1.  The status of implementation 
      - floats deployed and their performance
      From the last AST meeting, China deployed 24 floats (11 APEX, 1 PROVOR and 12 HM2000) in the South China Sea (SCS), the northwestern Pacific and Indian Ocean via 4 cruises. It was the first time for China to deploy profiling floats in the SCS and share data within Argo community. It could be regarded as the beginning of the SCS Argo regional observing network (~25 operational floats) that will be dominated by China Argo. The 10 floats deployed in the SCS are HM2000 floats which use BeiDou satellite for data transmission and GPS for positioning. One float (WMO: 2902699) did not transmit data after its deployment, and it was recovered by fisherman from Vietnam in this February and is kept by the Sub Department of Fisheries of Binh Thuan Province of Vietnam. Until now the other 9 HM2000 floats are all active and report good TS profiles with a 5-day cycle time. About 376 floats have been deployed by China from 2000, and 118 floats are still active as of 25 February 2016.
      - technical problems encountered and solved
      A set of (5) Iridium APEX floats stopped transmitting data prematurely after about 110 cycles and showed an energy drop at the end of their lifetime. These floats observed intensive profiles during the passages of several typhoons. To estimate the energy consumption with Alkaline batteries, we sent the technical data from those floats to TWR as well as UW. The results based on their energy budget models supported the observed data. So we suggest each Iridium APEX float should be installed Lithium battery packs either by the manufacturer or by float users before deployment.
      -status of contributions to Argo data management (including status of pressure     corrections, technical files, etc)
      From the last AST meeting, CSIO received data from 177 active floats (69 APEX, 89 PROVOR, 14 HM2000 and 5 ARVOR) and submitted 5383 TS profiles to GDACs. CLS still helps us to insert profiles from the old floats into GTS, moreover, CSIO distributes all data into GTS via Chinese Meteorological Agency (Beijing). All Argo profiles are converted to BUFR format through a Perl script developed by JMA. However, there were a few interruptions in 2016 because the breakdown of FTP server at Zhejiang Meteorological Bureau. In 2016, we took a lot of time to convert historical profile, technical and trajectory files into Version 3.1. Now all of the metafiles, and most of the technical and trajectory files have been updated. 
      - status of delayed mode quality control process

      In the past year, CSIO didn t submit any D-files to GDAC because the lack of manpower. We plan to restore DMQC and to eliminate the backlog this year. A construction of the SCS reference CTD dataset is scheduled for DMQC of all Argo floats in the SCS.

2.  Present level of and future prospects for national funding for Argo including a summary of the level of human resources devoted to Argo.
      Unlike other countries, China Argo is mainly supported by research programs, which leads to an unstable number of yearly deployment. In the past two years, the number of the operational floats reduced because the number of new deployments relied on related research programs. The situation is expected to be changed this year with the appeals from PIs. In the period of the "13th Five-Year Plan" (2016-2020), the government is expected to increase the support to China Argo as a part of implementing China s strategic initiative of the "21st Century Maritime Silk Road", and to help the countries along "Maritime Silk Road" to strengthen capability of addressing climate change, as a result, China will make more contribution to global Argo. If this proposal is granted, about 200 floats (100 HM2000, 50 Iridium APEX and 50 PROVOR) will be deployed during its first implement year (2017). During its second year, 400 floats will be deployed, then the Chinese Argo real-time observing network consisted of 400 operational floats will be constructed. Following 2019, ~200 floats/year will be routinely deployed to maintain the network for at least 5 years.
      Currently there are 6 staffs working for float deployment, data processing and data application at CSIO.
    
3.  Summary of deployment plans (level of commitment, areas of float Deployment, low or high resolution profiles) and other commitments to Argo (data management) for the upcoming year and beyond where possible.

      The identified number of floats to be purchased this year is 45 including 15 Iridium floats, 10 BGC-Argo, 15 HM2000 and 5 deep-Argo. These floats are funded by the Ministry of Finance who will support the purchase of scientific instruments for those state key laboratories passed a 5-year assessment. China Argo Real-time Data Centre is an important basic platform for SOED (state key laboratory of Satellite Ocean Environment Dynamics). As a result, China will deploy ~45 floats at least this year, however, the number will be more than 200 if the proposal of China Argo real-time observing network along Maritime Silk Road could be granted. Of course, it is a great challenge to deploy so many floats via various investigation cruises and volunteer ships.

