1. Introduction
The Coupled Ocean–Atmosphere Response Experiment (COARE; Webster and Lukas 1992) triggered a resurgence of interest in measurement and parameterization of air–sea turbulent and radiative fluxes. The program set a 10 W m−2 goal for the total uncertainty in long-term (∼1 month) measurement of the surface energy budget for the COARE region. A COARE Flux Working Group (FWG) was formed, which developed a plan for pre-COARE flux cruises, intercomparisons between ships, buoys, and aircraft during COARE, and post-COARE field efforts and calibration studies (Bradley and Weller 1997). Flux estimates originated from ship, buoy, aircraft, satellite, and model data, so to ensure compatibility when comparing results the FWG developed a bulk turbulent flux algorithm as a common standard. The new algorithm needed to accommodate the light wind conditions and large near-surface ocean temperature gradients often encountered in this region. It evolved over several years and was eventually frozen at version 2.5b and published (Fairall et al. 1996a).
The algorithm was developed using COARE measurements, albeit with a relatively limited wind speed range (0–12 m s−1), and has been used by many research groups for COARE analyses (more than 200 citations at the time of this writing). However, it has also been used outside the Tropics in a wide variety of conditions, raising obvious questions about its applicability in colder waters, midlatitudes, and much higher wind speeds. The algorithm was “globalized” in the sense that, wherever possible, physical variables are computed as functions of ambient conditions (e.g., temperature, latitude, solar flux) rather than hardwired to tropical values. Since 1996, work has continued to extend it to higher wind speeds and to verify it against high-quality data outside the Tropics. In 1998 a new version was developed (Bradley et al. 2000), based on six National Oceanic and Atmospheric Administration (NOAA) Environmental Technology Laboratory (ETL) cruises conducted between 1991 and 1993, preliminary results from two other ETL programs, and other published measurements from high wind regions, to extend the applicability to 20 m s−1. It has evolved continuously since then, and is now considered ready for public release as COARE 3.0. Between 1997 and 1999 ETL conducted six more cruises, obtaining direct covariance and inertial-dissipation flux estimates and high-quality bulk meteorological variables, suitable for verifying the new algorithm.
In this paper we describe the new version of the COARE algorithm and its verification. Following this introduction, we give some general background on flux algorithms and the original COARE version (section 2). The new version of the algorithm is described in detail in section 3, and evaluated against the entire ETL database of 7216 1-h values obtained between 1991 and 1999. In section 5 we discuss these results in relation to other algorithms and other measurements. Our conclusions are given in section 6. The measurement system, flux processing methods, and accuracy issues are discussed in the appendix.
2. Background on similarity relationships and flux algorithms
a. Bulk scaling theory
b. COARE 2.5
In typical execution of a bulk algorithm, the atmospheric variables (U, V, T, q) at reference height z are provided through measurement or model output; the surface properties (current vector, water temperature) are also provided. Strictly speaking, (3) requires the true interface temperature, Ts, but usually only the temperature at some depth, Tw(D), is available so that a method of estimating Ts from Tw is needed. The COARE algorithm incorporates submodels that represent the millimeter-scale cool skin near the interface and the diurnal (solar) warm layer in the upper few meters of the ocean (Fairall et al. 1996b; Webster et al. 1996). The cool skin implies that the true interface temperature is several tenths (°C) cooler than the bulk water near the surface. In light wind and sunny conditions the sun may warm the upper few meters of the ocean by 1°–3°C. The surface value for specific humidity is computed from the surface temperature and the vapor pressure of seawater (0.98 times the vapor pressure of pure water; Kraus and Businger 1994).
c. Discussion of recent bulk models
Almost two decades have passed since Blanc (1985) published his careful and rather alarming review of the large differences between more than 10 bulk flux algorithms. At that time, many of the physical processes involved in air–sea transfer of heat or momentum were embedded within the transfer coefficient for a particular entity. While transfer coefficients were known to be a function of wind speed, height, and atmospheric stability, more often than not the accuracy of available test data did not justify anything other than a constant value for the transfer coefficient of scalars, or a simple wind speed–dependent value for stress. Such simple expressions have become inadequate for several reasons, mostly connected with the burgeoning requirements of climate research, and recognition of the sensitivity of numerical climate models to small changes in air–sea flux values, particularly when attempting to couple oceanic and atmospheric GCMs. We will briefly describe some other bulk flux algorithms that have emerged over the period of development of the COARE algorithm, and give their main features in Table 1. The list is not exhaustive or even possibly up to date.
The algorithm described by Zeng et al. (1998, hereafter ZZD) was developed to suit the needs of numerical model codes. As with COARE 2.5, ZZD use the Kansas profile functions for near-neutral atmospheric stability, with the convective forms of Kader and Yaglom (1990) and the relations of Holtslag et al. (1990) in very stable conditions. Equation (6), with α = 0.013, is used for zo and the Brutsaert (1982) formulation [see Eq. (29)] is used for the scalar roughness lengths, with constant coefficients obtained from the R/V Moana Wave COARE data (Fairall et al. 1996a). ZZD include a “gustiness” correction, with β = 1.0, and allowance for the 2% reduction in saturated specific humidity over seawater, which they find decreases latent heat flux by 20% at 14 m s−1. The algorithm is tuned to the same dataset (the R/V Moana Wave observations by Fairall et al. 1996a) as COARE version 2.5 for low to moderate wind speeds, and the Humidity Exchange Over the Sea (HEXOS) data (De Cosmo et al. 1996) up to 18 m s−1. However, they use the HEXOS data as presented by the authors, rather than modified as described in section 3e. Various empirical constants are given values that ensure reasonable agreement with observations over the wind speed range 0–18 m s−1.
The Bourassa et al. (1999, hereafter BVW) bulk model is notable for its attempt to relate surface roughness lengths, and hence the exchange coefficients, to various aspects of sea state, swell, gravity waves, and capillary waves. They draw on, and extend, published representations of wave structure in their analyses, but adequate validating data are not yet available to test the potential of this algorithm. BVW point out that, particularly under low wind conditions where swell, wind velocity, stress, and current directions are not necessarily parallel, their algorithm allows the cross-wind component of stress to be calculated. They note that, with the coexistence of these different wave types and interactions between them, it is unclear what the proper coordinate frame of reference should be for the wind and water velocities. For zo, BVW use a version of Eq. (6) in which the “Charnock” term is replaced by one involving wave parameters. For the scalar roughness lengths, they use the Reynolds parameters, Rt = 0.4 and Rq = 0.62, given by Brutsaert (1982; section 5). BVW adopt the profile stability functions of Beljaars and Holtslag (1991) for stable and of Benoit (1977) for unstable conditions, and gustiness with β = 1.25.
The Clayson et al. (1996, hereafter CFC) algorithm is based on the surface renewal theory of Brutsaert (1975), with an alternative timescale parameterization. CFC use a simplified form of the BVW sea state surface roughness model, and the same profile stability functions as BVW, which lead to Brutsaert-like expressions for the scalar roughness lengths. Unlike BVW, they do not include a gustiness term, relying on their capillary wave parameterization and surface renewal theory to obtain correct fluxes in low wind, convective conditions. Because this algorithm was developed for the assessment of fluxes from satellite data, they incorporate a model of the ocean cool skin (Wick et al. 1996). They also adjust surface humidity for the 2% reduction of vapor pressure over saline water. However, they do not correct SST for the diurnal thermocline.
