1. Introduction
Accurate estimation of the exchanges of momentum, heat, and freshwater between the ocean and atmosphere is critical for a wide range of weather- and climate-related studies. The turbulent air–sea fluxes (TASFs), which include wind stress, evaporation, and the latent and sensible heat flux components, are the primary mechanism by which these exchanges occur. As such, TASFs influence the variability and climatology of the net surface heat flux at all scales. In contrast to the radiative shortwave and longwave heat flux components, the spatial pattern of TASFs is characterized by strongly localized maxima over the mid- and high latitudes, making them crucial drivers of the variability of many ocean processes, such as deep convection in the subpolar waters (Visbeck et al. 2003; Moore et al. 2014; Holdsworth and Myers 2015). TASFs also have a strong impact on the atmospheric circulation and climate. For instance, in the western boundary current regions, TASFs can modulate the atmospheric responses at mesoscales and synoptic scales (Zolina and Gulev 2003; Small et al. 2008; Minobe et al. 2008; Ma et al. 2015b,a).
Since direct measurements of TASFs tend to be idealized, infrequent, and highly localized, they cannot be used to build global or regional climatologies of TASFs (Brunke et al. 2003, 2011). Rather, these data provide valuable constraints for the development and improvement of parameterizations (Fairall et al. 1996b, 2003, 2011; Edson et al. 2013).
Hence, global estimates of TASFs are derived using bulk formulas, which relate each TASF to more easily measurable and widely available meteorological surface state variables (SSVs), and a bulk transfer coefficient (BTC). BTCs are determined with what we will refer to as a bulk algorithm. The core of a typical bulk algorithm includes dependencies of BTCs on the wind speed and the stability of the atmospheric surface layer (ASL) as well as the adjustment of atmospheric scalars to the standard height through the flux–profile relationships. Traditionally, bulk algorithms are developed for in situ point measurements of SSVs, with neutral drag, heat, and moisture BTCs derived from ship and buoy measurements (Large and Pond 1981, 1982; Smith 1988; Fairall et al. 1996b, 2003; Bradley and Fairall 2007). In this sense, bulk parameterizations are most suited to applications utilizing voluntary observing ship (VOS) and buoy data only. Nevertheless, for most purposes, bulk formulas are used to compute TASFs using a variety of SSV datasets.
These datasets include SSVs measured by VOS instrumentation (Josey et al. 1999; Gulev et al. 2007; Berry and Kent 2005, 2009), satellite-retrieved SSVs (Bentamy et al. 2003; Chou et al. 2004; Bourras 2006; Grodsky et al. 2009; Andersson et al. 2011; Tomita and Kubota 2011), and SSVs derived from NWP systems, particularly those from reanalysis datasets (Josey et al. 2013). This latter approach is used for producing global TASF climatologies and surface forcing fields for numerical experimentation with OGCMs (Yu et al. 2008; Large and Yeager 2009; Brodeau et al. 2010).
Discrepancies between state-of-the-art bulk algorithms have been repeatedly shown to lead to a relatively large spread in the computed TASFs (Blanc 1985, 1986, 1987; Zeng et al. 1998; Eymard et al. 1999; Brunke et al. 2002, 2003). Moreover, given the large uncertainties prevalent in SSVs from different sources (Josey et al. 1999; Berry and Kent 2005; Josey et al. 2014), it is common practice to neglect the accuracy of various constants and parameter approximations in bulk formulas. Yet, the uncertainties associated with these assumptions remain too poorly quantified to constitute actionable information for concerned users. An important question is whether or not the uncertainties associated with assumptions and approximations in the bulk formulas are significant for climatological estimates of TASFs. In this study, we address these issues and quantify the effect of various assumptions and approximations in the bulk formulas on the global TASF estimates. This is assessed through numerical experiments in which global TASFs are computed over a set of configurable bulk routines using prescribed daily SST and 3-hourly atmospheric SSVs from the ERA-Interim reanalysis for the period 1982–2014.
By computing TASFs with a fixed bulk formulas frame, modifiable constants, interchangeable bulk algorithms, and various parameter approximations, we quantify errors with respect to a reference control experiment.
This paper addresses the sensitivity of computed TASFs to various aspects of the parameterization of the bulk formulas with respect to the uncertainties arising due to the choice of the bulk algorithm. These aspects include the air density, sea surface saturation humidity, mean sea level pressure, cool skin and warm layer, and sea surface currents.
This paper is organized as follows: In section 2, we provide a short overview of the bulk formulas and some well-established bulk algorithms and describe the setup of the control experiment. In section 3, we quantify TASF uncertainties related to various assumptions and approximations in the bulk formulas, each tested with an individual sensitivity experiment. In section 4, we focus on the uncertainties related to the choice and parameterization of the bulk algorithm used to compute the BTCs. Our conclusions are summarized in section 5.
