Evaluating the contribution of satellite measurements to the reconstruction of three-dimensional ocean temperature fields in combination with Argo profiles

Jian Chen; Hengqian Yan; Senliang Bao; Xindong Cui; Chengzu Bai; Huizan Wang

doi:10.1007/s13131-021/1858-3

Volume 41 Issue 2

Feb. 2022

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Jian Chen, Hengqian Yan, Senliang Bao, Xindong Cui, Chengzu Bai, Huizan Wang. Evaluating the contribution of satellite measurements to the reconstruction of three-dimensional ocean temperature fields in combination with Argo profiles[J]. Acta Oceanologica Sinica, 2022, 41(2): 65-79. doi: 10.1007/s13131-021/1858-3

Citation:

Jian Chen, Hengqian Yan, Senliang Bao, Xindong Cui, Chengzu Bai, Huizan Wang. Evaluating the contribution of satellite measurements to the reconstruction of three-dimensional ocean temperature fields in combination with Argo profiles[J]. Acta Oceanologica Sinica, 2022, 41(2): 65-79. doi: 10.1007/s13131-021/1858-3

Citation:

Jian Chen, Hengqian Yan, Senliang Bao, Xindong Cui, Chengzu Bai, Huizan Wang. Evaluating the contribution of satellite measurements to the reconstruction of three-dimensional ocean temperature fields in combination with Argo profiles[J]. Acta Oceanologica Sinica, 2022, 41(2): 65-79. doi: 10.1007/s13131-021/1858-3

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Evaluating the contribution of satellite measurements to the reconstruction of three-dimensional ocean temperature fields in combination with Argo profiles

doi: 10.1007/s13131-021/1858-3

1.
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2.
Beijing Institute of Applied Meteorology, Beijing 100029, China
3.
College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

Funds: The National Natural Science Foundation of China under contract Nos 41706021 and 41976188.

More Information

Corresponding author: E-mail: chenj03@126.com
Received Date: 2021-03-01
Accepted Date: 2021-05-20

Available Online: 2021-12-09

Publish Date: 2022-02-01

Abstract

Abstract

Assimilation systems absorb both satellite measurements and Argo observations. This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis, allowing for better configuration of assimilation parameters. To achieve this, two comparative reconstruction schemes were designed under the optimal interpolation framework. Using a static scheme, an in situ-only field of ocean temperature was derived by correcting climatology with only Argo proﬁles. Through a dynamic scheme, a synthetic ﬁeld was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection, and then a combined field was reconstructed by correcting the synthetic ﬁeld with in situ proﬁles. For both schemes, a diagnostic iterative method was performed to optimize the background and observation error covariance statics. The root mean square difference (RMSD) of the in situ-only field, synthetic ﬁeld and combined field were analyzed toward assimilated observations and independent observations, respectively. The rationale behind the distribution of RMSD was discussed using the following diagnostics: (1) The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters, as in the Northwest Pacific Ocean; this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity. (2) The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes; this is determined by the actual noise-to-signal ratio of ocean temperature. (3) The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics; this is determined by both the geographical feature of the synthetic field RMSD (smaller at depth in the extratropics) and that of the covariance correlation scales (smaller in the extratropics).
- satellite measurements,
- Argo,
- three-dimensional reconstruction,
- ocean temperature

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References(38)

