Estimation of global subsurface temperature anomaly based on multisource satellite observations

Wen′e LI; Hua SU; Xiaoqin WANG; Xiaohai YAN

doi:10.11834/jrs.20177026

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Estimation of global subsurface temperature anomaly based on multisource satellite observations
Vol. 21, Issue 6, Pages: 881-891(2017)
Received：20 March 2017，

Accepted：14 June 2017，

Published：15 September 2017
DOI： 10.11834/jrs.20177026
稿件说明：

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黎文娥, 苏华, 汪小钦, 严晓海. 2017. 多源卫星观测的全球海洋次表层温度异常信息提取. 遥感学报, 21(6): 881–891 DOI： 10.11834/jrs.20177026.

Li W E, Su H, Wang X Q and Yan X H. 2017. Estimation of global subsurface temperature anomaly based on multisource satellite observations. Journal of Remote Sensing, 21(6): 881–891 DOI： 10.11834/jrs.20177026.

摘要

基于表层卫星遥感观测的中深层海洋遥感对于了解海洋内部异常及其动力过程有重要意义。如何从现有的海洋表层遥感观测资料提取海洋内部关键动力环境信息场是具有挑战性的海洋遥感技术前沿。本文采用支持向量回归(SVR)方法，通过卫星遥感观测获取的多源海表参量(海表高度异常(SSHA)、海表温度异常(SSTA)、海表盐度异常(SSSA)和海表风场异常(SSWA))，选择最优参量输入组合，感知海洋次表层温度异常(STA)，并用实测Argo数据作精度验证。结果表明SVR模型可准确估算全球尺度的STA(1000 m深度以浅)；当SVR输入变量为2个(SSHA、SSTA)、3个(SSHA、SSTA、SSSA)、4个(SSHA、SSTA、SSSA、SSWA)时对应的平均均方差(MSE)分别为0.0090、0.0086、0.0087，平均决定系数(

)分别为0.443、0.457、0.485。因此，除了SSHA和SSTA外，SSSA与SSWA的输入对SVR模型的估算有积极影响，有助于提高STA的估算精度。在全球增暖与减缓背景下，该研究可为从表层卫星遥感观测提取海洋内部热力异常信息研究提供重要技术支持，有利于拓展卫星对海观测范围。

Abstract

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 (

) 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

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

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.

关键词

Keywords

references

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

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

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

Chang C C and Lin C J. 2013. LIBSVM: a library for support vector machines. [2015-11-23]. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf

陈锦年, 宋贵霆, 褚健婷, 许兰英. 2003. 赤道太平洋次表层海水温度异常的信号通道. 水科学进展, 14(2): 152–157

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 (

Chen X Y and Tung K K. 2014. Varying planetary heat sink led to global-warming slowdown and acceleration. Science, 345(6199): 897–903

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

Fischer M. 2000. Multivariate projection of ocean surface data onto subsurface sections. Geophysical Research Letters, 27(6): 755–757

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

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

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

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

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

Hsu C W, Chang C C and Lin C J. 2010. A practical guide to support vector classification. National Taiwan University. [2016-08-16]. http://www.vis.lbl.gov/~romano/mlgroup/papers/practical-svm-guide.pdf http://www.vis.lbl.gov/~romano/mlgroup/papers/practical-svm-guide.pdf

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

Huber M and Knutti R. 2014. Natural variability, radiative forcing and climate response in the recent hiatus reconciled. Nature Geoscience, 7(9): 651–656

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]

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

Kosaka Y and Xie S P. 2013. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501(7467): 403–407

李崇银, 穆明权. 2001. 赤道印度洋海温偶极子型振荡及其气候影响. 大气科学, 25(4): 433–443

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 (

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

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

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

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

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

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

Trenberth K E and Fasullo J T. 2013. An apparent hiatus in global warming?. Earth’s Future, 1(1): 19–32

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

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

王喜冬, 韩桂军, 李威, 齐义泉. 2011. 利用卫星观测海面信息反演三维温度场. 热带海洋学报, 30(6): 10–17

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 (

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

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

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

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

冼广铭, 曾碧卿. 2008. ε-支持向量回归机算法及其应用. 计算机工程与应用, 44(17): 40–42

Xian G M and Zeng B Q. 2008. ε-SVR algorithm and its application. Computer Engineering and Applications, 44(17): 40–42 (

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