Components of IndOOS.

The IndOOS consists of a suite of in situ observing networks as well as a constellation of satellites that return surface measurements (Beal et al. 2020; Fig. 1). As part of the in situ network, the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) and the Ocean Moored buoy Network for the Northern Indian Ocean (OMNI) form the moored arrays in the tropical Indian Ocean. The mooring arrays offer an important backbone for the IndOOS, designed to provide new insights into ocean dynamics and for the investigation of air–sea interaction related to weather and climate events and their prediction, including cyclone tracking and marine heatwave events (Acharya and Chattopadhyay 2019; McPhaden et al. 2009). All moorings measure meteorological parameters at the sea surface (such as air temperature and surface wind speed), and ocean temperature, salinity, and currents at discrete depths in the upper few hundred meters of the ocean. A few sites at the equator also measure the ocean current profile in the upper hundred meters, while some other selected sites measure high-quality air–sea flux and/or biogeochemical variables. Other components of the IndOOS consist of free-floating independent platforms. Surface drifters provide observations of ocean circulation at the drogue depth of 15 m, sea surface temperature (SST), and sea level pressure. The drifter network is designed at a density of one drifter per 5° × 5° region (Centurioni et al. 2017). Argo is designed to observe our changing climate and provides heat storage estimates and hence projections of sea level rise. Core Argo profiling floats measure temperature, salinity, and pressure from the surface to 2,000 m depth every 10 days with a nominal coverage of 3° × 3° grid (Roemmich et al. 2019) while the sparser array of biogeochemical (BGC) Argo floats measure pH, oxygen, nitrate, chlorophyll, suspended particles, and downwelling irradiance, in addition to temperature, salinity, and pressure. Nearer to coasts or on islands, tide gauges measure the sea level fluctuations (Merrifield et al. 2009). Finally, ship-based measurements also significantly contribute to the IndOOS. The near-repeat XBT transects provide important information about circulation in boundary currents and heat transport by measuring temperature to ∼850 m depth from voluntary observing ships such as cargo vessels (Goni et al. 2019). GO-SHIP conducts transbasin, full-depth, high-quality hydrographic surveys of the ocean’s physical and biogeochemical properties including carbon occupying select transects approximately once every decade to measure long-term change (Talley et al. 2016).

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Recommended design of IndOOS for 2020–30, including in situ measurements such as Argo floats, RAMA/OMNI moored buoys, XBT network, tide gauge network, surface drifters, and GO-SHIP surveys, as well as remotely sensed data (after Beal et al. 2020).

Citation: Bulletin of the American Meteorological Society 105, 3; 10.1175/BAMS-D-22-0270.1

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Governance and implementation of IndOOS.

Funding to facilitate the development and implementation of IndOOS is the responsibility of the multi-institutional, international Indian Ocean Resources Forum (IRF). The IRF is specifically tasked with facilitating and coordinating the resources that may be needed to maintain the IndOOS, such as the ship time needed for the RAMA deployments, and in turn provide feedback on its activities to the heads of the institutions providing those resources. Scientific and technological initiatives in the participating countries are encouraged. The IRF is also responsible for facilitating and enhancing data and information sharing with regard to IndOOS. In performing its tasks, the IRF is guided by the scientific objectives and research strategy formulated by the Climate and Ocean: Variability, Predictability and Change (CLIVAR)–Global Ocean Observing System (GOOS) Indian Ocean Region Panel (IORP), as discussed in Beal et al. (2020) and the Sustained Indian Ocean Biogeochemistry and Ecosystem Research (SIBER) Scientific Coordinating Committee, which are regarded as the main scientific bodies to advise the IRF. Good regional and international cooperation along with strong partnerships are key to successful management and implementation.

