Ocean observation data are essential for ocean predictions (e.g., Davidson et al., 2019) and also contribute to weather and climate predictions based on coupled atmosphere–ocean models, especially at subseasonal and longer time scales (e.g., Subramanian et al., 2019). However, developing and sustaining the ocean observation network requires a huge amount of human and financial resources. Therefore, the ocean observation network should be designed to efficiently acquire effective observation data, and their adequacy should be continually evaluated (e.g., Fujii et al., 2019). Especially nowadays, many ocean observing systems (e.g., the global Argo array, mooring arrays, ocean observing satellites, ocean gliders, and autonomous underwater vehicles) require scientific support for their usefulness in order to sustain them.

There are several strategies to demonstrate the importance of observation data and to scientifically support the maintenance and expansion of the ocean observation network. Among them, observing system experiments (OSEs) are widely used in order to evaluate the importance of the ocean observation data and the effectiveness of ocean observing systems for ocean predictions (e.g., Vidard et al., 2007; Balmaseda et al., 2009; Oke and Schiller, 2007; Lea et al., 2014; Fujii et al., 2015a; Turpin et al., 2016; Xue et al., 2017). OSEs are data assimilation and prediction experiments in which specific observation types are excluded from or added to the data being assimilated, and the impact of the observation types is assessed by comparing it with regular data assimilation and prediction. Observing system simulation experiments (OSSEs), which are the same as OSEs but use synthetic observation data generated from a reference simulation instead of real observation data, are also performed for evaluating the future observing system or proposing a new design of an observation network (e.g., Halliwell et al., 2017; Bonaduce et al., 2018; Gasparin et al., 2020; Gasparin et al., 2023; Waters et al., 2024). However, the results of OSEs and OSSEs severely depend on the property of the system, including systematic errors of the system (i.e., model biases), physical parameterizations, and data assimilation schemes applied in the system, as demonstrated in previous studies (e.g., Fujii et al., 2015b, 2019; Gasparin et al., 2019). Therefore, it is preferable to conduct the OSEs/OSSEs using multiple prediction systems in order to mitigate the differences in evaluations caused by the system dependency and to draw robust and reliable conclusions.

The United Nations (UN) Decade of Ocean Science for Sustainable Development, that is, the UN Ocean Decade (UNESCO-IOC, 2021; see also https://oceandecade.org/), provides a good opportunity to make a close collaboration between ocean observing and ocean and coupled prediction communities, as well as among various groups in the ocean prediction community, for evaluating the importance of ocean observation systems and co-designing the future evolution of the ocean observing network. “Synergistic Observing Network for Ocean Prediction (SynObs)” was launched as a UN Ocean Decade Project in 2022 to take advantage of this collaborative opportunity (see https://oceanpredict.org/un-decade-of-ocean-science/synobs-2/). SynObs is currently coordinating the flagship OSEs/OSSEs, a collaborative OSEs/OSSEs using multiple ocean and subseasonal-to-seasonal (S2S) prediction systems to make robust and reliable evaluations that are seen across a variety of substantially varying ocean data assimilation and prediction systems. The flagship OSEs/OSSEs has the following five goals. The first is to demonstrate the importance of major ocean observing systems [Argo array, tropical mooring arrays, satellite altimetry, Surface Water and Ocean Topography (SWOT) mission, etc.] for ocean and coupled predictions. The second is to evaluate the synergy among different observing systems, typically between satellite and in situ observing systems, in ocean predictions. The third is to contribute to the future design of the ocean observing network to improve its effectiveness and efficiency. The fourth is to build a coordinated framework to co-design the future ocean observing network among ocean observing and prediction communities. The fifth is, then, to establish the best practice to evaluate the current ocean observing network and to design its future evolution.

