Banks, R. (2006).
Variability of Indian Ocean Surface Fluxes Using a New Objective Method. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: A new objective technique is used to analyze monthly mean gridded fields of air and sea temperature, scalar and vector wind, specific humidity, sensible and latent heat flux, and wind stress over the Indian Ocean. A variational method produces a 1°x1° gridded product of surface turbulent fluxes and the variables needed to calculate these fluxes. The surface turbulent fluxes are forced to be physically consistent with the other variables. The variational method incorporates a state of the art flux model, which should reduce regional biases in heat and moisture fluxes. The time period is January 1982 to December 2003. The wind vectors are validated through comparison to monthly scatterometer winds. Empirical orthogonal function (EOF) analyses of the annual cycle emphasize significant modes of variability in the Indian Ocean. The dominant monsoon reversal and its connection with the southeast trades are linked in eigenmodes one and two of the surface fluxes. The third eigenmode of latent and sensible heat flux reveal a structure similar to the Indian Ocean Dipole (IOD) mode. The variability in surface fluxes associated with the monsoons and IOD are discussed. September-October-November composites of the surface fluxes during the 1997 positive IOD event and the 1983 negative IOD event are examined. The composites illustrate characteristics of fluxes during different IOD phases.
Bourassa, M. A., and P.J. Hughes. (2018). Surface Heat Fluxes and Wind Remote Sensing. In and J. Verron J. Tintoré A. Pascual E. P. Chassignet (Ed.), (pp. 245–270). Tallahassee, FL: GODAE OceanView.
Abstract: The exchange of heat and momentum through the air-sea surface are critical aspects of ocean forcing and ocean modeling. Over most of the global oceans, there are few in situ observations that can be used to estimate these fluxes. This chapter provides background on the calculation and application of air-sea fluxes, as well as the use of remote sensing to calculate these fluxes. Wind variability makes a large contribution to variability in surface fluxes, and the remote sensing of winds is relatively mature compared to the air sea differences in temperature and humidity, which are the other key variables. Therefore, the remote sensing of wind is presented in greater detail. These details enable the reader to understand how the improper use of satellite winds can result in regional and seasonal biases in fluxes, and how to calculate fluxes in a manner that removes these biases. Examples are given of high-resolution applications of fluxes, which are used to indicate the strengths and weakness of satellite-based calculations of ocean surface fluxes.
Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., et al. (2020). FluxSat: Measuring the Ocean-Atmosphere Turbulent Exchange of Heat and Moisture from Space.
Remote Sensing, 12(11), 1796.
Abstract: Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean-atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air-sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean-atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean-atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.
Morey, S. L., Bourassa, M. A., Dukhovskoy, D. S., & O'Brien, J. J. (2006). Modeling studies of the upper ocean response to a tropical cyclone.
Ocean Dynamics, 56(5-6), 594–606.
Scott, J. P. (2011).
An Intercomparison of Numerically Modeled Flux Data and Satellite-Derived Flux Data for Warm Seclusions. Master's thesis, Florida State University, Tallahassee, FL.
Wallcraft, A. J., Kara, A. B., Hurlburt, H. E., Chassignet, E. P., & Halliwell, G. H. (2008). Value of bulk heat flux parameterizations for ocean SST prediction.
Journal of Marine Systems, 74(1-2), 241–258.
Wang, S., Kranz, S. A., Kelly, T. B., Song, H., Stukel, M. R., & Cassar, N. (2020). Lagrangian Studies of Net Community Production: The Effect of Diel and Multiday Nonsteady State Factors and Vertical Fluxes on O
2/Ar in a Dynamic Upwelling Region. J. Geophys. Res. Biogeosci., 125(6), e2019JG005569.
Abstract: The ratio of dissolved oxygen to argon in seawater is frequently employed to estimate rates of net community production (NCP) in the oceanic mixed layer. The in situ O2/Ar‐based method accounts for many physical factors that influence oxygen concentrations, permitting isolation of the biological oxygen signal produced by the balance of photosynthesis and respiration. However, this technique traditionally relies upon several assumptions when calculating the mixed‐layer O2/Ar budget, most notably the absence of vertical fluxes of O2/Ar and the principle that the air‐sea gas exchange of biological oxygen closely approximates net productivity rates. Employing a Lagrangian study design and leveraging data outputs from a regional physical oceanographic model, we conducted in situ measurements of O2/Ar in the California Current Ecosystem in spring 2016 and summer 2017 to evaluate these assumptions within a �worst‐case� field environment. Quantifying vertical fluxes, incorporating nonsteady state changes in O2/Ar, and comparing NCP estimates evaluated over several day versus longer timescales, we find differences in NCP metrics calculated over different time intervals to be considerable, also observing significant potential effects from vertical fluxes, particularly advection. Additionally, we observe strong diel variability in O2/Ar and NCP rates at multiple stations. Our results reemphasize the importance of accounting for vertical fluxes when interpreting O2/Ar‐derived NCP data and the potentially large effect of nonsteady state conditions on NCP evaluated over shorter timescales. In addition, diel cycles in surface O2/Ar can also bias interpretation of NCP data based on local productivity and the time of day when measurements were made.
Wei, J., Dirmeyer, P. A., Guo, Z., Zhang, L., & Misra, V. (2010). How Much Do Different Land Models Matter for Climate Simulation? Part I: Climatology and Variability.
J. Climate, 23(11), 3120–3134.
Zavala-Hidalgo, J., Pares-Sierra, A., & Ochoa, J. (2002). Seasonal variability of the temperature and heat fluxes in the Gulf of Mexico.
Atmosfera, 15(2), 81–104.