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Twenty years of ocean observations with China Argo
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Zenghong Liu1, 2, Xiaogang Xing1, 2, Zhaohui Chen3, Shaolei Lu1, 2, Xiaofen Wu1, 2, Hong Li4, Chunling Zhang5, Lijing Cheng6, Zhaoqin Li1, 2, Chaohui Sun1, 2, Jianping Xu1, 2, Dake Chen1, Fei Chai1, 2, *
Acta Oceanologica Sinica | 2023, 42(2) : 1 - 16
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Acta Oceanologica Sinica | 2023, 42(2): 1-16
• Review •
Twenty years of ocean observations with China Argo
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Zenghong Liu1, 2, Xiaogang Xing1, 2, Zhaohui Chen3, Shaolei Lu1, 2, Xiaofen Wu1, 2, Hong Li4, Chunling Zhang5, Lijing Cheng6, Zhaoqin Li1, 2, Chaohui Sun1, 2, Jianping Xu1, 2, Dake Chen1, Fei Chai1, 2, *
Affiliations
  • 1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • 2 Observation and Research Station of Global Ocean Argo System (Hangzhou), Ministry of Natural Resources, Hangzhou 310012, China
  • 3 Frontier Science Center for Deep Ocean Multispheres and Earth System/Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266100, China
  • 4 Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
  • 5 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  • 6 International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Published: 2023-02-25 doi: 10.1007/s13131-022-2076-3
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The international Argo program, a global observational array of nearly 4 000 autonomous profiling floats initiated in the late 1990s, which measures the water temperature and salinity of the upper 2 000 m of the global ocean, has revolutionized oceanography. It has been recognized one of the most successful ocean observation systems in the world. Today, the proposed decade action “OneArgo” for building an integrated global, full-depth, and multidisciplinary ocean observing array for beyond 2020 has been endorsed. In the past two decades since 2002, with more than 500 Argo deployments and 80 operational floats currently, China has become an important partner of the Argo program. Two DACs have been established to process the data reported from all Chinese floats and deliver these data to the GDACs in real time, adhering to the unified quality control procedures proposed by the Argo Data Management Team. Several Argo products have been developed and released, allowing accurate estimations of global ocean warming, sea level change and the hydrological cycle, at interannual to decadal scales. In addition, Deep and BGC-Argo floats have been deployed, and time series observations from these floats have proven to be extremely useful, particularly in the analysis of synoptic-scale to decadal-scale dynamics. The future aim of China Argo is to build and maintain a regional Argo fleet comprising approximately 400 floats in the northwestern Pacific, South China Sea, and Indian Ocean, accounting for 9% of the global fleet, in addition to maintaining 300 Deep Argo floats in the global ocean (25% of the global Deep Argo fleet). A regional BGC-Argo array in the western Pacific also needs to be established and maintained.

Argo program  /  China Argo  /  ocean observation  /  core Argo  /  Deep Argo  /  BGC-Argo
Zenghong Liu, Xiaogang Xing, Zhaohui Chen, Shaolei Lu, Xiaofen Wu, Hong Li, Chunling Zhang, Lijing Cheng, Zhaoqin Li, Chaohui Sun, Jianping Xu, Dake Chen, Fei Chai. Twenty years of ocean observations with China Argo[J]. Acta Oceanologica Sinica, 2023 , 42 (2) : 1 -16 . DOI: 10.1007/s13131-022-2076-3
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1 Introduction
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In 1998, scientists from the United States (U.S.), Australia, France, and Japan proposed a design for a global array of autonomous profiling floats to strengthen the capability of researchers to obtain temperature and salinity measurements of the upper ocean (Argo Science Team, 1998). This array, known as Argo, was initially named as an abbreviation of “Array for Real-time Geostrophic Oceanography”. The initial design of Argo required the deployments of over 3 000 profiling floats in a 300 km×300 km array covering the ice-free regions of the world ocean (Argo Science Team, 1998), and is known as “core Argo”; this array only measures the water temperature and salinity of the upper 2 000 m of the global ocean. The profiling floats used in Argo are freely drifting instruments that achieve descent and ascent by changing their buoyancy. When a float is deployed at the sea surface, it dives to a nominal depth of 1 000 m (known as the parking depth) and drifts freely with the ocean current for ~9 days. It then dives another 1 000 m, reaching 2 000 m depth, and ascends to the sea surface at a speed of ~10 cm/s. During its ascent, it measures water temperature and salinity with the mounted CTD sensor (Table 1). When the float reaches the sea surface, the data are transmitted to land stations via satellite, and the float then descends to the parking depth to begin another cycle (Roemmich et al., 2009).
By the end of 2007, the initial design of 3 000 core Argo floats was reached through the joint efforts of more than 30 countries worldwide. Now, with ~3 900 active floats across the global ocean, the Argo program has become a major component of the GOOS, which provides a description of the mean state and variability of the upper 2 000 m of the global ocean on seasonal to decadal timescales (Roemmich and Owens, 2000; Riser et al., 2016; Wong et al., 2020; Johnson et al., 2022). An open data policy at all levels of processing was implemented when the array was initially designed. Data acquired by this array are publicly and freely available via the two GDACs, which makes the Argo program a pioneer in scientific ocean data delivery (Wong et al., 2020).
Over the past decade, new sensors have been installed onto the profiling floats that allow BGC variables (e.g., dissolved oxygen, chlorophyll, backscattering, nitrate, pH, downwelling irradiance) to be measured in real time (Johnson et al., 2009; Claustre et al., 2010; Biogeochemical-Argo Planning Group, 2016). In addition, new ice-detection algorithms have been developed that greatly increase the survival rate of floats in regions with seasonal ice cover (Wong and Riser, 2011, 2013). The development of Deep Argo has been recognized as a crucial step in the evolution of the Argo program (Riser et al., 2016). An initial design of the Deep Argo array (5°×5°×15-day target) of ~1200 floats has been proposed by Johnson et al. (2015) to provide the capacity to resolve global decadal trends in ocean heat content and thermosteric sea level anomalies, which is considered to be necessary for a complete accounting of the Earth’s heat budget (Hakuba et al., 2021; von Schuckmann et al., 2020). Since that proposal, pilot deployments of hundreds of 4 000-m and 6 000-m profiling floats have been implemented by countries such as the U.S., Japan, and France, and the performances of Deep Argo CTDs are being tested and improved. Today, the renamed international Argo program, “OneArgo”, has been endorsed by the “United Nations Decade of Ocean Science for Sustainable Development (2021–2030),” which is expected to transform the revolutionary core Argo array into one with truly global reach, including the polar oceans and marginal seas, extending to the full ocean depth and including ocean biogeochemical measurements (Roemmich et al., 2019a; Johnson et al., 2022).
China’s participation in the Argo program was suggested at the 14th joint working group meeting of the U.S.-China Marine and Fishery Science and Technology Protocol (Hangzhou, China, 1999). As early as 2002, China formally joined the international Argo program with the State Council’s approval, becoming the ninth country to participate in this program (Xu, 2002; Xu and Liu, 2007; Liu et al., 2017). The initial objectives of China Argo were to construct a real-time observation network comprising 100–150 Argo floats in the adjacent northwestern Pacific Ocean and Indian Ocean, and to become an important member of the international Argo program, providing global Argo data and related data products for oceanic research, weather and climate forecasts, marine resources development and protection in China (Xu, 2002). After two decades, the objectives of China Argo have been fully realized. The Argo data have become a primary data source of fundamental research and operational forecast and prediction of ocean states, weather, and climate.
This paper reviews the progress achieved in the first two decades of China Argo since 2002, including float deployment, data acquisition, data QC, product development, float technology development, and extension of China Argo. Finally, some perspectives on the future development of China Argo are also provided.
