Anterior|Siguiente

Estimation of global subsurface temperature anomaly based on multisource satellite observations

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-spatial Information Technology, Fuzhou University, Fuzhou 350002, China

  • Email：1836682015@qq.com
  • Introduction：黎文娥(1992—　)，女，硕士研究生，研究方向为中深层海洋遥感。E-mail：1836682015@qq.com
Wen′e LI1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-spatial Information Technology, Fuzhou University, Fuzhou 350002, China

  • Email：suhua@fzu.edu.cn
  • Introduction：苏华(1985—　)，男，副研究员，研究方向为海洋环境遥感。E-mail：suhua@fzu.edu.cn
Hua SU1*,  
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-spatial Information Technology, Fuzhou University, Fuzhou 350002, China

Xiaoqin WANG1,  
  • Affiliation：

    Xiamen University/University of Delaware Joint Institute for Coastal Research and Management (Joint-CRM), Xiamen University, Xiamen 361005, China

Xiaohai YAN2

resumen

Subsurface thermal structure of the global ocean is a key factor that reflects the impact of global climate variability and change. Accurately determining and describing the global subsurface and deeper ocean thermal structure from satellite measurements are becoming even more important for understanding the ocean interior anomaly and dynamic processes during recent global warming and hiatus. The extent to which such surface remote sensing observations can be used to develop information about the global ocean interior is essential but challenging. This work proposes a Support Vector Regression (SVR) method, a popular machine learning method for data regression used to estimate Subsurface Temperature Anomaly (STA) in the global ocean. The SVR model can well estimate the global STA upper 1000 m through a suite of satellite remote sensing observations of sea surface parameters [including Sea Surface Height Anomaly (SSHA), Sea Surface Temperature Anomaly (SSTA), Sea Surface Salinity Anomaly (SSSA), and Sea Surface Wind Anomaly (SSWA)] with in situ Argo data for training and testing at different depth levels. In this study, we employed the Mean Squared Error (MSE) and squared correlation coefficient ( R2) to assess the performance of SVR on STA estimation. Results from the SVR model were validated to test the accuracy and reliability using the worldwide Argo STA data (upper 1000 m depth). The average MSE and R2 of the 15 levels are 0.0090/0.0086/0.0087 and 0.443/0.457/0.485 for two attributes (SSHA, SSTA)/three attributes (SSHA, SSTA, SSSA)/four attributes (SSHA, SSTA, SSSA, SSWA) SVR, respectively. The estimation accuracy was improved by including SSSA and SSWA for SVR input (MSE decreased by 0.4%/0.3% and R2 increased by 1.4%/4.2% on average). The estimation accuracy gradually decreased with the increase in depth from 500 m. With the increase in depth, the absolute value of STA became smaller, i.e., it became more indistinctive in the spatial heterogeneity. The STA became less intensive in the deeper ocean due to the water stratification and stability. Results showed that SSSA and SSWA, in addition to SSTA and SSHA, are useful parameters that can help estimate the subsurface thermal structure and improve the STA estimation accuracy. Moreover, an obvious advantage for SVR is the absence of limitation on the input of sea surface parameters. Therefore, we can figure out more potential and useful sea surface parameters from satellite remote sensing as input attributes to further improve the STA sensing accuracy from SVR machine learning. This study provides a helpful technique for studying thermal variability in the ocean interior, which has played an important role in recent global warming and hiatus from satellite observations over global scale.

palabra clave

multisource satellite observation;subsurface temperature anomaly;support vector regression;information extraction;global ocean