A detailed nomenclature relating the acronyms and symbols used in this paper to the terms or physical parameters they stand for is outlined in appendix A.
2. Method and experiment design
a. The bulk model
Accuracy of the bulk formulas strongly relies on the BTCs CD, CH, and CE. In a typical bulk algorithm, the BTCs under neutral stability conditions are defined using in situ flux measurements, while their dependence on the stability is accounted through the Monin–Obukhov similarity theory and the flux–profile relationships (e.g., Paulson 1970). BTCs are functions of the wind speed and the near-surface stability of the ASL and hence depend on UB, Ts, Tz, qs, and qz.
In this study, we focus on three of the most common algorithms used by the GCM community: COARE, after Fairall et al. (1996b, 1997, 2003, 2011) and more recently Edson et al. (2013); NCAR, following the work of (Large and Pond 1981, 1982; Large et al. 1997; Large and Yeager 2004, 2009); and ECMWF (Miller et al. 1992; Beljaars 1995, 1997; Zeng and Beljaars 2005). By COARE, we explicitly refer to the COARE 3.0 algorithm (Fairall et al. 2003), by NCAR we refer to the algorithm developed by Large and Yeager (2009), and by ECMWF we refer to the version of the bulk algorithm used in recent cycles of the Integrated Forecast System (IFS) of the ECMWF, such as cycle 40 (ECMWF 2014).
b. Experimental setup
We use bulk formulas with 3-hourly atmospheric SSVs and daily mean SST from ERA-Interim (Dee et al. 2011) to compute global 3-hourly estimates of the TASFs from 1982 to 2014. Our experiments are initiated in 1982 as the SST data product used as the surface boundary condition in ERA-Interim becomes truly daily from the end of 1981. Prior to this, daily mean SSTs were derived through interpolation of monthly mean data.
In the control experiment CtrlE (Table 1), TASFs are computed at the standard height of z = 10 m, according to Eqs. (1) and the procedure outlined in Fig. 1. BTCs are computed using the COARE 3.0 algorithm, which has consistently performed well in algorithm intercomparisons (e.g., Brunke et al. 2003). Sensitivity experiments using the NCAR and ECMWF bulk algorithms are also performed to assess the algorithm-related TASFs uncertainties (section 4).
(columns 1 to 9) Definition of the main experiments. C. is for computed, P. is for prescribed, and f. is for fixed value; bold notation highlights how the setup of each sensitivity experiments differs from that of the control experiment CtrlE. Time mean of the globally averaged difference in (column 10) wind stress and (columns 12 to 16) turbulent heat flux, and the (column 11) globally integrated difference in evaporation, between the sensitivity experiments and CtrlE (1982–2014, 
Schematic of the bulk approach used to compute turbulent air–sea fluxes based on the sea surface and near-surface atmospheric state variables. Note that T and q are not always provided at the same height as the wind speed (usually 2 and 10 m, respectively). Therefore, prior to using bulk formulas [Eq. (1)], θ and q are adjusted to the standard height of 10 m during the computation of transfer coefficients (in the bulk algorithm).
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
We use instantaneous values of CP and Lυ, calculated according to Eqs. (B4) and (B5) (appendix B, sections c and d). TASFs are set to zero over ice-covered regions. For Uz, we use the absolute 10-m wind speed vector. The effect of using Uz relative to the sea surface current is tested in experiment CurrE (section 3e).
The 1982–2014 climatologies of wind stress and total turbulent heat flux QT (i.e., QL + QH) derived in CtrlE are shown in Fig. 2.
Mean climatology (1982–2014) of (a) the wind stress module and (b) the total turbulent heat flux QT (latent + sensible), obtained with the control experiment CtrlE. Corresponding zonal averages: (c) wind stress and (d) the total turbulent heat flux and its latent QL and sensible QH components and the net longwave radiative flux QIR.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
All the FORTRAN routines used to compute TASFs in this study are developed and maintained by the authors as part of the AeroBulk1 package and are currently being implemented as the default forcing function of the upcoming major release of the NEMO2 OGCM (version 4.0).
The approximations and assumptions studied in sections 3 and 4 are chosen to reflect many of the common parameterization issues faced by ocean and atmosphere practitioners when configuring bulk formulas for use in modeling experiments. As such, aspects of the bulk parameterization that are more esoteric in nature, and generally unknown to the practitioner, such as the choice of the stability functions or the Webb correction (Webb et al. 1980), are not considered in this study. They do, however, represent a source of uncertainty in computed TASFs and remain to be quantified in the manner presented here.