Zhang and McPhaden (1995, hereafter ZM95) studied the relationship between SST and latent heat flux in the equatorial Pacific Ocean, using data from the TOGA Tropical Atmosphere Ocean (TAO) moored array. Their bulk flux algorithm takes the simplest possible route consistent with the need to take account of low wind and highly convective conditions, and is computationally economical. They adopt the standard Kansas stability profile functions across the entire unstable range from near-neutral to free convection, and Eq. (6) with α = 0.011 for the momentum roughness length. However, following Geernaert (1987) they set the temperature and moisture roughness lengths equal and constant at 2 × 10−5 m.
Beljaars (1995) approaches the problem from the perspective of the numerical modeler seeking economical solutions without violating physical reality. He shows that normal MOST can be used in the surface layer without modification for free convection (i.e., no ⅓ power law), as long as a convective scaling velocity [Eq. (8)] is included in the bulk equation. He adopts the standard Kansas expressions, arguing that the behavior of the stability functions for large (−ζ) is not too critical because vertical gradients are small in the well-mixed regime, and contribute little to the air–surface velocity, temperature, or moisture difference. His analysis is general for the surface layer over land and ocean, rough and smooth surfaces, and suggests a typical value β = 1.2 for the gustiness parameter. For air–sea transfer over the ocean, Beljaars (1995) uses Eq. (6) for zo, with α = 0.018, and the same Brutsaert (1982) expressions for the scalar roughness lengths as BVW, applied to both smooth and rough flow. He justifies this cautiously, on the grounds that empirical evidence points to an almost constant moisture transfer coefficient over the entire low to high wind speed regime (Smith 1989). Approaching zero wind speed, his transfer coefficients for both heat and moisture follow quite well the increasing trend shown by the low wind data of Bradley et al. (1991).
Using COARE and TAO mooring data, ZZD have compared momentum and scalar fluxes from several of the algorithms described above and show that, up to around 6 m s−1, their performance is very similar. Differences appear at higher wind speeds, but uncertainty about the quality of the test data in this regime makes judgement difficult. ZZD also consider the parameterizations employed in several numerical models, and conclude that these are seriously defective. It is probably fair to comment that almost any of the modern algorithms, with reasonable roughness parameterizations and stability correction, would considerably improve the performance of the models.
3. Advances for COARE 3.0
For the improved algorithm three major issues were addressed. First, the COARE model was fit to the average flux observed but was not a perfect fit to the observed wind speed dependence (e.g., the peak in the 10-m neutral transfer coefficient Ce10n in the LKB model at wind speeds around 6 m s−1 was not apparent in the data). Second, the model was published as being valid over the COARE wind range from 0 to 10 m s−1, and extension to higher wind speeds was needed. Third, it needed to be generalized for more global applications, and tested against a much broader dataset. Preliminary discussion of these and other minor improvements appear in Bradley et al. (2000) and Fairall et al. (2001); they are summarized as follows:
The empirical constant in the convective portion of the scalar profile function has been changed for improved matching to direct profile observations (Grachev et al. 2000).
The Kansas stable profile functions (Businger et al. 1971) have been replaced by those from Beljaars and Holtslag (1991) which, based on new profile data taken over the Arctic ice cap (Persson et al. 2002), appear to be a better fit at extreme stability.
The stability iteration loop has been reduced from 20 to 3 by taking advantage of a bulk Richardson number parameterization for an improved first guess (Grachev and Fairall 1997).
The latent heat flux has been reformulated in terms of mixing ratio instead of water vapor density.
Above 10 m s−1 the Charnock parameter takes a simple wind speed dependence based on data from various sources (e.g., Hare et al. 1999).
An option has been added to allow the velocity roughness to be affected by wave parameters.
The LKB scalar roughness relationship [fx(Rr)] has been replaced with a much simpler one that fits both the COARE and Humidity Exchange Over the Sea Main Experiment (HEXMAX) databases.
MATLAB and FORTRAN versions of both COARE 2.5b and 3.0 have been made publicly available online at ftp://ftp.etl.noaa.gov/et7/anonymous/cfairall/bulkalg/. Included is a description of the codes and a test dataset file. The programs are set up to read the test file and output the results; output files and graphs of results are also provided.
a. Stability function considerations
The unstable profile stability functions used in the COARE algorithm are a blend of Kansas forms (valid for −1 < ζ < 0) and forms that scale as (axζ)−1/3 in convective conditions (ax is an empirical constant and the −1/3 power is the asymptotic limit of MOST; see Grachev et al. 2000). In COARE 2.5 au and as were set to 12.87; Grachev et al. (2000) showed that the values au = 10.15 and as = 34.15 gave the smoothest blend of Kansas and convective forms so these values have been adopted for COARE 3.0. The modification of the profile function on the stable side is based on more extensive observations. The original Kansas observations were limited to 0 < ζ < 1. The form of the stable side functions as ζ ≫ 1 has implications for the numerical characteristics of the stability iteration.
This result is consistent with the original Kansas analysis that suggested a critical gradient Richardson number on the order of 0.2. The physical interpretation is debatable, but numerically the iteration based on (9) does not converge if Rib exceeds the threshold described by (13). If one views this condition as complete suppression of turbulent transport, then the fluxes should simply be set to zero. Using extensive measurements from a 100-m tower over land, Beljaars and Holtslag (1991) found finite, but highly intermittent, values for fluxes in very stable conditions. They produced empirical functions that fit their data (and the Kansas data) and do not result in a critical Richardson number, but lead to rapidly decreasing fluxes as stability increases. A preliminary analysis of tower data over sea ice in the Surface Heat Budget of the Arctic (SHEBA) experiment (Persson et al. 2002) has also found small but finite fluxes in very stable conditions. These small fluxes may be caused by breaking atmospheric gravity waves or some other process of nonshear-driven turbulence for which Monin–Obukov scaling is inappropriate. Pending more information, we adopt the Bejaars and Holtslag functions, which have eliminated occasional pathological results obtained with version 2.5.
b. Conservative moisture variables and the Webb et al. (1980) correction
c. The Charnock parameter
Yelland and Taylor's (1996) results were a major factor in our decision to allow α to increase with wind speed above 10 m s−1. Ironically, in more recent papers Yelland and Taylor have reanalyzed their ID-based flux estimates so that they no longer support the increase in α specified in COARE 3.0. Yelland et al. (1998) revised their 1996 results using corrections to mean winds and measurement heights from computational fluid dynamics (CFD) calculations on models of their ships. Another revision (Taylor and Yelland 2000) was made after adopting an improved dimensionless dissipation function to calculate their ID stress values. These changes have reduced their values for α to about 0.011 for all wind speeds. Thus, the behavior of Charnock's parameter at wind speeds above 10 m s−1 remains controversial, and we will look to analysis of the large number of direct covariance and ID stress measurements that are accumulating in the ETL database to help resolve the matter. It is important because of the increasing application of bulk flux algorithms to severe storm situations.
d. Surface wave influence on roughness parameters
For wind speeds greater than about 5 m s−1, surface waves are a dominant factor in the surface roughness of the ocean. A simple description of surface roughness such as (6) represents the average surface wave climate of an ensemble of measurements. If this is reasonably similar to that of the open ocean, then the results are useful in many applications. However, it is sometimes desirable to know the stress appropriate for the actual wave conditions, for example, in coastal regions where the wave climate is different from that of the open ocean (Gulev et al. 1998). Furthermore, if we knew more about the linkage between wave properties and surface roughness, measurements from diverse regions could be associated more rationally than by simple averaging. The literature abounds with analyses and models that address this issue, ranging from crude parameterizations based on the simplest wave properties (e.g., significant wave height or phase speed of the dominant waves) to complicated integrations based on the two-dimensional wave spectrum.