2 Implementation status of China Argo
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2.1 Float deployment
The first China Argo float deployment (led by Weidong Yu, First Institution of Oceanography, SOA) took place in March 2002 in the tropical eastern Indian Ocean. In January 2003, a pilot project supported by the MOST, China Argo deployed the first set of 13 floats (seven PROVOR and six APEX floats) in the Philippine Sea, symbolizing the initial implementation of China Argo. The float deployment of China Argo has been sponsored by the MOST, SOA (incorporated into the MNR in 2017), and other government agencies. By the end of 2021, 544 floats had been deployed in the Pacific Ocean, Indian Ocean, Atlantic Ocean, and Southern Ocean, and some of them were deployed in marginal seas such as the South China Sea, Sea of Japan, Mediterranean Sea, and Bering Sea (Fig. 1), accounting for approximately 3% of the global deployment. These floats were provided by 16 scientists from the three institutions of oceanography and the NMEFC of the MNR, the two institutions of oceanography of the CAS, OUC, SHOU, and ZJU. Unlike countries such as the U.S., Japan, France, the United Kingdom (U.K.), Canada, and India, whose national Argo programs are typically funded by the budget in each fiscal year, China Argo is mainly sponsored by research programs, leading to notably unstable yearly deployment over the past two decades (Fig. 2). The maximum yearly deployment (91 floats) occurred in 2014; however, most yearly deployments have been fewer than 40 floats. The deployment of China Argo has focused on regions such as the northwestern Pacific Ocean, South China Sea, and Indian Ocean, which have important impacts on China’s climate and weather, disaster forecasts and prevention, and marine transportation.
APEX (Teledyne Webb Research, U.S.), PROVOR (NKE Instrumentation, France), and HM2000 (HSOE, China) are the principal float models used in China Argo, accounting for ~92% of the floats that China has deployed. In the past five years, with the development of float and sensor technology, BGC and Deep Argo floats such as PROVOR_IV, NAVIS_BGCi, and ARVOR_D have been used by China Argo (Fig. 3). For each float used by the Argo program, temperature and salinity (core variables) are the two elemental and necessary variables that a float must observe; in other words, the core Argo is the basic mission of the Argo program. In addition to temperature and salinity, more than 50 BGC floats deployed by China Argo have enabled us to obtain data on dissolved oxygen concentration, chlorophyll, particulate backscattering, downwelling irradiance, nitrate, and pH profiles in the upper 1 000 m or 2 000 m.
2.2 Data acquisition
Of the 544 floats deployed by China Argo, 539 have reported and transmitted data successfully via satellites (ARGOS, Iridium, and Beidou). As of December 2021, approximately 70 353 core profiles and 15 311 BGC profiles had been observed and processed (see Table 2), accounting for 2.8% of the total core (temperature and salinity) profiles collected by the Argo program.
In the early period of Argo, almost all floats transmitted their data via the ARGOS satellite system operated by CLS (Toulouse, France). This is a one-way and low-bandwidth satellite system, with data throughput of no more than 1 bit/s. To guarantee error-free data reception and location in all weather conditions, it takes at least 10 h for floats to transmit data at the sea surface. At present, approximately 89% of the active floats in the China Argo float array use the Iridium satellite constellation for data transmission. For this satellite system, users often receive data via RUDICS when large quantities of data are to be transmitted, such as those from BGC-Argo floats with multiple sensors and high-resolution sampling schemes. Another method is to use SBD for data transmission; for instance, data (SBD messages) from several PROVOR and PROVORDOI (with additional dissolved oxygen sensor) floats deployed by China are transferred to users via e-mail. The Iridium satellite system is a two-way communication system, users are able to send commands to modify the mission configuration of a float. In recent years, the Beidou navigation system has been adopted for profiling floats manufactured by China (e.g., the HM2000 float). In addition to positioning, the Beidou satellite system has the capacity to transmit short messages (~1.25 bytes/s). Like the Iridium system, the Beidou satellite system is also a two-way communication system.
2.3 Data assembly centers
To process (including data decoding, quality control, and data encapsulation and distribution) the data obtained by the floats in the global Argo fleet, countries such as the U.S., U.K., Australia, Japan, Korea, India, Canada, and France have established DACs. All the data assembled by DACs are immediately submitted to the two GDACs: the Coriolis Data Center in France and the U.S. Navy’s FNMOC. In China, there are two DACs: CSIO DAC, the China Argo Real-time Data Center at the Second Institute of Oceanography, MNR, and NMDIS DAC, the China Argo Data Center of the NMDIS, MNR. CSIO DAC processes data from the majority of China Argo floats (97% of the floats deployed as of December 2021), and NMDIS DAC processes data from the remaining floats.
CSIO DAC was established in 2002 with the support of the MOST and SOA, and submitted the first core profile from Float 5900019 to the GDACs. The Argo processing procedure was initially developed in 2002, by two young CSIO technicians whom received POGO-sponsored technical training at NOAA’s AOML in Miami. Because of the application of diverse new firmware versions of various float models, this procedure could no longer satisfy the needs of automatic data processing. Therefore, CSIO developed a new Argo data processing system in 2014 (Fig. 4). Allowing data from the majority of profiling floats (including BGC-Argo floats with fully equipped sensors) used by the Argo program to be processed automatically in real time, this system has been operating for the last seven years. In addition, the profile data are encoded into TESAC or BUFR messages, and over 90% of the profiles have been inserted into the GTS within 12 h via the GTS node at the CMA (Tran, 2019).
2.4 Data quality control
Upon the operation of the China Argo DACs, the unified data QC procedures specified by the ADMT have been applied. At the beginning of the Argo program, an initial design of Argo CTD QC, including a set of automatic quality tests, was drafted based on the QC tests of the GTSPP. Each DAC is required to apply these QC tests and assign quality flags to each core profile within 24 h after the data are received. That is called Argo Real-Time QC, and through this process, grossly bad data that may result from sensor malfunction or corrupted satellite transmission can be detected (Wong et al., 2020, 2022).
Because Argo floats are disposable instruments, retrieving floats for sensor recalibration is nearly impossible; thus, float data, especially salinity measurement, can be affected by sensor drift caused by biological fouling and biocide leakage into the conductivity cell (Wong et al., 2003, 2020). To calibrate the data affected by sensor drift, DMQC methods and toolkits were developed (Wong et al., 2003; Böhme and Send, 2005; Owens and Wong, 2009), which are typically conducted for the first time 12 months after float deployment (Wong et al., 2020). The effect of adjustment relies upon the quality and quantity of the nearby CTD reference data set; therefore, it is critical to collect recent high-quality shipboard CTD casts for Argo DMQC. At present, China Argo is making use of the OWC toolkit (https://github.com/ArgoDMQC/matlab_owc) for DMQC of all floats. The DMQC operator typically sends the results of comparisons between float data and the historical time-series to the scientists or experts from the DAC, who will then determine whether the adjustment will be adopted based on their knowledge of the regional oceanography. As of December 2021, approximately 75% of the core profiles acquired by China had been subjected to DMQC.
When a core profile is submitted to the Coriolis GDAC, a statistical procedure (Gaillard et al., 2009) and a daily MIN/MAX test (Gourrion et al., 2020) for detecting outliers are performed. If a profile fails these tests, a warning e-mail is sent to the associated DAC, where further examination will be implemented and anomalies flagged.
Of the 544 floats deployed by China Argo, 59 are BGC-Argo floats with a range of biogeochemical sensors. Because QC methods and procedures for all BGC variables are still under development, in the early stage, CSIO DAC did not conduct any QC tests for BGC variables. In 2019, CSIO DAC began to conduct delayed-mode bio-fouling tests on the dissolved oxygen profiles from 14 PROVORDOI floats deployed by OUC in 2014, to identify and flag oxygen measurements suspected of contamination by bio-fouling. Recently, standard RTQC procedures for oxygen and chlorophyll profile data have been determined by the BGC-ADMT; thus, CSIO has begun to follow these procedures to produce adjusted data in real time.
