Ocean is characterized by the existence of vertically quasi-uniform layer of temperature (T, isothermal layer) and density (ñ, mixed layer). Underneath it, there exists a layer with strong vertical gradient such as the thermocline (in temperature) and pycnocline (in density). The thermocline is the transition layer between the mixed layer at the surface and the deep water layer. Thermocline gradient (i.e., thermocline strength) affects the exchange of heat and water mass between the mixed layer and deep oceans, ocean circulation, and in turn influences marine ecology, and climate. Therefore, determination of thermocline (or pycnocline) strength (defined as maximum gradient) is important. A good method for determination of thermocline strength from profile data should satisfy the following requirements: (1) objective determination, (2) capability of handling noisy data (i.e., no smoothing needed), (3) capability of handling high and low resolution data, and (4) no iteration needed.

In this paper, a noise-filtering maximum gradient method is developed to identify thermocline strength. Starting from the surface downward, a depth (zmin) with minimum magnitude of gradient is identified. This minimum gradient is the best representation of the mixed layer gradient although theoretically it should be zero. The vertical temperature difference from zK (bottom of the profile) to zmin, ÄT = Tzmin – TK, is the variability of the temperature across the mixed layer, thrmocline, and deep layer. Since the vertical gradient is strongest in the thermocline, and weakest in the mixed layer, the main part of the thermocline can be roughly identified between the two depths: z(0.1) with the gradient of 0.1ÄT and z(0.7) with the gradient of 0.7ÄT. The purpose to select the two depths [z(0.1) and z(0.7)] is to filter the noise in upper part of temperature profiles. The maximum gradient between the two depths z(0.1) and z(0.7) is used to estimate the maximum thermocline gradient.

This noise filtering maximum gradient method is used to process the data (1990-2010) from the Global Temperature and Salinity Profile Program (GTSPP). GTSPP is a cooperative international project since 1990, and handles all temperature and salinity profile data including XBT, CTDs, thermistor chain data, and Argo observations. These data will reach data processing centers of the Program through the real-time channels of the IGOSS program or in delayed mode through the IODE system. Real-time data in GTSPP are acquired from the Global Telecommunications System in the bathythermal (BATHY) and temperature, salinity & current (TESAC) codes forms supported by the WMO. Delayed mode data are contributed directly by member states of IOC. The thermocline strength has evident spatial and temporal variability. The probability density function (PDF) of the thermocline strength satisfies the Weibull distribution. Interpretations of the observational results will also be presented.

References Chu, P.C., and C.W. Fan, 2010: Optimal linear fitting for objective determination of ocean mixed layer depth from glider profiles. Journal of Atmospheric and Oceanic Technology, 27, 1893-1898. Chu, P.C., and C.W. Fan, 2011: Maximum angle method for determining mixed layer depth from seaglider data. Journal of Oceanography, 67, 219-230