The original COARE bulk flux model did not consider wave conditions, primarily because no detailed wave measurements were made but also partly because the wave stress community lacked consensus on how to handle waves. Subsequently some progress has been made in this regard, although it is fair to say that consensus is still lacking. However, we have incorporated into COARE 3.0 two recent parameterizations, each of which allows the Charnock parameter or velocity roughness length to be calculated from specified wave properties.
We cannot claim that the wave climate of the dataset used to develop COARE 3.0 is, on average, well developed at all wind speeds, so Fig. 2 does not imply that one model is better than the other. The duration/fetch required to reach the fully developed state increases with mean wind speed, so it seems likely that well-developed conditions are more commonly observed at moderate winds. However, this issue is confused by possible contributions to the stress of swell waves that are not associated with the local winds; this becomes increasingly significant at low wind speeds and/or in high wind regions where larger swell is more common.
e. Scalar roughness length parameterization
LKB (and COARE 2.5) parameterized the scalar roughness Reynolds numbers in terms of Rr [Eq. (7)], based on sublayer transfer and surface renewal theory, supported with a limited amount of field data. To produce the new scalar roughness parameterization for COARE 3.0, we combined some ETL datasets (see section 4) and added a reanalysis of data from HEXOS conducted on the Dutch tower in the North Sea (Smith et al. 1996). The COARE measurements on R/V Moana Wave produced about 850 h of usable humidity flux data. We focus on the behavior of moisture parameters, because the range of latent heat flux is much greater than that for sensible heat in our datasets, and historically it has received the greater attention. Data from two field programs prior to COARE [the Tropical Instability Wave Experiment (TIWE) and the Atlantic Stratocumulus Transition Experiment (ASTEX)] and one subsequent to COARE [the San Clemente Ocean Probing Experiment (SCOPE)] provided an additional 450 usable values. All measurements in this combined set, which we call COARE-plus, were made with the same system and processing methods. The SCOPE data were obtained from R/P FLIP which, because of minimal motion and flow distortion, are of unusually high quality and consistency (Grachev and Fairall 1997). The fluxes and bulk variables were averaged in 10-m neutral wind speed bins, and transfer coefficients were computed. This analysis gave a reasonably clean depiction of the wind speed dependence of Ce10n for U between 0 and 10 m s−1.
For the higher wind speed regime, we used published results (DeCosmo et al. 1996) from HEXMAX. To make the HEXMAX transfer coefficients consistent with our COARE-plus data, they were modified to account for the cool skin (the cooler interface implies lower water vapor pressure, so that a larger transfer coefficient is required to produce the observed flux), the 2% reduction in water vapor pressure over seawater, and the Webb et al. (1980) correction. These changes increased their transfer coefficients by about 8%; the DeCosmo et al. (1996) median values were used to reduce the sensitivity to outliers and a skewed distribution. The maximum wind speed for usable humidity data from HEXMAX was about 18 m s−1. The two datasets are shown in Fig. 3 as a function of wind speed. Because the HEXMAX conditions were much rougher than typical open ocean regions at the same wind speed, we have converted the results to roughness Reynolds number, Rr, which we take to be a more fundamental representation of air–sea surface interaction properties than the wind speed; these results are shown in Fig. 4. The compatibility of the ship-based, primarily tropical data with the platform-based, North Sea data is striking and encouraging.
4. Flux and transfer coefficient evaluation
Table 2 lists the series of 12 deployments of the ETL seagoing flux system, beginning before the COARE field program and ending in late 1999. The basic flux measuring system and software (see the appendix) remained essentially the same but sensor models, data acquisition structure, computers, etc., were upgraded over the period. As described in section 3e, data from the first six cruises formed the COARE-plus database. Because this contained only 67 h of data at wind speed greater than 10 m s−1, it was augmented with 94 h of HEXMAX data and used to formulate COARE 3.0. Data from the six later cruises have been combined with the first six to form a larger database (ETL1999) containing 7216 h of data, including about 800 h with wind speeds exceeding 10 m s−1 and 2200 h at high latitudes. This allows us to test the algorithm over the wind speed range from 0 to 20 m s−1.
We first scan the database to select a subset of covariance and ID flux estimates that satisfy criteria designed to reject invalid or unreliable points. The criteria include experimental aspects (e.g., relative wind direction within a certain sector to avoid interference by the ship's structure), instrument performance indicators, avoidance of ship maneuvers, and requirements that certain variables (e.g., variances) fall within physically reasonable limits. We might reject a specific flux value if the standard deviation of wind speed normalized by the mean wind speed exceeded some limit, but never reject a flux value based on its comparison with the bulk model. At sea, the shipboard system records continuously, irrespective of weather or operational conditions, so that such quality controls are needed to ensure a dataset that is clean, coherent, and relevant to the geophysical problem under investigation. In the present case, 4946 h for stress and 4276 h for latent heat flux were accepted (covariance, ID, and bulk).
The next step is to compare the values of fluxes obtained from the bulk algorithm with the measurements in some rational fashion. Usually we are interested in the average performance of the bulk algorithm, with information on its statistical scatter about the observations. In this analysis we show comparisons of quantities averaged in bins of 10-m neutral wind speed with the additional condition that the air–sea specific humidity difference exceeds 2.0 g kg−1. Figure 6 shows such a comparison for latent heat flux. The bin width increases slightly at the data-sparse higher wind speeds; bins with fewer than five values are not shown. The turbulence values are the average of covariance and ID values; the bulk values are COARE 3.0. We plot both medians and means to reveal skewed distributions or effects of outliers. Figure 7 shows these same values plotted on an x–y linear scale. Figure 8 shows a similar comparison for stress, using only medians and averaging the covariance and ID values separately.
The bulk algorithm is used to compute bulk fluxes and transfer coefficients that are then averaged in each bin. The required transfer coefficient is the mean bulk coefficient multiplied by the ratio of mean measured to mean computed (bulk) fluxes. If the bulk model is reasonably accurate, (33) yields the transfer coefficient associated with the measured average flux.
In Fig. 9 we show the comparison between measured and modeled Ce10p using (33) and the average of mean and median values (we also show results of the SM using medians). The rms deviation of these values from the model is 4.0%. The statistical uncertainty in the mean 10-m neutral transfer coefficients, shown as error bars, was estimated by dividing the standard deviation of points within each wind speed bin, σ
Corresponding results for the momentum transfer coefficient are shown in Fig. 10, where again the error bars correspond to the uncertainty in the estimates. Figure 11 shows the Charnock parameter computed from the transfer coefficients given in Fig. 10 using (21) and (22). The large amount of new data at higher wind speeds strongly support the increasing α built into COARE 3.0 on the evidence of Fig. 1, and suggest that it continues to increase beyond 18 m s−1. The comparison is not valid below 5 m s−1 because zo approaches the smooth flow limit 0.11 ν/u∗.
Since the total variation of Rq is relatively small over the range of Rr from 0.1 to 100, a two-parameter fit is adequate.
5. Discussion
Here we compare the results presented above with previous measurements of air–sea exchange coefficients. There have been several recent comparisons of flux algorithms (CFC; ZZD; Chang and Grossman 1999) that showed that for U < 10 m s−1 results agreed quite closely, but diverged at higher wind speeds. However, few quality observations for U > 10 m s−1 existed to validate these comparisons. Shallow-water observations (e.g., HEXMAX) require some physical model to relate to open-ocean conditions. For stress, a consensus model is presently lacking; for scalar transfers we use roughness Reynolds number similarity, as described in the previous section. The new results presented in this paper, for air–sea fluxes and exchange coefficients, and on which the revised COARE bulk algorithm is based, derive from a very large database (ETL1999). The total number of hours of observation used in this study significantly exceeds the total number considered in previous reviews. They also benefit from great advances in sensor and computational technology as described in the appendix.