References

[1]	Agarwal N, Sharma R, Basu S, et al. 2007. Derivation of salinity profiles in the Indian Ocean from satellite surface observations. IEEE Transactions on Geoscience and Remote Sensing, 4(2): 322–325. doi: 10.1109/LGRS.2007.894163
[2]	Alves J O S, Haines K, Anderson D L T. 2001. Sea level assimilation experiments in the tropical Pacific. Journal of Physical Oceanography, 31(2): 305–323. doi: 10.1175/1520-0485(2001)031<0305:SLAEIT>2.0.CO;2
[3]	An Yuzhu, Zhang Ren, Wang Huizan, et al. 2012. Study on calculation and spatio-temporal variations of global ocean mixed layer depth. Chinese Journal of Geophysics, 55(7): 2249–2258
[4]	Antonov J I, Levitus S, Boyer T P. 2004. Climatological annual cycle of ocean heat content. Geophysical Research Letters, 31(4): L04304. doi: 10.1029/2003GL018851
[5]	Ballabrera-Poy J, Mourre B, Garcia-Ladona E, et al. 2009. Linear and non-linear T–S models for the eastern North Atlantic from Argo data: Role of surface salinity observations. Deep-Sea Research I, 56: 1605–1614. doi: 10.1016/j.dsr.2009.05.017
[6]	Carnes M R, Teague W J, Mitchell J L, 1994. Inference of subsurface thermohaline structure from fields measurable by satellite. Journal of Atmospheric and Oceanic Technology, 11(2): 551–566
[7]	Chapnik B, Desroziers G, Rabier F, et al. 2004. Properties and first application of an error-statistics tuning method in variational assimilation. Quarterly Journal of the Royal Meteorological Society, 130: 2253–2275. doi: 10.1256/qj.03.26
[8]	Chapnik B, Desroziers G, Rabier F, et al. 2006. Diagnosis and tuning of observational error in a quasi-operational data assimilation setting. Quarterly Journal of the Royal Meteorological Society, 132(615): 543–565. doi: 10.1256/qj.04.102
[9]	Cooper M, Haines K. 1996. Altimetric assimilation with water property conservation. Journal of Geophysical Research: Oceans, 101(C1): 1059–1077. doi: 10.1029/95JC02902
[10]	Dee D P, da Silva A M. 1999. Maximum-likelihood estimation of forecast and observation error covariance parameters. Part I: Methodology. Monthly Weather Review, 127(8): 1822–1834. doi: 10.1175/1520-0493(1999)127<1822:MLEOFA>2.0.CO;2
[11]	Desroziers G, Berre L, Chapnik B, et al. 2005. Diagnosis of observation, background and analysis-error statistics in observation space. Quarterly Journal of the Royal Meteorological Society, 131(613): 3385–3396. doi: 10.1256/qj.05.108
[12]	Desroziers G, Ivanov S. 2001. Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation. Quarterly Journal of the Royal Meteorological Society, 127(574): 1433–1452. doi: 10.1002/qj.49712757417
[13]	Eden C, Willebrand J. 2001. Mechanism of interannual to decadal variability of the North Atlantic circulation. Journal of Climate, 14(10): 2266–2280. doi: 10.1175/1520-0442(2001)014<2266:MOITDV>2.0.CO;2
[14]	Fox D N, Teague W J, Barron C N, et al. 2002. The modular ocean data assimilation system (MODAS). Journal of Atmospheric and Oceanic Technology, 19(2): 240–252. doi: 10.1175/1520-0426(2002)019<0240:TMODAS>2.0.CO;2
[15]	Gaillard F. 2012. ISAS-Tool version 6: Method and Configuration. Plouzané, France: Ifremer Cnrs Ird Ubo
[16]	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
[17]	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
[18]	Guinehut S, Dhomps A L, Larnicol G, et al. 2012. High resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Science, 8(5): 845–857. doi: 10.5194/os-8-845-2012
[19]	Guinehut S, Le Traon P Y, Larnicol G, et al. 2004. Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—A first approach based on simulated observations. Journal of Marine Systems, 46(1–4): 85–98
[20]	Guinehut S, Le Traon P Y, Larnicol G, et al. 2006. What can we learn from Global Altimetry/Hydrography comparisons?. Geophysical Research Letters, 33(10): L10604
[21]	Harrison D E, Carson M. 2007. Is the world ocean warming? Upper-ocean temperature trends: 1950–2000. Journal of Physical Oceanography, 37(2): 174–187. doi: 10.1175/JPO3005.1
[22]	Hollingsworth A, Lönnberg P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus A, 38(2): 111–136. doi: 10.3402/tellusa.v38i2.11707
[23]	Larnicol G, Guinehut S, Rio M H, et al. 2006. The global observed ocean products of the French Mercator project. In: Proceedings of the Symposium on 15 Years of Progress in Radar Altimetry. Noordwijk, Netherlands: ESA
[24]	Lellouche J M, Greiner E, Le Galloudec O, et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1/12° high-resolution system. Ocean Science, 14(5): 1093–1126. doi: 10.5194/os-14-1093-2018
[25]	Lellouche J M, Le Galloudec O, Drévillon M, et al. 2013. Evaluation of global monitoring and forecasting systems at Mercator Océan. Ocean Science, 9(1): 57–81. doi: 10.5194/os-9-57-2013
[26]	Maes C, Behringer D. 2000. Using satellite–derived sea level and temperature profiles for determining the salinity variability: A new approach. Journal of Geophysical Research: Oceans, 105(C4): 8537–8547. doi: 10.1029/1999JC900279
[27]	Meinen C S, Watts D R. 2000. Vertical structure and transport on a transect across the North Atlantic Current near 42 N: Time series and mean. Journal of Geophysical Research: Oceans, 105(C9): 21869–21891. doi: 10.1029/2000JC900097
[28]	Mitchell D, Wimbush M, Watts D, et al. 2004. The residual GEM technique and its application to the southwestern Japan East Sea. Journal of Atmospheric and Oceanic Technology, 21(12): 1895–1909. doi: 10.1175/JTECH-1668.1
[29]	Nardelli B B, Guinehut S, Pascual A, et al. 2012. Towards high resolution mapping of 3-D mesoscale dynamics from observations. Ocean Science, 8(5): 885–901. doi: 10.5194/os-8-885-2012
[30]	Nardelli B B, Santoleri R. 2004. Reconstructing synthetic profiles from surface data. Journal of Atmospheric and Oceanic Technology, 21(4): 693–703. doi: 10.1175/1520-0426(2004)021<0693:RSPFSD>2.0.CO;2
[31]	Nardelli B B, Santoleri R. 2005. Methods for the reconstruction of vertical profiles from surface data: Multivariate analyses, residual GEM, and variable temporal signals in the North Pacific Ocean. Journal of Atmospheric and Oceanic Technology, 22(11): 1762–1781. doi: 10.1175/JTECH1792.1
[32]	Reynolds R W, Smith T M, Liu Chunying, et al. 2007. Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22): 5473–5496. doi: 10.1175/2007JCLI1824.1
[33]	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
[34]	Saji N H, Goswami B N, Vinayachandran P N, et al. 1999. A dipole mode in the tropical Indian Ocean. Nature, 401(6751): 360–363
[35]	Servain J, Wainer I, McCreary J P, et al. 1999. Relationship between the equatorial and meridional modes of climatic variability in the tropical Atlantic. Geophysical Research Letters, 26(4): 485–488. doi: 10.1029/1999GL900014
[36]	Talagrand O. 1999. A posteriori verification of analysis and assimilation algorithms. In: Proceedings of the ECMWF Workshop on Diagnosis of Data Assimilation Systems. Reading, UK: European Centre for Medium-Range Weather Forecasts
[37]	Von Schuckmann K, Gaillard F, Le Traon P Y. 2009. Global hydrographic variability patterns during 2003–2008. Journal of Geophysical Research, 114(C9): C09007
[38]	Wu X, Yan X H, Jo Y H, et al. 2012. Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network. Journal of Atmospheric and Oceanic Technology, 29(11): 1675–1688. doi: 10.1175/JTECH-D-12-00013.1