4.  Summary of national research and operational uses of Argo data as well as contributions to Argo Regional Centers.  Please also include any links to national program Argo web pages to update links on the AST and AIC websites. 
      Argo data has become an important data source in basic research and operational application. CSIO maintains a monthly global Argo gridded dataset (called BOA_Argo) and updates once a year. We are preparing an English version of the user manual of this dataset and put it into Argo-UCSD website.

      There are two websites maintained by China, one is maintained by NMDIS (www.argo.gov.cn) at Tianjin (China Argo data center), and another is maintained by CSIO (www.argo.org.cn) at Hangzhou (China Argo Real-time data center). The implement status of China Argo, real-time data display including T/S/O2 profiles, float trajectory, profile data, the derived products and status of global Argo are provided. 

5.  Problems encountered during the operation of international Argo and suggestions

      No.

6.  To continue improving the number of CTD cruise data being added to the reference database by Argo PIs, it is requested that you include the number and location of CTD cruise data uploaded by PIs within your country to the CCHDO website in the past year.  

      No CTD data were submitted.

7.  Keeping the Argo bibliography
Bai, P., Y. Gu, P. Li, and K. Wu, 2016: Modelling the upwelling offthe east Hainan Island coast in summer 2010. Chinese Journal of Oceanology and Limnology, 34, 1358-1373, http://dx.doi.org/10.1007/s00343-016-5147-5
Chen, C., B. Lei, Y.-L. Ma, and R. Duan, 2016: Investigating sound speed profile assimilation: An experiment in the Philippine Sea. Ocean Engineering, 124, 135-140, http://dx.doi.org/10.1016/j.oceaneng.2016.07.062
Chen, G. and X. Wang, 2016: Vertical Structure of Upper-Ocean Seasonality: Annual and Semiannual Cycles with Oceanographic Implications. Journal of Climate, 29, 37-59, http://dx.doi.org/10.1175/JCLI-D-14-00855.1
Chen, G., X. Wang, and C. Qian, 2016: Vertical Structure of Upper-Ocean Seasonality: Extratropical Spiral versus Tropical Phase Lock.Journal of Climate, 29, 4021-4030, http://dx.doi.org/10.1175/JCLI-D-15-0805.1
Cheng, L. and J. Zhu, 2016: Benefits of CMIP5 Multimodel Ensemble in Reconstructing Historical Ocean Subsurface Temperature Variations. Journal of Climate, 29, 5393-5416, http://dx.doi.org/10.1175/JCLI-D-15-0730.1
Deng, Z. a., G. Wei, T. Yu, H. Wei, L. Kang, and L. Han, 2016: The diapycnal mixing in the upper Pacific estimated from GTSPP observations.Acta Oceanologica Sinica, 35, 46-52, http://dx.doi.org/10.1007/s13131-016-0795-z
Dong, D., X. Yang, X. Li, and Z. Li, 2016: SAR Observation of Eddy-Induced Mode-2 Internal Solitary Waves in the South China Sea. Ieee Transactions on Geoscience and Remote Sensing, 54, 6674-6686, http://dx.doi.org/10.1109/TGRS.2016.2587752
Fu, D., H. Luan, D. Pan, Y. Zhang, L. a. Wang, D. Liu, Y. Ding, and X. Li, 2016: Impact of two typhoons on the marine environment in the Yellow Sea and East China Sea. Chinese Journal of Oceanology and Limnology, 34, 871-884, http://dx.doi.org/10.1007/s00343-016-5049-6
Gao, W., P. Li, S.-P. Xie, L. Xu, and C. Liu, 2016: Multicore structure of the North Pacific subtropical mode water from enhanced Argo observations. Geophysical Research Letters, 43, 1249-1255, http://dx.doi.org/10.1002/2015GL067495