In Fig. 9, the increase in Ce10n toward very low wind speeds confirms, with about 700 h of data below 2.5 m s−1, the behavior observed by Bradley et al. (1991) and embodied in LKB and both versions 2.5 and 3.0 of the COARE algorithm. For wind speeds above 4 m s−1, previous estimates of Ce10n are usually given as a constant value because the accuracy ascribed to the measurements does not warrant more detail. The review by Smith (1989) found Ce10n = 1.2 (±0.1) × 10−3 for winds from 4 to 14 m s−1; Garratt (1992) and Smith et al. (1996) quote 1.1 × 10−3 ±15% for winds between 3 and 20 m s−1; for the HEXMAX data DeCosmo et al. (1996) give 1.1 × 10−3 and find no significant variation with wind speed up to 18 m s−1. To compare with these, we calculate the constant value for Ce10n which fits the data above 5 m s−1, and find 1.15 × 10−3 within 5.3%. This value may not compare directly because it includes the following: 1) reduction in seawater vapor pressure by 2% due to salinity, 2) a true air–water interface temperature (i.e., cool skin corrected), and 3) the Webb et al. correction. These factors combine to increase the mean moisture transfer coefficient by more than 6%.
However, as shown in Fig. 9, both the early data used to tune COARE 3.0 and the large ETL1999 database clearly demonstrate that Ce10n increases steadily toward higher winds. Previously, the HEXMAX data were the most significant high wind speed results for the moisture transfer coefficient. After adjustment as described in section 3e, HEXMAX is consistent with our measurements, whether graphed as Ce10n versus wind speed or zoq versus Rr.
The neutral drag coefficient is well known to increase with wind speed and is often given an empirical linear form above about 5 m s−1 (e.g., Garratt 1992; Smith 1980; Yelland et al. 1998). The ETL1999 data in Fig. 10 confirm that a linear representation is not unreasonable, but is less enlightening than a model based on the physical concepts of air–sea exchange described in section 2. The Smith (1988) model embodied in COARE 3.0, Eq. (6), predicts an increase in Cd10n toward low wind speeds. As shown in both the covariance and ID measurements in Fig. 10, only for wind speeds less than about 1 m s−1, does Cd10n increase above its minimum value of about 1.0 × 10−3, which appears constant to about 5 m s−1. Between about 6 and 14 m s−1, the ID values are on average 3.0% lower than the covariance values, but the difference increases above 15 m s−1. Agreement between COARE 3.0 and combined ID and covariance 10-m neutral drag coefficients is about 4%. In fact, the ID values for stress and drag coefficient agree closely with the model, while the covariance values tend to be higher at high wind speed and slightly lower at low wind speed. Covariance could be overestimated by about 10% because of ship flow distortion; the ID results could be underestimated by a similar amount because of loss of pressure transport production of turbulent kinetic energy (TKE) to the growing waves (Janssen 1999).
Most of our measurements for U > 12 m s−1 were acquired in the Fronts and Atlantic Storms Experiment (FASTEX) (North Atlantic) and Moorings (North Pacific) field studies and the number of useable observations for U > 15 m s−1 is fairly small (133 h for stress; 85 h for moisture). In the high wind regime, our drag coefficient values are somewhat higher than those of Smith (1980) and Taylor and Yelland (2000). For simplicity, consider values of 103Cd10n at U = 20 m s−1: Smith, 1.93; Taylor and Yelland, 1.92; this dataset, covariance, 2.30 and ID, 2.07. The average of these four values is 2.06 and the spread is from +12% to −7%. Part of the difference between our ID result and that of Taylor and Yelland lies in the choice of dimensionless dissipation function. A change in Kolmogorov constant from 0.55 to 0.53 [the value consistent with Edson and Fairall (1998, hereafter referred to as EF)] would increase the Taylor and Yelland neutral transfer coefficient by 3.8% to 1.99. The COARE 3.0 value at 20 m s−1 is 2.06. Overall, we estimate that the COARE 3.0 transfer coefficients when applied to 1-h bulk measurements over the open ocean are accurate to about 5% for wind speeds 0–10 m s−1 and better than 10% for 10–20 m s−1.
In the wind speed range 0–20 m s−1 the major remaining surface physics issue is the influence of surface waves on the fluxes. With present techniques, a huge number of observations will be required to obtain definitive results because of the addition of one or two independent variables. High-quality, routine measurements of wave properties is an important technical challenge, so we must hope for a breakthrough in theory or modeling. A second major issue is the application of our measurement-based algorithms in numerical models. One aspect is the difference between one-dimensional and two-dimensional representations (Vickers and Esbensen 1998) but most of the problems are associated with resolved versus unresolved (subgrid scale) processes and variability. The COARE algorithm accounts for velocity variability caused by boundary layer–scale eddies, but that is the only unresolved process explicitly in the model. Of course, when the model is applied to point measurements of the appropriate timescale, other sources of variability are explicitly resolved by the input data. In many numerical models moist convective processes are at least partly unresolved and this is a large source of variability. Some investigators (Jabouille et al. 1996; Zulauf and Krueger 1997; Redelsperger et al. 2000; Zeng et al. 2002) have tried adding a second gustiness velocity term, based on convective mass flux or precipitation rate, to the boundary layer convection-driven gustiness velocity (8). These studies used high-resolution models to simulate convective variability and examined the effect of spatial–temporal averages on the bulk flux relationships. The results have been encouraging but observational verification with conventional flux measurements is not straightforward. Rainfall studies encounter sampling problems and a point measurement of rainfall is a poor indicator of convective activity (at sea one can be surrounded by rainstorms for days and not collect any rain at the ship). Aircraft and ship-based scanning radars may be well suited to attack this problem.
The above analysis has ignored the possible effects of sea spray droplets on the moisture transfer coefficient. Our measurements of water vapor flux do not differentiate between evaporation from the sea surface and subsequent evaporation from sea spray droplets below (or above) the sensors. Such effects are expected to become significant at winds exceeding 15–20 m s−1, but production of spray droplets as a function of wind speed are still uncertain (Andreas et al. 1995) and the threshold for measurable effects is not known. Recent model studies (Pattison and Belcher 1999; Andreas 2001) suggest droplet effects on the order of 10 W m−2 for HEXOS-type conditions where direct evaporation is about 250 W m−2. In our opinion, such models are not yet sufficiently accurate to justify their inclusion in COARE 3.0.
6. Conclusions
The COARE bulk flux algorithm has been updated and its range of wind speed validity is extended to 0–20 m s−1; we designate this version COARE 3.0. The updates include improvements to the stability functions, shortening the stability iteration, and eliminating the need for a Webb correction to latent heat flux. The wind speed dependence of both velocity and scalar transfer coefficients is changed slightly, particularly above 10 m s−1. The modifications were based on nearly 2800 h of direct flux measurements during six ETL cruises in the COARE era (referred to as COARE-plus), augmented with about 100 h of data at wind speeds above 10 m s−1 from the HEXMAX experiment. Neutral moisture exchange coefficients from the two data sources merged extremely well after the published HEXMAX results were adjusted for three established correction factors.
In LKB and COARE 2.5, the scalar exchange coefficients were based on a relationship between scalar and velocity surface roughness Reynolds numbers. Analysis of the combined dataset suggested a much simpler mathematical relationship for COARE 3.0, directly relating the scalar roughness length to roughness Reynolds number. The drag coefficient was changed via a revised Charnock constant, which increases above 10 m s−1 on the evidence of observations from several sources, including some of the COARE-plus cruises.
