If model parameterizations of unresolved physics, such as the variety of upper ocean mixing processes, are to hold over the large range of time and space scales of importance to climate, they must be strongly physically based. Observations, theories, and models of oceanic vertical mixing are surveyed. Two distinct regimes are identified: ocean mixing in the boundary layer near the surface under a variety of surface forcing conditions (stabilizing, destabilizing, and wind driven), and mixing in the ocean interior due to internal waves, shear instability, and double diffusion (arising from the different molecular diffusion rates of heat and salt). Mixing schemes commonly applied to the upper ocean are shown not to contain some potentially important boundary layer physics. Therefore a new parameterization of oceanic boundary layer mixing is developed to accommodate some of this physics. It includes a scheme for determining the boundary layer depth h, where the turbulent contribution to the vertical shear of a bulk Richardson number is parameterized. Expressions for diffusivity and nonlocal transport throughout the boundary layer are given. The diffusivity is formulated to agree with similarity theory of turbulence in the surface layer and is subject to the conditions that both it and its vertical gradient match the interior values at h. This nonlocal “K profile parameterization” (KPP) is then verified and compared to alternatives, including its atmospheric counterparts. Its most important feature is shown to be the capability of the boundary layer to penetrate well into a stable thermocline in both convective and wind-driven situations. The diffusivities of the aforementioned three interior mixing processes are modeled as constants, functions of a gradient Richardson number (a measure of the relative importance of stratification to destabilizing shear), and functions of the double-diffusion density ratio, Rρ. Oceanic simulations of convective penetration, wind deepening, and diurnal cycling are used to determine appropriate values for various model parameters as weak functions of vertical resolution. Annual cycle simulations at ocean weather station Papa for 1961 and 1969–1974 are used to test the complete suite of parameterizations. Model and observed temperatures at all depths are shown to agree very well into September, after which systematic advective cooling in the ocean produces expected differences. It is argued that this cooling and a steady salt advection into the model are needed to balance the net annual surface heating and freshwater input. With these advections, good multiyear simulations of temperature and salinity can be achieved. These results and KPP simulations of the diurnal cycle at the Long-Term Upper Ocean Study (LOTUS) site are compared with the results of other models. It is demonstrated that the KPP model exchanges properties between the mixed layer and thermocline in a manner consistent with observations, and at least as well or better than alternatives.

Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization

Island Biogeography; ecology, evolution, and conservation

textA term map is a map that visualizes the structure of a scientific field by showing the relations between important terms in the field. The terms shown in a term map are usually selected manually with the help of domain experts. Manual term selection has the disadvantages of being subjective and labor-intensive. To overcome these disadvantages, we propose a methodology for automatic term identification and we use this methodology to select the terms to be included in a term map. To evaluate the proposed methodology, we use it to construct a term map of the field of operations research. The quality of the map is assessed by a number of operations research experts. It turns out that in general the proposed methodology performs quite well.

/pdf/automatic-term-identification-for-bibliometric-mapping-1utn18clpt.pdf

Automatic Term Identification for Bibliometric Mapping

Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach

Sea surface temperature (SST) is a vitally important parameter of the global ocean, which can profoundly affect the climate and marine ecosystems. To achieve an accurate and holistic prediction of the short and mid-term SST field, a spatiotemporal deep learning model is proposed which can capture the correlations of SST across both space and time. The model uses the convolutional long short-term memory (ConvLSTM) as the building block and is trained in an end-to-end manner. Experiments using 36-year satellite-derived SST data in a subarea of the East China Sea demonstrate that the proposed model outperforms the persistence model, the linear support vector regression (SVR) model, and two LSTM models with different settings, when judged using multiple statistics and from different perspectives. The results suggest that the proposed model is highly promising for short and mid-term daily SST field prediction accurately and conveniently.

A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data

Prediction of sea surface temperature in the tropical Atlantic by support vector machines

https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers11-06/010050933.pdf

High-resolution regional ocean dynamics simulation in the southwestern tropical Atlantic

A study of the Brazilian Fernando de Noronha island and Rocas atoll wakes in the tropical Atlantic

Amazon River plume influence on Western Tropical Atlantic dynamic variability

Climate is closely related to the dynamics of the surface layer of the tropical Atlantic and the exchange between this latter and the atmosphere, and wearther forecasting will improve with increasing understanding of the processes that govern the relative distribution of thermodynamic properties of the water column. This paper focuses on the isolation of warm surface waters from the cold ones of the deep ocean by a salinity induced barrier layer (BL) in the western equatorial Atlantic (3oS-7oN; 40o-52oW), based on 487 CTD profiles (REVIZEE - 1995-2001). The main process contributing to the seasonal BL formation is the discharge of low salinity waters from the Amazon river. During boreal late winter/spring (Mar-May; high river discharge), deeper isothermal (Z T) and mixed layers (Z M) prevail and the formation of a 16m-thick BL was clearly determined the formation of a salt-induced marked pycnocline within a deeper isothermal layer. However, during the boreal autumn (Oct-Dec; low river discharge), density stratification was mainly determined by temperature distribution (Z M m Z T; BLT = Z M - Z T m 0). There was no clear register of a BL on the Amazon shelf, but a maximum BL (40 m) formed near the shelf break at 45°W.

http://www.scielo.br/pdf/bjoce/v53n3-4/01.pdf

Marcus Silva

Papers

Prediction of sea surface temperature in the tropical Atlantic by support vector machines

High-resolution regional ocean dynamics simulation in the southwestern tropical Atlantic

A study of the Brazilian Fernando de Noronha island and Rocas atoll wakes in the tropical Atlantic

Amazon River plume influence on Western Tropical Atlantic dynamic variability

Seasonal changes in the mixed and barrier layers in the western Equatorial Atlantic