At the end of 2019, the status and implementation of the IndOOS looked bright and promising (Table 1). RAMA buoy sites north of 15°S were fully occupied except for a few sites in the western equatorial Indian Ocean with many sites returning 100% of data in real time (Fig. 2). OMNI moorings were also being maintained with an annual servicing schedule by India including four deep-sea cruises (two each in Arabian Sea where seven OMNI buoys are maintained and Bay of Bengal where five OMNI moorings are maintained) and additional coastal mooring servicing. By 2008, the core Argo float array fully covered the Indian Ocean north of 40°S at the desirable resolution (Nagura and McPhaden 2018), and that status was still being maintained in 2019 (Fig. 3). At the time of the Beal et al. (2020) report release, there were 538 active core Argo floats and the BGC Argo program was beginning to ramp up with 61 BGC Argo floats deployed and profiling in the Indian Ocean. Surface drifters have typically populated about 90% of the Indian Ocean at their nominal optimally desired coverage since 2014 (Fig. 4). XBT transects were occupied fortnightly in the Indonesian seas (IX01) and seasonally in the Agulhas boundary current (IX21). The tide gauge network was enhanced after the 2004 Indian Ocean tsunami and the array continued to be sustained in 2019. Finally, GO-SHIP occupations were largely on-track to be repeated decadally, some since the 1980s, in order to monitor long-term variations in physical and biogeochemical water properties.

Table 1.

The status of IndOOS components pre- and during/postpandemic. Asterisk: Prepandemic (January 2018–March 2020); during/postpandemic (April 2020–December 2022).

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The status of RAMA moorings in (top) December 2019 and (bottom) December 2022 showing real-time data return from each buoy. Data return for each site, expressed as a percentage, is based on the number of days of data acquired divided by the number of days of data expected in a given time period, summed over all sensors on a particular mooring.

Citation: Bulletin of the American Meteorological Society 105, 3; 10.1175/BAMS-D-22-0270.1

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Average monthly coverage of Argo observations within each 3° square in (top) 2019 and (bottom) 2022. Note that most grid points in the Indian Ocean show many more months of coverage (red colors) in 2019 compared to 2022, indicating that the Argo network in the Indian Ocean has still not fully recovered from pandemic-related deployment opportunities.

Citation: Bulletin of the American Meteorological Society 105, 3; 10.1175/BAMS-D-22-0270.1

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Density of drifting buoys per 5° square in (top) December 2019 and (bottom) December 2022. Note the huge decline in drifters in the Indian Ocean.

Citation: Bulletin of the American Meteorological Society 105, 3; 10.1175/BAMS-D-22-0270.1

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Collectively, all these networks are purposely designed to complement and provide some redundancy by partly overlapping each other. This approach enables estimates of robust statistics and characteristics that are otherwise not possible from a single platform alone. It is also vital that all these observational datasets are transmitted and available in real time so that they can be assimilated in numerical weather prediction models that forecast the extreme weather events such as tropical cyclones, and that then feed into national advisory alert systems.

Degradation of IndOOS during COVID.

The COVID crisis had a quick and dramatic impact on our ability to sustain and implement the IndOOS in unexpected and largely unanticipated ways. Beginning suddenly in early 2020, oceanographic research vessels were confined to home ports and the global commercial shipping industry was thrown into disarray. These circumstances alone had a manifold impact on the maintenance of IndOOS in several ways. Opportunities to service and turn around the instruments on the moored buoys were severely curtailed, and so the chances for deployment of autonomous instruments such as Argo and drifters needed to replenish and sustain their networks evaporated. The XBT transects and other underway measurements, such as surface pCO₂, SST, and meteorological observations, that rely on the Ship of Opportunity network were limited as it was difficult for ship riders and Port Meteorological Officers to transit on the cargo vessels or visit the ships in port to repair or calibrate equipment. In addition, there was total disorder in the supply chain needed for the instruments and their refurbishment (e.g., sensors, batteries, wire cable, flotation). In this section, we detail the decimation of specific platforms that comprise the IndOOS (see Table 1 for a summary) while the following section provides an overview of the harmful impacts—both immediate and longer term—that occur as a direct result of the gaps experienced in the array during the COVID crisis.