Flux measurements from six later cruises (1997–99) have been added to COARE-plus to form a very large air–sea interaction database (ETL1999). ETL1999 contains about 7200 h of direct covariance and inertial-dissipation flux observations, 800 h for wind speeds greater than 10 m s−1 and 2200 h outside the Tropics, as well as concurrent measurements of bulk meteorological, radiation, and ocean variables. When sorted into wind speed bins of about 1-m s−1 width, the average latent heat flux ranges from 40 to 250 W m−2 and average stress from 0.001 to 1.0 N m−2. The large number of observations in the database removes most of the statistical uncertainty in determining the mean fluxes and transfer coefficients. There is much more data than previously existed for both high (>10 m s−1) and low winds (<2 m s−1). When subjected to quality control filters, there remain about 4500 direct flux observations of very high quality for research and validation purposes, including here the evaluation of COARE 3.0.
The observations in ETL1999 strongly support new relationships built into the revised algorithm, for the scalar roughness length dependence on roughness Reynolds number (Fig. 12) and the Charnock parameter, α (Fig. 11). Our combined covariance and ID measurements indicate α constant at 0.011 up to about 10 m s−1, increasing thereafter to about 0.020 at 19 m s−1.
On the basis that Ce10n is often represented as a constant value between 5 and 20 m s−1, our analysis indicates a value 1.15 × 10−3, close to several other recent determinations. However, our measurements clearly indicate that Ce10n increases steadily with wind speed from about 1.08 × 10−3 at 5 m s−1 to 1.2 × 10−3 at 18 m s−1. COARE 3.0 fits these bin-averaged measurements with an rms deviation of 4.0%. The measured Cd10n values increase from about 1.0 × 10−3 at 3 m s−1 to 2.30 × 10−3 at 20 m s−1 if covariance fluxes are used, or 2.07 × 10−3 for ID fluxes. The difference may be partly due to flow distortion effects on the covariance values, and above 15 m s−1 a possible indication of the wave-pressure effects on TKE dissipation and hence ID estimates (Janssen 1999). The average of 2.18 × 10−3 is significantly larger (i.e., separated by several standard deviations) than the classic covariance measurements of Smith (1980) and the large ID database of Taylor and Yelland (2000). The COARE 3.0 value at 20 m s−1 is 2.06. For wind speed greater than 2 m s−1 it fits the combined covariance and ID measurements of Cd10n within 4.2%. At low wind speeds both Ce10n and Cd10n increase toward lower wind speeds as has been observed previously.
Two alternative wave parameterizations (Taylor and Yelland 2001; Oost et al. 2002) have been incorporated into COARE 3.0, to enable wave conditions to be used in the calculation of surface roughness. The purpose is to enable the algorithm to be applied in regions, such as the coastal zone, where the wave climate is different from the open ocean. This option has not been evaluated by the authors, for lack of detailed wave data. However, we hope that this capability will encourage users to apply the arcane field of wind–wave relationships to practical situations where wave measurements are available, and thereby advance this area of study.
Twenty years ago, a survey of bulk flux schemes by Blanc (1985) found 30% differences in moisture and momentum transfer coefficients at moderate wind speeds with major problems noted at low and high wind speeds. With advancements in technology, air–sea flux measurements from ships are being made almost routinely by several research groups around the world and the progress has been impressive. Extensive low wind speed measurements in the COARE program and the adoption of gustiness have significantly improved the situation for light winds. We submit that, when applied to 1-h bulk measurements over the open ocean, the COARE 3.0 transfer coefficients are accurate to about 5% for wind speeds 0–10 m s−1. There is still need to resolve differences in the 10–20 m s−1 regime, but since Blanc's review these differences have reduced to around 10%. At the highest wind speeds the effects of spray have yet to be satisfactorily quantified. The issue of the effects of flow distortion on fluxes, particularly stress, still needs to be addressed through a combination of numerical, laboratory, and field studies. The great advancement in ship-based covariance measurements is highlighted by recent results on air–sea gas transfer where direct measurements of CO2 fluxes and transfer velocity have been made (Fairall et al. 2000; McGillis et al. 2001). Until recently, such measurements were considered to range from the unacceptable to the impossible (Csanady 2001).
Acknowledgments
This work was supported by the ONR Marine Meteorology program, the DOE Atmospheric Radiation Measurement program, the NOAA Office of Global Programs, the National Science Foundation Climate Dynamics program, and Grant ATM-9727054 from NSF's Mesoscale Dynamics Meteorology program. The authors especially thank Scott Abbott, Dave Costa, Jesse Leach, Cat Russell, Dan Gottas, Jim Jordan, and Dave Gregg for their work, dedication, and personal sacrifice beyond all reason. Numerous discussions with Drs. Peter K. Taylor (SOC), Ed Andreas (CRREL), and Meghan Cronin (NOAA/PMEL) are gratefully acknowledged.
REFERENCES
Andreas, E. L., 2001: A review of the sea spray generation function for the open ocean. Preprints, 11th Conf. on Interaction of the Sea and Atmosphere, San Diego, CA, Amer. Meteor. Soc., 1–4.
Andreas, E. L., J. B. Edson, E. C. Monahan, M. P. Rouault, and S. D. Smith, 1995: The spray contribution to net evaporation from the sea: A review of recent progress. Bound.-Layer Meteor., 72 , 3–52.
Beljaars, A. C. M., 1995: The parameterization of surface fluxes in large-scale models under free convection. Quart. J. Roy. Meteor. Soc., 121 , 255–270.
Beljaars, A. C. M., and A. A. M. Holtslag, 1991: Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor., 30 , 327–341.
Benoit, R., 1977: On the integral of the surface layer profile-gradient functions. J. Appl. Meteor., 16 , 859–860.
Blanc, T. V., 1985: Variation of bulk-derived surface flux, stability, and roughness results due to the use of different transfer coefficient schemes. J. Phys. Oceanogr., 15 , 650–669.
Bourassa, M. A., D. G. Vincent, and W. L. Wood, 1999: A flux parameterization including the effects of capillary waves and sea state. J. Atmos. Sci., 56 , 1123–1139.
Bradley, E. F., and R. A. Weller, 1997: Fourth workshop of the TOGA-COARE air–sea interaction (flux) working group. University Corporation for Atmospheric Research Report, UCAR, Boulder, CO, 61 pp.
Bradley, E. F., P. A. Coppin, and J. S. Godfrey, 1991: Measurements of sensible and latent heat flux in the western tropical Pacific Ocean. J. Geophys. Res., 96 , 3375–3389.
Bradley, E. F., C. W. Fairall, J. E. Hare, and A. A. Grachev, 2000: An old and improved bulk algorithm for air–sea fluxes: COARE 2.6A. Preprints, 14th Symp. on Boundary Layer and Turbulence, Aspen, CO, Amer. Meteor. Soc., 294–296.
Brutsaert, W., 1975: A theory for local evaporation (or heat transfer) from rough and smooth surfaces at ground level. Water Resour. Res., 11 , 543–550.
Brutsaert, W., 1982: Evaporation into the Atmosphere. D. Reidel, 299 pp.
Businger, J. A., J. C. Wyngaard, Y. Izumi, and E. F. Bradley, 1971: Flux profile relationships in the atmospheric surface layer. J. Atmos. Sci., 28 , 181–189.
Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81 , 639–640.
Chang, H-R., and R. L. Grossman, 1999: Evaluation of bulk surface flux algorithms for light wind conditions using data from the Coupled Ocean–Atmosphere Response Experiment (COARE). Quart. J. Roy. Meteor. Soc., 125 , 1551–1588.
Chertock, B., C. W. Fairall, and A. B. White, 1993: Surface-based measurements and satellite retrievals of broken cloud properties in the equatorial Pacific. J. Geophys. Res., 98 , 18489–18500.
