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A global tuning experiment for the semidiurnal tide is performed with a barotropic model. The model is forced with the M2 equilibrium tide and accounts for the self-attraction and loading (SAL) term. In addition to a quadratic drag, various linear internal wave drag terms adjusted by a scale factor of O(1) are applied. The drag terms include the original Nycander (2005) tensor scheme, the Nycander tensor scheme reduced at supercritical slopes, and their scalar sisters, a Nycander scalar scheme computed for additional abyssal hill roughness, and the Jayne and St. Laurent (2001) scalar scheme. The Nycander scheme does not have a tunable parameter, but to obtain the best tidal solutions, it is demonstrated that some tuning is unavoidable. It is shown that the scalar Nycander schemes yield slightly lower root-mean square (RMS) elevation errors vs. the data-assimilative TPXO tide model than the tensor schemes. Although the simulation with the optimally tuned original Nycander scalar yields dissipation rates close to TPXO, the RMS error is among the highest. The RMS error is lowered for the reduced schemes, which place relatively more dissipation in deeper water. The inclusion of abyssal hill roughness improves the regional agreement with TPXO dissipation rates, without changing the RMS errors. It is difficult to have each ocean basin optimally tuned with the application of a constant scale factor. The relatively high RMS error in the Atlantic Ocean is reduced with a spatially varying scale factor with a larger value in the Atlantic. Our best global mean RMS error of 4.4 cm for areas deeper than 1000 m and equatorward of 66 is among the lowest obtained in a forward barotropic tide model., The original document contains color images. Published in Ocean Modelling, v85 p42-55, 2015. Prepared in collaboration with the University of Southern Mississippi, Stennis Space Center, MS, the University of Michigan, Ann Arbor, and Bangor University, Menai Bridge, Anglesey, UK. Sponsored in part by NRL N000173-06-2-C003.

Ocean mesoscale eddies are non-deterministic in that small errors grow in time so that accurate prediction is not possible without continual correction from observations. Ocean frontogenesis can be forced by mesoscale eddies through straining of buoyancy gradients, which produces filaments of surface divergence related to ageostrophic upwelling. The upwelling can result in thinning of the mixed layer. The frontogenesis predictability is tested through a series of Observation System Experiments (OSEs), the results of which indicate that if the strength and location of the mesoscale eddies are accurately predicted, then the associated frontogenesis features can be predicted. The frontogenesis features have a conditional deterministic predictability . The OSEs are started with perturbed initial conditions, and the OSEs assimilate an increasing number of satellite altimeter data streams. One experiment uses all available data to provide the most accurate analysis, which is labeled as the nature run. Relative to the nature run, ocean steric height correlations increases from about 0.87 with one altimeter and asymptotically reaches 0.99 with four altimeters, showing increasing skill in mesoscale prediction. Satellite data provide no information to dynamically correct frontogenesis processes in the numerical models. Even though not corrected by data, as the number of satellite altimeters increases from 1 to 4, the spatial correlation to the nature run of the frontogenesis forcing increases linearly from 0.27 to 0.59, the surface divergence correlation increases linearly from 0.27 to 0.57 and mixed layer depth correlation increases linearly from 0.67 to 0.89. The conclusion is that within the simulations the frontogenesis filaments are deterministically predictable conditioned on accurate prediction of the mesoscale., Pub. in Ocean Modelling, v78, p1-16, 2014.

A new method is developed to utilize the relatively abundant satellite observations of sea surface temperature (SST) and sea surface height (SSH) and estimates of mixed layer depth (MLD) to create synthetic profiles of temperature and salinity for assimilation in the Navy's Global Ocean Forecasting System (GOFS). The traditional methods for creating synthetics have limitations particularly with regard to accuracy of the vertical ocean gradients. To address this issue, the Improved Synthetic Ocean Profile (ISOP) system has been developed to more correctly represent the ocean's vertical gradients using a three-layer approach accounting for the ocean's (1) surface mixed layer, (2) vertical gradients below the mixed layer through the thermocline, and (3) relatively quiescent depths. The present document covers the details of the background historical in situ observation database, the three-layer approach, the algorithms for each layer, and the global ISOP stand-alone synthetic validation., Prepared in collaboration with QinetiQ North America, Stennis Space Center, MS. The original document contains color images.