2.5 Global Argo data set repository
By the end of 2021, the Argo program has obtained more than 2.5 million core profiles from over 17 000 profiling floats. Various data quality problems still exist in the global data set because of uneven levels of data processing by different DACs and anomalous profiles resulting from sensor drift without timely adjustment. Therefore, CSIO developed a fast reception and post_QC system for global Argo data based on the QC method used by the ADMT combined with the practices and improved methods proposed by various DACs (Li et al., 2020b; Liu et al., 2021). With this system, the local host at CSIO is able to synchronize with the remote host at the Coriolis GDAC four times a day; the updated data are then added to the post_QC system, and anomalies in each core profile are detected and re-flagged. The ISAS13 climatology (Gaillard, 2012), a high-quality temperature and salinity climatology data set, is employed as a reference field to detect sensor drift in Argo core profiles. If a temperature or salinity measurement falls beyond ±6.5 times the standard deviation derived from the nearby climatology values, the measurement is flagged as a suspicious point (the whole profile is flagged if more than 1/3 points are beyond the threshold). Finally, the post_QC data are subjected to visual examination by oceanographic experts and re-flagged when necessary. The data set is expected to be updated quarterly and made freely available via the CSIO’s FTP site (ftp://ftp.argo.org.cn/pub/ARGO/global/core). Approximately 2 551 028 temperature and salinity profiles were downloaded from the GDACs, ranging from July 1997 to December 2021, 2 411 825 of which were retained after QC, accounting for approximately 94.5% of the total profiles (Fig. 5). The daily post_QC Argo data set provided by CSIO (excluding the visual examination) has been transferred to several departments and institutions for application in their operational forecast and reanalysis systems. The data set has also been used to evaluate the quality of remotely sensed sea surface salinity (SSS) (Yan et al., 2021), and the results indicated the high quality of this data set.
In addition to temperature and salinity, the BGC profiles provided by the GDACs are also synchronized daily to the local host. To date, the QC procedures for most BGC variables are still under revision, and the original quality flags assigned by the associated DAC are used, no BGC measurements will be re-flagged in our present system before they are written in a readable txt file.
2.6 Development of Argo products
The Argo fleet, comprising over 3 500 floats, provides more than 120 000 temperature and salinity profiles each year across the global ocean, although these floats are unevenly distributed both in time and space. To facilitate the usage of the Argo data in the oceanographic and atmospheric realms, different groups around the world have developed various products based on Argo data in addition to other data sources. These products are typically monthly averaged fields, and include measurements such as temperature, salinity, and mixed layer depth (Ridgway et al., 2002; Hosoda et al., 2008; Roemmich and Gilson, 2009; Good et al., 2013; Gaillard et al., 2016; Holte et al., 2017).
The first gridded product of China Argo was developed by Wang et al. (2006) based on a data interpolating empirical orthogonal function (DIN-EOF) method. It contains a three-dimensional temperature, salinity, and ocean velocity data set across the Pacific Ocean. Subsequently, related products were developed, which have been particularly useful in investigations of physical oceanography and climate change (Li et al., 2013, 2017; Liu et al., 2017; Cheng et al., 2017; Li et al., 2020a). Some gridded products combine Argo data with other observations (such as data from gliders, CTD probes, XBTs, and MBTs) and extend back to 1940–1960 (Cheng et al., 2017; Li et al., 2020a). However, the products developed in the early stage were generally regional in scale and rarely updated, which greatly limited their further usage. In this context, we introduced the applications of products with global coverage and that are routinely updated: BOA_Argo (Li et al., 2013, 2017; Lu et al., 2020), IAP data (Cheng and Zhu, 2016; Cheng et al., 2020; Li et al., 2020a), and GDCSM_Argo (Zhang et al., 2013).
2.6.1 BOA_Argo data set
BOA_Argo, a global gridded Argo data product developed by CSIO in 2012 (ftp://data.argo.org.cn/pub/ARGO/BOA_Argo/) (Li et al., 2013, 2017), is a monthly product containing data on temperature, salinity, isothermal layer depth, mixed layer depth, and composite mixed layer depth, with 1°×1° horizontal resolution and 58 vertical levels from the surface to 1 975×104 Pa, covering a period of 17 years from 2004 to 2020 (Lu et al., 2020). The product was generated based on Argo data only and was first released on the Argo program office’s website (https://argo.ucsd.edu/data/argo-data-products/) in 2017. Recently, BOA_Argo has drawn considerable attention in scientific research, such as in studies of the interannual variability of OHC (Liu et al., 2019; Liang et al., 2021; Yang et al., 2021; Lyu et al., 2021), salinity/the hydrological cycle (Tesdal et al., 2018; Cheng et al., 2020; Li et al., 2019; Liu et al., 2019, 2020; Duan et al., 2021; Ponte et al., 2021; Wu et al., 2021), eddy-induced heat transport (Gonaduwage et al., 2021), the Earth's energy imbalance (Hakuba et al., 2021), and sea level change (Amin et al., 2020; Camargo et al., 2020). In addition, it has been adopted in the field of biochemistry (Dilmahamod et al., 2019; Guerreiro et al., 2019; Herr et al., 2019; Park et al., 2020; Wang et al., 2021b) and in assessment of the quality of satellite remotely sensed SSS (Bao et al., 2019, 2021; Liu and Wei, 2021).
In summary, the extensive usage of BOA_Argo has proven its high quality and reliability. Furthermore, the strict quality control procedures (Li et al., 2017, 2020b) and refined Barnes successive method employed in the generation of BOA_Argo allow mesoscale signals to be accurately captured, compared with other datasets with the same horizontal resolution (Li et al., 2017). For example, the abnormal eddy structure with high SSS in June 2016 from EN.4.2.1 (Good et al., 2013) was invisible both in corresponding satellite observations and BOA_Argo (Bao et al., 2021), indicating the high quality of BOA_Argo. The interannual variability of OHC from BOA_Argo is nearly 50% larger than that of other gridded datasets in regions with active mesoscale signals (e.g., the Antarctic Circumpolar Current (ACC) and western boundary currents) (Liang et al., 2021) (Fig. 6), consistent with the greater portion of mesoscale signals retained in BOA_Argo (Li et al., 2017).
2.6.2 IAP data set
The IAP data set (Cheng et al., 2017, 2020; Li et al., 2020a, http://www.ocean.iap.ac.cn/) is another widely used global ocean gridded data set. In contrast to BOA_Argo, other available profiles from various instruments (e.g., XBT, MBT, and shipboard CTD) are also used to produce this data set. It includes 1°×1° monthly temperature fields starting from 1940, from the sea surface to 2 000 m depth. At present, other variables such as salinity, potential density, stratification, and OHC are also estimated (Cheng et al., 2020; Li et al., 2020a). The relatively long time series of the IAP data set gives it advantages in scientific studies associated with climate change and variability (Cheng et al., 2017, 2019, 2020; Frederikse et al., 2020; Li et al., 2020a). The usage of the internationally recommended bias correction scheme for XBT developed by Cheng et al. (2014) and an ensemble optimal interpolation scheme have greatly reduced uncertainty in the estimation of the long-term variability of OHC (Cheng et al., 2017), salinity (Cheng et al. 2020), and stratification (Li et al., 2020a) (Fig. 7). For example, a recent study explicitly stated the advantage of the IAP data set when comparing different products with independent sea level observations: “the SDSLEOF reconstruction based on the ref. 24 dataset has the smallest trend differences to the SDSLTG in most regions (extended data Fig. 5) and also provides the best representation of variability.” (Dangendorf et al., 2021). Furthermore, the IAP analysis has been comprehensively evaluated using the knowledge of recent well-observed ocean states during the Argo period (since 2005), but subsampled using the sparse distribution of observations in the more distant past, to show that the method produced a less biased (compared with irregular sampling) historical reconstruction back to 1960. This method provides a new strategy to evaluate data sets and increases the value of Argo data.
The IAP data products have supported many scientific studies and climate services since their initial release in 2016. For example, these products directly supported a key assessment result in an IPCC special report for the ocean and cryosphere: “Critically, the high confidence and high agreement in the ocean temperature data means we can detect discernable rates of increase in ocean heat uptake (Gleckler et al., 2012; Cheng et al., 2019)” (Bindoff et al., 2019). The IAP data are updated every 3–5 months and provide climate information to society (Cheng et al., 2021); for example, they have been used as key climate indicators by several governmental organizations, such as the U.S. Environmental Protection Agency (https://www.epa.gov/climate-indicators/climate-change-indicators-ocean-heat).