Clayson, C. A., C. W. Fairall, and J. A. Curry, 1996: Evaluation of turbulent fluxes at the ocean surface using surface renewal theory. J. Geophys. Res., 101 , 28503–28513.
Csanady, G. T., 2001: Air–Sea Interaction. Cambridge University Press, 239 pp.
DeCosmo, J., K. B. Katsaros, S. D. Smith, R. J. Anderson, W. A. Oost, K. Bumke, and H. Chadwick, 1996: Air–sea exchange of water vapor and sensible heat: The Humidity Exchange Over the Sea (HEXOS) results. J. Geophys. Res., 101 , 12001–12016.
Edson, J. B., and C. W. Fairall, 1998: Similarity relationships in the marine atmospheric surface layer for terms in the TKE and scalar variance budgets. J. Atmos. Sci., 55 , 2311–2328.
Edson, J. B., C. W. Fairall, P. G. Mestayer, and S. E. Larsen, 1991: A study of the inertial-dissipation method for computing air–sea fluxes. J. Geophys. Res., 96 , 10689–10711.
Edson, J. B., A. A. Hinton, K. E. Prada, J. E. Hare, and C. W. Fairall, 1998: Direct covariance flux estimates from moving platforms at sea. J. Atmos. Oceanic Technol., 15 , 547–562.
Fairall, C. W., and S. E. Larsen, 1986: Inertial dissipation methods and fluxes at the air ocean interface. Bound.-Layer Meteor., 34 , 287–301.
Fairall, C. W., and G. S. Young, 1991: A field evaluation of shipboard performance of an infrared hygrometer. Preprints, Seventh Symp. on Meteorological Observations and Instrumentation, New Orleans, LA, Amer. Meteor. Soc., 311–315.
Fairall, C. W., E. F. Bradley, D. P. Rogers, J. B. Edson, and G. S. Young, 1996a: Bulk parameterization of air–sea fluxes in TOGA COARE. J. Geophys. Res., 101 , 3747–3767.
Fairall, C. W., E. F. Bradley, J. S. Godfrey, G. A. Wick, J. B. Edson, and G. S. Young, 1996b: Cool skin and warm layer effects on the sea surface temperature. J. Geophys. Res., 101 , 1295–1308.
Fairall, C. W., A. B. White, J. B. Edson, and J. E. Hare, 1997: Integrated shipboard measurements of the marine boundary layer. J. Atmos. Oceanic Technol., 14 , 338–359.
Fairall, C. W., J. E. Hare, J. B. Edson, and W. R. McGillis, 2000: Measurement and parameterization of the air–sea gas transfer. Bound.-Layer Meteor., 96 , 63–105.
Fairall, C. W., J. E. Hare, A. A. Grachev, E. F. Bradley, and J. B. Edson, 2001: Preliminary results from the ETL open ocean air–sea flux database. Preprints, 11th Conf. on Interaction of the Sea and Air, San Diego, CA, Amer. Meteor. Soc., 5–8.
Finkelstein, P. L., and P. F. Sims, 2001: Sampling error in eddy correlation flux measurements. J. Geophys. Res., 106 , 3503–3509.
Garratt, J. R., 1992: The Atmospheric Boundary Layer. Cambridge University Press, 316 pp.
Geernaert, G. L., 1987: On the importance of the drag coefficient in air–sea interactions. Dyn. Atmos. Oceans, 11 , 19–38.
Godfrey, J. S., and A. C. M. Beljaars, 1991: On the turbulent fluxes of buoyancy, heat, and moisture at the air–sea interface at low wind speeds. J. Geophys. Res., 96 , 22043–22048.
Grachev, A. A., and C. W. Fairall, 1997: Dependence of the Monin–Obukhov stability parameter on the bulk Richardson number over the ocean. J. Appl. Meteor., 36 , 406–414.
Grachev, A. A., and C. W. Fairall, 2001: Upward momentum transfer in the marine boundary layer. J. Phys. Oceanogr., 31 , 1698–1711.
Grachev, A. A., C. W. Fairall, and E. F. Bradley, 2000: Convective profile constants revisited. Bound.-Layer Meteor., 94 , 495–515.
Gulev, S. K., D. Cotton, and A. Sterl, 1998: Intercomparison of the North Atlantic wave climatology from voluntary observing ships, satellite data, and modelling. Phys. Chem. Earth, 23 , 587–592.
Hare, J. E., P. O. G. Persson, C. W. Fairall, and J. B. Edson, 1999: Behavior of Charnock's relationship for high wind conditions. Preprints, 13th Symp. on Boundary Layers and Turbulence, Dallas, TX, Amer. Meteor. Soc., 252–255.
Holtslag, A. A. M., E. I. F. de Bruijn, and H-L. Pan, 1990: A high-resolution air mass transformation model for short-range weather forecasting. Mon. Wea. Rev., 118 , 1561–1575.
Jabouille, P., J. L. Redelsperger, and J. P. Lafore, 1996: Modification of surface fluxes by atmospheric convection in the TOGA COARE region. Mon. Wea. Rev., 124 , 816–837.
Janssen, P. A. E. M., 1999: On the effect of ocean waves on the kinetic energy balance and consequences for the inertial dissipation technique. J. Phys. Oceanogr., 29 , 530–534.
Kader, B. A., and A. M. Yaglom, 1990: Mean fields and fluctuation moments in unstably stratified turbulent boundary layers. J. Fluid Mech., 212 , 637–662.
Kraus, E. B., and J. A. Businger, 1994: Atmosphere–Ocean Interaction. Oxford University Press, 352 pp.
Liu, W. T., and J. A. Businger, 1975: Temperature profile in the molecular sublayer near the interface of a fluid in turbulent motion. Geophys. Res. Lett., 2 , 403–404.
Liu, W. T., K. B. Katsaros, and J. A. Businger, 1979: Bulk parameterization of the air–sea exchange of heat and water vapor including the molecular constraints at the interface. J. Atmos. Sci., 36 , 2052–2062.
McGillis, W. R., J. B. Edson, J. E. Hare, and C. W. Fairall, 2001: Direct covariance air–sea CO2 fluxes. J. Geophys. Res., 106 , 16729–16745.
Oncley, S. P., L. K. Kristensen, J. Mann, and J. C. Wyngaard, 1997: How close is close enough when measuring scalar fluxes with displaced sensors? J. Atmos. Oceanic Technol., 14 , 814–821.
Oost, W. A., G. J. Komen, C. M. J. Jacobs, and C. van Oort, 2002: New evidence for a relation between wind stress and wave age from measurements during ASGAMAGE. Bound.-Layer Meteor., 103 , 409–438.
Pattison, M. J., and S. E. Belcher, 1999: Production rates of sea-spray droplets. J. Geophys. Res., 104 , 18397–18407.
Persson, O. G. P., C. W. Fairall, E. L. Andreas, P. S. Guest, and D. K. Perovich, 2002: Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near-surface conditions and surface energy budget. J. Geophys. Res., 107 (C10) 8045. doi: 10.1029/2000JC000705.
Redelsperger, J. L., F. Guichard, and S. Mondon, 2000: Parameterization of mesoscale enhancement of surface fluxes for regional and large-scale models. J. Climate, 13 , 402–421.
Smith, S. D., 1980: Wind stress and heat flux over the ocean in gale force winds. J. Phys. Oceanogr., 10 , 709–726.
Smith, S. D., 1988: Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J. Geophys. Res., 93 , 15467–15472.
Smith, S. D., 1989: Water vapor flux at the sea surface. Bound.-Layer Meteor., 47 , 277–293.