The SynObs flagship OSEs/OSSEs comprises ocean prediction (OP) OSEs, OP OSSEs, and S2S OSEs. The OP OSEs are designed to evaluate the impacts of existing observing systems on ocean predictions, particularly those made by relatively high-resolution ocean data assimilation and prediction systems. The period of data assimilation (analysis) runs for the OP OSEs is at least 1 year during 2020 and preferably extended to 3 years until 2022 (with the possibility of further extension in the future). It is also encouraged to perform 10-day forecast runs for the same period, with particular priority given to the period from June 2021 to May 2022. The OP OSSEs are designed to evaluate new or future observing systems and the observing network design on ocean predictions. For example, we aim to evaluate SWOT data and the new Tropical Pacific Observing System (TPOS) design (Kessler et al., 2019; Kessler and Cravatte, 2021) here. It is planned to perform assimilation runs for approximately 1 year and 10-day forecast runs from the assimilation results. For the OP OSSEs, the use of relatively high-resolution ocean data assimilation and prediction systems is also expected, and the GEOS/NASA high-resolution coupled atmosphere–ocean simulation of approximately 1 year (Menemenlis, 2023) will be used as the Nature Run from which virtual observation data will be synthesized. The S2S OSEs are designed to evaluate observation impacts on atmosphere and ocean predictions at S2S timescales. They cover a long analysis period (2003–2022, with possible extension to 2023), and execution of 1-month or 4-month forecasts with coupled ocean–atmosphere models is also encouraged. Output data from the analysis and forecast runs performed for the flagship OSEs/OSSEs will be collected and shared as the SynObs common database with volunteer groups responsible for the diagnostics and will be eventually publicly available to the broader scientific community.

This paper presents the outline of the OP OSE and the early results from analyzing the OSE output of analysis runs made by multiple systems. We then discuss future perspectives on SynObs and the flagship OSEs/OSSEs activities.

SynObs is currently coordinating the OP OSEs as part of the flagship OSEs/OSSEs in order to evaluate the impacts of the ocean observing network on ocean prediction systems using relatively high-resolution (typically finer than 0.1°) ocean models that resolve mesoscale eddies. For this purpose, prediction centers are requested to perform ocean data assimilation runs with control and observation denial settings listed in Table 1 . It is also encouraged to perform ocean forecast runs from the oceanic initial condition reproduced by the assimilation runs using their ocean prediction systems.

As for the data assimilation runs, prediction centers are requested to provide outputs of ocean variables and diagnostics for at least 1 year from Jan. 1 to Dec. 31, 2020, and preferably for 3 years until Dec. 20, 2022. The assimilation runs are recommended to be started at least 3 months before the beginning of the output period mentioned above from the ocean state generated by regular data assimilation or free simulation in order to allow sufficient spin-up time.

Prediction centers are, then, requested to perform forecast runs from the end of every pentad in 2020–2022 from the oceanic initial condition generated by the above assimilation runs with each OSE setting. The requested forecast length is 10 days. It is recommended that the forecast runs in the period from Jun. 5, 2021, to May 31, 2022, be prioritized over the forecast runs in other periods, as mentioned in the previous section. Ideally, the ocean model should be forced by the results of atmospheric forecasts in the ocean forecast run if the model is not coupled to any atmospheric models. However, atmospheric forcing calculated from atmospheric reanalysis data can be used in the forecast run if the atmospheric forecast data are not available.

Outputs of the data assimilation runs and forecasts run with the OSE settings conducted in prediction centers are converted to data in a common regular latitude–longitude coordinate grid. Some variables at the times and locations of observations by Argo floats and mooring buoys are also output. The variables and diagnostics to be output, their resolution, and time intervals have been discussed and agreed upon. The output data are, then, collected and compiled in the SynObs database, as well as S2S OSE and OP OSSE outputs, and will be eventually open to the public. SynObs plans to ask several volunteer diagnostic groups of experts in specific observing systems, target regions, phenomena, applications, etc., to examine the impacts of observation data on several targeted variables or diagnostics (typically, the difference in the variables/diagnostics among OSEs). The diagnostic groups will mainly use the database and conduct the diagnostics focusing on their own expertise.