2.6.3 GDCSM_Argo data set
GDCSM_Argo (ftp://data.argo.org.cn/pub/ARGO/GDCSM_Argo/) is a product developed by CSIO DAC and SHOU. In contrast with BOA_Argo, a method of GDCSM was used to construct the three-dimensional gridded data (Zhang et al., 2013). The development of the GDCSM data set is based on OI and adopts anisotropic correlation scales. This method can be easily derived as a variation problem to find a vector of estimates that minimizes the total error variance of the field being estimated. It deduces the correlation scale function from the Fourier solution of the anisotropic diffusion equation. According to the needs of the objective analysis, the horizontal gradient change of oceanographic elements is evaluated based on climate data (Zhang et al., 2015). The longitudinal and latitudinal correlation scales in the objective analysis are limited by horizontal gradients. By adding the anisotropic background error correlations, the GDCSM can effectively extract shorter wavelength information zonally with a large gradient (Zhang et al., 2015, 2021). Error tests and statistics are used to determine the key parameters, such as correlation scale extrema, the radius of influence, and the relative ratio of background to observation errors.
Following a similar procedure to that of BOA_Argo, a global Argo gridded data set (GDCSM_Argo) is then generated. The product provides monthly three-dimensional temperature, salinity, sound velocity, and thermocline parameters for the global ocean from January 2004 through December 2020. The horizontal resolution is 1°×1°, with 58 vertical layers at depths between 0 m and 1 950 m. The GDCSM_Argo data set was verified based on in-situ observations and other gridded data sets. Its temperature and salinity differences relative to TAO/TRITON moored buoy observations are less than ±0.5°C and ±0.02 respectively in the equatorial Pacific (Fig. 8). In general, this data set is in good agreement with other gridded data sets such as WOA18 (Garcia et al., 2019), Roemmich-Argo (Roemmich and Gilson, 2009), JAMSTEC-Argo (Hosoda et al., 2008), EN4.2.1, and IPRC-Argo (http://apdrc.soest.hawaii.edu/projects/argo/). However, in contrast to WOA18 and other gridded data sets estimated by traditional OI, GDCSM_Argo tends to retain more small-scale information (Zhang et al., 2015). The GDCSM_Argo has been applied in the fishery oceanography (Xie et al., 2019; Zhang et al., 2021, 2022), and revealed more accurate environmental characters of fishing ground.
2.7 Development of float technology
Two float models have been developed in China during the past two decades, i.e., HM2000 and COPEX float developed by HSOE and NOTC, respectively. Both of them adopt a hydraulic system to change their buoyancy for controlling the descending and ascending of the floats. By SBE 41/41CP CTD sensor that the floats equipped, temperature and salinity profile data are acquired as they ascend to the sea surface. After reaching the sea surface, they transmit data and positions via ARGOS, Iridium or Beidou satellite. In November 2014, CSIO organized a field-testing experiment in the northwestern Pacific, where two COPEX, one HM2000 and two APEX floats were deployed for a comparison of the temperature and salinity profiles between the floats and concurrent shipboard CTD (Lu et al., 2016). Based on the results from this experiment, the HM2000 float was then introduced for the first time to the AST-16 meeting (Brest, France, March 2015), and received certification from the AST. Since then HSOE has continued to develop floats equipped with oxygen sensor.
2.8 Expansion of China Argo
2.8.1 BGC-Argo
With the rapid technical development of miniaturization and low-power consumption of optical, photochemical, and electrochemical sensors, profiling floats equipped with a wide variety of sensors represent promising technology and platforms for future observations in ocean biogeochemistry and bio-optics (Chai et al., 2020). The BGC-Argo program aims to develop a 1000-float global array, equipped with biogeochemical and bio-optical sensors, measuring, for example, oxygen, nitrate, pH, chlorophyll, particulate backscattering coefficients, and downwelling irradiance, in support of fundamental research on oceanic biogeochemical cycles and ecosystems and the needs of ocean resource management (Claustre et al., 2020).
As early as 2009, the first two BGC-Argo floats (APEX float equipped with an oxygen sensor) contributed by China were cast in the Luzon Strait and southeast off Taiwan Island, respectively. This could be recognized as an early trial of the BGC-Argo project in China. However, until 2018, BGC-Argo float deployments from China were very rare, with the exception of 14 PROVORDOI floats deployed in 2014 (mentioned above, but their valid oxygen data only lasted approximately 3 months). Since then, CSIO has deployed 14 additional floats in the northwestern Pacific to build up a regional BGC-Argo array, and nine floats are still operational at the end of 2021 (Fig. 9a). Aside from the northwestern Pacific, some BGC-Argo floats were also deployed in the Bay of Bengal, South Atlantic, and Southern Ocean; these were typically Argo-equivalent floats sponsored by several institutions and universities.
In particular, the first China-made BGC-Argo float (using the Beidou satellite system for data transmission), a HM2000 profiling float equipped with an Aanderaa optode 4330 oxygen sensor (Fig. 9b), was deployed in March 2021 in the Bay of Bengal, and the second one was deployed in August 2021 in the northwestern Pacific (WMO number: 2902757; Fig. 9a). In addition to temperature and salinity measurements, both of these floats reported fairly good dissolved oxygen profiles.
The observed data from the CSIO northwestern Pacific BGC-Argo array have proven to be extraordinarily valuable to regional ecosystem dynamics studies, including the typhoon-induced response of phytoplankton vertical distribution (Chai et al., 2021), mid-latitude northwestern Pacific winter blooms and fast carbon exports (Xing et al., 2020), seasonal and daily-scale phytoplankton photoacclimation effects (Xing et al., 2021), eddy-induced physical and biogeochemical response (Ding et al., 2022), as well as the neural-network-based regional model of chlorophyll profile estimate (Chen et al., 2022). Particularly, Xing et al. (2020) reported the wintertime fast carbon exports due to the synoptic-scale mixed-layer dynamics driven by the winter storms (Fig. 10), the observed carbon-export process, called as “mixed layer pump (MLP)”, was unexpectedly strong in the mid-latitude oceans, substantially extending the window of time where MLP processes can be important. Their findings also indicated that carbon export via MLP processes during winter occurred on time scales too short to be adequately sampled with the 5- or 10-day profiling intervals of regular Argo floats, and highlighted the significance of high-frequency observation for quantifying carbon exports in the mid-latitudes.
2.8.2 Deep Argo
The proposed Deep Argo array, with the goal of maintaining a global fleet of 1 200 floats collecting temperature, salinity, and pressure data from the sea surface to near the ocean bottom, will expand Argo’s current monitoring capabilities to the full ocean volume, allowing for assessment of year-to-year variability in the deep ocean’s heat and freshwater contents, circulation, and contribution to steric sea level rise (Johnson et al., 2015). Recent technological advancements have led to the development of four new float models capable of reaching the deep and abyssal ocean: Deep Arvor, Deep NINJA, Deep SOLO and Deep APEX, with maximum profiling depths of 4 000 m and 6 000 m, respectively (Kobayashi et al., 2013; Petzrick et al., 2013; Le Reste et al., 2016; Roemmich et al., 2019b). By the end of 2021, approximately 320 Deep Argo floats have been deployed by 10 countries, and 188 of these are still reporting data.
In 2016, from the “Wenhai” project that was launched and funded by the Laoshan Laboratory, China began its pioneering development of a 4 000-m profiling float. Several 4 000-m float prototypes developed by OUC, Tianjin University, and Laoshan Laboratory were manufactured and tested in the South China Sea and western Pacific during 2018–2019, and over a thousand 4 000-m temperature and salinity profiles were obtained (Gao et al., 2021). After Phase I of the “Wenhai” project, the HM4000 deep profiling float designed by Laoshan Laboratory was ultimately chosen as the primary model to engage in pilot regional deployment.