He, Q., H. Zhan, S. Cai, and Z. Li, 2016: Eddy effects on surface chlorophyll in the northern South China Sea: Mechanism investigation and temporal variability analysis. Deep Sea Research Part I: Oceanographic Research Papers, 112, 25-36,http://dx.doi.org/10.1016/j.dsr.2016.03.004
He, Q., H. Zhan, S. Cai, and G. Zha, 2016: On the asymmetry of eddy-induced surface chlorophyll anomalies in the southeastern Pacific: The role of eddy-Ekman pumping. Progress in Oceanography, 141, 202-211, http://dx.doi.org/10.1016/j.pocean.2015.12.012
Hu, S., D. Hu, C. Guan, F. Wang, L. Zhang, F. Wang, and Q. Wang, 2016: Interannual Variability of the Mindanao Current/Undercurrent in Direct Observations and Numerical Simulations. Journal of Physical Oceanography, 46, 483-499, http://dx.doi.org/10.1175/JPO-D-15-0092.1
Hu, S. and J. Sprintall, 2016: Interannual variability of the Indonesian Throughflow: The salinity effect. Journal of Geophysical Research: Oceans, 121, 2596-2615, http://dx.doi.org/10.1002/2015JC011495
Huang, G. Y. Yan, and Y. Qi, 2016: Seasonal and interannual variability of mixed layer salinity in the tropical Pacific ocean, Journal of Tropical Meteorology, 32(2), 219:228.
Huang, X., Z. Chen, W. Zhao, Z. Zhang, C. Zhou, Q. Yang, and J. Tian, 2016: An extreme internal solitary wave event observed in the northern South China Sea. Scientific Reports, 6, 30041, http://dx.doi.org/10.1038/srep30041
Li, C., H. Zhao, H. Li, and K. Lv, 2016: Statistical Models of Sea Surface Salinity in the South China Sea Based on SMOS Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 2658-2664.
Li, J., C. Liang, Y. Tang, C. Dong, D. Chen, X. Liu, and W. Jin, 2016: A new dipole index of the salinity anomalies of the tropical Indian Ocean.Scientific Reports, 6, 24260, http://dx.doi.org/10.1038/srep24260
Li, Q., B. Wang, X. Chen, X. Chen, and J.-H. Park, 2016: Variability of nonlinear internal waves in the South China Sea affected by the Kuroshio and mesoscale eddies. Journal of Geophysical Research: Oceans, 121, 2098-2118, http://dx.doi.org/10.1002/2015JC011134
Li, Y., X. Li, J. Wang, and S. Peng, 2016: Dynamical analysis of a satellite-observed anticyclonic eddy in the northern Bering Sea. Journal of Geophysical Research: Oceans, 121, 3517-3531, http://dx.doi.org/10.1002/2015JC011586
Liu, L., R. Huang, and F. Wang, 2016: Subduction/obduction rate in the North Pacific diagnosed by an eddy-resolving model. Chinese Journal of Oceanology and Limnology, 34, 835-846, http://dx.doi.org/10.1007/s00343-016-5036-y
Liu, Q. and Y. Lu, 2016: Role of horizontal density advection in seasonal deepening of the mixed layer in the subtropical Southeast Pacific.Advances in Atmospheric Sciences, 33, 442-451, http://dx.doi.org/10.1007/s00376-015-5111-x
Liu, Y., G. Chen, M. Sun, S. Liu, and F. Tian, 2016: A Parallel SLA-Based Algorithm for Global Mesoscale Eddy Identification. Journal of Atmospheric and Oceanic Technology, 33, 2743-2754, http://dx.doi.org/10.1175/jtech-d-16-0033.1
Lu, S., Z. Liu, and C. Sun, 2016: Application of improved Argo salinity delayed-mode quality control method, Journal of PLA university of Science and Technology (Natural Science Edition), 17(2), 165-173 (in Chinese).
Lu, S., C. Sun, Z. Liu, and J. Xu, 2016: Comparative testing and data quality evaluation for COPEX, HM2000 and APEX buoys, Journal of Ocean Technology, 35(1), 84-92 (in Chinese).
Lu, Z., L. Cheng, J. Zhu, and R. Lin, 2016: The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state. Journal of Geophysical Research: Oceans, 121, 3672-3691, http://dx.doi.org/10.1002/2015JC011480