We characterize the global ocean seasonal variability of the temperature versus salinity gradients in the transition layer just below the mixed layer using observations of conductivity temperature and depth and profiling float data from the National Ocean Data Center s World Ocean Data set. The balance of these gradients determines the temperature versus salinity control at the mixed layer depth (MLD). We define the MLD as the shallowest of the isothermal, isohaline, and isopycnal layer depths (ITLD, IHLD, and IPLD), each with a shared dependence on a 0.2 deg C temperature offset. Data are gridded monthly using a variational technique that minimizes the squared analysis slope and data misfit. Surface layers of vertically uniform temperature, salinity, and density have substantially different characteristics. By examining differences between IPLD, ITLD, and IHLD, we determine the annual evolution of temperature or salinity or both temperature and salinity vertical gradients responsible for the observed MLD. We find ITLD determines MLD for 63% and IHLD for 14% of the global ocean. The remaining 23% of the ocean has both ITLD and IHLD nearly identical. It is found that temperature tends to control MLD where surface heat fluxes are large and precipitation is small. Conversely, salinity controls MLD where precipitation is large and surface heat fluxes are small. In the tropical ocean, salinity controls MLD where surface heat fluxes can be moderate but precipitation is very large and dominant., Published in the Journal of Geophysical Research, v117 article no. C05006, 3 May 2012.

Predictive ability of five different embedded turbulent mixing models that range from second-order turbulent closure to bulk mixing parameterization is examined in the Mediterranean Sea. Each is embedded in the HYbrid Coordinate Ocean Model (HYCOM). Mixed layer depth (MLD), which is one of the most important upper ocean variables, is used to evaluate the treatment of turbulent processes in each model run. In addition to overall spatial and temporal variability, analyses of MLD are presented using an extensive set (3976) of temperature and salinity profiles from various data sources during 2003-2006. Results obtained from simulations (with no data assimilation and relaxation only to salinity) for the five mixing models are compared with observed MLDs obtained from in situ temperature and salinity profile observations. To ensure the robustness of the validation statistics MLD is computed using both curvature and threshold based methodologies. Results indicate that while all mixing schemes represent the MLD well, the bulk mixing models have substantial accuracy deficiencies relative to the higher order mixing models. The modeled MLDs are slightly deeper than observed MLDs with the mean bias error ~10 m for the higher order mixing models while the bulk mixing model bias error is 15 m or more. The RMS error for the higher order mixing models is --40 m while it is -50 m for the bulk mixing models. The bulk mixing models had substantially larger errors particularly for the curvature MLD definition., Published in Ocean Modelling, v34 p166-184, 2010. Work no. 73-5732-19-5

Since the 1970s, the U.S. Navy has maintained a state-of-the-art gridded global monthly full-depth climatology of temperature and salinity and their standard deviations called the Generalized Digital Environmental Model (GDEM). The present document describes the development and evaluation of GDEM4, the newest version of GDEM. As part of the evaluation of GDEM4, comparisons are made in this report to GDEM3 and to four other ocean climatologies: the NODC World Ocean Atlas (WOA2005), the NODC 1/4 degree resolution climatology, the WOCE Global Hydrographic Climatology (WGHC), and the NRLMODAS 2D (MODAS2D) surface temperature climatology. GDEM4 combines the methodology originally developed for GDEM3 with an expanded observation data set. In the evaluations performed, the GDEM4 climatology has been shown to be a significant improvement over GDEM3 and to compare favorably and in many ways improve on fields from the other climatologies used in the evaluations., The original document contains color images.

The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001-03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 18 radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods., Published in the Journal of Atmospheric and Oceanic Technology v 27 p528-546, 2010.

This paper describes the relative impact of the number of satellite altimeters used in the data assimilation portion of the U.S. Navy ocean prediction system. Using two different statistical and two different comparison methods, four different sets of results are evaluated. Much needed information is provided, regarding the ocean prediction accuracy that is achieved with satellite altimeters determined relative to in situ ocean observations.

The impact of short-term ocean variability on acoustic transmission loss (TL) is examined in the nature run of an observation system simulation experiment (C)SSK) centered on the Okinawa Trough in the western North Pacific. Range-dependent examinations of TL in the upper ocean show the impact of variability as it modifies the sonic layer depth and thickness of the surface duct. Short term variations in the marine environment are shown to have potentially significant impact on acoustic propagation, particularly for applications using active sonar. We examine case studies highlighting the effects of three types of phenomena: diurnal warming, typhoon-induced mixing, and internal waves. These phenomena were identified as important in the OSSE study area during an extensive Navy data collection effort during the summer and fall of 2007. Publicly available observations are used to evaluate the fidelity of the nature run, which then serves as the standard for both range-dependent TL computations and comparisons of ocean prediction alternatives. Some of the alternatives do not represent short-term ocean variability. Thus the case studies reveal the types of errors that arise when acoustic calculations fail to account for a sufficient spectrum of environmental influences. TL differences in the variable environments demonstrate acoustic prediction benefits provided by increasingly capable Navy ocean models., The original document contains color images. All DTIC reproductions will be in black and white.

