• Abram, N. J. , M. K. Gagan , M. T. McCulloch , J. Chappell , and W. S. Hantoro , 2003: Coral reef death during the 1997 Indian Ocean dipole linked to Indonesian wildfires. Science, 301, 952955, https://doi.org/10.1126/science.1083841.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Achuthavarier, D. , and V. Krishnamurthy , 2011: Role of Indian and Pacific SST in Indian summer monsoon intraseasonal variability. J. Climate, 24, 29152930, https://doi.org/10.1175/2010JCLI3639.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allison, E. H. , and Coauthors, 2009: Vulnerability of national economies to the impacts of climate change on fisheries. Fish Fish ., 10, 173196, https://doi.org/10.1111/j.1467-2979.2008.00310.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Annamalai, H. , P. Liu , and S.-P. Xie , 2005a: Southwest Indian Ocean SST variability: Its local effect and remote influence on Asian monsoons. J. Climate, 18, 41504167, https://doi.org/10.1175/JCLI3533.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Annamalai, H. , J. Potemra , R. Murtugudde , and J. P. McCreary , 2005b: Effect of preconditioning on the extreme climate events in the tropical Indian Ocean. J. Climate, 18, 34503469, https://doi.org/10.1175/JCLI3494.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Annamalai, H. , S.-P. Xie , J. P. McCreary , and R. Murtugudde , 2005c: Impact of Indian Ocean sea surface temperature on developing El Niño. J. Climate, 18, 302319, https://doi.org/10.1175/JCLI-3268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Annamalai, H. , H. Okajima , and M. Watanabe , 2007: Possible impact of the Indian Ocean SST on the Northern Hemisphere circulation during El Niño. J. Climate, 20, 31643189, https://doi.org/10.1175/JCLI4156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Annamalai, H. , J. Hafner , K. P. Sooraj , and P. Pillai , 2013: Global warming shifts the monsoon circulation, drying South Asia. J. Climate, 26, 27012718, https://doi.org/10.1175/JCLI-D-12-00208.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Annamalai, H. , B. Taguchi , J. P. McCreary , M. Nagura , and T. Miyama , 2017: Systematic errors in South Asian monsoon simulation: Importance of equatorial Indian Ocean processes. J. Climate, 30, 81598178, https://doi.org/10.1175/JCLI-D-16-0573.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K. , Z. Guan , and T. Yamagata , 2001: Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. Geophys. Res. Lett., 28, 44994502, https://doi.org/10.1029/2001GL013294.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K. , Z. Guan , and T. Yamagata , 2003: Influence of the Indian Ocean dipole on the Australian winter rainfall. Geophys. Res. Lett., 30, 1821, https://doi.org/10.1029/2003GL017926.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barange, M. , and Coauthors, 2014: Impacts of climate change on marine ecosystem production in societies dependent on fisheries. Nat. Climate Change, 4, 211216, https://doi.org/10.1038/nclimate2119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beal, L. M. , W. P. De Ruijter , A. Biastoch , and R. Zahn , 2011: On the role of the Agulhas system in ocean circulation and climate. Nature, 472, 429436, https://doi.org/10.1038/nature09983.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beal, L. M. , V. Hormann , R. Lumpkin , and G. R. Foltz , 2013: The response of the surface circulation of the Arabian Sea to monsoonal forcing. J. Phys. Oceanogr., 43, 20082022, https://doi.org/10.1175/JPO-D-13-033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beal, L. M. , J. Vialard , and M. K. Roxy , 2019: IndOOS-2: A roadmap to sustained observations of the Indian Ocean for 2020-2030. CLIVAR Rep. CLIVAR-4/2019, 206 pp., https://doi.org/10.36071/clivar.rp.4.2019.

    • Crossref
    • Export Citation
  • Behera, S. K. , and T. Yamagata , 2001: Subtropical SST dipole events in the southern Indian Ocean. Geophys. Res. Lett., 28, 327330, https://doi.org/10.1029/2000GL011451.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrenfeld, M. J. , and P. G. Falkowski , 1997: Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr., 42, 120, https://doi.org/10.4319/lo.1997.42.1.0001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bjerknes, J. , 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bopp, L. , and Coauthors, 2013: Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences, 10, 62256245, https://doi.org/10.5194/bg-10-6225-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Broecker, W. S. , 1991: The great ocean conveyor. Oceanography, 4 (2), 7989, https://doi.org/10.5670/oceanog.1991.07.

  • Bryden, H. L. , and L. M. Beal , 2001: Role of the Agulhas Current in Indian Ocean circulation and associated heat and freshwater fluxes. Deep-Sea Res. I, 48, 18211845, https://doi.org/10.1016/S0967-0637(00)00111-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burns, J. M. , B. Subrahmanyam , E. S. Nyadjro , and V. S. N. Murty , 2016: Tropical cyclone activity over the southwest tropical Indian Ocean. J. Geophys. Res. Oceans, 121, 63896402, https://doi.org/10.1002/2016JC011992.

    • Search Google Scholar
    • Export Citation
  • Cai, W. , and Coauthors, 2019: Pantropical climate interactions. Science, 363, eaav4236, https://doi.org/10.1126/science.aav4236.

  • Centurioni, L. , A. Horanyi , C. Cardinali , E. Charpentier , and R. Lumpkin , 2017: A global ocean observing system for measuring sea level atmospheric pressure: Effects and impacts on numerical weather prediction. Bull. Amer. Meteor. Soc., 98, 231238, https://doi.org/10.1175/BAMS-D-15-00080.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G. , W. Han , Y. Li , D. Wang , and M. McPhaden , 2015: Seasonal-to-interannual time scale dynamics of the equatorial undercurrent in the Indian Ocean. J. Phys. Oceanogr., 45, 15321553, https://doi.org/10.1175/JPO-D-14-0225.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, L. , K. E. Trenberth , J. Fasullo , T. Boyer , J. Abraham , and J. Zhu , 2017: Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv., 3, e1601545, https://doi.org/10.1126/sciadv.1601545.

    • Search Google Scholar
    • Export Citation
  • Clarke, A. J. , and S. Van Gorder , 2003: Improving El Niño prediction using a space-time integration of Indo-Pacific winds and equatorial Pacific upper ocean heat content. Geophys. Res. Lett., 30, 1399, https://doi.org/10.1029/2002GL016673.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, M. , and Coauthors, 2019: Extremes, abrupt changes and managing risks. IPCC Special Report on Oceans and Cryosphere in a Changing Climate, portner et al., Eds., Cambridge University Press, 589655.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Currie, J. C. , M. Lengaigne , J. Vialard , D. M. Kaplan , O. Aumont , S. W. A. Naqvi , and O. Maury , 2013: Indian Ocean dipole and El Niño/Southern Oscillation impacts on regional chlorophyll anomalies in the Indian Ocean. Biogeosciences, 10, 66776698, https://doi.org/10.5194/bg-10-6677-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A. , N. P. Klingaman , and S. J. Woolnough , 2015: Atmosphere-ocean coupled processes in the Madden-Julian oscillation. Rev. Geophys., 53, 10991154, https://doi.org/10.1002/2014RG000478.

    • Search Google Scholar
    • Export Citation
  • Desbruyères, D. , E. L. McDonagh , B. A. King , and V. Thierry , 2017: Global and full-depth ocean temperature trends during the early twenty-first century from Argo and repeat hydrography. J. Climate, 30, 1985–1997, https://doi.org/10.1175/JCLI-D-16-0396.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doi, T. , A. Storto , S. K. Behera , A. Navarra , and T. Yamagata , 2017: Improved prediction of the Indian Ocean dipole mode by use of subsurface ocean observations. J. Climate, 30, 79537970, https://doi.org/10.1175/JCLI-D-16-0915.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, L. , and M. J. McPhaden , 2017: Why has the relationship between Indian and Pacific Ocean decadal variability changed in recent decades? J. Climate, 30, 19711983, https://doi.org/10.1175/JCLI-D-16-0313.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, L. , T. Zhou , and B. Wu , 2014: Indian Ocean warming during 1958–2004 simulated by a climate system model and its mechanism. Climate Dyn ., 42, 203217, https://doi.org/10.1007/s00382-013-1722-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • do Rosário Gomes, H. , J. I. Goes , S. P. Matondkar , E. J. Buskey , S. Basu , S. Parab , and P. Thoppil , 2014: Massive outbreaks of Noctiluca scintillans blooms in the Arabian Sea due to spread of hypoxia. Nat. Commun., 5, 4862, https://doi.org/10.1038/ncomms5862.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drushka, K. , J. Sprintall , S. Gille , and S. Wijffels , 2012: In situ observations of Madden–Julian oscillation mixed layer dynamics in the Indian and western Pacific Oceans. J. Climate, 25, 23062328, https://doi.org/10.1175/JCLI-D-11-00203.1.

