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Margaret Yelland and Peter K. Taylor

Abstract

An automatic inertial dissipation system was used during three cruises of the RRS Discovery in the Southern Ocean to obtain a large dataset of open ocean wind stress estimates. The wind speed varied from near calm to 26 m s−1, and the sea-air temperature differences ranged from −15° to +7°C. The data showed that the assumption of a balance between local production and dissipation of turbulent kinetic energy is false and that the sign and magnitude of the imbalance depends critically on both stability and wind speed. The wide range of stability conditions allowed a new formulation for the nondimensional dissipation function under diabatic conditions.

A minimum in the 10-m neutral value of the drag coefficient occurred at 6 m s−1. At lower wind speeds the data were fitted by the relationship
i1520-0485-26-4-541-eq1
where U 10n. is the 10-m neutral wind speed (m s−1). At higher wind speeds
i1520-0485-26-4-541-eq2
which gives drag coefficients that are about 10% higher than those from previous open ocean studies (which assumed a balance between production and dissipation). Wave measurement suggested that the sea state was not, on average, fully developed at wind speeds above 15 m s−1. However, contrary to findings from other studies, no large anomalies in the drag coefficient were detected despite the range of conditions and sea states encountered. It is believed that the ideal conditions (such as the absence of swell) needed to defect the effects of sea state on the wind stress may occur rather infrequently over the open ocean.
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Peter K. Taylor and Margaret J. Yelland

Abstract

A synthetic dataset is used to show that apparent variations between different stability classes in the mean drag coefficient, C D10n, to wind speed relationship can be explained by random errors in determining the friction velocity u∗. Where the latter has been obtained by the inertial dissipation method, the variations in C D10n have previously been ascribed to an imbalance between production and dissipation in the turbulent kinetic energy budget. It follows that the application of “imbalance corrections” when calculating u∗ is incorrect and will cause a positive bias in C D10n, by about 10−4.

With no imbalance correction, random errors in u∗ result in scatter in the C D10n values, but for most wind speeds, there is no mean bias. However, in light winds under unstable conditions random errors in u∗ act to positively bias the calculated C D10n values. This is because the stability related effects are nonlinear and also because for some records for which C D10n would be decreased, the iteration scheme does not converge. The threshold wind speed is typically 7 m s−1, less for cleaner datasets. The biased C D10n values can be avoided by using a u∗ value calculated from a mean C D10nU 10n relationship to determine the stability. The choice of the particular relationship is not critical. Recalculating previously published C D10n values without imbalance correction, but with anemometer response correction, results in a decrease of C D10n but only by about 0.05 × 10−3.

In addition to removing a previous cause of scatter and uncertainty in inertial dissipation data, the results suggest that spurious stability effects and low wind speed biases may be present in C D10n estimates obtained by other methods.

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Elizabeth C. Kent and Peter K. Taylor

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The effect of heating due to solar radiation on measurements of humidity obtained from ships is examined. Variations in wet- and dry-bulb temperature measured on each side of a research ship are shown to correlate with solar radiation. However, the resulting variations in the measured humidity are shown to be negligible (less than 0.1 g kg−1). Any radiation induced humidity errors in data from merchant ships participating in the Voluntary Observing Ship Special Observing Programme for the North Atlantic are also shown to be small, less than 0.2 g kg−1. For calculation of the latent heat flux, the near-surface humidity should be calculated from the observed wet- and dry-bulb temperatures, whereas the dry-bulb temperature should be corrected for radiation-induced errors before calculation of the atmospheric stability or the sensible heat flux.

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Elizabeth C. Kent and Peter K. Taylor

Abstract

Previously published estimates of the surface turbulent fluxes over the North Atlantic Ocean have been compared by applying the calculation methods used by each author to a common dataset of ship observations. The major differences between the various flux estimations were due to whether or not the data were corrected for observation height, the method of calculating averaged fluxes and the choice of transfer coefficient.

By correcting the ship observations obtained from the Voluntary observing ship Special Observing Programme–North Atlantic for known observational biases an estimate of the correct values for the fluxes has been made. The previous studies were found to have overestimated the North Atlantic sea to air heat transfer by up to 30%. All of the schemes overestimated the annual cycle, giving fluxes approximately equal to the best estimate values in the summer but much higher fluxes in the winter. The use of transfer coefficient values much larger than the values determined by air-sea interaction experiments could not be justified by either the effects of measurement errors or by fair weather bias. The lower turbulent flux values are compatible with a balanced North Atlantic heat budget, given the uncertainty in the other flux terms.

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Elizabeth C. Kent and Peter K. Taylor

Abstract

Beaufort equivalent scales from the literature have been compared to find the scale that gives the most homogenous, internally consistent, combined anemometer and visual wind speed data from the Comprehensive Ocean–Atmosphere Data Set. Anemometer wind speeds have been height corrected using the individual anemometer height for each ship, where that could be identified, resulting in a more consistent dataset than that used in previous studies. Monthly mean 1° averages were constructed for visual and anemometer wind speeds separately from data between 1980 and 1990. The anemometer and visual means were compared where there were enough observations to give confidence in both means. The equivalent scale of Lindau was the most effective at giving similar anemometer and visual wind distributions from this mean dataset. The scale of daSilva et al. also performed well. The Lindau scale is, however, preferred because of its more rigorous derivation. The results for the different scales are in agreement with Lindau’s suggestion that the characteristic biases of earlier Beaufort scales could be explained by the statistical method of derivation.