Similar to the features of the other deep floats, a HM4000 float can park at depths ranging from 500 m to 4 000 m, with a nominal parking depth of 1 000 m. Users can easily adjust the ascending/descending speed of the float according to their observation requirements via the bidirectional satellite system. Moreover, the HM4000 float has the advantages of self-protection strategies, such as grounding detection and emergent ascending for a timeout or excessively deep profiling. To prolong the float’s lifetime, a low-power-consumption strategy in the profiling phase was also designed. At present, the measurement module is compatible with both SBE61 and RBRargo3 deep CTD sensors.
In 2020, the deep float R&D team from Laoshan Laboratory upgraded the existing testing processes and manufacturing for HM4000, based on the results and feedback from field testing in 2019 in the western Pacific. In May and June 2021, nine HM4000 floats were deployed around the region of the Kuroshio and Oyashio Extension in the North Pacific, with eight of them equipped with RBRargo3 deep CTD and one with an SEB61 CTD sensor (Fig. 11). By the end of 2021, seven floats were still reporting data and over a hundred 4 000-m temperature and salinity profiles had been acquired (Fig. 12). To verify the accuracy of the CTD sensors equipped on the floats, a CTD rosette equipped with a SBE 911 plus CTD and a Guildline Autosal 8400B salinometer were taken onboard the research vessel. When a float was deployed, a full-depth CTD cast was conducted and several water samples were taken from the CTD rosette. After collection, all samples were stored in the salinometer laboratory and were analyzed for salinity. The comparisons between observations from the floats, shipboard CTD, salinometer, and climatology indicated that half of the floats were able to obtain fairly good temperature and salinity profiles at least at the time they were deployed (e.g., float #16 equipped with the RBRargo3 CTD sensor, shown in Fig. 13), whereas the remainder observed systematic or pressure-dependent salinity errors (e.g., float #18 equipped with the RBRargo3 CTD sensor, shown in Fig. 14). Data obtained from this field test are very useful for the sensor manufacturers to verify and improve their Deep Argo CTDs.
3 Perspectives on China Argo
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3.1 Future of the China Argo network
Because the physical and biogeochemical profiles from the global Argo fleet have been widely used in scientific research and ocean forecasts, the value of the Argo program has been fully realized by the scientific community. However, in contrast to the U.S., Australia, France, Germany, U.K., and Japan, China’s contribution to the global Argo network was not sufficient during the past two decades. For instance, both the total number of the floats deployed and the current number of operational floats account for no more than 3.5% of the global array. As mentioned in Section 2.1, the majority of float deployments were sponsored by research projects, which is considered to be unsustainable. Figure 15 shows the time evolution of the China Argo fleet over the past decade. The number of the active floats reached a peak of 204 in January 2015, when a large number of floats sponsored by a special project were added into China Argo during 2010–2014, and brought the number of active floats of China Argo to fourth place in the global array (~5.3% of the total number). Since then, float deployment reverted to the previous situation again. As of December 2021, the number of the active floats decreased to 85, only accounting for 2.16% of the total number. It is worth noting that float deployments were greatly affected by the COVID-19 pandemic around the world since the end of 2019, which resulted in a 2% reduction of operational floats globally. Therefore, it is a great opportunity and obligation for China Argo to make a greater contribution to the international Argo program, e.g., “OneArgo”, for the next decade.
It has been suggested that China Argo should maintain a fleet of 400 floats in the North Pacific, South China Sea, and Indian Ocean, with a yearly float deployment of approximately 100 floats, and remarkably increase China’s contribution to the Argo program. In addition, the percentage of domestically produced profiling floats using the Beidou satellite system for data transmission is expected to be increased. Furthermore, the regional Deep Argo and BGC-Argo observing networks are considered to be crucial to satisfy the needs of multidisciplinary studies and novel discoveries.
3.2 BGC-Argo
Presently, China BGC-Argo relies on scattered contributions from several research institutions, without overall planning. Nevertheless, time series of subsurface observations from BGC-Argo have proven to be extraordinarily valuable to studies on meso- and submesoscale processes (Wang et al., 2021a) and daily to synoptic-scale ecosystem dynamics (Xing et al., 2020; Chai et al., 2021).
CSIO expects to maintain the present regional BGC-Argo array in the Northwest Pacific, and to extend it to the tropical and subarctic Pacific, as well as the South China Sea. In addition, more contributions by China in the Indian Ocean, Southern Ocean, and Arctic Ocean have been suggested, supporting more studies of biogeochemical cycles, ecosystem health (e.g., warming, acidification, and deoxygenation), and biological carbon pump processes. In the next decade (2021–2030), it is expected that China will contribute (and maintain) approximately 50–100 BGC-Argo floats in the global array (i.e., 5%–10%), and that China-made BGC-Argo float and sensor technology will be further promoted and developed. As for the BGC data quality control, CSIO has participated in the development of novel QC methods (e.g., on irradiance, chlorophyll, and backscattering data) along with other DACs, towards a better application of BGC-Argo data. Some DMQC procedures (e.g., on oxygen, nitrate, chlorophyll) have begun to be tested, and CSIO expects to achieve the operational application of DMQC procedures of all biogeochemical variables in the near future.
3.3 Deep Argo
It is also expected that China will maintain at least 300 Deep Argo floats in the global ocean, particularly in the North Pacific (e.g., the South China Sea, Kuroshio Extension regions, and tropical western Pacific regions) and in the Southern Ocean, building up a regional deep-ocean observation network and providing observations as deep as 6 000 m for the scientific community. This would be approximately 25% of the total floats in the global Deep Argo array, and would require a yearly deployment of approximately 100 floats.
Improvement and upgrading of the HM4000 float will be continued, with a target of at least 100 mission cycles in the sea or a three-year mean float lifetime. At Laoshan Laboratory, the R&D team is developing the 6 000-m float, Xuanwu, which will allow the water column from the sea surface to the sea floor to be measured. In addition, to accommodate the increasing requirements for future deployments of Deep Argo, high-precision CTD sensors are being developed. In the next decade, the 4 000-m (6 000-m) profiling floats are planned to be manufactured industrialized before 2025, and a data center will be established by collaboration between Laoshan Laboratory and CSIO.
4 Summary and discussion
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The international Argo program achieved its initial design of a global array comprising 3 000 core Argo floats within seven years after 2000. It took twelve years to collect one million profiles and only six years for another million profiles across the global ocean. The open-data policy adopted by Argo has driven an explosion in ocean and climate research using Argo data. It should be noted that the Argo program would never be successful without the international collaborations of more than 30 countries. Today, “OneArgo” is endorsed by the UN Ocean Decade, aiming to build a truly global, full-depth, and multidisciplinary network beyond 2020. However, to accomplish this goal, sufficient resource investment from each member country and new partners is a challenge we must face.
In this paper, we reviewed the progress achieved by China Argo over the past two decades. Relying on research projects and a few special programs, China has maintained a regional Argo fleet consisting of approximately 100 floats across the Pacific Ocean, South China Sea, and Indian Ocean. These float deployments have been supported by the majority of institutions and universities in China. Data reported by each operational float (via different communication systems) are transmitted to the associated DAC and then submitted to GDACs in real time after automatic data QC with the ADMT-specified QC procedure. Through fast reception and the post_QC system, CSIO DAC is able to provide high-quality global Argo temperature and salinity profiles for operational departments in near real time. Several Argo data products based on different objective analysis methods have been developed and distributed. These gridded data sets may include variables such as temperature, salinity, potential density, sound velocity, OHC, mixed-layer depth, and thermocline depth. Many scientific studies and intergovernmental climate change reports have cited and benefited from these data sets. In China, Argo data have been used and produced over 900 scientific publications, the second most around the world (refer to https://argo.ucsd.edu/outreach/publications/bibliography/).