Smith, S. D., C. W. Fairall, G. L. Geernaert, and L. Hasse, 1996: Air–sea fluxes: 25 years of progress. Bound.-Layer Meteor., 78 , 247–290.
Tanner, C. B., and G. W. Thurtell, 1969: Anemoclinometer measurements of Reynolds stress and heat transport in then atmospheric surface layer. University of Wisconsin Tech. Rep. ECOM-66-G22-F, 82 pp. [Available from U.S. Army Electronic Command, Atmospheric Sciences Laboratory, Ft. Huachuca, AZ 85613.].
Taylor, P. K., and M. A. Yelland, 2000: On the apparent “imbalance” term in the turbulent kinetic energy budget. J. Atmos. Oceanic Technol., 17 , 82–89.
Taylor, P. K., and M. A. Yelland, 2001: The dependence of sea surface roughness on the height and steepness of the waves. J. Phys. Oceanogr., 31 , 572–590.
Vickers, D., and S. K. Esbensen, 1998: Subgrid surface fluxes in fair weather conditions during TOGA COARE: Observation estimates and parameterization. Mon. Wea. Rev., 126 , 620–633.
Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapour transfer. Quart. J. Roy. Meteor. Soc., 106 , 85–100.
Webster, P. J., and R. Lukas, 1992: The Coupled Ocean–Atmosphere Response Experiment. Bull. Amer. Meteor. Soc., 73 , 1377–1416.
Webster, P. J., C. A. Clayson, and J. A. Curry, 1996: Clouds, radiation and the diurnal cycle of sea surface temperature in the tropical western Pacific. J. Climate, 9 , 1712–1730.
White, A. B., C. W. Fairall, and J. B. Snider, 1995: Surface-based remote sensing of marine boundary layer cloud properties. J. Atmos. Sci., 52 , 2827–2838.
Wick, G. A., W. J. Emery, L. H. Kantha, and P. Schlussel, 1996: The behavior of the bulk-skin sea surface temperture difference under varying wind speed and heat flux. J. Phys. Oceanogr., 26 , 1969–1988.
Yelland, M., and P. K. Taylor, 1996: Wind stress measurements from the open ocean. J. Phys. Oceanogr., 26 , 541–558.
Yelland, M., B. I. Moat, P. K. Taylor, R. W. Pascal, J. Hutchings, and V. C. Cornell, 1998: Measurements of the open ocean drag coefficient corrected for airflow disturbance by the ship. J. Phys. Oceanogr., 28 , 1511–1526.
Zeng, X., M. Zhao, and R. E. Dickinson, 1998: Comparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using the TOGA COARE data. J. Climate, 11 , 2628–2644.
Zeng, X., Q. Zhang, D. Johnson, and W-K. Tao, 2002: Parameterization of wind gustiness for the computation of ocean surface fluxes at different spatial scales. Mon. Wea. Rev., 130 , 2125–2133.
Zhang, G. J., and M. J. McPhaden, 1995: The relationship between sea surface temperature and latent heat flux in the equatorial Pacific. J. Climate, 8 , 589–605.
Zulauf, M., and S. K. Krueger, 1997: Parameterization of mesoscale enhancement of large-scale surface fluxes over tropical oceans. Preprints, 22nd Conf. on Hurricanes and Tropical Meteorology, Ft. Collins, CO, Amer. Meteor. Soc., 164–165.
APPENDIX
The ETL Shipboard Flux Measuring System
The ETL seagoing flux and bulk meteorology measurement system was fully described in Fairall et al. (1997). The following deals with specific aspects relevant to computing bulk transfer coefficients.
Instruments
The basic measurements used in this paper are covariance and inertial-dissipation turbulent flux estimates, combined with measurements of the basic bulk variables as described in section 2. A sonic anemometer (Gill/INUSA RS-2 or RS-2A) is used to obtain the three components of the wind vector (u′, υ′, w′) and the sonic temperature (T′). A high-speed infrared hygrometer (Ophir Corporation IR-2000) is used to obtain Q′. Velocity fluctuations in fixed-earth coordinates are obtained from the raw anemometer output by applying rotations to account for pitch, roll, and yaw plus corrections for the ship's velocity vector. High-frequency (i.e., surface wave–induced) motions are measured with an integrated package of angular rate sensors and accelerometers (Systron Donner Motionpak) which forms the mounting base of the sonic anemometer. Lower-frequency motions are obtained from GPS, a gyrocompass, and the ship's Doppler speed log. Details of the motion correction are given in Edson et al. (1998) and discussion on higher-frequency data and other covariance processing issues appear in EF. Sonic temperature is corrected for velocity cross talk and the humidity contribution as in Fairall et al. (1997). The ID flux estimates are computed from the variance spectral density of u′, T′, and Q′ in the inertial subrange of locally isotropic turbulence, which is usually at frequencies sufficiently above the wave-induced platform motions so corrections are not needed.
The optics of the high-speed hygrometer can be contaminated by salt (Fairall and Young 1991; Fairall et al. 1997) and require daily washing using specially installed water jets. Data obtained with water on the optics (e.g., during rainfall) are unreliable. In some conditions, sunlight also invalidates the data. The condition of the optics is monitored in the data stream and a threshold is set to reject such data. Because of these three sources of error, usable data for latent heat flux are significantly less than for stress. The hygrometer is located as near to the anemometer as flow distortion considerations allow (usually about 1 m), and below rather than alongside or above the anemometer. To account for the loss of correlation caused by the physical separation of the w′ and Q′ sensors, a correction (typically 2%–4%) is applied to
Mean wind speed and mean vector wind magnitude are obtained from the sonic anemometer after transformation to fixed-earth coordinates. A floating thermistor is used to obtain a near-surface value for the ocean temperature (the depth is about 5 cm). The COARE cool skin algorithm is used to obtain the interface value, typically 0.3°C cooler than the bulk. Mean air temperature and humidity are obtained with a combined temperature–relative humidity sensor in an aspirated radiation shield. In the early 1990s a Vaisala HMP35 sensor (0.1°C, 3% RH quoted accuracy) was used, later replaced with a Vaisala HMP-235 (0.1°C, 2% RH quoted accuracy).
Flux processing methods
Covariance and ID fluxes and mean variables are computed in 10-min chunks from a nominally 1-h time section and then averaged to 1-h. A coordinate rotation of the high-speed time series is performed on the mean fixed-earth velocity vector, following Tanner and Thurtell (1969) to produce streamwise coordinates for the 1-h period. Thus, we compute fluxes normal to the mean flow vector, which is subject to a mean tilt of about 5° due to distortion by the ship's structure. Initially the 10-min covariance blocks were averaged and the ID fluxes were computed from the 1-h-averaged spectra. After 1993 we changed to spectral processing for the covariances, too. The time series is time tapered with a Hamming window, the cospectra computed, and the covariances obtained as the integral of the windowed cospectrum. This reduces the sampling noise caused by leakage of low-frequency variations associated with the rectangular window.
The structure function–based approach that we are using employs Fx functions from EF that are fits of spectral
Accuracy considerations
Covariance flux estimates are subject to random sampling errors associated with atmospheric variability (Wyngaard 1973; Finkelstein and Sims 2001) and other random errors caused by imperfect motion corrections or sensor noise and drift. Systematic errors are caused by incorrect sensor calibration, imperfect motion correction, and flow distortion. Of all the turbulent quantities over the ocean, the wind components (needed for the stress measurement) have the strongest signals, and the most dependable sensor. However, because of the cross talk between velocity components, stress is most susceptible to motion correction and flow distortion. Correlations between w′ and sonic-derived T′ are also subject to cross-talk errors because both are determined from the same time-of-flight measurements. Even humidity flux is subject to cross talk because of acceleration effects on the hygrometer's rotating light beam chopper. These effects are discussed in detail in Fairall et al. (2000) and McGillis et al. (2001). Near-zero flux measurements when ΔT and Δq are very small indicates that cross-talk errors in our scalar fluxes are small, but in the case of sensible heat flux they may not be negligible. The accuracy of humidity fluxes is mainly constrained by uncertainty in the response of the humidity sensor to moisture fluctuations in the flux-containing frequency range. Comparisons between sensors of similar and different types suggests this is about 5% with the sensor used here.