    • Search Google Scholar
    • Export Citation
  • Elsner, J. B. , J. P. Kossin , and T. H. Jagger , 2008: The increasing intensity of the strongest tropical cyclones. Nature, 455, 9295, https://doi.org/10.1038/nature07234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. , 2007: Quasi-equilibrium dynamics of the tropical atmosphere. The Global Circulation of the Atmosphere, T. Schneider and A. H. Sobel , Eds., Princeton University Press, 186218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, M. , Y. Li , and G. Meyers , 2004: Multidecadal variations of Fremantle sea level: Footprint of climate variability in the tropical Pacific. Geophys. Res. Lett., 31, L16302, https://doi.org/10.1029/2004GL019947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, M. , M. J. McPhaden , S. Xie , and J. Hafner , 2013: La Niña forces unprecedented Leeuwin Current warming in 2011. Sci. Rep., 3, 1277, https://doi.org/10.1038/srep01277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, M. , and Coauthors, 2020: Tracking air–sea exchange and upper ocean variability in the Indonesian–Australian Basin during the onset of the 2018/19 Australian summer monsoon. Bull. Amer. Meteor. Soc., 101, E1397E1412, https://doi.org/10.1175/BAMS-D-19-0278.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findlater, J. , 1969: A major low-level air current near the Indian Ocean during the northern summer. Quart. J. Roy. Meteor. Soc., 95, 362380, https://doi.org/10.1002/qj.49709540409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fonteneau, A. , V. Lucas , E. Tewkai , A. Delgado , and H. Demarcq , 2008: Mesoscale exploitation of a major tuna concentration in the Indian Ocean. Aquat. Living Resour., 21, 109121, https://doi.org/10.1051/alr:2008028.

    • Search Google Scholar
    • Export Citation
  • Funk, C. , M. D. Dettinger , J. C. Michaelsen , J. P. Verdin , M. E. Brown , M. Barlow , and A. Hoell , 2008: Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proc. Natl. Acad. Sci. USA, 105, 11 08111 086, https://doi.org/10.1073/pnas.0708196105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gadgil, S. , and S. Gadgil , 2006: The Indian monsoon, GDP, and agriculture. Econ. Political Wkly ., 41, 48874895.

  • Gentemann, C. L. , F. J. Wentz , M. Brewer , K. Hilburn , and D. Smith , 2010: Passive microwave remote sensing of the ocean: An overview. Oceanography from Space, V. Barale , J. Gower , and L. Alberotanza , Eds., Springer, 1944, https://doi.org/10.1007/978-90-481-8681-5_22.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giannini, A. , R. Saravanan , and P. Chang , 2003: Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science, 302, 10271030, https://doi.org/10.1126/science.1089357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Girishkumar, M. S. , J. Joseph , N. P. Thangaprakash , P. Vijay , and M. J. McPhaden , 2017: Mixed layer temperature budget for the northward propagating summer monsoon intraseasonal oscillation (MISO) in the central Bay of Bengal. J. Geophys. Res. Oceans, 122, 88418854, https://doi.org/10.1002/2017JC013073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gopika, S. , T. Izumo , J. Vialard , M. Lengaigne , I. Suresh , and M. R. Ramesh Kumar , 2020: Aliasing of the Indian Ocean anthropogenic warming spatial pattern by natural climate variability. Climate Dyn ., 54, 10931111, https://doi.org/10.1007/s00382-019-05049-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goswami, B. N. , 2005: South Asian monsoon. Intraseasonal Variability in the Atmosphere-Ocean Climate System, Springer, 1961.

  • Gould, J. , and Coauthors, 2004: Argo profiling floats bring new era of in situ ocean observations. Eos, Trans. Amer. Geophys. Union, 85, 185191, https://doi.org/10.1029/2004EO190002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, N. E. , and T. P. Barnett , 1987: Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science, 238, 657659, https://doi.org/10.1126/science.238.4827.657.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregg, W. W. , and C. S. Rousseaux , 2019: Global ocean primary production trends in the modern ocean color satellite record (1998–2015). Environ. Res. Lett., 14, 124011, https://doi.org/10.1088/1748-9326/ab4667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guemas, V. , S. Corti , J. Garcia-Serrano , F. J. Doblas-Reyes , M. Balmaseda , and L. Magnusson , 2013: The Indian Ocean: The region of highest skill worldwide in decadal climate prediction. J. Climate, 26, 726739, https://doi.org/10.1175/JCLI-D-12-00049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamlington, B. D. , M. Lengaigne , J. Vialard , D. M. Kaplan , O. Aumont , S. W. A. Naqvi , and O. Maury , 2014: Uncovering an anthropogenic sea-level rise signal in the Pacific Ocean. Nat. Climate Change, 4, 782785, https://doi.org/10.1038/nclimate2307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, W. , and Coauthors, 2010: Patterns of Indian Ocean sea level change in a warming climate. Nat. Geosci., 3, 546550, https://doi.org/10.1038/ngeo901.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, W. , and Coauthors, 2014a: Intensification of decadal and multi-decadal sea level variability in the western tropical Pacific during recent decades. Climate Dyn ., 43, 13571379, https://doi.org/10.1007/s00382-013-1951-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, W. , J. Vialard , M. J. McPhaden , T. Lee , Y. Masumoto , M. Feng , and W. P. M. de Ruijter , 2014b: Indian Ocean decadal variability: A review. Bull. Amer. Meteor. Soc., 95, 16791703, https://doi.org/10.1175/BAMS-D-13-00028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, W. , and Coauthors, 2019: Impacts of basin-scale climate modes on coastal sea level: A review. Surv. Geophys., 40, 14931541, https://doi.org/10.1007/s10712-019-09562-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerling, M. , J. W. Hurrell , T. Xu , G. Bates , and A. Phillips , 2004: Twentieth century north Atlantic climate change. Part II: Understanding the effect of Indian Ocean warming. Climate Dyn ., 23, 391405, https://doi.org/10.1007/s00382-004-0433-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, W. , J. Eischeid , J. Perlwitz , X. Quan , T. Zhang , and P. Pegion , 2012: On the increased frequency of Mediterranean drought. J. Climate, 25, 21462161, https://doi.org/10.1175/JCLI-D-11-00296.1.

    • Search Google Scholar
    • Export Citation
  • Hood, R. R. , L. E. B. Beckley , and J. D. Wiggert , 2017: Biogeochemical and ecological impacts of boundary currents in the Indian Ocean. Prog. Oceanogr., 156, 290325, https://doi.org/10.1016/j.pocean.2017.04.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horii, T. , H. Hase , I. Ueki , and Y. Masumoto , 2008: Oceanic precondition and evolution of the 2006 Indian Ocean dipole. Geophys. Res. Lett., 35, L03607, https://doi.org/10.1029/2007GL032464.

    • Search Google Scholar
    • Export Citation
  • Hu, S. , and J. Sprintall , 2017: Observed strengthening of interbasin exchange via the Indonesian seas due to rainfall intensification. Geophys. Res. Lett., 44, 14481456, https://doi.org/10.1002/2016GL072494.

    • Search Google Scholar
    • Export Citation
  • Hu, S. , and A. V. Fedorov , 2019: Indian Ocean warming can strengthen the Atlantic meridional overturning circulation. Nat. Climate Change, 9, 747751, https://doi.org/10.1038/s41558-019-0566-x.

    • Search Google Scholar
    • Export Citation
  • International CLIVAR Project Office, 2006: Understanding the role of the Indian Ocean in the climate system——Implementation plan for sustained observations. CLIVAR Publ. 100, 76 pp.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324

  • Izumo, T. , C. B. Montégut , J.-J. Luo , S. K. Behera , S. Masson , and T. Yamagata , 2008: The role of the western Arabian Sea upwelling in Indian monsoon rainfall variability. J. Climate, 21, 56035623, https://doi.org/10.1175/2008JCLI2158.1.