The pandemic created significant ongoing challenges for maintaining the RAMA ocean moored buoy array. RAMA moored buoys are usually replaced once a year because sensors fall out of calibration and batteries run down. This servicing is typically done using research vessels primarily from Indonesia, India, and South Korea in partnerships with NOAA. However, these research cruises were put on hold for more than two years during the pandemic, with only a single servicing cruise that turned around two buoys in the southwest Indian Ocean in January 2022. As a result, most of the buoys stopped transmitting data and many were lost. Simple metrics tell the story: just before the start of the pandemic in December 2019, 21 out of 22 RAMA moorings were transmitting good data and data return overall was 87%; whereas in December 2022, every buoy in the RAMA array (except for those turned around in January 2022) had been deployed for over 3 years with the bleak result that average data return for the entire array dropped to 2% (Fig. 2). In contrast, the GOOS OCG 2022 Report Card (www.ocean-ops.org/reportcard2022/) noted that 50% of the global tropical moored buoy array was operational in the Atlantic and 75% in the Pacific, due in part to more servicing opportunities (Connell et al. 2023). Irrespective, the inability to regularly service the RAMA array was a problem not just for maintaining data flow, but also for the costs of replacing the lost equipment which is estimated to be more than $3 million (U.S. dollars).

While still impacted, the OMNI array focused on the northern Indian Ocean (Fig. 1) fared much better than RAMA. Around 73% of the Indian OMNI buoy network remained functional during the pandemic in 2020 even when cruises were reduced to 33% (Venkatesan et al. 2021). Likely the availability of ship time and hence the ability to service the regional network around India combined with readily available inventories of sensors and buoy systems contributed to the better outcome for the OMNI array (Venkatesan et al. 2021). Data return of the Arabian Sea OMNI buoy surface meteorological data remained relatively high (>80%) during the pandemic, whereas that of the Bay of Bengal OMNI buoys, which are subject to more vandalism, was reduced to 50%. Similarly, data return from stand-alone subsurface velocity sensors and the temperature–conductivity–pressure sensors with self-recording and some telemetry data capability fared much better in the Arabian Sea OMNI buoys (93% and 80%, respectively) than in the Bay of Bengal OMNI buoys (58.3% and 85.7%, respectively) (Venkatesan et al. 2021).

The Argo program in the Indian Ocean also suffered serious multiyear declines in array size and spatial coverage of floats. In 2022, only 607 core Argo floats were operating (Fig. 3), which is about 85% of the target number of 697. Typically, the average age of failure of an Argo float is ∼4.82 years, but in 2022, about 54% of the core Argo floats in the Indian Ocean were much older than 3 years, and so will likely stop working in the next 2 years. Normally around 170 floats are targeted for annual deployment in the Indian Ocean to maintain the nominal sampling array. However, an ongoing gap in deployment opportunities means that about 250 floats needed to be deployed in 2022 so as to reach the recommended Argo coverage.

BGC Argo deployments and coverage in the Indian Ocean did not fare any better than core Argo. Typically, around 20 BGC Argo floats are deployed in the northern Indian Ocean, supplied by Indian National Centre for Ocean Information Services (INCOIS) and other international agencies, but that fell to only 5 in 2020 and 1 in 2021. By December 2021, the number of operational BGC Argo floats in the northern Indian Ocean had fallen to 21, with enormous gaps in the southern Indian Ocean between 10° and 30°S with little to no coverage. As of August 2022, over 80% of the 40 active Indian Ocean BGC floats were more than 3 years old.

Surface drifters were similarly severely impacted. The number of drifters actively reporting in December 2019 was 261 units. During the pandemic, drifter coverage went essentially to zero in the Arabian Sea and equatorial Indian Ocean (Fig. 4). The number of deployments of drifters in the Indian Ocean was less than half compared to the yearly average and less than one-third of target deployments. In total, 287 drifters were deployed in 2019, compared to only 76 drifters deployed in 2022, and so well below the level required to reach the typical average target of 212 deployments per year.

