Highlights

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

  • Ashafahani, A.A., Wirasatriya, A., Pranowo, W.S., Sugianto, D.N., and Maslukah, L.(in press). 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.
  • Essink, S., Hormann, V., Centurioni, L., and Mahadevan, A. (2022). On Characterizing Ocean Kinematics from Surface Drifters, J. Atmos. Ocean. Tech., doi: https://doi.org/10.1175/JTECH-D-21-0068.1.
  • 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.
  • 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.
  • 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, Submitted to Climate Dynamics.
  • 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.
  • 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.
  • Chacko, N. and Jayaram, C. (2021). Response of the Bay of Bengal to super cyclone Amphan examined using synergistic satellite and in-situ observations, Oceanologia, doi: 10.1016/j.oceano.2021.09.006.
  • 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.
  • 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.
  • Greaser, S., Subrahmanyam, B., Trott, C., and Roman-Stork, H. (2020). Interactions Between Mesoscale Eddies and Synoptic Oscillations in the Bay of Bengal During the Strong Monsoon of 2019, J. Geophys. Res. Oceans, 125(10), e2020JC016772, doi: 10.1029/2020JC016772.
  • Hackert, E., Kovach, R.M., Molod, A., Vernieres, G., Borovikov, A., Marshak, J., and Chang, Y. (2020). Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies From the NASA GEOS Seasonal Forecast System, J. Geophys. Res.-Oceans, 125(4), doi: 10.1029/2019JC015788.
  • Molod, A., Hackert, E., Akella, S., Andrews, L., Arnold, N., Barahona, D., Borovikov, A., Cullather, R., Chang, Y., and Kovach, R. (2020). An Introduction to the NASA GMAO Coupled Atmosphere-Ocean System - GEOS-S2S Version 3, NASA Technical Reports Server, GSFC-E-DAA-TN78568, 22 p.
  • Roman‐Stork, H., Subrahmanyam, B., and Trott, C. (2020). Monitoring Intraseasonal Oscillations in the Indian Ocean Using Satellite Observations, J. Geophys. Res. Oceans, 125(2), e2019JC015891, doi: 10.1029/2019JC015891.
  • Merryfield, W. et al. (2020). Current and Emerging Developments in Subseasonal to Decadal Prediction, Bull. Amer. Meteorol. Soc., doi: 10.1175/BAMS-D-19-0037.1.
  • Roman-Stork, H., Subrahmanyam, B., and Murty, V. (2020). The Role of Salinity in the Southeastern Arabian Sea in Determining Monsoon Onset and Strength, J. Geophys. Res.-Oceans, 125(1), e2019JC015592, doi: 10.1029/2019JC015592.
  • Zedler, S., Powell, B., Qiu, B., and Rudnick, D. (2019). Energy Transfer in the Western Tropical Pacific, Oceanography, 32(4), 136–145, doi: 10.5670/oceanog.2019.419.
  • Trott, C.B., Subrahmanyam, B., Roman-Stork, H.L., Murty, V.S.N., and Gnanaseelan, C. (2019). Variability of Intraseasonal Oscillations and Synoptic Signals in Sea Surface Salinity in the Bay of Bengal, J. Climate, 32 (20), 6703-6728, doi: 10.1175/JCLI-D-19-0178.1.
  • Hu, S., Zhang, Y., Feng, M., Du, Y., Sprintall, J., Wang, F., Hu, D., Xie, Q., and Chai, F. (2019). Interannual to Decadal Variability of Upper-Ocean Salinity in the Southern Indian Ocean and the Role of the Indonesian Throughflow, J. Climate, 32 (19), 6403-6421, doi: 10.1175/JCLI-D-19-0056.1.
  • Zhu, J. and Kumar, A. (2019). Role of Sea Surface Salinity Feedback in MJO Predictability: A Study with CFSv2, J. Climate, 32, 5745-5759, doi: 10.1175/JCLI-D-18-0755.1.
  • Shoup, C.G., Subrahmanyam, B., and Roman-Stork, H.L. (2019). Madden-Julian Oscillation-Induced Sea Surface Salinity Variability as Detected in Satellite-Derived Salinity, Geophys. Res. Lett., 46 (16), 9748-9756, doi: 10.1029/2019GL083694.
  • Roman-Stork, H.L., Subrahmanyam, B., and Murty, V.S.N. (2019). Quasi-biweekly Oscillations in the Bay of Bengal in Observations and Model Simulations, Deep-Sea Res. Pt. II, 168, 104609, doi: 10.1016/j.dsr2.2019.06.017.
  • Hackert, E.C., Kovach, R.M., Busalacchi, A.J., and Ballabrera-Poy, J. (2019). Impact of Aquarius and SMAP Satellite Sea Surface Salinity Observations on Coupled El Niño/Southern Oscillation Forecasts, J. Geophys. Res.-Oceans, 124 (7), 4546-4556, doi: 10.1029/2019JC015130.
  • Trott, C. (2019). Upper Ocean Dynamics and Mixing in the Arabian Sea During Monsoons, Thesis (Ph.D.).
To view all salinity publications, visit the publications page.

Interview: Drs. Heather Roman-Stork and Subrahmanyam Bulusu

[Click on the main image for videos]

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.

Reference

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.

Reference

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.

Reference

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