    • Search Google Scholar
    • Export Citation
  • Izumo, T. , and Coauthors, 2010: Influence of the state of the Indian Ocean dipole on the following year's El Niño. Nat. Geosci., 3, 168172, https://doi.org/10.1038/ngeo760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joseph, S. , L. A. Joseph , and J. Lingala , 2019: On the super cyclonic storm “Kyarr” currently active in the Arabian Sea. IIOE-2 Newsletter, No. 3, Indian National Centre for Ocean Information Services, Hyderabad, India, 1–2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kataoka, T. , T. Tozuka , S. K. Behera , and T. Yamagata , 2014: On the Ningaloo Niño/Niña. Climate Dyn ., 43, 14631482, https://doi.org/10.1007/s00382-013-1961-z.

    • Search Google Scholar
    • Export Citation
  • Kim, H. , F. Vitart , and D. E. Waliser , 2018: Prediction of the Madden–Julian oscillation: A review. J. Climate, 31, 94259443, https://doi.org/10.1175/JCLI-D-18-0210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, M. , 2014: Priorities for installation of continuous global navigation satellite system (GNSS) near to tide gauges. University of Tasmania Tech. Rep., 20 pp., https://doi.org/10.13140/RG.2.1.1781.7049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A. , B. J. Soden , and N. C. Lau , 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917932, https://doi.org/10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S. K. , W. Park , M. O. Baringer , A. L. Gordon , B. Huber , and Y. Liu , 2015: Pacific origin of the abrupt increase in Indian Ocean heat content during the warming hiatus. Nat. Geosci., 8, 445449, https://doi.org/10.1038/ngeo2438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L'Hégaret, P. , L. M. Beal , S. Elipot , and L. Laurindo , 2018: Shallow cross-equatorial gyres of the Indian Ocean driven by seasonally reversing monsoon winds. J. Geophys. Res. Oceans, 123, 89028920, https://doi.org/10.1029/2018JC014553.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Y. , W. Han , and L. Zhang , 2017: Enhanced decadal warming of the southeast Indian Ocean during the recent global surface warming slowdown. Geophys. Res. Lett., 44, 98769884, https://doi.org/10.1002/2017GL075050.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, I. I. , C.-H. Chen , I.-F. Pun , W. T. Liu , and C.-C. Wu , 2009: Warm ocean anomaly, air sea fluxes, and the rapid intensification of Tropical Cyclone Nargis (2008). Geophys. Res. Lett., 36, L03817, https://doi.org/10.1029/2008GL035815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H. , Y. Tang , D. Chen , and T. Lian , 2017: Predictability of the Indian Ocean dipole in the coupled models. Climate Dyn ., 48, 20052024, https://doi.org/10.1007/s00382-016-3187-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Q. , M. Feng , D. Wang , and S. Wijffels , 2015: Interannual variability of the Indonesian Throughflow transport: A revisit based on 30-year expendable bathythermograph data. J. Geophys. Res. Oceans, 120, 82708282, https://doi.org/10.1002/2015JC011351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, W. , S. P. Xie , and J. Lu , 2016: Tracking ocean heat uptake during the surface warming hiatus. Nat. Commun., 7, 10926, https://doi.org/10.1038/ncomms10926.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Llovel, W. , and T. Lee , 2015: Importance and origin of halosteric contribution to sea level change in the southeast Indian Ocean during 2005-2013. Geophys. Res. Lett., 42, 11481157, https://doi.org/10.1002/2014GL062611.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, J.-J. , S. Behera , Y. Masumoto , H. Sakuma , and T. Yamagata , 2008: Successful prediction of the consecutive IOD in 2006 and 2007. Geophys. Res. Lett., 35, L14S02, https://doi.org/10.1029/2007GL032793.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, J.-J. , R. Zhang , S. K. Behera , Y. Masumoto , F. F. Jin , R. Lukas , and T. Yamagata , 2010: Interaction between El Niño and extreme Indian Ocean dipole. J. Climate, 23, 726742, https://doi.org/10.1175/2009JCLI3104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, J.-J. , W. Sasaki , and Y. Masumoto , 2012: Indian Ocean warming modulates Pacific climate change. Proc. Natl. Acad. Sci. USA, 109, 18 70118 706, https://doi.org/10.1073/pnas.1210239109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCreary, J. P. , R. Murtugudde , J. Vialard , P. N. Vinayachandran , J. D. Wiggert , R. R. Hood , D. Shankar , and S. Shetye , 2009: Biophysical processes in the Indian Ocean. Indian Ocean Biogeochemical Processes and Ecological Variability, Geophys. Monogr., Vol. 185, Amer. Geophys. Union, 932, https://doi.org/10.1029/2008GM000768.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McDonagh, E. L. , H. L. Bryden , B. A. King , and R. J. Sanders , 2008: The circulation of the Indian Ocean at 32°S. Prog. Oceanogr., 79, 2036, https://doi.org/10.1016/j.pocean.2008.07.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J. , and M. Nagura , 2014: Indian Ocean dipole interpreted in terms of recharge oscillator theory. Climate Dyn ., 42, 15691586, https://doi.org/10.1007/s00382-013-1765-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J. , and Coauthors, 2009: RAMA: The Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction. Bull. Amer. Meteor. Soc., 90, 459480, https://doi.org/10.1175/2008BAMS2608.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J. , and Coauthors, 2010: The Global Tropical Moored Buoy Array. Proc. OceanObs’09: Sustained Ocean Observations and Information for Society Conf ., Venice, Italy, ESA, https://doi.org/10.5270/OceanObs09.cwp.61.

    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J. , Y. Wang , and M. Ravichandran , 2015: Volume transports of the Wyrtki jets and their relationship to the Indian Ocean dipole. J. Geophys. Res. Oceans, 120, 53025317, https://doi.org/10.1002/2015JC010901.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merrifield, M. , and Coauthors, 2009: The Global Sea Level Observing System (GLOSS). Proc. OceanObs’09: Sustained Ocean Observations and Information for Society Conf., Venice, Italy, ESA, https://doi.org/10.5270/OceanObs09.cwp.63.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyers, G. , 1996: Variation of Indonesian throughflow and the El Niño-southern oscillation. J. Geophys. Res. Oceans, 101, 12 255-12 263, https://doi.org/10.1029/95JC03729.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morioka, Y. , T. Tozuka , and T. Yamagata , 2013: How is the Indian Ocean subtropical dipole excited? Climate Dyn ., 41, 19551968, https://doi.org/10.1007/s00382-012-1584-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H. , G. A. Vecchi , and S. Underwood , 2017: Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nat. Climate Change, 7, 885889, https://doi.org/10.1038/s41558-017-0008-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murtugudde, R. , J. P. McCreary Jr. , and A. J. Busalacchi , 2000: Oceanic processes associated with anomalous events in the Indian Ocean with relevance to 1997–1998. J. Geophys. Res., 105, 32953306, https://doi.org/10.1029/1999JC900294.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagura, M. , and M. J. McPhaden , 2010: Wyrtki jet dynamics: Seasonal variability. J. Geophys. Res., 115, C07009, https://doi.org/10.1029/2009JC005922.