Both the commercial shipping industry and research vessels were severely impacted by restrictions due to the pandemic in 2020–22. Fortunately, XBT launches along the IX01 frequently repeated transect from Fremantle to Jakarta that samples the Indonesian Throughflow continued at intervals of every 9–14 days with only a brief pause in January/February 2022 (two voyages) due to a COVID-impacted delayed delivery of XBT probes. However, the IX21 transect that crosses the Agulhas was halted in March 2020 and sampling only resumed at low density by the ship’s crew in August 2021, returning to high density sampling with a ship rider in September 2022. Finally, the high-quality decadal repeat full-depth physical and BGC measurements along GO-SHIP transects were halted in 2020 when transect IO7 was occupied by Japan, as research vessels were largely confined to their home ports. The resumption of GO-SHIP transects in the Indian Ocean is expected in July 2023 with the reoccupation of the I05 transect, from Fremantle, Australia, to Durban, South Africa.

Degradation of regional weather and climate forecasts.

Various national agencies use the IndOOS network for forecasts applied at various time scales. For example, RAMA/OMNI mooring data are used extensively to provide accurate weather and monsoon forecasts over South Asia, to improve understanding and impacts of the MJO, to provide initial conditions for operational global coupled forecasting systems, to verify ocean–atmosphere flux products and satellite retrievals in the Indian Ocean, among many other applications (e.g., Pradhan et al. 2021; Balmaseda et al. 2013).

In situ ocean observations are important for constraining the temperature, salinity, and currents in ocean state analysis and reanalysis, thereby improving the mixed layer, circulation, and water mass simulations of the ocean (Oke et al. 2015a). These ocean state reanalyses are used as initial conditions for the regional and global model forecasts, and also for assessing the model forecast skills (Balmaseda et al. 2013). Ocean initial conditions are a key source of predictability at subseasonal time scales, particularly for Indian Ocean phenomena such as the MJO and monsoon intraseasonal oscillations, as well as important in predicting El Niño–Southern Oscillation (ENSO), the IOD, and the monsoons. Decadal forecasts and climate change projections initialized with ocean observations are found to reproduce more reliable climate predictions compared with uninitialized climate projections (Smith et al. 2007; Kirtman et al. 2013). The data loss in the Indian Ocean hence had critical implications for monsoon, IOD, and MJO forecasts, which may cascade to impact ENSO forecasts (Wang et al. 2022) and longer-term decadal predictions.

While satellite systems dominate the available observational data in volume and so have a strong influence on forecast accuracy, in situ ocean observations are shown to have a greater impact per observation, despite their relatively low numbers (Dahoui et al. 2017). In particular, buoys were shown to have the highest impact on average (Dahoui et al. 2017). OSE experiments show that local errors in global and basin-scale prediction systems can be significantly reduced when assimilating observations from regional observing systems like the IndOOS (Oke et al. 2015b). On a basin scale, Argo data have the largest impact, particularly in simulating model temperature, salinity, sea surface height, and mesoscale circulation. Argo data are essential in correcting the systematic cold bias that models have in the ocean from the surface to a depth range of 2,000 m, and furthermore capture the warming trend at these depths (Oke et al. 2015a). Argo has a substantial effect in correcting the heat content of the subtropical Indian Ocean, thereby improving the ocean model’s vertical mixing and the freshwater flux (Balmaseda et al. 2007). The role of the Indian Ocean in storing additional heat from global warming in its interior and its importance in modulating global climate variability has been made apparent by Argo and other in situ data (Lee et al. 2015; Beal et al. 2020). The deterioration of Indian Ocean in situ observations can hence hinder our understanding of climate-scale changes in the Indo-Pacific region.

On a local scale, mooring data have a similar or greater impact than Argo data (Oke et al. 2015a). Assimilation of subsurface temperature fields obtained through mooring and Argo profiles in the Pacific is found to improve ENSO forecasts at long lead times (Fujii et al. 2011; Oke et al. 2015a). XBT data generally have a smaller effect except near the XBT transects themselves where they affect both temperature and velocity fields (Verdy et al. 2017; Gwyther et al. 2022). Notably, XBT transects typically cross strong boundary currents, such as the ITF (IX01 transect) and the Agulhas (IX21) in the Indian Ocean, where strong currents make it challenging to maintain Argo coverage. So, the effect of XBT transects on model fidelity becomes significant in these regions.

