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.
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
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
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).
Numbers of the IndOOS-2 review exercise.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
Numbers of the IndOOS-2 review exercise.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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 (Shenoi et al. 2002) and more intense cyclones (Sengupta et al. 2008; Neetu et al. 2019), to reduce oceanic productivity (Prasanna Kumar et al. 2002), and to lead to an OMZ in the Bay of Bengal (Sarma et al. 2016).
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
The annual-mean westerly winds along the equator in the Indian Ocean damp the equatorial upwelling that is found to the east of other tropical oceans. Instead, wind-driven open ocean upwelling is found in the southwestern tropical Indian Ocean, forming the Seychelles–Chagos thermocline ridge (SCTR; Fig. 2). The SCTR hosts warm SSTs and shallow thermocline (Fig. 3a), such that small perturbations in the atmosphere can easily induce an SST response, and vice versa. This results in strong air–sea coupling at various time scales (e.g., Xie et al. 2002; Vialard et al. 2009; Yokoi et al. 2012) linked to tropical cyclones, the MJO and the Indian Ocean dipole (IOD), as described below. Observational studies have also documented concentrated tuna fishing activities in the SCTR upwelling (Fonteneau et al. 2008).
The breeding ground of atmospheric intraseasonal variability.
The Indian Ocean is the breeding ground of the MJO, which modulates rainfall and cyclogenesis throughout the global tropics at 30–90-day time scales (Zhang 2005). The MJO propagates eastward over the Indian Ocean into the western Pacific Ocean and beyond, also impacting midlatitude weather (Figs. 4a,b). In summer, intraseasonal variability is often associated with northward-propagating rainfall anomalies known as the MISO, which manifest as the active and break phases of the Indian monsoon (Figs. 4c,d; e.g., Goswami 2005). The MJO and MISO induce SST variations (Fig. 4) that are larger in the Indian Ocean than in the Pacific Ocean, particularly in the northwestern Australian Basin, SCTR, and Bay of Bengal (e.g., Vialard et al. 2012, 2013). Accounting for these SST responses and their feedbacks can improve the forecast range of the MJO by about 10 days (Fig. 4e), yielding enhanced rainfall predictability throughout the tropics (De Mott et al. 2015).
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
Interannual climate variability.
Until 20 years ago, the Indian Ocean was seen as passively responding to its giant Pacific neighbor, which hosts ENSO, the dominant mode of year-to-year climate variability globally (e.g., Timmermann et al. 2018). We now know that there is intrinsic climate variability in the Indian Ocean, with important climatic consequences regionally and beyond. El Niño events induce subsidence over the Indian Ocean, which warms almost uniformly as a result (Fig. 5a; Yu and Rienecker 1999; Klein et al. 1999). This Indian Ocean Basin Mode (IOBM) is maintained through local air–sea interactions, extending the regional climate impacts (Fig. 5d) beyond those of ENSO (Xie et al. 2009; Taschetto et al. 2011; Roxy et al. 2014). The Indian Ocean also hosts modes of intrinsic climate variability arising from regional air–sea interactions, such as the IOD (Saji et al. 1999; Webster et al. 1999; Murtugudde et al. 2000; Fig. 5b), Ningaloo Niños (Fig. 5c; Feng et al. 2013), and Indian Ocean subtropical dipole (IOSD; Behera and Yamagata 2001; Fig. 5d). In addition to their impacts on regional rainfall, these climate modes have biogeochemical and ecosystem signatures (Fig. 5; e.g., Currie et al. 2013; Parvathi et al. 2017; Wiggert et al. 2009; Zinke et al. 2014). The IOD and IOBM are thought to feedback on the ENSO cycle in the Pacific (Annamalai et al. 2005b; Izumo et al. 2010; Cai et al. 2019). The IOD, IOSD, and Ningaloo Niños are sometimes forced by ENSO (Yamagata et al. 2004; Morioka et al. 2013; Feng et al. 2013; Kataoka et al. 2014), which is thus a source of predictability. The subsurface structure of the Indian Ocean also yields predictability for the IOD (Annamalai et al. 2005c; Doi et al. 2017; Ummenhofer et al. 2017; McPhaden and Nagura 2014).
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
A globally relevant heat buffer at decadal time scales.
Due to its large heat capacity, the ocean absorbs more than 90% of the anthropogenically induced excess heat into the Earth system (IPCC 2013). The Indian Ocean has contributed to one-quarter of this global oceanic heat uptake over the last two decades, despite representing only 13% of the global ocean surface (Fig. 6a; Cheng et al. 2017). This heat uptake has contributed strongly to regional sea level rise (Thompson et al. 2016). The heat budget of the entire Indian Ocean, north of its open southern boundary around 35°S, is dominated by three oceanic flux components estimated to have similar magnitude (see Fig. 2 schematic). First, an inflow of fresh tropical waters via narrow and deep passages through the Indonesian Seas [Indonesian Throughflow (ITF); Sprintall et al. 2009; Zhang et al. 2018; Roberts et al. 2017]. Second, a meridional overturning circulation linking subduction of waters into the thermocline from seasonal deep mixed layers at the southern reaches of the basin and an inflow of Antarctic Intermediate Water with upwelling in the SCTR and in the Arabian Sea (cross-equatorial and subtropical cells; Schott et al. 2002, 2009; Han et al. 2014b; McDonagh et al. 2008). Third, a horizontal subtropical gyre circulation dominated by the poleward-flowing warm and salty waters of the Agulhas Current at the western boundary (Bryden and Beal 2001). Over the last decade or so, the largest changes in Indian Ocean heat content have occurred over the subtropics (Fig. 6b). While variations in the ITF (Wainwright et al. 2008; Lee et al. 2015) and Leeuwin Current (Feng et al. 2004; Zhang et al. 2018) have been linked to Indian Ocean heat content and sea level changes, lack of measurements in the Agulhas Current and the large uncertainties in surface heat fluxes (Fig. 7; Yu et al. 2007) currently make it difficult to constrain the basin-scale heat budget at interannual and longer time scales.
(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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
(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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1
(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.
Citation: Bulletin of the American Meteorological Society 101, 11; 10.1175/BAMS-D-19-0209.1