    • Search Google Scholar
    • Export Citation
  • Nagura, M. , and M. J. McPhaden , 2012: The dynamics of wind-driven intraseasonal variability in the equatorial Indian Ocean. J. Geophys. Res., 117, C02001, https://doi.org/10.1029/2011JC007405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naqvi, S. W. A. , and Coauthors, 2009: Seasonal anoxia over the western Indian continental shelf. Indian Ocean Biogeochemical Processes and Ecological Variability, Geophys. Monogr., Vol. 185, Amer. Geophys. Union, 333345, https://doi.org/10.1029/2008GM000745.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Needham, H. F. , B. D. Keim , and D. Sathiaraj , 2015: A review of tropical cyclone-generated storm surges: Global data sources, observations, and impacts. Rev. Geophys., 53, 545591, https://doi.org/10.1002/2014RG000477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neetu, S. , M. Lengaigne , J. Vialard , G. Samson , S. Masson , K. S. Krishnamohan , and I. Suresh , 2019: Premonsoon/postmonsoon Bay of Bengal tropical cyclones intensity: Role of air-sea coupling and large-scale background state. Geophys. Res. Lett., 46, 21492157, https://doi.org/10.1029/2018GL081132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neumann, B. , A. T. Vafeidis , J. Zimmermann , and R. J. Nicholls , 2015: Future coastal population growth and exposure to sea-level rise and coastal flooding—A global assessment. PLOS ONE, 10, e0118571, https://doi.org/10.1371/journal.pone.0118571.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nidheesh, A. G. , M. Lengaigne , J. Vialard , T. Izumo , A. S. Unnikrishnan , B. Meyssignac , B. Hamlington , and C. de Boyer Montegu , 2017: Robustness of observation-based decadal sea level variability in the Indo-Pacific Ocean. Geophys. Res. Lett., 44, 73917400, https://doi.org/10.1002/2017GL073955.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nieves, V. , J. K. Willis , and W. C. Patzert , 2015: Recent hiatus caused by decadal shift in Indo-Pacific heating. Science, 349, 532535, https://doi.org/10.1126/science.aaa4521.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oliver, E. C. , and Coauthors, 2018: Longer and more frequent marine heatwaves over the past century. Nat. Commun., 9, 1324, https://doi.org/10.1038/s41467-018-03732-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parvathi, V. , and Coauthors, 2017: Positive Indian Ocean dipole events prevent anoxia along the west coast of India. Biogeosciences, 14, 15411559, https://doi.org/10.5194/bg-14-1541-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prasanna Kumar, S. , P. M. Muraleedharan , T. G. Prasad , M. Gauns , N. Ramaiah , S. N. de Souza , S. Sardesai , and M. Madhupratap , 2002: Why is the Bay of Bengal less productive during summer monsoon compared to the Arabian Sea? Geophys. Res. Lett., 29, 2235, https://doi.org/10.1029/2002GL016013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajeevan, M. , C. K. Unnikrishnan , and B. Preethi , 2012: Evaluation of the ENSEMBLES multi-model seasonal forecasts of Indian summer. Climate Dyn ., 38, 22572274, https://doi.org/10.1007/s00382-011-1061-x.

    • Search Google Scholar
    • Export Citation
  • Rajeevan, M. , J. Srinivasan , K. N. Kumar , C. Gnanaseelan , and M. M. Ali , 2013: On the epochal variation of intensity of tropical cyclones in the Arabian Sea. Atmos. Sci. Lett., 14, 249255, https://doi.org/10.1002/asl2.447.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rao, S. A. , A. R. Dhakate , S. K. Saha , S. Mahapatra , H. S. Chaudhari , S. Pokhrel , and S. K. Sahu , 2012: Why is Indian Ocean warming consistently? Climatic Change, 110, 709719, https://doi.org/10.1007/s10584-011-0121-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reason, C. J. C. , 2001: Subtropical Indian Ocean SST dipole events and southern African rainfall. Geophys. Res. Lett., 28, 22252227, https://doi.org/10.1029/2000GL012735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Resplandy, L. , M. Lévy , L. Bopp , V. Echevin , S. Pous , V. V. S. S. Sarma , and D. Kumar , 2012: Controlling factors of the oxygen balance in the Arabian Sea’s OMZ. Biogeosciences, 9, 50955109, https://doi.org/10.5194/bg-9-5095-2012.

    • Search Google Scholar
    • Export Citation
  • Roberts, C. D. , M. D. Palmer , R. P. Allan , D. G. Desbruyeres , P. Hyder , C. Liu , and D. Smith , 2017: Surface flux and ocean heat transport convergence contributions to seasonal and interannual variations of ocean heat content. J. Geophys. Res. Oceans, 122, 726744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodrigues, R. R. , A. S. Taschetto , A. S. Gupta , and G. R. Foltz , 2019: Common cause for severe droughts in South America and marine heatwaves in the South Atlantic. Nat. Geosci ., 12, 620626, https://doi.org/10.1038/s41561-019-0393-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M. K. , and Y. Tanimoto , 2007: Role of SST over the Indian Ocean in influencing the intraseasonal variability of the Indian summer monsoon. J. Meteor. Soc. Japan, 85, 349358, https://doi.org/10.2151/jmsj.85.349.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M. K. , K. Ritika , P. Terray , and S. Masson , 2014: The curious case of Indian Ocean warming. J. Climate, 27, 85018509, https://doi.org/10.1175/JCLI-D-14-00471.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M. K. , K. Ritika , P. Terray , R. Murtugudde , K. Ashok , and B. N. Goswami , 2015: Drying of Indian subcontinent by rapid Indian Ocean warming and a weakening land-sea thermal gradient. Nat. Commun., 6, 7423, https://doi.org/10.1038/ncomms8423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M. K. , and Coauthors, 2016: A reduction in marine primary productivity driven by rapid warming over the tropical Indian Ocean. Geophys. Res. Lett., 43, 826833, https://doi.org/10.1002/2015GL066979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M. K. , P. Dasgupta , M. J. McPhaden , T. Suematsu , C. Zhang , and D. Kim , 2019: Twofold expansion of Indo-Pacific warm pool warps MJO lifecycle. Nature, 575, 647651, https://doi.org/10.1038/s41586-019-1764-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S. K. , A. Hazra , S. Pokhrel , H. S. Chaudhari , K. Sujith , A. Rai , H. Rahaman , and B. N. Goswami , 2019: Unraveling the mystery of Indian summer monsoon prediction: Improved estimate of predictability limit. J. Geophys. Res. Atmos., 124, 19621974, https://doi.org/10.1029/2018JD030082.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saji, N. H. , B. N. Goswami , P. N. Vinayachandran , and T. Yamagata , 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360363, https://doi.org/10.1038/43854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarma, V. V. S. S. , and Coauthors, 2016: Effects of freshwater stratification on nutrients, dissolved oxygen, and phytoplankton in the Bay of Bengal. Oceanography, 29, 222231, https://doi.org/10.5670/oceanog.2016.54.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmidtko, S. , L. Stramma , and M. Visbeck , 2017: Decline in global oceanic oxygen content during the past five decades. Nature, 542, 335339, https://doi.org/10.1038/nature21399.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schott, A. F. , and J. P. McCreary Jr. , 2001: The monsoon circulation of the Indian Ocean. Prog. Oceanogr., 51, 1123, https://doi.org/10.1016/S0079-6611(01)00083-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schott, A. F. , M. Dengler , and R. Schoenefeldt , 2002: The shallow overturning circulation of the Indian Ocean. Prog. Oceanogr., 53, 57103, https://doi.org/10.1016/S0079-6611(02)00039-3.