Details of the OSE settings listed in Table 1 are given below. CNTL is the reference run in the set of OSEs. In CNTL, Sea Surface Temperature (SST) data, 80% of Argo data, mooring data, other in situ temperature and salinity profile data, and data from satellite nadir altimeters are assimilated. Other types of observation data (e.g., sea ice concentration ratio) can also be assimilated in some systems if they are used in the operation. Other settings of the model and assimilation system can be different from their operational settings. It is noted that 20% of the Argo data are withheld from assimilation in order to use as independent reference Argo data for validation. Which data are withheld is determined by the World Meteorological Organization (WMO) number of the floats, that is, the data for the floats whose last digit of the WMO number is 8 or 9 are withheld. With this method, the common reference Argo data are easily identified among prediction centers, and they are expected to distribute homogeneously in the global ocean as shown in Figure 1 (also see Fujii et al., 2015a). It is recommended that the World Ocean Database (WOD; Boyer et al., 2016; Locarnini et al., 2013; Zweng et al., 2013) be used to collect the in situ temperature and salinity (TS) profiles, which will be assimilated because Argo, mooring, and other profiles are already sorted in different files, and thus, it is easy to identify Argo and mooring profiles. However, it is approved to use other datasets if necessary.

In the following five OSEs, specific types of observations are excluded from the data to be assimilated. The impact of that observation type will be assessed by comparing the specific OSE with CNTL, where all observation types are assimilated. The impact of the satellite nadir altimeter data is assessed using NoAlt, in which the altimeter data are excluded. The impact of Argo floats is assessed using NoArgo, in which all TS profiles observed by Argo floats are excluded. The impact of moorings, which are mainly in the tropical ocean, is assessed using NoMoor, in which all TS profiles observed by moorings are excluded. The impact of SST data, which are mainly observed by satellites, is assessed using NoSST, in which SST data are excluded. Then, the impact of all in situ data is assessed using NoInsitu, in which all kinds of in situ TS profiles are excluded.

SSTonly is implemented for checking the performance of the system when only SST data are ingested. SSTonly is performed with the same setting as CNTL, but only SST data are assimilated. Free is implemented for checking the model performance without data assimilation and is performed with the same setting of the ocean model as CNTL and without any procedures related to data assimilation (including climatological nudging and bias correction). In Free, information related to the actual ocean condition only comes from atmospheric forcing. HalfArgo is implemented for evaluating the impact of changing the density of Argo floats and is performed with the same setting as CNTL, but the number of Argo data being assimilated is reduced from 80% to 40%. The data for floats whose last digit of the WMO number is 4, 5, 6, 7, 8, or 9 are withheld. Oper is the result of data assimilation runs with identical settings as used in the operational system and provided for checking the performance of the actual operational systems.

Table 1 lists OSEs following their general priority order. However, it is left up to each prediction center to decide the order of implementing the OSEs on their own according to their own priority. It should also be mentioned that the OSE settings listed in Table 1 are common with those for S2S OSEs. Therefore, some prediction centers are currently conducting ocean reanalyses with the OSE settings for 2003–2022.

In this section, we present early evaluation results of OP OSEs using the output of the data assimilation runs of the four systems (FOAM, GIOPS, MOVE-G3, and JCOPE-FGO) with the CNTL, NoAlt, and NoArgo settings. A brief summary of the configuration of the four prediction systems is presented in Table 2 , and further descriptions of those systems are given in the Supplementary Material . Detailed descriptions of the diagnostics used for evaluating observation impacts in this section are also provided in the Supplementary Material .

The UN Ocean Decade Project SynObs has been promoting various activities associated with the evaluation of the ocean observation network since its launch in 2022. In particular, SynObs has started the internationally coordinated multi-system OSEs/OSSEs, or the flagship OSEs/OSSEs, to make a fair evaluation of the ocean observing network, which does not depend on any particular ocean prediction system, and provide a scientifically justified rationale for its maintenance and evolution. The flagship OSEs/OSSEs consists of three components: the OP OSEs for high-resolution ocean predictions, the S2S OSEs for long lead-time coupled ocean–atmosphere predictions, and the OP OSSEs for evaluating new and future observing systems.