References

  1. 1.
    Ali M M, Swain D and Weller R A. 2004. Estimation of ocean subsurface thermal structure from surface parameters: a neural network approach. Geophysical Research Letters, 31(20): L20308
  2. 2.
    Atlas R, Hoffman R N, Ardizzone J, Leidner S M, Jusem J C, Smith D K and Gombos D. 2011. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic application. Bulletin of the American Meteorological Society, 92(2): 157–174
  3. 3.
    Balmaseda M A, Trenberth K E and Källén E. 2013. Distinctive climate signals in reanalysis of global ocean heat content. Geophysical Research Letters, 40(9): 1754–1759
  4. 4.
    Chang C C and Lin C J. 2013. LIBSVM: a library for support vector machines. [2015-11-23].
  5. 5.
    Chen J N, Song G T, Chu J T and Xu L Y. 2003. Oceanic temperature anomalous signal pathway in the equatorial Pacific. Advances in Water Science, 14(2): 152–157 (
  6. 6.
    Chen X Y and Tung K K. 2014. Varying planetary heat sink led to global-warming slowdown and acceleration. Science, 345(6199): 897–903
  7. 7.
    Drijfhout S S, Blaker A T, Josey S A, Nurser A J G, Sinha B and Balmaseda M A. 2014. Surface warming hiatus caused by increased heat uptake across multiple ocean basins. Geophysical Research Letters, 41(22): 7868–7874
  8. 8.
    Fischer M. 2000. Multivariate projection of ocean surface data onto subsurface sections. Geophysical Research Letters, 27(6): 755–757
  9. 9.
    Fox D N, Teague W J, Barron C N, Carnes M R and Lee C M. 2002. The modular ocean data assimilation system (MODAS). Journal of Atmospheric and Oceanic Technology, 19(2): 240–252
  10. 10.
    Guinehut S, Coatanoan C, Dhomps A L, Le Traon P Y and Larnicol G. 2009. On the use of satellite altimeter data in Argo quality control. Journal of Atmospheric and Oceanic Technology, 26(2): 395–402
  11. 11.
    Guinehut S, Dhomps A L, Larnicol G and Le Traon P Y. 2012. High resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Science, 8(5): 845–857
  12. 12.
    Guinehut S, Le Traon P Y and Larnicol G. 2006. What can we learn from Global Altimetry/Hydrography comparisons?. Geophysical Research Letters, 33(10): L10604
  13. 13.
    Guinehut S, Le Traon P Y, Larnicol G and Philipps S. 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
  14. 14.
    Hsu C W, Chang C C and Lin C J. 2010. A practical guide to support vector classification. National Taiwan University. [2016-08-16].
  15. 15.
    Huang C, Davis L S and Townshend J R G. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4): 725–749
  16. 16.
    Huber M and Knutti R. 2014. Natural variability, radiative forcing and climate response in the recent hiatus reconciled. Nature Geoscience, 7(9): 651–656
  17. 17.
    Khedouri E, Szczechowski C and Cheney R. 1983. Potential oceanographic applications of satellite altimetry for inferring subsurface thermal structure // Proceedings of OCEANS’83. San Francisco, CA, USA: IEEE: 274–280 [DOI: 10.1109/OCEANS.1983.1152138]
  18. 18.
    Klemas V and Yan X H. 2014. Subsurface and deeper ocean remote sensing from satellites: an overview and new results. Progress in Oceanography, 122: 1–9
  19. 19.
    Kosaka Y and Xie S P. 2013. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501(7467): 403–407
  20. 20.
    Li C Y and Mu M Q. 2001. The dipole in the equatorial Indian Ocean and its impacts on climate. Chinese Journal of Atmospheric Sciences, 25(4): 433–443 (
  21. 21.
    Liu L, Peng S Q, Wang J B and Huang R X. 2014. Retrieving density and velocity fields of the ocean’s interior from surface data. Journal of Geophysical Research, 119(12): 8512–8529
  22. 22.
    Mayer D A, Molinari R L, Baringer M O and Goni G J. 2001. Transition regions and their role in the relationship between sea surface height and subsurface temperature structure in the Atlantic Ocean. Geophysical Research Letters, 28(20): 3943–3946
  23. 23.
    Meijers A J S, Bindoff N L and Rintoul S R. 2011. Estimating the four-dimensional structure of the southern ocean using satellite altimetry. Journal of Atmospheric and Oceanic Technology, 28(4): 548–568
  24. 24.
    Song Y T and Colberg F. 2011. Deep ocean warming assessed from altimeters, gravity recovery and climate experiment, in situ measurements, and a non-Boussinesq ocean general circulation model. Journal of Geophysical Research, 116(C2): C02020
  25. 25.
    Su H, Wu X B, Yan X H and Kidwell A. 2015. Estimation of subsurface temperature anomaly in the Indian Ocean during recent global surface warming hiatus from satellite measurement: a support vector machine approach. Remote Sensing of Environment, 160: 63–71
  26. 26.
    Swart S, Speich S, Ansorge I J and Lutjeharms J R E. 2010. An altimetry-based gravest empirical mode south of Africa: 1. development and validation. Journal of Geophysical Research, 115(C3): C03002
  27. 27.
    Trenberth K E and Fasullo J T. 2013. An apparent hiatus in global warming?. Earth’s Future, 1(1): 19–32
  28. 28.
    Vapnik V, Golowich S E and Smola A. 1997. Support vector method for function approximation, regression estimation, and signal processing // Mozer M, Jordan M and Petsche T, eds. Advances in Neural Information Processing Systems 9. Cambridge, MA: MIT Press: 281–287
  29. 29.
    Wang J B, Flierl G R, LaCasce J H, McClean J L and Mahadevan A. 2013. Reconstructing the ocean’s interior from surface data. Journal of Physical Oceanography, 43(8): 1611–1626
  30. 30.
    Wang X D, Han G J, Li W and Qi Y Q. 2011. Reconstruction of ocean temperature profile using satellite observations. Journal of Tropical Oceanography, 30(6): 10–17 (
  31. 31.
    Weston J and Watkins C. 1999. Support vector machines for multi-class pattern recognition // Verleysen M, ed. Proceedings of the 7th European Symposium on Artificial Neural Networks. Bruges: D-Facto Press
  32. 32.
    Willis J K, Roemmich D and Cornuelle B. 2003. Combining altimetric height with broadscale profile data to estimate steric height, heat storage, subsurface temperature, and sea-surface temperature variability. Journal of Geophysical Research, 108(C9): 3292
  33. 33.
    Wilson C and Coles V J. 2005. Global climatological relationships between satellite biological and physical observations and upper ocean properties. Journal of Geophysical Research, 110(C10): C10001
  34. 34.
    Wu X B, Yan X H, Jo Y H and Liu W T. 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
  35. 35.
    Xian G M and Zeng B Q. 2008. ε-SVR algorithm and its application. Computer Engineering and Applications, 44(17): 40–42 (

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website