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|Smith, S. R., Lopez, N., & Bourassa, M. A. (2016). SAMOS air-sea fluxes: 2005-2014. Geosci. Data J., 3(1), 9–19.|
|Smith, S. R., Servain, J., Legler, D. M., Stricherz, J. N., Bourassa, M. A., & O'Brien, J. J. (2004). In Situ-Based Pseudo-Wind Stress Products for the Tropical Oceans. Bull. Amer. Meteor. Soc., 85(7), 979–994.|
Steffen, J., & Bourassa, M. (2018). Barrier Layer Development Local to Tropical Cyclones based on Argo Float Observations. J. Phys. Oceanogr., 48(9), 1951–1968.
Abstract: The objective of this study is to quantify barrier layer development due to tropical cyclone (TC) passage using Argo float observations of temperature and salinity. To accomplish this objective, a climatology of Argo float measurements is developed from 2001 to 2014 for the Atlantic, eastern Pacific, and central Pacific basins. Each Argo float sample consists of a prestorm and poststorm temperature and salinity profile pair. In addition, a no-TC Argo pair dataset is derived for comparison to account for natural ocean state variability and instrument sensitivity. The Atlantic basin shows a statistically significant increase in barrier layer thickness (BLT) and barrier layer potential energy (BLPE) that is largely attributable to an increase of 2.6 m in the post-TC isothermal layer depth (ITLD). The eastern Pacific basin shows no significant changes to any barrier layer characteristic, likely due to a shallow and highly stratified pycnocline. However, the near-surface layer freshens in the upper 30 m after TC passage, which increases static stability. Finally, the central Pacific has a statistically significant freshening in the upper 20-30 m that increases upper-ocean stratification by similar to 35%. The mechanisms responsible for increases in BLPE vary between the Atlantic and both Pacific basins; the Atlantic is sensitive to ITLD deepening, while the Pacific basins show near-surface freshening to be more important in barrier layer development. In addition, Argo data subsets are used to investigate the physical relationships between the barrier layer and TC intensity, TC translation speed, radial distance from TC center, and time after TC passage.
Keywords: SEA-SURFACE TEMPERATURE; UPPER-OCEAN RESPONSE; NINO SOUTHERN-OSCILLATION; MIXED-LAYER; INDIAN-OCEAN; HEAT-BUDGET; NUMERICAL SIMULATIONS; HURRICANES; VARIABILITY; PACIFIC
|Subrahmanyam, B., Legler, D. M., Barnier, B., O'Brien, J. J., de Miranda, A. P., & Bourassa, M. (2001). Sensitivity of an ocean general circulation model to changes in surface momentum forcing. WMO WORLD Climate Research Programme, CAS/JSC Working Group on Numerical Experimentation, Research Activities in Atmospheric and Oceanic Modeling.|
Venugopal, T., Ali, M. M., Bourassa, M. A., Zheng, Y., Goni, G. J., Foltz, G. R., et al. (2018). Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall. Sci Rep, 8(1), 12092.
Abstract: This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 degrees C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Nino Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Keywords: SEA-SURFACE TEMPERATURE; EL-NINO; EQUATORIAL PACIFIC; IMPACT; PREDICTION; ENSO; DIPOLE; REGION; SST
|Verschell, M. A., Bourassa, M. A., Weissman, D. E., & O'Brien, J. J. (1999). Ocean model validation of the NASA scatterometer winds. J. Geophys. Res., 104(C5), 11359–11373.|
|Verzone, K. V., Bourassa, M. A., Bachiochi, D., Cocke, S. D., LaRow, T. E., & O'Brien, J. J. (2000). Double Ensemble Estimates of Precipitation in the Southeastern United States for Extreme ENSO Events (H. Ritchie, Ed.).|
Villas Bôas, A. B., Ardhuin, F., Ayet, A., Bourassa, M. A., Brandt, P., Chapron, B., et al. (2019). Integrated Observations of Global Surface Winds, Currents, and Waves: Requirements and Challenges for the Next Decade. Front. Mar. Sci., 6.
Abstract: Ocean surface winds, currents, and waves play a crucial role in exchanges of momentum, energy, heat, freshwater, gases, and other tracers between the ocean, atmosphere, and ice. Despite surface waves being strongly coupled to the upper ocean circulation and the overlying atmosphere, efforts to improve ocean, atmospheric, and wave observations and models have evolved somewhat independently. From an observational point of view, community efforts to bridge this gap have led to proposals for satellite Doppler oceanography mission concepts, which could provide unprecedented measurements of absolute surface velocity and directional wave spectrum at global scales. This paper reviews the present state of observations of surface winds, currents, and waves, and it outlines observational gaps that limit our current understanding of coupled processes that happen at the air-sea-ice interface. A significant challenge for the coming decade of wind, current, and wave observations will come in combining and interpreting measurements from (a) wave-buoys and high-frequency radars in coastal regions, (b) surface drifters and wave-enabled drifters in the open-ocean, marginal ice zones, and wave-current interaction �hot-spots,� and (c) simultaneous measurements of absolute surface currents, ocean surface wind vector, and directional wave spectrum from Doppler satellite sensors.
|Vose, R. S., Applequist, S., Bourassa, M. A., Pryor, S. C., Barthelmie, R. J., Blanton, B., et al. (2014). Monitoring and Understanding Changes in Extremes: Extratropical Storms, Winds, and Waves. Bull. Amer. Meteor. Soc., 95(3), 377–386.|
|Wallcraft, A. J., Kara, A. B., Barron, C. N., Metzger, E. J., Pauley, R. L., & Bourassa, M. A. (2009). Comparisons of monthly mean 10 m wind speeds from satellites and NWP products over the global ocean. J. Geophys. Res., 114(D16).|