break (dry) cycles of the southwest monsoon rainfall. In this study, we use satellite‐derived precipitation, sea level anomalies, sea surface salinity, sea surface temperature, and surface winds to monitor the 30‐90‐day, 10‐20‐day, and 3‐7‐day ISOs, and how they influence local dynamics.


Roman‐Stork, H., Subrahmanyam, B., and Trott, C. (2020). Read the full paper.

Madden-Julian oscillation diagram

As a dominant source of tropical variability, the Madden‐Julian oscillation (MJO) influences the ocean in many ways. One approach to observe the atmosphere‐ocean relationship is by examining sea surface salinity (SSS) due to direct freshening by MJO precipitation. The convectively enhanced (suppressed) phase of the MJO is associated with negative (positive) SSS anomalies that propagate eastward along the equatorial Indian and Pacific oceans. In this study, primary MJO events are identified, and their SSS signatures are compared for the first time across multiple satellite salinity products from 2010 to 2017.


Shoup, C.G., Subrahmanyam, B., and Roman-Stork, H.L. (2019). Read the full paper.