When ocean observations are not assimilated, seasonal climate forecast errors grow rapidly over a 1–2-month time period, eventually saturating to a 50% increase in errors after 6 months of integration with no data assimilation (Oke et al. 2015a,b). When data from only half the Argo floats are assimilated, the ocean forecast error increases by about 15% for temperature and salinity, compared to an assimilation including all Argo data (Oke et al. 2015a; Turpin et al. 2016). The relative degradation of forecasts without Argo data assimilated is approximately uniform down to 2,000 m depth. Similar significant impacts are found when mooring and XBT data are not assimilated.

Pradhan et al. (2021) performed coupled experiments with the operational model used for seasonal forecasts for India. They found that irrespective of the assimilation system, the absence of moored buoys can result in a cold SST bias and subsurface temperature errors in initial conditions that adversely impact the prediction of Indian summer monsoon rainfall. Excluding mooring data from ocean analysis can deteriorate the seasonal forecasts of Indian Ocean SSTs and the IOD, along with the associated atmospheric circulation. The prediction error in the summer monsoon rainfall over India caused by the exclusion of moored buoy observations is as high as 39%. However, the Pradhan et al. (2021) coupled experiment was carried out for only a single year and used all tropical moored buoy observations together rather than isolating the impacts of individual basins.

In situ ocean observations play an integral role in early warning systems in the case of extreme weather events across the Indian Ocean rim nations. A national impact assessment report (Venkatesan et al. 2020) highlighted that in situ ocean observations are key to improvements in weather forecasts from the India Meteorological Department. The inclusion of IndOOS data avoided losses that would have been suffered in the absence of timely weather warnings. These real-time in situ ocean observations have provided the crucial lead time for early warnings, helping evacuate millions of people during Cyclone Fani in Odisha in May 2019, Cyclone Amphan in May 2020 in West Bengal, and Cyclone Nisarga in Maharashtra in June 2020.

Impacts on ocean data products and validation.

The salinity information from Argo floats plays a dominant role in documenting salinity variability of most of the global ocean (Balmaseda et al. 2007). As a result, the halosteric component of sea level rise has been better computed since the start of the Argo era. This is particularly important for the Indian Ocean, where the relative contribution of thermosteric and halosteric components have been less understood until recently, although numerous studies have shown the halosteric component can be significant in the Indian Ocean Basin (Llovel and Lee 2015; Hu and Sprintall 2016). A degradation of available Argo data in the Indian Ocean can thus lead to errors in the determination of basinwide sea level variability.

Subsurface temperature data from moorings, core Argo floats, and XBTs are key inputs to global ocean heat content estimates that provide an essential metric for understanding climate change and Earth’s energy budget. The percentage of coverage of these subsurface temperature data are directly linked to the uncertainty of the heat content estimate (Boyer et al. 2023). Similarly, subsurface temperature measurements can be critical for understanding marine heatwaves that often extend below the sea surface (Zhang et al. 2021), where they can have their largest impact on marine ecosystems and also increase the potential heat storage that can act to intensify tropical cyclones (Rathore et al. 2022). Regional forecasts of marine heatwaves rely on assimilation of the oceanic subsurface data into ocean models to enable more accurate forecasts and better preparation for marine heatwaves and their impacts by stakeholders such as managers of marine fisheries.

An unexpected impact from the deficit in the coverage of BGC Argo floats arose in November 2022 when the Indian space agency (ISRO) launched the OceanSat-3 with an Ocean Color Monitor (OCM). The ISRO team had initially planned to use BGC Argo data during the pilot period December 2022–March 2023 for analysis and validation for the satellite mission to improve the algorithm for the OCM. However, the lack of in situ BGC data at that time prevented this effort.

Cost–benefit analysis of observations to society and the blue economy.