Expansion of China Argo from core to Deep and BGC has begun recently. This includes establishing the regional BGC-Argo observation array in the northwestern Pacific and pilot deployments of deep floats (4 000 m). Moreover, BGC (equipped with dissolved oxygen sensors) and deep profiling floats (4 000 m) are being developed and tested. Despite the small number of BGC floats being deployed, their time series of subsurface observations have proven to be very useful in studies of multi-scale biogeochemical cycles and ecosystem dynamics. The pilot deployments of the HM4000 float in the North Pacific have obtained valuable 4 000-m temperature and salinity profiles, as well as simultaneous shipboard CTD casts and seawater salinity analyses by salinometer, which are expected to be useful for the sensor manufacturers to improve their CTD products.
In general, China Argo’s contribution to the global Argo network over the past two decades is not sufficient in contrast with the outcomes using Argo data. To sustain the growth of the China Argo fleet and respond to the UN decade action of “OneArgo”, a regional Argo observation network comprising 400 profiling floats across the northwestern Pacific, South China Sea, and Indian Ocean has been proposed. In addition, the building of a regional Deep Argo fleet of 300 deep floats had been suggested by the Laoshan Laboratory, which would account for 1/4 of the global array. For BGC-Argo, the regional fleet focusing on the western Pacific has been regarded as a promising observation system for studying biogeochemical cycles, ecosystem health, and biological carbon pump processes. For the next ten years, the construction and maintenance of the China regional Argo array should be incorporated into the national fiscal budget. In addition, a China Argo alliance that includes the majority of oceanography institutions and universities needs to be established for better coordination and effectively use of float resources.
Acknowledgements
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We thank those institutions, universities, principal investigators and ships who have made contributions to China Argo either in Argo fleet maintenance or float deployment. We thank Sara J. Mason, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.
Funding
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  • The National Natural Science Foundation of China under contract Nos 42122046, 42076202, U1811464 and 4210060098; the Project Supported by Laoshan Laboratory under contract No. LSKJ202201500; the Project Supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. SML2021SP102.
References
Less
Amin H, Bagherbandi M, Sjöberg L E. 2020. Quantifying barystatic sea-level change from satellite altimetry, GRACE and Argo observations over 2005–2016. Advances in Space Research, 65(8): 1922–1940, doi: 10.1016/j.asr.2020.01.029
Argo Science Team. 1998. On the design and implementation of Argo: an initial plan for a global array of profiling floats. ICPO Report No. 21. Melbourne, Victoria: GODAE International Project Office, Bureau of Meteorology
Bao Senliang, Wang Huizan, Zhang Ren, et al. 2019. Comparison of satellite-derived sea surface salinity products from SMOS, Aquarius, and SMAP. Journal of Geophysical Research: Oceans, 124(3): 1932–1944, doi: 10.1029/2019JC014937
Bao Senliang, Wang Huizan, Zhang Ren, et al. 2021. Application of phenomena-resolving assessment methods to satellite sea surface salinity products. Earth and Space Science, 8(8): e2020EA001410
Bindoff N L, Cheung W W L, Kairo J G, et al. 2019. Changing ocean, marine ecosystems, and dependent communities. In: Pörtner H O, Roberts D C, Masson-Delmotte V, et al. , eds. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Cambridge, UK and New York, NY, USA: Cambridge University Press, 447–587
Biogeochemical-Argo Planning Group. 2016. The scientific rationale, design and implementation plan for a biogeochemical-Argo float array. https://biogeochemical-argo.org/cloud/document/relevant-reports/BGC-Argo_Science_Implementation_Plan.pdf[2016-04-12/2020-12-03]
Böhme L, Send U. 2005. Objective analyses of hydrographic data for referencing profiling float salinities in highly variable environments. Deep-Sea Research Part II: Topical Studies in Oceanography, 52(3–4): 651–664
Camargo C M L, Riva R E M, Hermans T H J, et al. 2020. Exploring sources of uncertainty in steric sea-level change estimates. Journal of Geophysical Research: Oceans, 125(10): e2020JC016551
Chai Fei, Johnson K S, Claustre H, et al. 2020. Monitoring ocean biogeochemistry with autonomous platforms. Nature Reviews Earth & Environment, 1(6): 315–326
Chai Fei, Wang Yuntao, Xing Xiaogang, et al. 2021. A limited effect of sub-tropical typhoons on phytoplankton dynamics. Biogeosciences, 18(3): 849–859, doi: 10.5194/bg-18-849-2021
Chen Jianqiang, Gong Xun, Guo Xinyu, et al. 2022. Improved perceptron of subsurface chlorophyll maxima by a deep neural network: A case study with BGC-Argo float data in the northwestern Pacific Ocean. Remote Sensing, 14(3): 632, doi: 10.3390/rs14030632
Cheng Lijing, Abraham J, Hausfather Z, et al. 2019. How fast are the oceans warming?. Science, 363(6423): 128–129, doi: 10.1126/science.aav7619
Cheng Lijing, Abraham J, Trenberth K E, et al. 2021. Upper ocean temperatures hit record high in 2020. Advances in Atmospheric Sciences, 38(4): 523–530, doi: 10.1007/s00376-021-0447-x
Cheng Lijing, Trenberth K E, Fasullo J, et al. 2017. Improved estimates of ocean heat content from 1960 to 2015. Science Advances, 3(3): e1601545, doi: 10.1126/sciadv.1601545
Cheng Lijing, Trenberth K E, Gruber N, et al. 2020. Improved estimates of changes in upper ocean salinity and the hydrological cycle. Journal of Climate, 33(23): 10357–10381, doi: 10.1175/JCLI-D-20-0366.1
Cheng Lijing, Zhu Jiang. 2016. Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variations. Journal of Climate, 29(15): 5393–5416, doi: 10.1175/JCLI-D-15-0730.1
Cheng Lijing, Zhu Jiang, Cowley R, et al. 2014. Time, probe type, and temperature variable bias corrections to historical expendable bathythermograph observations. Journal of Atmospheric and Oceanic Technology, 31(8): 1793–1825, doi: 10.1175/JTECH-D-13-00197.1
Claustre H, Bishop J, Boss E, et al. 2010. Bio-optical profiling floats as new observational tools for biogeochemical and ecosystem studies: Potential synergies with ocean color remote sensing. In: Hall J, Harrison D E, Stammer D, eds. Proceedings of the “OceanObs’09: Sustained Ocean Observations and Information for Society”. Venice, Italy: ESA Publication
Claustre H, Johnson K S, Takeshita Y. 2020. Observing the global ocean with biogeochemical-Argo. Annual Review of Marine Science, 12(1): 23–48, doi: 10.1146/annurev-marine-010419-010956
Dangendorf S, Frederikse T, Chafik L, et al. 2021. Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level. Nature Climate Change, 11(6): 514–520, doi: 10.1038/s41558-021-01046-1
Dilmahamod A F, Penven P, Aguiar-González B, et al. 2019. A new definition of the South-East Madagascar Bloom and analysis of its variability. Journal of Geophysical Research: Oceans, 124(3): 1717–1735, doi: 10.1029/2018JC014582
Ding Ya’nan, Yu Fei, Ren Qiang, et al. 2022. The physical-biogeochemical responses to a subsurface anticyclonic eddy in the Northwest Pacific. Frontiers in Marine Science, 8: 766544, doi: 10.3389/fmars.2021.766544
