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In this paper, different partitioning techniques and methods to identify wind sea and swell are investigated, addressing both 1D and 2D schemes. Current partitioning techniques depend largely on arbitrary parameterizations to assess if wave systems are significant or spurious. This makes the implementation of automated procedures difficult, if not impossible, to calibrate. To avoid this limitation, for the 2D spectrum, the use of a digital filter is proposed to help the algorithm keep the important features of the spectrum and disregard the noise. For the 1D spectrum, a mechanism oriented to neglect the most likely spurious partitions was found sufficient for detecting relevant spectral features. Regarding the identification of wind sea and swell, it was found that customarily used methods sometimes largely differ from one another. Evidently, methods using 2D spectra and wind information are the most consistent. In reference to 1D identification methods, attention is given to two widely used methods, namely, the steepness method used operationally at the National Data Buoy Center (NDBC) and the Pierson–Moskowitz (PM) spectrum peak method. It was found that the steepness method systematically overestimates swell, while the PM method is more consistent, although it tends to underestimate swell. Consistent results were obtained looking at the ratio between the energy at the spectral peak of a partition and the energy at the peak of a PM spectrum with the same peak frequency. It is found that the use of partitioning gives more consistent identification results using both 1D and 2D spectra.

[1] Mean fields, seasonal cycles, and interannual variability are examined for fields of satellite-derived chlorophyll pigment concentrations (CHL), sea surface height (SSH), and sea surface temperature (SST) during 1997-2002. The analyses help to identify three dynamic regions: an upwelling zone next to the coast, the Ensenada Front in the north, and regions of repeated meanders and/or eddy variability west and southwest of Point Eugenia. High values of CHL are found in the upwelling zone, diminishing offshore. The exception is the area north of 31°N (the Ensenada Front), where higher CHL are found about 150 km offshore. South of 31°N, the long-term mean dynamic topography decreases next to the coast, creating isopleths of height parallel to the coastline, consistent with southward geostrophic flow. North of 31°N the mean flow is toward the east, consistent with the presence of the Ensenada Front. The mean SST reveals a more north-south gradient, reflecting latitudinal differences in surface heating due to solar radiation. Harmonic analyses and EOFs reveal the seasonal and interannual patterns, including the region of repeated eddy activity to the west and southwest of Point Eugenia. A maximum CHL occurs in spring in most of the inshore regions, reflecting the growth of phytoplankton in response to the seasonal maximum in upwelling-favorable winds. SST and SSH anomalies are negative in the coastal upwelling zone in spring, also consistent with a response to the seasonal maximum in upwelling. When the seasonal cycle is removed, the strongest signal in the EOF time series is the response to the strong 1997-1998 El Niño, with a weaker signal representing La Niña (1998)(1999) conditions. El Niño conditions consist of low chlorophyll, high SSH, and high SST, with opposite conditions during La Niña.

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Theoretical study and experimental verification of wind wave generation and evolution focus generally on ideal conditions of steady state and quiescent initial background, of which the ideal fetch-limited wind wave growth is an important benchmark. In nature, unsteady winds and swell presence are more common. Here, the observations of wind wave development in mixed seas under unsteady and quasi-steady wind forcing are presented. With reference to the ideal fetch-limited growth functions established under steady wind forcing in the absence of swell, the analysis shows that the wind-steadiness factor impacts wave growth. The wind wave variance in mixed sea is enhanced in both accelerating and decelerating phases of an unsteady wind event, with a larger enhancement in the accelerating phase than in the decelerating phase. Spatial and temporal wind wave measurements under similar environmental conditions are also compared; the quantifiable differences in the wave development are attributable to the wind-steadiness factor. Coupled with the empirical observation that the average wind stress is decreased in mixed sea, these results suggest that wind wave generation and development are more efficient in mixed sea than in wind sea. Possible causes include (i) oscillatory modulation of surface roughness increases air–sea exchanges, (ii) background surface motion reduces energy waste for cold start of wind wave generation from a quiescent state, and (iii) breaking of short waves redistributes wind input and allows more of the available wind power to be directed to the longer waves for their continuous growth.

In an earlier paper by Wang and Hwang, a wave steepness method was introduced to separate the wind sea and swell of the 1D wave spectrum without relying on external information, such as the wind speed. Later, the method was found to produce the unreasonable result of placing the swell–sea separation frequency higher than the wind sea peak frequency. Here, the following two factors causing the erratic performance are identified: (a) the wave steepness method defines the swell–sea separation frequency to be equal to the wind sea peak frequency with a wave age equal to one, and, (b) for more mature wave conditions, the peak frequency of the wave steepness function may not continue monotonic downshifting in high winds if the high-frequency portion of the wave spectrum has a spectral slope milder than −5. Conceptually, the swell–sea separation frequency should be placed between the swell and wind sea peak frequencies rather than at the wind sea peak frequency. Furthermore the wind sea wave age can vary over a considerable range, thus factor a above can lead to incorrect results. Also, because the slope of the wind sea equilibrium spectrum is typically close to −4, factor b becomes a serious restriction in more mature wave conditions. A spectrum integration method generalized from the wave steepness method is presented here for wind sea and swell separation of the 1D wave spectrum without requiring external information. The new spectrum integration method works very well over a wide range of wind wave development stages in the ocean.

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[1] Wind stress is a key parameter for oceanic and atmospheric modeling, forecasting, and hydrodynamic studies. It is generally accepted that wind stress depends on the sea state. In particular, it has been shown that the presence of swell can modify both magnitude and direction of the wind stress. The presence of swell enhances momentum flux when swell propagates opposite to the wind direction and reduces it when it travels along the wind direction. However, those conclusions are mainly based on data acquired in low wind speed conditions and it is not clear to what extent an effect of swell persists at higher winds. Here simultaneous measurements of wind stress and waves, carried out in an area characterized by the occurrence of strong offshore winds with counter long-period swell, are presented and analyzed. The observations indicate that swell causes substantial changes to the wind stress at all observed wind conditions, including wind speeds as high as 20 ms À1 . It is believed that in low wind conditions swell increases drag by directly interacting with the air flow, whereas at higher winds, swell reduces drag by modifying the wind-sea-associated roughness.Citation: García-Nava, H., F. J. Ocampo-Torres, P. Osuna, and M. A. Donelan (2009), Wind stress in the presence of swell under moderate to strong wind conditions,