    • Search Google Scholar
    • Export Citation
  • Schott, A. F. , S.-P. Xie , and J. P. McCreary Jr. , 2009: Indian Ocean circulation and climate variability. Rev. Geophys., 47, RG1002, https://doi.org/10.1029/2007RG000245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sengupta, D. , B. R. Goddalehundi , and D. S. Anitha , 2008: Cyclone-induced mixing does not cool SST in the post-monsoon north Bay of Bengal. Atmos. Sci. Lett., 9, 16, https://doi.org/10.1002/asl.162.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, H. , A. C. Subramanian , A. J. Miller , and N. R. Cavanaugh , 2014: Coupled impacts of the diurnal cycle of sea surface temperature on the Madden–Julian oscillation. J. Climate, 27, 84228443, https://doi.org/10.1175/JCLI-D-14-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shenoi, S. S. C. , D. Shankar , and S. R. Shetye , 2002: Differences in heat budgets of the near-surface Arabian Sea and Bay of Bengal: Implications for the summer monsoon. J. Geophys. Res., 107, 3052, https://doi.org/10.1029/2000JC000679.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shinoda, T. , 2005: Impact of diurnal cycle of solar radiation on intraseasonal SST variability in the western equatorial Pacific. J. Climate, 18, 26282636, https://doi.org/10.1175/JCLI3432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sprintall, J. , S. E. Wijffels , R. Molcard , and I. Jaya , 2009: Direct estimates of the Indonesian Throughflow entering the Indian Ocean: 2004–2006. J. Geophys. Res., 114, C07001, https://doi.org/10.1029/2008JC005257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strutton, P. G. , V. J. Coles , R. R. Hood , R. J. Matear , M. J. McPhaden , and H. E. Phillips , 2015: Biogeochemical variability in the central equatorial Indian Ocean during the monsoon transition. Biogeosciences, 12, 23672382, https://doi.org/10.5194/bg-12-2367-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Subramanian, A. , and Coauthors, 2019: Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability. Front. Mar. Sci., 6, 427, https://doi.org/10.3389/fmars.2019.00427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takahashi, T. , and Coauthors, 2002: Global sea-air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep-Sea Res. II, 49, 16011622, https://doi.org/10.1016/S0967-0645(02)00003-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Talley, L. D. , and Coauthors, 2016: Changes in ocean heat, carbon content, and ventilation: A review of the first decade of GO-SHIP global repeat hydrography. Ann. Rev. Mar. Sci., 8, 185215, https://doi.org/10.1146/annurev-marine-052915-100829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanizaki, C. , T. Tozuka , T. Doi , and T. Yamagata , 2017: Relative importance of the processes contributing to the development of SST anomalies in the eastern pole of the Indian Ocean dipole and its implication for predictability. Climate Dyn ., 49, 12891304, https://doi.org/10.1007/s00382-016-3382-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taschetto, A. S. , A. Sen Gupta , H. H. Hendon , C. C. Ummenhofer , and M. H. England , 2011: The relative contribution of Indian Ocean sea surface temperature anomalies on Australian summer rainfall during El Niño events. J. Climate, 24, 37343747, https://doi.org/10.1175/2011JCLI3885.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, P. R. , C. G. Piecuch , M. A. Merrifield , J. P. McCreary , and E. Firing , 2016: Forcing of recent decadal variability in the equatorial and North Indian Ocean. J. Geophys. Res. Oceans, 121, 67626778, https://doi.org/10.1002/2016JC012132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timmermann, A. , and Coauthors, 2018: El Niño–Southern Oscillation complexity. Nature, 559, 535545, https://doi.org/10.1038/s41586-018-0252-6.

    • Search Google Scholar
    • Export Citation
  • Tokinaga, H. , S. P. Xie , C. Deser , Y. Kosaka , and Y. M. Okumura , 2012: Slowdown of the Walker circulation driven by tropical Indo-Pacific warming. Nature, 491, 439443, https://doi.org/10.1038/nature11576.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tozuka, T. , T. Kataoka , and T. Yamagata , 2014: Locally and remotely forced atmospheric circulation anomalies of Ningaloo Niño/Niña. Climate Dyn ., 43, 21972205, https://doi.org/10.1007/s00382-013-2044-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C. , M. H. England , P. C. McIntosh , G. A. Meyers , M. J. Pook , J. S. Risbey , A. S. Gupta , and A. S. Ta , 2009: What causes Southeast Australia’s worst droughts? Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801.

    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C. , A. Biastoch , and C. W. Böning , 2017: Multidecadal Indian Ocean variability linked to the Pacific and implications for preconditioning Indian Ocean dipole events. J. Climate, 30, 17391751, https://doi.org/10.1175/JCLI-D-16-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Unnikrishnan, A. S. , A. G. Nidheesh , and M. Lengaigne , 2015: Sea-level-rise trends off the Indian coasts during the last two decades. Curr. Sci., 108, 966971.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vialard, J. , G. R. Foltz , M. J. McPhaden , J. P. Duvel , and C. de Boyer Montégut , 2008: Strong Indian Ocean sea surface temperature signals associated with the Madden-Julian oscillation in late 2007 and early 2008. Geophys. Res. Lett., 35, L19608, https://doi.org/10.1029/2008GL035238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vialard, J. , and Coauthors, 2009: Cirene: Air–sea interactions in the Seychelles–Chagos thermocline ridge region. Bull. Amer. Meteor. Soc., 90, 4562, https://doi.org/10.1175/2008BAMS2499.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vialard, J. , A. Jayakumar , C. Gnanaseelan , M. Lengaigne , D. Sengupta , and B. N. Goswami , 2012: Processes of 30-90 day sea surface temperature variability in the northern Indian Ocean during boreal summer. Climate Dyn ., 38, 19011916, https://doi.org/10.1007/s00382-011-1015-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vialard, J. , K. Drushka , H. Bellenger , M. Lengaigne , S. Pous , and J. P. Duvel , 2013: Understanding Madden-Julian-induced sea surface temperature variations in the north western Australian Basin. Climate Dyn ., 41, 32033218, https://doi.org/10.1007/s00382-012-1541-7.

    • Search Google Scholar
    • Export Citation
  • Vincent, E. M. , K. A. Emanuel , M. Lengaigne , J. Vialard , and G. Madec , 2014: Influence of upper ocean stratification interannual variability on tropical cyclones. J. Adv. Model. Earth Syst., 6, 680699, https://doi.org/10.1002/2014MS000327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wainwright, L. , G. Meyers , S. Wijffels , and L. Pigot , 2008: Change in the Indonesian Throughflow with the climatic shift of 1976/77. Geophys. Res. Lett., 35, L03604, https://doi.org/10.1029/2007GL031911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J. , A. M. Moore , J. P. Loschnigg , and R. R. Leben , 1999: Coupled oceanic–atmospheric dynamics in the Indian Ocean during 1997–98. Nature, 401, 356360, https://doi.org/10.1038/43848.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wernberg, T. , and Coauthors, 2016: Climate-driven regime shift of a temperate marine ecosystem. Science, 353, 169172, https://doi.org/10.1126/science.aad8745.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiggert, J. D. , J. Vialard , and M. J. Behrenfeld , 2009: Basin-wide modification of dynamical and biogeochemical processes by the Indian Ocean dipole during the SeaWiFS era. Indian Ocean Biogeochemical Processes and Ecological Variability, Geophys. Monogr., Vol. 185, Amer. Geophys. Union, 385408.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wijesekera, H. W. , and Coauthors, 2016: ASIRI: An ocean–atmosphere initiative for Bay of Bengal. Bull. Amer. Meteor. Soc., 97, 18591884, https://doi.org/10.1175/BAMS-D-14-00197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wijffels, S. E. , G. Meyers , and J. S. Godfrey , 2008: A 20-yr average of the Indonesian Throughflow: Regional currents and the interbasin exchange. J. Phys. Oceanogr., 38, 19651978, https://doi.org/10.1175/2008JPO3987.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wojtasiewicz, B. , and Coauthors, 2020: Autonomous profiling float observations reveal the dynamics of deep biomass distributions in the denitrifying oxygen minimum zone of the Arabian Sea. J. Mar. Syst., 207, 103103, https://doi.org/10.1016/j.jmarsys.2018.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolnough, S. J. , F. Vitart , and M. A. Balmaseda , 2007: The role of the ocean in the Madden–Julian oscillation: Implications for MJO prediction. Quart. J. Roy. Meteor. Soc., 133, 117128, https://doi.org/10.1002/qj.4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P. , H. Annamalai , F. A. Schott , and J. P. McCreary , 2002: Structure and mechanisms of south Indian Ocean climate variability. J. Climate, 15, 864878, https://doi.org/10.1175/1520-0442(2002)015<0864:SAMOSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P. , K. Hu , J. Hafner , H. Tokinaga , Y. Du , G. Huang , and T. Sampe , 2009: Indian Ocean capacitor effect on Indo–western Pacific climate during the summer following El Niño. J. Climate, 22, 730747, https://doi.org/10.1175/2008JCLI2544.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamagata, T. , and Coauthors, 2004: Coupled ocean-atmosphere variability in the tropical Indian Ocean. Earth’s Climate: The Ocean-Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 189211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yokoi, T. , T. Tozuka , and T. Yamagata , 2012: Seasonal and interannual variations of the SST above the Seychelles Dome. J. Climate, 25, 800814, https://doi.org/10.1175/JCLI-D-10-05001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoneyama, K. , C. Zhang , and C. N. Long , 2013: Tracking pulses of the Madden–Julian oscillation. Bull. Amer. Meteor. Soc., 94, 18711891, https://doi.org/10.1175/BAMS-D-12-00157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, L. , and M. M. Rienecker , 1999: Mechanisms for the Indian Ocean warming during the 1997-1998 El Niño. Geophys. Res. Lett., 26, 735738, https://doi.org/10.1029/1999GL900072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, L. , and M. J. McPhaden , 2011: Ocean preconditioning of Cyclone Nargis in the Bay of Bengal: Interaction between Rossby waves, surface fresh waters, and sea surface temperatures. J. Phys. Oceanogr., 41, 17411755, https://doi.org/10.1175/2011JPO4437.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, L. , X. Jin , and R. A. Weller , 2007: Annual, seasonal, and interannual variability of air–sea heat fluxes in the Indian Ocean. J. Climate, 20, 31903209, https://doi.org/10.1175/JCLI4163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yun, Q. , and Coauthors, 2019: Upper ocean response to the Super Tropical Cyclone Phailin (2013) over the freshwater region of the Bay of Bengal. J. Phys. Oceanogr., 49, 12011228, https://doi.org/10.1175/JPO-D-18-0228.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C. , 2005: Madden-Julian oscillation. Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158.

