Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters

Empirical Orthogonal Function （EOF） analysis is used in this study to generate main eigenvector fields of historical temperature for the China Seas （here referring to Chinese marine territories） and adjacent waters from 1930 to 2002 （510 143 profiles）. A good temperature profile is reconstructed based on several subsurface in situ temperature observations and the thermocline was estimated using the model. The results show that： 1） For the study area, the former four principal components can explain 95% of the overall variance, and the vertical distribution of temperature is most stable using the in situ temperature observations near the surface. 2） The model verifications based on the observed CTD data from the East China Sea （ECS）, South China Sea （SCS） and the areas around Taiwan Island show that the reconstructed profiles have high correlation with the observed ones with the confidence level 〉95%, especially to describe the characteristics of the thermocline well. The average errors between the reconstructed and observed profiles in these three areas are 0.69℃, 0.52℃ and 1.18℃ respectively. It also shows the model RMS error is less than or close to the climatological error. The statistical model can be used to well estimate the temperature profile vertical structure. 3） Comparing the thermocline characteristics between the reconstructed and observed profiles, the results in the ECS show that the average absolute errors are 1.5m, 1.4 m and 0.17~C/m, and the average relative errors are 24.7%, 8.9% and 22.6% for the upper, lower thermocline boundaries and the gradient, respectively. Although the relative errors are obvious, the absolute error is small. In the SCS, the average absolute errors are 4.1 m, 27.7 m and 0.007℃/m, and the average relative errors are 16.1%, 16.8% and 9.5% for the upper, lower thermocline boundaries and the gradient, respectively. The average relative errors are all 〈20%. Although the average absolute error of the lower thermocline boundary is considerable, but contrast to the spatial scale of average depth of the lower thermocline boundary （165 m）, the average relative error is small （16.8%）. Therefore the model can be used to well estimate the thermocline.

Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

The sensitive area of targeted observations for short-term(7 d)prediction of vertical thermal structure(VTS)in summer in the Yellow Sea was investigated.We applied the Conditional Nonlinear Optimal Perturbation(CNOP)method and an adjoint-free algorithm with the Regional Ocean Modeling System(ROMS).We used vertical integration of CNOP-type temperature errors to locate the sensitive areas,where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area.The identified sensitive areas were northeast−southwest orientated northeast to the verification area,which were possibly related to the southwestward background currents.Then,we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas.Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas(e.g.,the verification area and areas to its east and northeast).Moreover,removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself.Our results suggest that implementation of targeted observation in the CNOP-based sensitive areas is an effective method to improve short-term prediction of VTS in summer in the Yellow Sea.

A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.