We have begun to collect the outputs of the OP OSEs, and, as shown in the previous section, early analysis of those outputs indicates that there are observation impacts consistent across the four ocean prediction systems. For example, the impacts of the altimetry data on the assimilated SSH field are generally large in the western boundary current regions and around the ACC where SSH has large variability but are small in the tropical regions despite the relatively large SSH variability there. We also found characteristics of observation impacts that are specific to some ocean prediction systems. Our analysis indicates the possibility that there are some characteristic differences in the observation impacts between low-resolution and eddy-resolving ocean prediction systems. In addition, the OSEs have shown synergies between the observing systems, with profile observations improving fits to SSH observations in regions where the altimeter observations alone cannot improve the simulation. Similarly, altimeter observations universally demonstrate improvements in fits to non-assimilated Argo profiles, to the same extent the remaining assimilated Argo profiles do. These results generally demonstrate the importance of satellite altimetry data and the global Argo array and provide scientific support for maintaining and further developing these observation systems.

However, the results here are just an illustration of the potential of the SynObs project. Since outputs of the OP OSEs for only four ocean prediction systems are currently available, it is not sufficient to conclude what the observation impacts are consistent across most ocean prediction systems and what are impacts specific to particular prediction systems. It is also difficult to analyze what characteristics cause observation impacts specific to some prediction systems, for example, how observation impacts are affected by the resolution of the prediction system. In addition, it should be noted that we have analyzed only outputs of data assimilation runs so far, and the observation impacts on forecast runs have not been assessed yet. The impacts depend on the limits of the model’s forecast ability and are likely to be reduced compared to the impacts on data assimilation runs because the forecast errors deteriorate information from observations.

Currently, some other ocean prediction centers will conduct or are conducting the analysis runs for the OP OSEs and plan to provide outputs. Some of the submitted OSE outputs are planned to be replaced by a new version that fixes inadvertent defects. We will conduct further investigation on the consistency of observation impacts, the characteristics of observation impacts specific to some ocean prediction systems, and their contributing factors using the original and newly submitted OSE outputs in the near future. In addition, forecast runs from the analysis fields of the assimilation runs for each OSE are planned to be performed, and observation impacts on the forecast accuracy will be also investigated.

Meanwhile, some ocean/climate prediction centers are currently running ocean reanalysis runs for S2S OSEs and plan to perform the coupled atmosphere–ocean model forecasts using those reanalysis fields as oceanic initial conditions. The results of these coordinated OSEs are expected to provide a clear indication of ocean observation impacts on the atmospheric fields in the coupled forecasts, which have only been identified for a few forecasting systems following different experimental protocols (Balan-Sarojini et al., 2024; Balmaseda et al., 2024). We are currently preparing virtual ocean observations for OP OSSEs and discussing the setting of the OSSE runs. The evaluation of the OSSE integrations will target the suitability of the new TPOS design and demonstrate the effectiveness of SWOT SSH data and some other new ocean observations. All outputs of the flagship OSEs/OSSEs will be compiled as a database and shared with several expert groups in order to carry out SynObs multi-system evaluation according to the area of expertise: specific observing systems, target regions, phenomena, applications, and so forth. Finally, the database will eventually be made available to the public. We expect that sharing the flagship OSE/OSSE outputs with various expert groups will boost the impact of SynObs by providing a broader and more rounded evaluation of the observing system evaluation and design, enhancing the communication between the producers and users of data, and increasing the understanding and awareness of the impact of the ocean observations on predictions of the ocean and atmosphere at a range of time scales.

SynObs will promote the sharing of the achievements of research activities and community efforts on the ocean observing system design and evaluation, including the results of the flagship OSEs/OSSEs, with the broader oceanic and earth system research communities through academic papers, scientific meetings, web pages, etc. SynObs also aims to provide significant inputs to the reports on the ocean observation impacts and the design of the ocean observing network made by international organizations, such as the WMO and Global Ocean Observing System (GOOS), and internationally coordinated projects, such as the UN Ocean Decade Programme “Ocean Observing Co-Design” (https://goosocean.org/what-we-do/goos-at-the-heart-of-the-ocean-decade/ocean-observing-co-design), and to contribute to the international designing of the ocean observing network from the perspectives of ocean and coupled predictions. We believe that these SynObs activities will enhance the communications between ocean observing and ocean and coupled prediction communities and lead to a transformative change in the ocean observing network that will generate substantial societal benefits.