For well-placed sensors on ships, flow distortion is a serious concern only for stress. Stress measurements from two research vessels were found to be 10%–15% greater than those obtained on R/P FLIP (considered to be largely distortion and motion free) during side-by-side intercomparisons (Edson et al. 1998; EF). The distortion effect will depend on the specific arrangement of sensors relative to the ship structure, so the above results may not translate exactly to the present measurements. Therefore we have applied no empirical distortion correction to our covariance stress data but note a possible systematic uncertainty of about 10%.
The covariance stress vector is composed of streamwise (τx) and cross-stream (τy) horizontal components, which combine to give a magnitude of the stress vector, τ =
The ID flux estimates do not require motion corrections, and variance estimates (i.e., variance spectra) have smaller sampling variability than covariances (Wyngaard 1973). The statistical uncertainty of covariance and ID stress measurements is contrasted in Fig. A2 where variations about the bin medians are plotted as a function of wind speed. Here σU∗ is computed as one-half the difference in the stress values corresponding to 84% and 16% cumulative probability within each wind speed bin. One important difference between covariance and ID stress estimates is the positive-definite nature of ID algorithms for stress. In covariance measurements we obtain a distribution of stress values (including negatives) that we average to obtain a mean estimate. In ID measurements we obtain a distribution of
The ID estimates are subject to another major error source: uncertainty in the dimensionless structure function parameter. We are using Fx functions from EF that are fits of spectral
The absolute accuracy of transfer coefficient measurements is subject to uncertainties in the mean measurements, the fluxes, and in the case of neutral transfer coefficients (or roughness length), the MOST stability functions (see discussion in Fairall et al. 1996a). Table A1 is a reproduction of accuracies claimed by Fairall et al. (1996a). These accuracies are not derived from factory calibrations for most instruments, but from comparisons with multiple instruments and platforms such as were done in the COARE, FASTEX, and the 1999 cruises on R/V Ronald H. Brown. In most cruises, mean and fast humidity sensors are scaled to match a high-quality handheld ventilated psychrometer; the adjustments are typically ±2%. The near-surface water temperature sensor is checked against the ship's thermosalinograph at night when vertical gradients are expected to be low. Flow distortion corrections have been applied for mean wind speed and for the height of measurements based on wind tunnel measurements (R/V Moana Wave) and wind flow patterns obtained by computational fluid dynamics (R/V Knorr and R/V Ronald H. Brown). Height adjustments to mean observations and ID fluxes are made as described by Yelland et al. (1998). The corrections are typically 2%–4% and the height changes on the order of 1 m. Of the 7216 1-h observations in the database, only 390 h from the R/V Malcolm Baldridge have no independent distortion estimates. When possible, the wind vector is referenced to the ocean surface to remove the effects of currents. For COARE the currents at a nearby buoy were used to correct the measured earth-frame wind vectors; for the 1999 cruises the ship's Doppler speed log was used as the reference in the motion corrections.
Estimates of Charnock parameter from various field programs: ×, COARE; ○, SCOPE; ▵, MBL; □, Yelland and Taylor (1996). The dashed line is the COARE 3.0 relationship
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Wind speed dependence of the 10-m neutral momentum transfer coefficient from several sources: solid line, COARE 3.0; dashed line, Smith (1980); line with ×, Oost et al. (2002) parameterization for fully developed seas; line with ○, Taylor and Yelland (2001) parameterization for fully developed seas
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Wind speed dependence of the 10-m neutral humidity transfer coefficient from the COARE-plus dataset (line with ×) and modified values from the HEXMAX program (line with ○)
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Roughness Reynolds number dependence of the humidity transfer roughness length: ×, COARE-plus; ○, modified HEXMAX; solid line, LKB model (COARE 2.5); dashed line, Brutsaert–Garratt parameterization; dotted line, COARE 3.0
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Wind speed dependence of the momentum and scalar transfer coefficients for COARE versions 2.5 (broken line) and 3.0 (solid line): (top) Cd10n; (bottom) Ce10n
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
The average of covariance and ID latent heat fluxes computed in 10-m neutral wind speed bins. Mean values are shown by lines and medians by symbols: the solid line and circles are measured fluxes, and the broken line and crosses are calculated with COARE 3.0
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
The same data points from Fig. 6, but plotted as bulk latent heat flux vs measured turbulent values. The × symbols are the means; the ○ symbols are medians; the solid line is 1:1
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
As in Fig. 7 but for median turbulent stress. The ○ symbols are the covariance values; the ⋄ symbols are the ID values.
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
The 10-m neutral transfer coefficient for moisture (○), calculated with Eq. (33) and using the average of mean and median values, as a function of the 10-m neutral wind. Error bars indicate the statistical uncertainty (one sigma) of the bin-average based on the distribution within the wind speed bin. The heavy solid line is COARE 3.0. The ⋄ symbols are computed with the SM (see text) using the median values
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Median 10-m neutral velocity transfer coefficient as a function of 10-m neutral wind using method 3 [Eq. (33)]. Error bars indicate the statistical uncertainty (one sigma) of the bin median based on the distribution within the mean wind speed bin: ○, combined covariance and ID values; ×, ID values only; ⋄, covariance values only
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Charnock parameter as a function of 10-m neutral wind using the mean turbulence values (○) from Fig. 10. The dashed line is COARE 3.0; the solid straight line a value of 0.01.
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Moisture roughness length as a function of the roughness Reynolds number, as in Fig. 4 but showing the relation of ETL1999 (⋄) to COARE 3.0 [dotted line, Eq. (28)]. COARE-plus data shown as × and modified HEXMAX as ○.
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Humidity roughness Reynolds number vs velocity roughness Reynolds number: ○, ETL1999 dataset results; solid line COARE 3.0 [Eq. (28)]; dashed line LKB and COARE 2.5; dotted line, Garratt–Brutsaert [Eq. (29)]; heavy dotted line, revised Garratt–Brutsaert [Eq. (35)]
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Fig. A1. Covariance measurements of the streamwise momentum flux (τx) as a function of 10-m neutral wind speed. The individual points are nominal 1-h averages. The solid line is COARE 3.0.
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Fig. A2. Std dev of u∗ within bins of mean 10-m neutral winds: ○ symbols are covariance [solid line = (0.0552 + 0.0036
Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2
Comparative features of various modern bulk flux algorithms
Summary of ETL air–sea flux and bulk meteorological data cruises used in the analysis: Tropical Instability Wave Experiment (TIWE); Atlantic Stratocumulus Transition Experiment (ASTEX); Coupled Ocean–Atmosphere Response Experiment (COARE); San Clemente Ocean Probing Experiment (SCOPE); Fronts and Atlantic Storms (FASTEX); Joint Air–Sea Monsoon Experiment (JASMINE); Nauru ’99 Experiment (NAURU99); Tropical Rainfall Measuring Mission (TRMM)/Kwajalein Experiment (KWAJEX); Pan American Climate Studies fall 1999 study (PACSF99)
Table A1. Measurement uncertainty estimates from Fairall et al. (1996a)