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An Objective Algorithm for Reconstructing the Three-Dimensional Ocean Temperature Field Based on Argo Profiles and SST Data

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An Objective Algorithm for Reconstructing the Three-Dimensional Ocean Temperature Field Based on Argo Profiles and SST Data Ocean Dynamics DOI 10.1007/s10236-017-1104-x An objective algorithm for reconstructing the three-dimensional ocean temperature field based on Argo profiles and SST data 1,2 1 2 2 1 Chaojie Zhou & Xiaohua Ding & Jie Zhang & Jungang Yang & Qiang Ma Received: 12 June 2017 /Accepted: 19 September 2017 # Springer-Verlag GmbH Germany 2017 Abstract While global oceanic surface information with large- resolution (0.25° ×0.25°), resulting in the capture of smaller- scale, real-time, high-resolution data is collected by satellite scale characteristics. Finally, both the accuracy and the superi- remote sensing instrumentation, three-dimensional (3D) obser- ority of the algorithm are validated. vations are usually obtained from in situ measurements, but with minimal coverage and spatial resolution. To meet the Keywords Three-dimensional temperature reconstruction . needs of 3D ocean investigations, we have developed a new Argo temperature profile . Sea surface temperature . Fitting algorithm to reconstruct the 3D ocean temperature field based method . Vertical temperature gradient on the Array for Real-time Geostrophic Oceanography (Argo) profiles and sea surface temperature (SST) data. The Argo tem- perature profiles are first optimally fitted to generate a series of 1 Introduction temperature functions of depth, with the vertical temperature structure represented continuously. By calculating the deriva- Various remote sensing instruments, such as an altimeter, tives of the fitted functions, the calculation of the vertical tem- scatterometer and radiometer, are designed to collect informa- perature gradient of the Argo profiles at an arbitrary depth is tion of the ocean surface, including the sea surface tempera- accomplished. A gridded 3D temperature gradient field is then ture (SST) and height (SSH). Accordingly, a considerable found by applying inverse distance weighting interpolation in amount of observations of high resolution with a global cov- the horizontal direction. Combined with the processed SST, the erage have been gathered. Unfortunately, the derived informa- 3D temperature field reconstruction is realized below the sur- tion generally only focuses on the sea surface without direct face using the gridded temperature gradient. Finally, to confirm investigation of the vertical structure of the deeper ocean. the effectiveness of the algorithm, an experiment in the Pacific Since 1998, over 1.5 million profiles have been collected Ocean south of Japan is conducted, for which a 3D temperature within the Array for Real-time Geostrophic Oceanography field is generated. Compared with other similar gridded prod- (Argo) project, which has built a real-time global ocean ob- ucts, the reconstructed 3D temperature field derived by the servation system for sampling the upper 2000 m of the ocean, proposed algorithm achieves satisfactory accuracy, with corre- thereby making available temperature and salinity (T-S) ob- lation coefficients of 0.99 obtained, including a higher spatial servations with a global coverage (Riser et al. 2016). While the data coverage and volume exceed all previous traditional Responsible Editor: Guoping Gao observations, the resolution and distribution of the profiles are insufficient in space and time. Therefore, to meet the needs of * Chaojie Zhou 3D ocean investigations, the high-resolution reconstruction of [email protected] the 3D temperature and salinity fields based on the available data is becoming a significant research issue. 1 Department of Mathematics, Harbin Institute of Technology at Since the 1980s, several methods have been proposed to Weihai, Weihai 264209, China reconstruct the 3D temperature and salinity field from sea 2 The First Institute of Oceanography, State Oceanic Administration, surface information (Guinehut et al. 2004;Carnesetal. No. 6 Xianxialing Road, Qingdao 266061, China 1994;NardelliandSantoleri2004), including physical Ocean Dynamics methods, data assimilation technology within an ocean model, Below, the data is introduced in Section 2, Section 3 refers and statistical methods. By taking characteristics of the water to the details of the algorithm process, while Section 4 de- movement and energy exchange into consideration, Hurlburt scribes the implementation and validation of the application (1986) built a numerical ocean model to dynamically transfer experiment, including the determination of the initial condi- simulated altimeter data into subsurface information. The tions, vertical gradient, and the comparison with other existing model reconstructs the deep pressure field even for situations Argo products. Some final