The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001-03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 1 deg radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods., Published in Journal of Atmospheric and Oceanic Technology, v27 p528-546, 2010.

Climatological fields of mixed layer depth (MLD) are presented over the Aegean, Marmara, Black and Azov Seas. Monthly fields of MLD are formed by historical individual temperature and salinity profiles from combination of various data sets with additional quality control procedures applied. Various definitions that are based solely on temperature (T) or those that include the impact of salinity (S) are applied to investigate the robustness in the pattern and values of the MLDs. Interpolation of the MLD fields to a 0.25 deg x 0.25 deg regular grid over the region is accomplished using a combination of median filter and ordinary kriging. Strong seasonal variability is noted in all regions. Given a density-based MLD criterion that includes both T and S, deep mixed layers (> 200 m) are noted in the Aegean Sea, especially eastern part of the region during winter while MLDs are generally much shallower (< 60 m) in the Black Sea. A criterion based on curvature method, which determines MLD according to first maximum of curvature of T may fail in representing deep MLDs in the Aegean Sea when the water column is well mixed. MLD fields obtained from all definitions are found to be strongly correlated to each other over the seasonal cycle, confirming the strong seasonal cycle. While the curvature method gives shallow MLDs only during winter, it has relatively large skill in comparison to the density-based MLD criterion. In general, MLD fields suffer from lack of input T and S profiles in the Marmara and Azov Seas, thus they may not be representative. Monthly MLD fields presented in this paper are available for various applications, such as mixed layer studies, ocean biology, ocean modeling and acoustic propagation., Published in Journal of Marine Systems, v78 pS169-S180, 2009.

This paper provides the first analysis of the seasonal evolution of the sonic layer depth (SLD) relative to the mixed layer depth (MLD) for the Aegean, Marmara, Black, and Azov Seas. SLD identifies the acoustic ducting capabilities of the upper ocean and is of interest to investigations of upper ocean acoustics. A monthly SLD climatology on a regular 0.25 deg x 0.25 deg grid is constructed from interpolation of available quality-controlled ocean temperature and salinity profiles using the kriging methodology. A four step pre-processing procedure is designed to reduce noise and the effects of sampling irregularities. Monthly SLD fields are then compared relative to the much more widely studied MLD as computed using four different methods from the recent scientific literature. The goals of this analysis are to characterize the SLD relative to the MLD and provide a means for computing SLD from limited hydrographic information and/or MLD estimates. Very deep SLD values are found during winter, in the Aegean and Black Seas, when the near surface temperature values become lower than the temperature below the permanent pycnocline. When this occurs, the SLD drops to the bottom while the MLD remains much shallower at the seasonal pycnocline. For the months of May through October the SLD tends to be less than 25 m for the entire region. It is demonstrated that MLD obtained from the four methodologies have high correlations with SLD over the annual cycle, indicating a robust relationship. As a result, SLD can be estimated using least squares regression coefficients when salinity is unavailable or when observation profiles do not extend to deeper levels., Published in Journal of Marine Systems, v78 pS181-S190, 2009.

Using a global set of in situ temperature and salinity profile observations, the sonic layer depth (SLD) and the mixed layer depth (MLD) are analyzed and compared over the annual cycle. The SLD characterizes the potential of the upper ocean to trap acoustic energy in a surface duct while MLD characterizes upper ocean mixing. The SLD is computed from temperature and salinity profile pairs using a new tunable method while MLD is computed using recently developed methods and either temperature only profiles or temperature and salinity profile pairs. Both SLD and MLD estimates provide information on different and important aspects of the upper ocean. The SLD and MLD often coincide because sound speed increases with depth down to the MLD, where (typically) a decrease in temperature occurs, resulting in a local maximum sound speed. The depth of this maximum sound speed is the SLD., The original document contains color images. All DTIC reproductions will be in black and white. Pub. in Jnl. of Geophysical Research, v113 p1-14, 2008.