  • Zhang, Y. , M. Feng , Y. Du , H. E. Phillips , N. L. Bindoff , and M. J. McPhaden , 2018: Strengthened Indonesian Throughflow drives decadal warming in the southern Indian Ocean. Geophys. Res. Lett., 45, 61676175, https://doi.org/10.1029/2018GL078265.

    • Search Google Scholar
    • Export Citation
  • Zinke, J. , A. Rountrey , M. Feng , S.-P. Xie , D. Dissard , K. Rankenburg , J. M. Lough , and M. T. McCulloch , 2014: Corals record long-term Leeuwin Current variability including Ningaloo Niño/Niña since 1795. Nat. Commun., 5, 3607, https://doi.org/10.1038/ncomms4607.

    • Search Google Scholar
    • Export Citation
  • View in gallery
    Fig. 1.

    Artist’s illustration of the Indian Ocean Observing System and its societal applications. IndOOS data support research to advance scientific knowledge about the Indian Ocean circulation, climate variability and change, and biogeochemistry, as well as societal applications due to its contribution to operational analyses and forecasts. Credit: JAMSTEC.

  • View in gallery
    Fig. SB1.

    Numbers of the IndOOS-2 review exercise.

  • View in gallery
    Fig. 2.

    Indian Ocean main oceanographic features and phenomena. The surface circulation seasonally reverses north of 10°S under the influence of monsoons. The summer monsoon also promotes the intense Somali current as well as upwellings and high productivity in the western Arabian Sea. High surface layer productivity, sinking of biomass, and its remineralization at depth also lead to the formation of subsurface oxygen minimum zones (OMZs) in the Arabian Sea and Bay of Bengal. The Indo-Pacific warm pool is a region of intense air–sea interactions, where the Madden–Julian oscillation, monsoon intraseasonal oscillation, and Indian Ocean dipole develop. The Indian Ocean is a gateway of the global oceanic circulation, with inputs of heat and freshwater through the Indonesian Throughflow, which exit the basin though boundary currents, mainly the Agulhas Current along Africa, but also the Leeuwin Current along Australia. There are two vertical overturning cells connecting subducted waters south of 30°S to the tropical Indian Ocean: the shallow subtropical overturning cell where water upwells in the “thermocline ridge” open-ocean upwelling region, and the cross-equatorial cell where water upwells farther north in the Arabian Sea of the coast of Somalia and Oman. These cells are the main source of subsurface ventilation due to the presence of continents to the north.

  • View in gallery
    Fig. 3.

    Boreal summer (JJAS) observed climatologies of (a) sea surface temperature (colors) and wind stress (vectors), (b) primary productivity estimate (colors) and 200–1,500-m average oxygen (contours), and (c) sea surface salinity (color) and rainfall (contours). See the online supplemental material (https://doi.org/10.1175/BAMS-D-19-0209.2) for the equivalent winter figure and for the details of datasets and methods for each figure. The heating of the Asian landmass by the sun’s movements yields strong winds and rainfall in the boreal summer. The alongshore winds induce upwelling of cold and nutrient-rich water in the western Arabian Sea, conductive to high oceanic productivity. The combined high oxygen demand from this oceanic productivity and weak ventilation due to the presence of land to the north results in a very extensive OMZ in the Arabian Sea and Bay of Bengal. More detailed methods for Fig. 3 and following are provided in the online supplemental information of this article.

  • View in gallery
    Fig. 4.

    Atmospheric convection perturbation (outgoing longwave radiation, contours every 10 W m‒2) and sea surface temperature (SST; colors) composites of two successive phases of (a),(b) the Madden–Julian oscillation (MJO) during December–March and (c),(d) the monsoon intraseasonal oscillation (MISO) during June–September. (e) MJO forecast skill as a function of lead time (days) for forecasts with fixed SST, observed SST, and active ocean–atmosphere coupling. The MJO and MISO modulate tropical rainfall during boreal winter and summer, respectively. They are associated with SST and oceanic mixed layer processes, which need to be better observed to improve their forecasts.

  • View in gallery
    Fig. 5.

    SST signals associated with the four main Indian Ocean climate modes: (a) Indian Ocean Basin Mode (IOBM), (b) Indian Ocean dipole (IOD), (c) Ningaloo Niño (NN), and (d) Indian Ocean subtropical dipole (IOSD). The four climate modes induce year-to-year SST and rainfall fluctuations over the Indian Ocean region, partly in response to El Niño but also independently. They peak in FMA, SON, DJF, and JFM, respectively. Each of these climate modes has important consequences around the Indian Ocean and beyond, with the most important climate impacts summarized on the figure.

  • View in gallery
    Fig. 6.

    (a) The 12-month running-mean time series of the 0–700-m-averaged temperature for the global ocean (black, with gray shading for 95% confidence interval) and Indian Ocean (red, with a thin line showing monthly time series). The 1998–2015 linear trends for both series are displayed as green dashed lines. (b) The 0–2,000-m heat content trend (W m‒2) during 2006–15, computed from the optimal interpolation of Argo profiles. Deep, 700–2,000-m heat content changes represent about 20% of the trend over the entire Indian Ocean. (c) CMIP5 historical and RCP8.5 multimodel-mean (23 models) projected changes (2080–2100 minus 1980–2000) in boreal summer (JJAS) primary productivity. Red ´ symbols indicate regions where less than 80% of the models agree on the sign of the projected change. The Indian Ocean has been warming faster than the global ocean over the last 20 years, accounting for about 25% of the global ocean heat content increase, with the strongest 0–2,000-m warming in the southeastern subtropics. Climate model projections agree on a large (∼20%) decrease of oceanic productivity in the Arabian Sea in the case of unabated carbon emissions and strong deoxygenation in the southern subtropics.

  • View in gallery
    Fig. 7.

    (a) Time mean of the net surface flux (Qnet, positive for oceanic heat gain) at the ocean surface from the ensemble mean of six different flux products for the 2001–15 period. (b) Standard deviations (STDs) around the mean of the six flux products over that period, giving an idea of the area where flux estimates are most uncertain. The STDs in climatological Qnet are up to 25 W m‒2 in a large part of the Indian Ocean north of 10°S, on the same order of magnitude as the mean Qnet itself. The large uncertainty in Qnet products hampers the quantification of basin-scale heat budgets at the interannual to decadal time scales. Buoy locations of RAMA-2.0 are superimposed (adapted from McPhaden et al. 2009), with diamonds denoting RAMA surface mooring sites and squares corresponding to “flux reference sites” that provide the essential benchmark time series for validating and improving air–sea parameterizations in models and for improving uncertainty quantification in air–sea flux products.

  • View in gallery
    Fig. 8.

    Main IndOOS-2 recommendations. Argo: Maintain the core 3° × 3° array; add 200 BGC-Argo floats; develop a Deep-Argo program. RAMA: New RAMA-2.0 design that better addresses operational constraints; occupy three remaining sites in Arabian Sea; increase resolution of upper-ocean measurements and add biogeochemical measurements at flux reference sites; add a new flux site off northwestern Australia. XBT: Maintain IX01 and IX21 lines, install autolaunchers, and increase near-coastal resolution on IX01. Tide gauges: Add collocated measurements of land motion; add sites in the southwestern Indian Ocean and on islands. Surface drifters: Maintain core 5° × 5° array; evaluate addition of barometric pressure measurements. Boundary current arrays: Add measurements of mass, heat, and freshwater fluxes of the Agulhas and Leeuwin Currents, including hydrographic end-point moorings to capture basin-scale overturning. GO-SHIP: Find national commitment for IO1-E and IO1-W sections; add measurements of phytoplankton community structure. Satellites: Maintain overlapping, intercalibrated missions; enhance spatial resolution of SSH or currents directly. These recommendations can be summarized in four core findings of the review, listed in green in the frames beside the map.