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Peter K. Taylor and Margaret J. Yelland

Abstract

It is proposed that the sea surface roughness z o can be predicted from the height and steepness of the waves, z o/H s = A(H s/L p)B, where H s and L p are the significant wave height and peak wavelength for the combined sea and swell spectrum; best estimates for the coefficients are A = 1200, B = 4.5. The proposed formula is shown to predict well the magnitude and behavior of the drag coefficient as observed in wave tanks, lakes, and the open ocean, thus reconciling observations that previously had appeared disparate. Indeed, the formula suggests that changes in roughness due to limited duration or fetch are of order 10% or less. Thus all deep water, pure windseas, regardless of fetch or duration, extract momentum from the air at a rate similar to that predicted for a fully developed sea. This is confirmed using published field data for a wide range of conditions over lakes and coastal seas. Only for field data corresponding to extremely young waves (U 10/c p > 3) were there appreciable differences between the predicted and observed roughness values, the latter being larger on average. Significant changes in roughness may be caused by shoaling or by swell. A large increase in roughness is predicted for shoaling waves if the depth is less than about 0.2L p. The presence of swell in the open ocean acts, on average, to significantly decrease the effective wave steepness and hence the mean roughness compared to that for a pure windsea. Thus the predicted open ocean roughness is, at most wind speeds, significantly less than is observed for pure wind waves on lakes. Only at high wind speeds, such that the windsea dominates the swell, do the mean open ocean values reach those for a fully developed sea.

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Elizabeth C. Kent and Peter K. Taylor

Abstract

To assess climatic changes in sea surface temperature (SST), changes in the measurement method with time and the effect of these changes on the mean SST must be quantified. Observations from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) have been analyzed for the period from 1970 to 1997 using both SST measurement metadata contained within the dataset and a World Meteorological Organization (WMO) catalog of observing ships. The WMO metadata were particularly important in identifying engine-intake SSTs during the 1970s, but increased method identification over the entire period. There are strong regional variations in the preferred SST measurement method, with engine-intake SST most common in the Pacific and bucket SST preferred by countries bordering the Atlantic. The number of engine-intake SSTs increases over time and becomes more numerous than buckets by the early 1980s.

There are significant differences between SST observations made by different methods. The rounding of reports is more common for engine-intake SST than for either bucket or hull sensor SST, which degrades its quality. Significant time-varying biases exist between SST derived from buckets and from engine intakes. The SST difference has a strong seasonal signal with bucket SST being relatively cold in winter, probably resulting from heat loss from the buckets, and warm in summer, probably resulting from solar warming or the sampling of a shallow warm layer. There is also a long-term trend with engine-intake SST being relatively warm in the early period but with a small annual mean difference between the two methods by 1990.

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Elizabeth C. Kent, Peter G. Challenor, and Peter K. Taylor

Abstract

The random observational errors for meteorological variables within the Comprehensive Ocean–Atmosphere Dataset (COADS) have been determined using the semivariogram statistical technique. The error variance has been calculated using four months of data, spanning summer and winter months and the start and end of the dataset. The random errors found range from 1.3 to 2.8 m s−1 for 10-m-corrected wind speed, 1.2 to 7.1 mb for surface pressure, 0.8° to 3.3°C for 10-m air temperature, 0.4° to 2.8°C for sea surface temperature, and 0.6 to 1.8 g kg−1 for 10-m specific humidity. The air temperature and specific humidity random observational errors contain a dependence on their mean values, but correlations between errors and mean values are low for the other variables analyzed. The accuracy of the error estimates increases with the number of observational data pairs used in the analysis. Wind speed random observational errors were reduced by height correction and by the use of the Lindau Beaufort Scale.

Taken over the latitude range 45°S–75°N, the mean random observational errors are 2.1 ± 0.2 m s−1 for 10-m-corrected wind speed, 2.3 ± 0.2 mb for surface pressure, 1.4° ± 0.1°C for 10-m air temperature, 1.5° ± 0.1°C for sea surface temperature, and 1.1 ± 0.2 g kg−1 for 10-m specific humidity.

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Elizabeth C. Kent, Peter K. Taylor, and Peter G. Challenor

Abstract

The effects on a dataset of smoothing by successive correction have been investigated. The resulting spatial resolution is estimated using a distribution of ship reports from a sample month. Although the smoothing uses the same characteristic radii over the whole globe, the resulting resolution is spatially variable and, in data-sparse regions, will show large month-to-month variability with changes in the distribution of the ship tracks. The climatological dataset, which is gridded at 1°, is shown to have a typical resolution of 3°. In some regions the resolution is much coarser.

Using sea surface temperature as an example, it is shown that the successive correction procedure as used, for example, in a recent climatological dataset, is not successful in removing all of the noise in data-sparse regions. Additionally, the well-defined intermonthly variability in the main shipping lanes, where there are many observations, is degraded by the influence of poorer-quality data in the surrounding regions. This typically increases the intermonthly variability estimates in the shipping lanes by a factor of 2. Further, the reduction of intermonthly variability, by up to a factor of 6, in highly variable regions such as the Gulf Stream, is greater than can be accounted for by noise in the individual ship reports. This reduction is due to the removal of small-scale variability by the smoothing process. Removal of coherent and persistent small-scale variability has an effect on the temporal and spatial characteristics of the data. It is suggested that smoothing by successive correction, although commonly used, is poorly suited to such spatially inhomogenous data as those from the merchant ships.

However, the effect of successive correction on variability analysis using empirical orthogonal functions (EOFs) is shown to be small for the most significant modes of variability identified in the Gulf Stream region. This is because the EOF analysis picks out the large-scale variability in the highest-order modes. However, too large a fraction of the total variance explained is ascribed to the large-scale modes of variability. Variability with small spatial scales is more likely to be significant if raw data are used in the EOF analysis. Little significance should be given to EOF modes with spatial scales similar to the size of gaps between shipping lanes; this varies from region to region.

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