Ali, A., Christensen, K. H., Breivik, Ø., Malila, M., Raj, R. P., Bertino, L., et al. (2019). A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans.
Ocean Modelling, 137, 76–97.
Abstract: Five different parameterizations of Langmuir turbulence (LT) effect are investigated in a realistic model of the North Atlantic and Arctic using realistic wave forcing from a global wave hindcast. The parameterizations mainly apply an enhancement to the turbulence velocity scale, and/or to the entrainment buoyancy flux in the surface boundary layer. An additional run is also performed with other wave effects to assess the relative importance of Langmuir turbulence, namely the Coriolis-Stokes forcing, Stokes tracer advection and wave-modified momentum fluxes. The default model (without wave effects) underestimates the mixed layer depth in summer and overestimates it at high latitudes in the winter. The results show that adding LT mixing reduces shallow mixed layer depth (MLD) biases, particularly in the subtropics all year-around, and in the Nordic Seas in summer. There is overall a stronger relative impact on the MLD during winter than during summer. In particular, the parameterization with the most vigorous LT effect causes winter MLD increases by more than 50% relative to a control run without Langmuir mixing. On the contrary, the parameterization which assumes LT effects on the entrainment buoyancy flux and accounts for the Stokes penetration depth is able to enhance the mixing in summer more than in winter. This parametrization is also distinct from the others because it restrains the LT mixing in regions of deep MLD biases, so it is the preferred choice for our purpose. The different parameterizations do not change the amplitude or phase of the seasonal cycle of heat content but do influence its long-term trend, which means that the LT can influence the drift of ocean models. The combined impact on water mass properties from the Coriolis-Stokes force, the Stokes drift tracer advection, and the wave-dependent momentum fluxes is negligible compared to the effect from the parameterized Langmuir turbulence.
Ali, M. M., Nagamani, P. V., Sharma, N., Venu Gopal, R. T., Rajeevan, M., Goni, G. J., et al. (2015). Relationship between ocean mean temperatures and Indian summer monsoon rainfall.
Atmos. Sci. Lett., 16(3), 408–413.
Armstrong, E. M., Bourassa, M. A., Cram, T., Elya, J. L., Greguska, F. R., III, Huang, T., et al. (2018). An information technology foundation for fostering interdisciplinary oceanographic research and analysis. In
American Geophysical Union (Vol. Fall Meeting).
Abstract: Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist's personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.
Arruda, W. Z., Campos, E. J. D., Zharkov, V., Soutelino, R. G., & da Silveira, I. C. A. (2013). Events of equatorward translation of the Vitoria Eddy.
Continental Shelf Research, 70, 61–73.
Baigorria, G., Jones, J., Shin, D., Mishra, A., & Ingram, K. T., Jones, J. W., O'Brien, J. J., Roncoli, M. C., Fraisse, C., Breuer, N. E., Bartels, W.-L., Zierden, D. F., Letson, D. (2007). Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs.
Clim. Res., 34, 211–222.
Bashmachnikov, I. L., Fedorov, A. M., Vesman, A. V., Belonenko, T. V., & Dukhovskoy, D. S. (2019). Thermohaline convection in the subpolar seas of the North Atlantic from satellite and in situ observations. Part 2: indices of intensity of deep convection.
Abstract: Variation in locations of the maximum development of deep convection in the subpolar seas, taking into account their small dimensions, represent difficulty in identifying its interannual variability from usually sparse in situ data. In this work, the interannual variability of the maximum convection depth, is obtained using one of the most complete datasets ARMOR, which combines in situ and satellite data. The convection depths, derived from ARMOR, are used for testing the efficiency of two indices of convection intensity: (1) sea-level anomalies from satellite altimetry and (2) the integral water density in the areas of the most frequent development of deep convection. The first index, capturing some details, shows low correlations with the interannual variability of the deep convection intensity. The second index shows high correlation with the deep convection intensity in the Greenland, Irminger and Labrador seas. Asynchronous variations in the deep convection intensity in the Labrador-Irminger seas and in the Greenland Sea are obtained. In the Labrador and in the Irminger seas, the quasi-seven-year variations in the convection intensity are identified.
Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States.
Environmental Modelling & Software, 73, 90–102.
Bastola, S., Misra, V., & Li, H. (2013). Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons.
Earth Interact., 17(25), 1–22.
Bourassa, M. A., & Weissman, D. E. (2003). The development and application of a sea surface stress model function for the QuikSCAT and ADEOS-II SeaWinds scatterometers. In
IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) (pp. 239–241).
Bourassa, M. A., Legler, D. M., O'Brien, J. J., & Smith, S. R. (2003). SeaWinds validation with research vessels.
J. Geophys. Res., 108(C2).