  • View in gallery
    Fig. 9.

    Range of skillful state-of-the-science forecasts for Indian Ocean weather and climate phenomena, as a function of their time scale. The MJO and MISO have quite a short skillful prediction range (1/4 to 1/3 of their time scale), but a better monitoring of upper-ocean variability may allow better forecasts (Fig. 4). IndOOS subsurface data enhance IOD forecast scores. While ENSO is a source of predictability for Indian Ocean climate, ocean–atmosphere interactions in the Indian Ocean itself are also important and can potentially feedback on ENSO. Indian Ocean natural decadal climate variability is currently a “gray area,” limiting our capacity to clearly delineate climate changes signals from those associated to forcing external to the climate system, such as anthropogenic climate change.

  • View in gallery
    Fig. SB2.

    The late Gary Meyers, former cochair of the Indian Ocean Region Panel, and one of the promoters of the IndOOS observing system.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 4629 1319 57
PDF Downloads 2967 682 39

A Road Map to IndOOS-2: Better Observations of the Rapidly Warming Indian Ocean

L. M. BealRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

Search for other papers by L. M. Beal in
Current site
Google Scholar
PubMed
Close
,
J. VialardInstitut de Recherche pour le Développement, Sorbonne Universités (UPMC, Université Paris 06)-CNRS-IRD-MNHN, LOCEAN Laboratory, IPSL, Paris, France

Search for other papers by J. Vialard in
Current site
Google Scholar
PubMed
Close
,
M. K. RoxyIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India

Search for other papers by M. K. Roxy in
Current site
Google Scholar
PubMed
Close
,
J. LiInternational CLIVAR Project Office, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China

Search for other papers by J. Li in
Current site
Google Scholar
PubMed
Close
,
M. AndresWoods Hole Oceanographic Institution, Woods Hole, Massachusetts

Search for other papers by M. Andres in
Current site
Google Scholar
PubMed
Close
,
H. AnnamalaiInternational Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu, Hawaii

Search for other papers by H. Annamalai in
Current site
Google Scholar
PubMed
Close
,
M. FengCentre for Southern Hemisphere Oceans Research, Hobart, Tasmania, and Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Crawley, Western Australia, Australia

Search for other papers by M. Feng in
Current site
Google Scholar
PubMed
Close
,
W. HanDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

Search for other papers by W. Han in
Current site
Google Scholar
PubMed
Close
,
R. HoodUniversity of Maryland Center for Environmental Science, Cambridge, Maryland

Search for other papers by R. Hood in
Current site
Google Scholar
PubMed
Close
,
T. LeeNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Search for other papers by T. Lee in
Current site
Google Scholar
PubMed
Close
,
M. LengaigneInstitut de Recherche pour le Développement, Sorbonne Universités (UPMC, Université Paris 06)-CNRS-IRD-MNHN, LOCEAN Laboratory, IPSL, Paris, France

Search for other papers by M. Lengaigne in
Current site
Google Scholar
PubMed
Close
,
R. LumpkinNOAA/Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

Search for other papers by R. Lumpkin in
Current site
Google Scholar
PubMed
Close
,
Y. MasumotoThe University of Tokyo, Tokyo, and Application Laboratory, JAMSTEC, Yokohama, Japan

Search for other papers by Y. Masumoto in
Current site
Google Scholar
PubMed
Close
,
M. J. McPhadenNOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

Search for other papers by M. J. McPhaden in
Current site
Google Scholar
PubMed
Close
,
M. RavichandranNational Centre for Polar and Ocean Research, Ministry of Earth Sciences, Goa, India

Search for other papers by M. Ravichandran in
Current site
Google Scholar
PubMed
Close
,
T. ShinodaTexas A&M University, Corpus Christi, Texas

Search for other papers by T. Shinoda in
Current site
Google Scholar
PubMed
Close
,
B. M. SloyanCentre for Southern Hemisphere Oceans Research, Hobart, Tasmania, and Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Crawley, Western Australia, Australia

Search for other papers by B. M. Sloyan in
Current site
Google Scholar
PubMed
Close
,
P. G. StruttonInstitute for Marine and Antarctic Studies, University of Tasmania, and Australian Research Council Centre of Excellence for Climate Extremes, Hobart, Tasmania, Australia

Search for other papers by P. G. Strutton in
Current site
Google Scholar
PubMed
Close
,
A. C. SubramanianDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

Search for other papers by A. C. Subramanian in
Current site
Google Scholar
PubMed
Close
,
T. TozukaThe University of Tokyo, Tokyo, Japan

Search for other papers by T. Tozuka in
Current site
Google Scholar
PubMed
Close
,
C. C. UmmenhoferWoods Hole Oceanographic Institution, Woods Hole, Massachusetts

Search for other papers by C. C. Ummenhofer in
Current site
Google Scholar
PubMed
Close
,
A. S. UnnikrishnanNational Institute of Oceanography, Council of Scientific and Industrial Research, Goa, India

Search for other papers by A. S. Unnikrishnan in
Current site
Google Scholar
PubMed
Close
,
J. WiggertUniversity of Southern Mississippi, Hattiesburg, Mississippi

Search for other papers by J. Wiggert in
Current site
Google Scholar
PubMed
Close
,
L. YuWoods Hole Oceanographic Institution, Woods Hole, Massachusetts

Search for other papers by L. Yu in
Current site
Google Scholar
PubMed
Close
,
L. ChengInternational Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China

Search for other papers by L. Cheng in
Current site
Google Scholar
PubMed
Close
,
D. G. DesbruyèresIfremer, University of Brest, CNRS, IRD, Laboratoire d’Océanographie Physique et Spatiale, IUEM, Brest, France

Search for other papers by D. G. Desbruyères in
Current site
Google Scholar
PubMed
Close
, and
V. ParvathiCenter for Prototype Climate Modeling, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

Search for other papers by V. Parvathi in
Current site
Google Scholar
PubMed
Close
Free access

Abstract

The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.

Corresponding author: J. Vialard, jerome.vialard@ird.fr

Abstract

The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.

Corresponding author: J. Vialard, jerome.vialard@ird.fr

While the Indian Ocean is the smallest of the four major oceanic basins, close to one-third of humankind lives in the 22 countries that border its rim. Many of these countries have developing or emerging economies, or are island states, and are vulnerable to extreme weather events, to changes in monsoon cycles, and to climate variations and climate change.

Many Indian Ocean rim countries depend on rain-fed agriculture. In India, for example, 60% of jobs are in agriculture, which accounts for 20% of GDP, and there is a tight link between grain production and monsoon rainfall (Gadgil and Gadgil 2006). Indian Ocean sea surface temperatures (SST) influence monsoon rainfall over India (Ashok et al. 2001; Annamalai et al. 2005a), floods and droughts over Indonesia, Africa, and Australia (Saji et al. 1999; Webster et al. 1999; Reason 2001; Ashok et al. 2003; Yamagata et al. 2004; Ummenhofer et al. 2009; Taschetto et al. 2011; Tozuka et al. 2014), and wildfires in Indonesia and Australia (Abram et al. 2003). The tropical Indian Ocean is the warmest among global oceans and is part of the Indo-Pacific warm pool (SST > 28°C), which plays a key role in sustaining deep-atmospheric convection (Graham and Barnett 1987; Emanuel 2007) and maintaining the tropical atmospheric circulation (Bjerknes 1969). Observations indicate that the Indian Ocean has been warming faster than any other basin in response to anthropogenic climate change (Annamalai et al. 2013; Dong et al. 2014; Roxy et al. 2014). This warming contributes to increasing droughts over South Asia (Roxy et al. 2015), and eastern Africa where it is predicted to increase the number of undernourished people by 50% by 2030 (Funk et al. 2008).