An ocean value chain built on a strong bedrock of ocean observations, modeling, information, and advisory services plays a critical role in all blue economy initiatives spanning fishing, shipping, energy, tourism, and other marine and coastal industries. With a greater appreciation of the ocean–climate nexus, and as more and more countries build their blue economy policies, it is important to document the importance of the ocean value chain. Cost–benefit case studies have been undertaken on various IndOOS ocean information services based on operational ocean services provided by INCOIS to various stakeholders in India and for the rim nations around the north Indian Ocean (Venkatesan et al. 2015, 2020; Nagaraja et al. 2018). The benefits due to these services and in some cases the presumptive losses due to their disruption have been estimated by various independent agencies as huge (Venkatesan et al. 2015). For example, Venkatesan et al. (2015) report that ∼1 trillion INR is spent annually by farmers for facilities such as irrigation, and a national survey indicated that 95% of these farmers considered the recent development of next-generation numerical weather forecasts with higher reliability increased the utility of the forecasts, with an economic benefit to farmers of ∼3.3 trillion INR. Similarly, the overall economic benefit from the Ocean State Forecast exceeds 3.7 trillion INR to the Indian Navy and Coast Guard, as well as for oil and gas exploration industries.

INCOIS has been providing Potential Fishing Zone (PFZ) advisories that help fishermen in conducting efficient fishing operations in an environmentally friendly way, acting to promote sustainable harvesting of the fishery resources. PFZ advisories are thought to reduce search time of fishers by 30%–70% and enhance catch by 2–4 times. With an objective to enhance maritime safety, INCOIS provides Ocean State Forecast (OSF) services with information on winds, waves, ocean currents, water temperature, etc. approximately every 3–6 h on a daily basis with predictions for the following 5 days. INCOIS also provides other important operational services to a wide range of coastal and maritime stakeholders such as tsunami early warning, storm surge early warning, high wave alerts, oil spill trajectories, marine search and rescue operations support, coral bleaching alerts, and harmful algal blooms. Cost–benefit analyses by INCOIS suggest that an advisory on sea state indicating a “no-go mission” can benefit strategic users by $580 million (U.S. dollars). A correct oil spill advisory can save as much as $13 million (U.S. dollars) in response. These services are of paramount importance as the need for food and resources from the ocean increases rapidly to meet the needs of the ever-growing population and for economic prosperity. Ocean observations, both in situ and remote sensing, play a key role in the generation of these services by enhancing our understanding of scientific processes, building models for prediction and forecasting, data assimilation, and validation of services.

Solidifying regional and international partnerships.

One way to increase resiliency is to establish solid regional and international partnerships across political borders with coordinated efforts contributing to the collection of ocean observations. Fortunately, many of these partnerships are already in place, either formally or informally, and there are many examples of successful cooperative international and regional efforts to build resiliency and revive the IndOOS in the face of the COVID pandemic challenges. For example, some of the formal agreements with NOAA in support of the RAMA array were renewed during the pandemic (e.g., with the Ministry of Earth Sciences, India, and BMKG, Indonesia), attesting to the resilience of these international partnerships even though the fieldwork was on hold.

In terms of regional collaboration, a step forward was the recent signing of an MoU between UNESCO’s Intergovernmental Oceanographic Commission (IOC) and the Indian Ocean Rim Association to promote capacity development in ocean science and related applications to the sustainable ocean economy. The MoU cites the need to “learn more about the nature and resources of the ocean and coastal areas and to apply that knowledge for improvement of management, sustainable development, protection of the marine environment, and decision-making processes” within the 23 member states. To ratify this major agreement in the wake of COVID demonstrates the resilience of the Indian Ocean community. It may also be timely for the establishment of an IOC–UNESCO Regional Training and Research Center (RTRC) focused on Indian Ocean observations and research. RTRCs are aimed at improving national and regional capability and capacity of marine science. They typically operate within the region’s national oceanographic institutes or universities to provide training and marine research opportunities to young scientists mainly from developing countries. Within the western Indian Ocean, Africa has established an Ocean Decade Task force to implement the African road map toward the U.N. Decade. This task force will focus on maximizing collaborations for enhanced ocean observations that are relevant to East African societies.

Identifying regional opportunities and establishing connections.