Duan Wei, Cheng Xuhua, Zhu Xiuhua, et al. 2021. Variability in upper-ocean salinity stratification in the tropical Pacific Ocean. Acta Oceanologica Sinica, 40(1): 113–125, doi: 10.1007/s13131-020-1597-x
Frederikse T, Landerer F, Caron L, et al. 2020. The causes of sea-level rise since 1900. Nature, 584(7821): 393–397, doi: 10.1038/s41586-020-2591-3
Gaillard F. 2012. ISAS-Tool Version 6: Method and configuration. Brest: via Ifremer
Gaillard F, Autret E, Thierry V, et al. 2009. Quality control of large Argo datasets. Journal of Atmospheric and Oceanic Technology, 26(2): 337–351, doi: 10.1175/2008JTECHO552.1
Gaillard F, Reynaud T, Thierry V, et al. 2016. In situ–based reanalysis of the global ocean temperature and salinity with ISAS: variability of the heat content and steric height. Journal of Climate, 29(4): 1305–1323, doi: 10.1175/JCLI-D-15-0028.1
Gao Zhiyuan, Chen Zhaohui, Huang Xiaodong, et al. 2021. Internal wave imprints on temperature fluctuations as revealed by rapid-sampling deep profiling floats. Journal of Geophysical Research: Oceans, 126(12): e2021JC017878
Garcia H E, Boyer T P, Baranova O K, et al. 2019. World Ocean Atlas 2018: Product Documentation. A. Mishonov, Technical Editor. https://www.ncei.noaa.gov/sites/default/files/2020-04/woa18documentation.pdf[2022-4-6]
Gleckler P, Santer B, Domingues C, et al. 2012. Human-induced global ocean warming on multidecadal timescales. Nature Climate Change, 2: 524–529, doi: 10.1038/nclimate1553
Gonaduwage L P, Chen Gengxin, Priyadarshana T, et al. 2021. Interannual variability of summertime eddy-induced heat transport in the western South China Sea and its formation mechanism. Climate Dynamics, 57(1): 451–468
Good S A, Martin M J, Rayner N A. 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. Journal of Geophysical Research: Oceans, 118(12): 6704–6716, doi: 10.1002/2013JC009067
Gourrion J, Szekely T, Killick R, et al. 2020. Improved statistical method for quality control of hydrographic observations. Journal of Atmospheric and Oceanic Technology, 37(5): 789–806, doi: 10.1175/JTECH-D-18-0244.1
Guerreiro C V, Baumann K H, Brummer G J A, et al. 2019. Transatlantic gradients in calcifying phytoplankton (coccolithophore) fluxes. Progress in Oceanography, 176: 102140, doi: 10.1016/j.pocean.2019.102140
Hakuba M Z, Frederikse T, Landerer F W. 2021. Earth’s energy imbalance from the ocean perspective (2005–2019). Geophysical Research Letters, 48(16): e2021GL093624
Herr A E, Kiene R P, Dacey J W H, et al. 2019. Patterns and drivers of dimethylsulfide concentration in the northeast subarctic Pacific across multiple spatial and temporal scales. Biogeosciences, 16(8): 1729–1754, doi: 10.5194/bg-16-1729-2019
Holte J, Talley L D, Gilson J, et al. 2017. An Argo mixed layer climatology and database. Geophysical Research Letters, 44(11): 5618–5626, doi: 10.1002/2017GL073426
Hosoda S, Ohira T, Nakamura T. 2008. A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. JAMSTEC Report of Research and Development, 8: 47–59, doi: 10.5918/jamstecr.8.47
Johnson K S, Berelson W M, Boss E S, et al. 2009. Observing biogeochemical cycles at global scales with profiling floats and gliders: prospects for a global array. Oceanography, 22(3): 216–225, doi: 10.5670/oceanog.2009.81
Johnson G C, Hosoda S, Jayne S R, et al. 2022. Argo-two decades: Global oceanography, revolutionized. Annual Review of Marine Science, 14(1): 379–403, doi: 10.1146/annurev-marine-022521-102008
Johnson G C, Lyman J M, Purkey S G. 2015. Informing Deep Argo array design using Argo and full-depth hydrographic section data. Journal of Atmospheric and Oceanic Technology, 32(11): 2187–2198
Kobayashi T, Watanabe K, Tachikawa M. 2013. Deep NINJA collects profiles down to 4, 000 meters. Sea Technology, 54(2): 41–44
Le Reste S, Dutreuil V, André X, et al. 2016. “Deep-Arvor”: A new profiling float to extend the Argo observations down to 4000-m depth. Journal of Atmospheric and Oceanic Technology, 33(5): 1039–1055, doi: 10.1175/JTECH-D-15-0214.1
Li Guancheng, Cheng Lijing, Zhu Jiang, et al. 2020a. Increasing ocean stratification over the past half-century. Nature Climate Change, 10(12): 1116–1123, doi: 10.1038/s41558-020-00918-2
Li Zhaoqin, Liu Zenghong, Lu Shaolei. 2020b. Global Argo data fast receiving and post-quality-control system. IOP Conference Series: Earth and Environmental Science, 502(1): 012012, doi: 10.1088/1755-1315/502/1/012012
Li Hong, Xu Jianping, Liu Zenghong, et al. 2013. Study on the global ocean Argo gridded dataset and its validation community in coastal waters of Yantai. Marine Science Bulletin (in Chinese), 32(6): 615–625
Li Hong, Xu Fanghua, Zhou Wei, et al. 2017. Development of a global gridded Argo data set with Barnes successive corrections. Journal of Geophysical Research: Oceans, 122(2): 866–889, doi: 10.1002/2016JC012285
Li Guancheng, Zhang Yuhong, Xiao Jingen, et al. 2019. Examining the salinity change in the upper Pacific Ocean during the Argo period. Climate Dynamics, 53(9): 6055–6074
Liang Xinfeng, Liu Chao, Ponte R M, et al. 2021. A comparison of the variability and changes in global ocean heat content from multiple objective analysis products during the Argo period. Journal of Climate, 34(19): 7875–7895
Liu Zenghong, Li Zhaoqin, Lu Shaolei, et al. 2021. Scattered dataset of global ocean temperature and salinity profiles from the international Argo Program. Journal of Global Change Data & Discovery (in Chinese), 5(3): 312–321
Liu Chao, Liang Xinfeng, Chambers D P, et al. 2020. Global patterns of spatial and temporal variability in salinity from multiple gridded Argo products. Journal of Climate, 33(20): 8751–8766, doi: 10.1175/JCLI-D-20-0053.1
Liu Hao, Lin Xiaopei, Lan Jian. 2019. Salt sinking in the upper South Pacific subtropical gyre from 2004 to 2016. Journal of Geophysical Research: Oceans, 124(10): 7011–7029, doi: 10.1029/2019JC015270
Liu Hao, Wei Zexun. 2021. Intercomparison of global sea surface salinity from multiple datasets over 2011–2018. Remote Sensing, 13(4): 811, doi: 10.3390/rs13040811
Liu Zenghong, Wu Xiaofen, Xu Jianping, et al. 2017. China Argo project: progress in China Argo ocean observations and data applications. Acta Oceanologica Sinica, 36(6): 1–11, doi: 10.1007/s13131-017-1035-x
Lu Shaolei, Liu Zenghong, Li Hong, et al. 2020. Manual of Global Ocean Argo Gridded Data Set (BOA_Argo). Hangzhou: China Argo Real-Time Data Center
Lu Shaolei, Sun Chaohui, Liu Zenghong, et al. 2016. Comparative testing and data quality evaluation for COPEX, HM2000 and APEX profiling buoys. Journal of Ocean Technology (in Chinese), 35(1): 84–92
Lyu Kewei, Zhang Xuebin, Church J A. 2021. Projected ocean warming constrained by the ocean observational record. Nature Climate Change, 11(10): 834–839, doi: 10.1038/s41558-021-01151-1
Owens W B, Wong A P S. 2009. An improved calibration method for the drift of the conductivity sensor on autonomous CTD profiling floats by θ-S climatology. Deep-Sea Research Part I: Oceanographic Research Papers, 56(3): 450–457, doi: 10.1016/j.dsr.2008.09.008
Park J E, Park K A, Kang C K, et al. 2020. Satellite-observed chlorophyll-a concentration variability and its relation to physical environmental changes in the East Sea (Japan Sea) from 2003 to 2015. Estuaries and Coasts, 43(3): 630–645, doi: 10.1007/s12237-019-00671-6
Petzrick E, Truman J, Fargher H. 2013. Profiling from 6, 000 meter with the APEX-Deep float. In: 2013 OCEANS. San Diego, CA, USA: IEEE, 1–3