The Indian Ocean hosts many countries dependent on fisheries and whose fisheries have poor adaptive capacity, including India, Indonesia, Sri Lanka, Maldives, Pakistan, Thailand, Madagascar, Mozambique, and Tanzania (Allison et al. 2009). Climate change is predicted to reduce fish catches for most of these nations (Barange et al. 2014). For instance, the intense marine productivity of the northern Indian Ocean is under threat (Bopp et al. 2013; Roxy et al. 2016; Gregg and Rousseaux 2019). In the Arabian Sea, oxygen-depleted waters reach the surface more frequently, causing more fish mortality events (Naqvi et al. 2009). Marine heatwaves also affect fisheries and ecosystems, with the first recorded bleaching of the pristine Ningaloo reef off Western Australia in 2011 (Feng et al. 2013).

The Bay of Bengal region already witnesses more than 80% of global fatalities due to tropical cyclones, because of coastal flooding (Needham et al. 2015). The frequency of extremely severe cyclones in the Arabian Sea is also projected to increase (Murakami et al. 2017), with 2019 already a highly unusual year (Joseph et al. 2019). Sea level rise in the northern Indian Ocean averaged 3.28 mm yr‒1 from 1992 to 2013 (Unnikrishnan et al. 2015) and is projected to rise at a faster pace in the future (Collins et al. 2019). Coastal population density around the Indian Ocean is projected to become the largest in the world by 2030, with 340 million people exposed to coastal hazards (Neumann et al. 2015). This rapid population growth is conflating with climate change–induced sea level rise and tropical cyclone intensification to increase vulnerability (Elsner et al. 2008; Rajeevan et al. 2013).

Beyond these direct impacts on rim countries, the Indian Ocean influences climate globally. The tropical Indian Ocean warm pool is the breeding ground for the Madden–Julian oscillation (MJO) and for monsoon intraseasonal oscillations (MISO), ocean–atmosphere coupled phenomena that modulate rainfall and tropical cyclone activity on subseasonal time scales (Zhang 2005). Year-to-year variability of Indian Ocean SST can influence the evolution of El Niño–Southern Oscillation (ENSO) in the neighboring Pacific Ocean (Clarke and Van Gorder 2003; Annamalai et al. 2005a; Luo et al. 2010; Izumo et al. 2010), and may force tropical–extratropical atmospheric variability with impacts extending over the northeast Pacific (Annamalai et al. 2007). The Indian Ocean is also an important component of the so-called global ocean conveyer belt that drives climate variability at multidecadal and longer time scales (Broecker 1991). A redistribution of heat from the Pacific to the Indian Ocean over the last decade is thought to have played a key role in regulating global mean surface temperatures (Tokinaga et al. 2012; Liu et al. 2016), with the Indian Ocean representing about one-quarter of the global ocean heat gain since 1990 (Lee et al. 2015; Nieves et al. 2015; Cheng et al. 2017). This Indian Ocean warming has had far-reaching impacts, causing droughts in the West Sahel, Mediterranean and South America (Giannini et al. 2003; Hoerling et al. 2012; Rodrigues et al. 2019), modulating the Pacific atmospheric circulation (Luo et al. 2012; Han et al. 2014a; Hamlington et al. 2014; Dong and McPhaden 2017), the Atlantic oceanic circulation and North Atlantic climate (Hu and Fedorov 2019; Hoerling et al. 2004). Finally, the basin accounts for about one-fifth of the global oceanic uptake of anthropogenic CO2 (Takahashi et al. 2002), helping to buffer the effects of global warming.

The role of the Indian Ocean in regional and global climate and the vulnerability of its rim populations articulate the need to better understand and predict its variability and change. The Indian Ocean Observing System (IndOOS; Fig. 1), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond (International CLIVAR Project Office 2006). With the accelerating pace of climatic and oceanic change there is an urgent need to develop a more resilient and capable observing system that can better meet scientific and societal requirements for climate information and prediction over the next decade and beyond: IndOOS-2.

Fig. 1.
Fig. 1.

Artist’s illustration of the Indian Ocean Observing System and its societal applications. IndOOS data support research to advance scientific knowledge about the Indian Ocean circulation, climate variability and change, and biogeochemistry, as well as societal applications due to its contribution to operational analyses and forecasts. Credit: JAMSTEC.

Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1

Here we provide an overview of the road map for IndOOS-2 (Beal et al. 2019), the result of a 3-yr internationally coordinated review of the IndOOS by more than 60 scientists (see “The IndOOS review” sidebar for details on the review process and sponsors, and a link to the full report). First, we briefly present the circulation and biogeochemistry of the Indian Ocean and their interaction with climate variability and change. We then describe the IndOOS and its components, summarizing past successes and limitations of the observing system in terms of the “state of the science,” thereby articulating the needed changes in its design. Finally, we present the core findings of the review, highlight some of the most important recommendations of the IndOOS-2 road map, and discuss some of the implementation challenges.

The IndOOS review

The IndOOS review and resulting IndOOS-2 road map were initiated as a system-based evaluation to update and fill gaps in the IndOOS and increase its readiness level, under the leadership of the Climate and Ocean: Variability, Predictability and Change (CLIVAR)/Intergovernmental Oceanographic Commission (IOC) Indian Ocean Region Panel (IORP) and in collaboration with the Integrated Marine Biosphere Research (IMBeR) project/Global Ocean Observing System (GOOS) Sustained Indian Ocean Biogeochemistry and Ecosystem Research (SIBER) panel. The review was conducted over the course of 3 years under the scrutiny of an independent review board appointed by sponsoring organizations (see acknowledgments for details). As background material for the review, a group of 60 international scientists drafted 25 white papers on observing system components and scientific drivers. The terms of reference for the review, as well as the chapters and their contents, and the framework for prioritizing the many resulting actionable recommendations, were developed, discussed, and evolved by this community during three workshops in Australia (2017), Indonesia (2018), and South Africa (2019).

The 136 actionable recommendations that came out of the IndOOS review were prioritized as follows. All chapters and recommendations were first reviewed by the board of six international experts. They were then presented and discussed at the second IndOOS review workshop. A synthesis of breakout discussions allowed classifying actionable recommendations into three tiers: I—high priority (maintain and consolidate essential capacities, while considering the practicalities of implementation); II—desirable (extend IndOOS capacities to better address scientific and operational drivers); and III—lower priority (pilot projects to investigate the efficacy, sustainability, and potential for integration into the IndOOS). With the final versions of chapters in hand, the impact of the actionable recommendations was assessed objectively according to the number of scientific and societal drivers each address and their niche importance.

Finally, the list of tiered and prioritized recommendations was sent out for final comments from the review board and from the CLIVAR to the broader science community. Results of the survey feedback were presented and discussed during the third and final IndOOS review workshop, and recommendations revised accordingly. This rigorous community-led review and discussion process resulted in a list of prioritized actionable recommendations that form a framework for the implementation of IndOOS-2 (Fig. SB1).

The full report (Beal et al. 2019) is available online (https://doi.org/10.36071/clivar.rp.4.2019).

Fig. SB1.
Fig. SB1.

Numbers of the IndOOS-2 review exercise.

Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1

Oceanic and climatic phenomena of the Indian Ocean

Monsoon-induced climatology.

The Indian Ocean is the only tropical ocean that is bounded by a landmass to the north, resulting in the strongest and most extensive monsoon on Earth and many unique oceanographic features. Perhaps the most significant is the monsoon-induced complete seasonal reversal of the oceanic circulation north of 10°S (Fig. 2). Strong alongshore winds in the western Arabian Sea during the southwest monsoon (Findlater 1969) induce coastal upwelling of cold subsurface waters (Fig. 3a; Schott and McCreary 2001), which modulate evaporation and moisture transport toward India (Izumo et al. 2008; Xie et al. 2009) and provide a globally significant source of atmospheric CO2 (Takahashi et al. 2002). The upwelled waters also bring nutrients to the surface, fostering intense oceanic productivity (Fig. 3b; McCreary et al. 2009; Hood et al. 2017), which induces large oxygen consumption within the poorly ventilated lower layers. The result is a thick oxygen minimum zone (OMZ) between about 200- and 1,500-m depth (Fig. 3b; Resplandy et al. 2012). In the Bay of Bengal, excess freshwater input from monsoon rains and river runoff creates a shallow, low-salinity surface layer (Fig. 3c). By inhibiting vertical mixing of heat, nutrients, and oxygen this salinity stratification is thought to favor warmer SSTs, which promote monsoon rainfall (Sh