In 2022, the GOOS Observations Coordination Group, through its OceanOPS arm, organized an Indian Ocean Planning Meeting with IRF and IORP participation, to not only address the serious challenges faced by the IndOOS that were accelerated by the COVID pandemic, but also look to the future to enable routine opportunities that might better fill unanticipated gaps in the observing system. The idea was for scientists and operators to share experience, plans, and deployment opportunities on identified cruises with partner ocean observing networks and regional experts so as to refine the overall implementation plan for some key platforms in the following year or so. The series of telecons were highly successful, with the number of Argo float deployment plans for 2023/24 in the Indian Ocean almost tripling since the beginning of the regional coordination meetings. In addition, the cruise planning list and deployment opportunities grew, thanks to awareness and input from the community.

Recovery of RAMA has been slow, with only one cruise carried out in 2023. More RAMA cruises are planned in 2024 with the intent of reestablishing a high degree of functionality to the array. Importantly, these cruises will also provide a platform for deployment of surface drifting buoys and Argo floats, helping build up those arrays which have been depleted in the Indian Ocean for lack of deployment opportunities during the pandemic. Nonetheless, some recent new challenges have also surfaced. Due to the Russia–Ukraine conflict, fuel prices have significantly risen so that the cost of chartering vessels for float deployments has become prohibitively high. Inflation has also increased the cost of new equipment, impacting budgets that were fixed and allocated years earlier. Sudden fluctuations in the exchange rate can be particularly crippling for underresourced countries trying to set up ocean observing systems.

Improving operational robustness and data sharing policies.

Resiliency in operational robustness can be gained by diversifying the composition of the observing system and introducing some level of redundancy. Evolving the observing system that is underpinned by new scientific understanding can take advantage of new technologies that are ready for deployment to fill in data gaps. Recent years have been a fruitful period for new ocean technology development, including air–sea measurements from uncrewed surface vessels as well as subsurface ocean gliders that have demonstrated efficacy in coastal regions. Such autonomous sampling also alleviates the costs associated with expensive cruises. Even so, it is important moving forward that new technology is first assessed according to its technological readiness, the relevance and ease of obtaining the measurements, and the potential role in the context of the existing networks.

Improving data and information management is key for better interoperability and dissemination of a product that will be accessible and useful to the broader community beyond the traditional ocean science domains. Value-added enhancement of the systems requires increased participation by countries and institutions willing to provide resources and financial backing. A recent example is the Joint OMNI–RAMA Indian Ocean Data Portal developed in 2021 by INCOIS jointly with NIOT and NOAA PMEL that is well-maintained and updated with the high-quality moored time series data from both the OMNI and RAMA Indian Ocean buoys (McPhaden et al. 2023). The portal stimulates broader utilization for both scientific research and applications, while showcasing the large inventory of meteorological and oceanographic datasets with direct ready access for data display and delivery along with supporting metadata information.

Sharing capacity and regional involvement.

Finally, codesigning scientific and technological capacity of regional underresourced nations will be critical for understanding and addressing the needs of these countries toward achieving enhanced ocean management and governance within their exclusive economic zones (EEZs), as well as contributing to the resilience and value of IndOOS. The ongoing recovery and success of IndOOS is reliant on increased involvement and cooperation of regional countries and agencies, along with their commitment to investment in their social capital, building and supporting national capacity and observing best practices, in addition to data sharing and dissemination.

The long road to recovery.

Despite the recent progress aimed at improving the IndOOS resiliency, it is still expected to be a slow road to recovery for the observing system. The value of in situ marine meteorological and oceanic measurements that constrain the weather and ocean models as well as contribute to assessments of water, maritime resources and environmental services cannot be overstated. On a longer term, these data play a pivotal role guiding advanced reanalysis products and other data assimilation systems that are essential for evaluating ocean climate variability and predictability. As noted, there is a critical need to document this gap now so that future climate scientists might be able to quantify the influence of the missing observations such as through the use of OSSEs. Such quantitative assessments will provide more fundamental evidence of the impacts of the data loss in IndOOS over the past 3 years on weather and climate analyses and forecasts.