Ma, X. and C. Sun, 2016: Equatorward shift of annual Rossby waves in the Equatorial Pacific Ocean. Chinese Journal of Oceanology and Limnology, 34, 212-218, http://dx.doi.org/10.1007/s00343-015-4405-2
Nan, F., F. Yu, H. Xue, L. Zeng, D. Wang, S. Yang, and K.-C. Nguyen, 2016: Freshening of the upper ocean in the South China Sea since the early 1990s. Deep Sea Research Part I: Oceanographic Research Papers, 118, 20-29, http://dx.doi.org/10.1016/j.dsr.2016.10.010
Pun, I.-F., J. F. Price, and S. R. Jayne, 2016: Satellite-Derived Ocean Thermal Structure for the North Atlantic Hurricane Season. Monthly Weather Review, 144, 877-896, http://dx.doi.org/10.1175/MWR-D-15-0275.1
Qian, J.-g. and Y.-l. Han, 2016: A New Method to Obtain Deep Ocean Current Velocity from Argo Floats. International Conference on Oriental Thinking and Fuzzy Logic: Celebration of the 50th Anniversary in the era of Complex Systems and Big Data, B.-Y. Cao, P.-Z. Wang, Z.-L. Liu, and Y.-B. Zhong, Eds., Springer International Publishing, 519-531.
Qin, H., R. X. Huang, W. Wang, and H. Xue, 2016: Regulation of South China Sea throughflow by pressure difference. Journal of Geophysical Research: Oceans, 121, 4077-4096, http://dx.doi.org/10.1002/2015JC011177
Qiu, C., J. Chen, M. Shangguan, and H. Mao, 2016: Seasonal variations of sea level anomaly in the subtropical front zone based on satellite-derived data from 2003–2009. Aquatic Ecosystem Health & Management, 19, 270-275,http://dx.doi.org/10.1080/14634988.2016.1210451
Sun, C. and X. Ma, 2016: Estimating thermohaline variability of the equatorial Pacific Ocean from satellite altimetry. Science China Earth Sciences, 59, 2213-2222, http://dx.doi.org/10.1007/s11430-016-0048-2
Sun, R., G. Wang, and C. Chen, 2016: The Kuroshio bifurcation associated with islands at the Luzon Strait. Geophysical Research Letters,43, 5768-5774, http://dx.doi.org/10.1002/2016GL069652
Wang, F., J. Wang, C. Guan, Q. Ma, and D. Zhang, 2016: Mooring observations of equatorial currents in the upper 1000 m of the western Pacific Ocean during 2014. Journal of Geophysical Research: Oceans, 121, 3730-3740, http://dx.doi.org/10.1002/2015JC011510
Wang, G., L. Wu, N. C. Johnson, and Z. Ling, 2016: Observed three-dimensional structure of ocean cooling induced by Pacific tropical cyclones. Geophysical Research Letters, 43, 7632-7638, http://dx.doi.org/10.1002/2016GL069605
Wang, G., C. Zhao, J. Xu, F. Qiao, and C. Xia, 2016: Verification of an operational ocean circulation-surface wave coupled forecasting system for the China’s seas. Acta Oceanologica Sinica, 35, 19-28, http://dx.doi.org/10.1007/s13131-016-0810-4
Wang, S., Z. Liu, C. Pang, and H. Liu, 2016: The decadally modulating eddy field in the upstream Kuroshio Extension and its related mechanisms. Acta Oceanologica Sinica, 35, 9-17, http://dx.doi.org/10.1007/s13131-015-0741-5
Wang, X. and H. Liu, 2016: Seasonal-to-interannual variability of the barrier layer in the western Pacific warm pool associated with ENSO.Climate Dynamics, 47, 375-392, http://dx.doi.org/10.1007/s00382-015-2842-4
Wu, G., F. Zhai, and D. Hu, 2016: Interannual variations of North Equatorial Current transport in the Pacific Ocean during two types of El Niño.Chinese Journal of Oceanology and Limnology, 34, 585-596, http://dx.doi.org/10.1007/s00343-015-4352-y
Xia, S., C. Ke, X. Zhou, and J. Zhang, 2016: Assessment and adjustment of sea surface salinity products from Aquarius in the southeast Indian Ocean based on in situ measurement and MyOcean modeled data. Acta Oceanologica Sinica, 35, 54-62,http://dx.doi.org/10.1007/s13131-016-0818-9