remarks are then provided. with energetic shallow and deep circulations, baroclinic instability and a vigorous vertical exchange of energy. However, the investigation was a pure simulation and the 2Data dynamic transfer of information was not feasible for the ocean or for all regions of parameter space relevant to the 2.1 Argo profiles ocean. Chu et al. (1997a, b) developed a thermal parametric model to analyze observed regional sea temperature profiles Argo temperature profiles are obtained from the Argo Real- based on a layered structure of the temperature fields. Though Time Data Center of China (http://www.argo.org.cn/). After some characteristics of each profile were obtained, including quality control, 83 reasonable profiles are retained, with the the mixed layer depth (MLD), thermocline depth and details of every profile employed in the experiment presented thermocline temperature gradient, reconstruction of the 3D in Table 1, including the profile number, measurement time, temperature field proved difficult due to the original prupose and position. The experimental domain and original Argo of the designed model. Yan et al. (2004) proposed a data profiles are shown in Fig. 1.Inthedevelopedalgorithm, assimilation scheme based on 3D variational analysis every profile is optimally fitted to obtain the continuous (3DVAR) to estimate T-S profiles from surface dynamic vertical variation of the temperature gradient, from which we height information. Both vertical correlations for temperature obtain a gridded 3D gradient field by application of the and salinity background errors, as well as the nonlinear T-S inverse distance weighted (IDW) method horizontally. relation, were taken into consideration. While the results of the designed experiment showed potential usefulness in altimetry 2.2 Sea surface temperature data assimilation, the conducted experiment does not repre- sent the complicated nature of the ocean state satisfactorily. To The National Oceanic and Atmospheric Administration meet the U.S. Navy’s requirement for rapid estimates of pres- (NOAA) Advanced Very High Resolution Radiometer ent and near-term ocean conditions, Fox et al. (2002)com- (AVHRR) 1/4° daily Optimum Interpolation Sea Surface bined in situ measurements, remotely sensed temperatures and Temperature (or daily OISST) is an analysis constructed by heights to form a single integrated analysis of temperature and combining observations from different platforms (satellites, salinity on a regular grid. The regression coefficients relating ships, buoys) on a regular global grid (Reynolds et al. 2007). the subsurface temperature to the SSH and SST were calculat- Here, the AVHRR SST data is applied to initialize the recon- ed based on a large number of in situ T-S profiles. struction algorithm at the surface. Combined with the vertical Benefiting from the Argo project, many near real-time temperature gradient obtained by the fitted Argo temperature monthly global gridded ocean T-S productions have been de- profiles, the surface temperature information may be readily veloped (e.g., Jamestec-Argo (Hosoda et al. 2008), transferred downward to the subsurface. Roemmich-Argo (Roemmich and Gilson 2009), EN4-Argo (Good et al. 2013), and BOA-Argo (Li et al. 2017)) by merg- 2.3 Validation data ing Argo T-S observations into a climatological initial condi- tion directly based on optimum interpolation or more sophis- According to the analysis of Li et al. (2017), we select two of ticated variational analysis methods (Troupin et al. 2010). the existing Argo-derived gridded products, the version 4 of However, the smaller-scale signals from the original observa- the Met Office Hadley Centre BEN^ series of data sets (EN4) tional data have been smoothed and concealed, and the hori- and Barnes objective analysis (BOA)-Argo datasets, to vali- zontal resolution of the most recent productions is 1° ×1°, date the reconstructed temperature, because of their good per- which is insufficient for mesoscale research. formance. The EN4 dataset is generated by the optimal inter- A new algorithm is proposed here to construct a monthly polation method, where the climatological World Ocean Atlas oceanic 3D temperature field with a horizontal resolution of (WOA98) is considered as the background. Moreover, tem- 0.25° ×0.25°, which is realized by the combination of Argo perature and salinity information from all types of ocean pro- temperature profiles and SST data. To validate the effective- filing instruments are merged. Unlike the EN4 dataset, the ness of the algorithm, an experiment is performed for the background condition of the BOA-Argo dataset is generated Pacific Ocean south of Japan (25° N–32° N, 136° E–143° from original Argo observations by the Cressman scheme, so E) with a gridded temperature product for January 2009. that signals from the original data are retained, with the noise Ocean Dynamics Table 1 The Argo profiles
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