Ponte R M, Sun Qiang, Liu Chao, et al. 2021. How salty is the global ocean: Weighing it all or tasting it a sip at a time?. Geophysical Research Letters, 48(11): e2021GL092935
Ridgway K R, Dunn J R, Wilkin J L. 2002. Ocean interpolation by four-dimensional weighted least squares—Application to the waters around Australasia. Journal of Atmospheric and Oceanic Technology, 19(9): 1357–1375, doi: 10.1175/1520-0426(2002)019<1357:OIBFDW>2.0.CO;2
Riser S C, Freeland H J, Roemmich D, et al. 2016. Fifteen years of ocean observations with the global Argo array. Nature Climate Change, 6(2): 145–153, doi: 10.1038/nclimate2872
Roemmich D, Alford M H, Claustre H, et al. 2019a. On the future of Argo: A global, full-depth, multi-disciplinary array. Frontiers in Marine Science, 6: 439, doi: 10.3389/fmars.2019.00439
Roemmich D, Gilson J. 2009. The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82(2): 81–100, doi: 10.1016/j.pocean.2009.03.004
Roemmich D, Johnson G C, Riser S, et al. 2009. The Argo Program: Observing the global oceans with profiling floats. Oceanography, 22(2): 34–43, doi: 10.5670/oceanog.2009.36
Roemmich D, Owens W B. 2000. The Argo Project: Global ocean observations for understanding and prediction of climate variability. Oceanography, 13(2): 45–50, doi: 10.5670/oceanog.2000.33
Roemmich D, Sherman J T, Davis R E, et al. 2019b. Deep SOLO: A full-depth profiling float for the Argo program. Journal of Atmospheric and Oceanic Technology, 36(10): 1967–1981, doi: 10.1175/JTECH-D-19-0066.1
Tesdal J E, Abernathey R P, Goes J I, et al. 2018. Salinity trends within the upper layers of the subpolar North Atlantic. Journal of Climate, 31(7): 2675–2698, doi: 10.1175/JCLI-D-17-0532.1
Tran A. 2019. Review of Argo data performance on the Global Telecommunication System (GTS). The 20th Argo Data Management Team Meeting, 16–18 October, 2019, Villefranche-sur-mer, France
von Schuckmann K, Cheng L J, Palmer M D, et al. 2020. Heat stored in the Earth system: Where does the energy go?. Earth System Science Data, 12(3): 2013–2041, doi: 10.5194/essd-12-2013-2020
Wang Tao, Chai Fei, Xing Xiaogang, et al. 2021a. Influence of multi-scale dynamics on the vertical nitrate distribution around the Kuroshio Extension: An investigation based on BGC-Argo and satellite data. Progress in Oceanography, 193: 102543, doi: 10.1016/j.pocean.2021.102543
Wang Guihua, Liu Zenghong, Xu Jianping. 2006. Three dimensional Pacific temperature, salinity and circulation reconstructions with Argo data. In: Xu Jianping, ed. A Collection of Research Articles on Argo Application (in Chinese). Beijing: China Ocean Press, 16–26
Wang Tao, Zhang Shuwen, Chen Fajin, et al. 2021b. Influence of sequential tropical cyclones on phytoplankton blooms in the northwestern South China Sea. Journal of Oceanology and Limnology, 39(1): 14–25, doi: 10.1007/s00343-020-9266-7
Wong A P S, Johnson G C, Owens W B. 2003. Delayed-mode calibration of autonomous CTD profiling float salinity data by θ-S climatology. Journal of Atmospheric and Oceanic Technology, 20(2): 308–318, doi: 10.1175/1520-0426(2003)020<0308:DMCOAC>2.0.CO;2
Wong A, Keeley R, Carval T, et al. 2022. Argo quality control manual for CTD and trajectory data. https://archimer.ifremer.fr/doc/00228/33951/[2021-8-3]
Wong A P S, Riser S C. 2011. Profiling float observations of the upper ocean under sea ice off the Wilkes Land coast of Antarctica. Journal of Physical Oceanography, 41(6): 1102–1115, doi: 10.1175/2011JPO4516.1
Wong A P S, Riser S C. 2013. Modified shelf water on the continental slope north of Mac Robertson Land, East Antarctica. Geophysical Research Letters, 40(23): 6186–6190, doi: 10.1002/2013GL058125
Wong A P S, Wijffels S E, Riser S C, et al. 2020. Argo data 1999–2019: Two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats. Frontiers in Marine Science, 7: 700, doi: 10.3389/fmars.2020.00700
Wu Yue, Zheng Xiaotong, Sun Qiwei, et al. 2021. Decadal variability of the upper-ocean salinity in the southeast Indian Ocean: role of local ocean-atmosphere dynamics. Journal of Climate, 34(19): 7927–7942, doi: 10.1175/JCLI-D-21-0122.1
Xie Chunhu, Xu Miaomiao, Cao Shasha, et al. 2019. Gridded Argo data set based on GDCSM analysis technique: establishment and preliminary applications. Journal of Marine Sciences (in Chinese), 37(4): 24–35
Xing Xiaogang, Boss E, Chen Shuangling, et al. 2021. Seasonal and daily-scale photoacclimation modulating the phytoplankton chlorophyll-carbon coupling relationship in the mid-latitude northwest Pacific. Journal of Geophysical Research: Oceans, 126(10): e2021JC017717
Xing Xiaogang, Wells M L, Chen Shuangling, et al. 2020. Enhanced winter carbon export observed by BGC-Argo in the Northwest Pacific Ocean. Geophysical Research Letters, 47(22): e2020GL089847
Xu Jianping. 2002. A Exploration of Global Ocean Argo Observing (in Chinese). Beijing: China Ocean Press
Xu Jianping, Liu Zenghong. 2007. The Experiment of China Argo Ocean Observing Array (in Chinese). Beijing: China Meteorological Press
Yan Hengqian, Wang Huizan, Zhang Ren, et al. 2021. The inconsistent pairs between in situ observations of near surface salinity and multiple remotely sensed salinity data. Earth and Space Science, 8(5): e2020EA001355
Yang Yuanyuan, Zhong Min, Feng Wei, et al. 2021. Detecting regional deep ocean warming below 2000 meter based on altimetry, GRACE, Argo, and CTD data. Advances in Atmospheric Sciences, 38(10): 1778–1790, doi: 10.1007/s00376-021-1049-3
Zhang Chunling, Wang Zhenfeng, Liu Yu. 2021. An Argo-based experiment providing near-real-time subsurface oceanic environmental information for fishery data. Fisheries Oceanography, 30(1): 85–98, doi: 10.1111/fog.12504
Zhang Chunling, Wang Danyang, Wang Zhenfeng. 2022. Fishery analysis using gradient-dependent optimal interpolation. Acta Oceanologica Sinica, 41(2): 116–126, doi: 10.1007/s13131-021-1895-y
Zhang Chunling, Xu Jianping, Bao Xianwen. 2015. Gradient-dependent correlation scale method based on Argo. Journal of PLA University of Science and Technology (Natural Science Edition) (in Chinese), 16(5): 476–483
Zhang Chunling, Xu Jianping, Bao Xianwen, et al. 2013. An effective method for improving the accuracy of Argo objective analysis. Acta Oceanologica Sinica, 32(7): 66–77, doi: 10.1007/s13131-013-0333-1
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Article Info
doi: 10.1007/s13131-022-2076-3
  • Receive Date：2022-04-12
  • Online Date：2025-11-21
  • Published：2023-02-25
Article Data
Affiliations
History
  • Received：2022-04-12
  • Accepted：2022-06-14
Funding
The National Natural Science Foundation of China under contract Nos 42122046, 42076202, U1811464 and 4210060098; the Project Supported by Laoshan Laboratory under contract No. LSKJ202201500; the Project Supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. SML2021SP102.
Affiliations
    1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
    2 Observation and Research Station of Global Ocean Argo System (Hangzhou), Ministry of Natural Resources, Hangzhou 310012, China
    3 Frontier Science Center for Deep Ocean Multispheres and Earth System/Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266100, China
    4 Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
    5 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    6 International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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