Xiao, J., Q. Xie, D. Wang, L. Yang, Y. Shu, C. Liu, J. Chen, J. Yao, and G. Chen, 2016: On the near-inertial variations of meridional overturning circulation in the South China Sea. Ocean Science, 12, 335-344, http://dx.doi.org/10.5194/os-12-335-2016
Xu, F. and A. Ignatov, 2016: Error characterization in iQuam SSTs using triple collocations with satellite measurements. Geophysical Research Letters, 43, 10,826-10,834, http://dx.doi.org/10.1002/2016GL070287
Xu, L., P. Li, S.-P. Xie, Q. Liu, C. Liu, and W. Gao, 2016: Observing mesoscale eddy effects on mode-water subduction and transport in the North Pacific. Nat Commun, 7, http://dx.doi.org/10.1038/ncomms10505
Yang, L. and D. Yuan, 2016: Absolute geostrophic currents in global tropical oceans. Chinese Journal of Oceanology and Limnology, 34,1383-1393, http://dx.doi.org/10.1007/s00343-016-5092-3
Yang, L. and D. Yuan, 2016: Heat and salt transport throughout the North Pacific Ocean. Chinese Journal of Oceanology and Limnology, 34,1347-1357, http://dx.doi.org/10.1007/s00343-016-5125-y
Yang, X., F. Wu, Z. Liu, 2016: Preliminary research on upper ocean heat & salt content of the West Pacific warm pool, Periodical of Ocean University of China,46(4), 1-12 (in Chinese).
Yin, X., J. Boutin, E. Dinnat, Q. Song, and A. Martin, 2016: Roughness and foam signature on SMOS-MIRAS brightness temperatures: A semi-theoretical approach. Remote Sensing of Environment, 180, 221-233, http://dx.doi.org/10.1016/j.rse.2016.02.005
Zeng, L., D. Wang, J. Chen, W. Wang, and R. Chen, 2016: SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919–2014. Scientific Data, 3, 160029, http://dx.doi.org/10.1038/sdata.2016.29
Zhang, N., M. Feng, Y. Du, J. Lan, and S. E. Wijffels, 2016: Seasonal and interannual variations of mixed layer salinity in the southeast tropical Indian Ocean. Journal of Geophysical Research: Oceans, 121, 4716-4731, http://dx.doi.org/10.1002/2016JC011854
Zhang, R., S.-P. Xie, L. Xu, and Q. Liu, 2016: Changes in mixed layer depth and spring bloom in the Kuroshio extension under global warming. Advances in Atmospheric Sciences, 33, 452-461, http://dx.doi.org/10.1007/s00376-015-5113-8
Zhang, Y., Y. Du, and T. Qu, 2016: A sea surface salinity dipole mode in the tropical Indian Ocean. Climate Dynamics, 1-13,http://dx.doi.org/10.1007/s00382-016-2984-z
Zhang, Z., J. Tian, B. Qiu, W. Zhao, P. Chang, D. Wu, and X. Wan, 2016: Observed 3D Structure, Generation, and Dissipation of Oceanic Mesoscale Eddies in the South China Sea. Scientific Reports, 6, 24349, http://dx.doi.org/10.1038/srep24349
Zhao, H., C. Li, H. Li, K. Lv, and Q. Zhao, 2016: Retrieve sea surface salinity using principal component regression model based on SMOS satellite data. Journal of Ocean University of China, 15, 399-406, http://dx.doi.org/10.1007/s11802-016-2817-z
Zhao, X., H. Li, Z. Liu, J. Xu, C. Sun, and S. Lu, 2016: Argo surface temperature, salinity estimation method by mixed layer model, Marine Science Bulletin, 35(5), 532-544 (in Chinese).
Zhi, H., R.-H. Zhang, F. Zheng, P. Lin, L. Wang, and P. Yu, 2016: Assessment of interannual sea surface salinity variability and its effects on the barrier layer in the equatorial Pacific using BNU-ESM. Advances in Atmospheric Sciences, 33, 339-351,http://dx.doi.org/10.1007/s00376-015-5163-y
Zhou, W., M. Chen, W. Zhuang, F. Xu, F. Zheng, T. Wu, and X. Wang, 2016: Evaluation of the tropical variability from the Beijing Climate Center’s real-time operational global Ocean Data Assimilation System. Advances in Atmospheric Sciences, 33, 208-220,http://dx.doi.org/10.1007/s00376-015-4282-9