3. Impacts of approximations in the bulk formulas
We perform a series of sensitivity experiments to quantify the impact of various approximations in the bulk formulas [Eq. (1)]. For every sensitivity experiment, we use the same configuration as in CtrlE (section 2b), except for the particular aspect being tested. The impact of the tested aspect is then assessed and discussed by analyzing global estimates of the differences in TASFs between the sensitivity experiment and CtrlE.
a. Density of air: Equation of state versus fixed value
In marine conditions, ρ [Eq. (4)] typically spans values from 1.1 kg m−3 in the tropics to 1.3 kg m−3 in the cold environment, implying a variation of roughly ±10% around the value of 1.2 kg m−3 often used as a reference3 (Table 2). Compared to the climatology of ρ computed in CtrlE using Eq. (4), a constant density of 1.2 kg m−3 overestimates ρ in the tropics by 3%–4% on average (Fig. 3). At higher latitudes, this underestimates ρ by about 2% in midlatitudes and 6% in subpolar regions.
Impact of the (second row) temperature on the density and the (third row) specific heat capacity of moist air for a fixed SLP of 1010 hPa and a relative humidity of 80%. For the density, values in parentheses are for a SLP of 1040 (bold) and 970 hPa (italic). The relative errors are calculated as the difference between values obtained with a temperature of 30°C or −5°C and the reference value obtained with a temperature of 15°C, divided by the same reference value.
Mean (1982–2014) relative error in the estimate of the air density due to the fixed density assumption (ρ = 1.2 kg m−3), with respect to the reference density calculated with equation of state [Eq. (4)].
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
In sensitivity experiment RhoE (Table 1), we use a fixed ρ value of 1.2 kg m−3. An error in ρ affects all computed TASFs [Eq. (1)]. The spatial pattern of the mean relative error in wind stress and QT (not shown) is almost identical to that of ρ (Fig. 3) but with the opposite sign. The momentum flux to the ocean increases by 3% in the tropics; however, because of relatively low winds, the resulting absolute effect is about 2 mN m−2 on average, and never exceeds 5 mN m−2 (Figs. 4a,e). The error is small in subtropical highs where the year-round ρ is close to 1.2 kg m−3. In mid- and high latitudes, the underestimation of ρ in RhoE leads to an underestimation of the wind stress with a mean difference being typically −10 mN m−2 (Fig. 4a) and the maximum differences (likely associated with winter storms) amounting to −30 mN m−2 (Fig. 4e).
Wind stress errors, with respect to the control experiment CtrlE, for various sensitivity experiments (Table 1). Errors are computed for the periods 1993–2014 in CurrE (denoted by *) and 1982–2014 for all other experiments. First and third rows indicate the time-mean and zonal-mean errors of the wind stress for all values (solid black lines), positive values only (red shading), and negative values only (blue shading). Positive values indicate a gain of momentum for the ocean. Second and fourth rows indicate the probability density functions of the 3-hourly time varying errors over the global ocean (60°S–60°N, gray shaded bars), tropics (23°S–23°N, red lines), and extratropics (60°–30°S in yellow and 30°–60°N in dashed blue, respectively). The distributions are computed using a bin size of 1 mN m−2.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
In the tropics, RhoE overestimates E by 0.15 mm day−1 on average (equivalent to a heat flux deficit of 4 W m−2; Figs. 5a,e). In the extratropics, the increase in QT, due to the underestimation of ρ during the cold season, dominates over less frequent decreases in QT due to warm air episodes in summer. The resulting surplus of heat and freshwater input for the ocean at midlatitudes is about +3 W m−2 and +0.1 mm day−1 on average and is slightly more pronounced for QT in the Northern Hemisphere (NH) due to the larger contribution of QH. North of 40°N, by limiting |QT| in winter, RhoE leads to an underestimation of the amplitude of the seasonal cycle of QT by almost 10 W m−2 (gray line in Fig. 5a). In cold-air outbreak conditions (mostly dominated by QH), |QT| can be underestimated by more than 20 W m−2 in RhoE (Fig. 5e). In February, the mean |QH| over the Labrador Sea is underestimated by about 15 W m−2 (not shown).
As in Fig. 4, but for the turbulent heat flux QT. First, second, and third rows display dashed black lines that show the zonal-mean error of the latent heat flux QE. Solid gray lines show the change in amplitude of the monthly mean annual cycle of QT. Positive values indicate a gain of heat for the ocean. The distributions are computed using a bin size of 1 W m−2.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
The mean response of the global ocean to the fixed density assumption is dominated by the overestimation of evaporation in the tropics, leading to a cooling of −0.4 W m−2 and a freshwater deficit of 0.1 Sv (1 Sv = 106 m3 s−1; Table 1).
b. Accuracy of the estimate of qs
Inaccuracy in the estimate of qs has a direct effect on E and QL, because of the linear dependence of E on the term Δq = qs − qz. It also has an indirect effect on other TASFs because of the dependence of all BTCs on the stability (which partly depends on Δq). Other indirect effects of an error in qs include slight modifications of the height-adjusted qz, ρ(z), and UB. Our results show that all these indirect effects are secondary and negligible compared to the direct error induced by Δq in Eq. (1c).
1) Salt-related reduction in qs
Sud and Walker (1997) showed the importance of the salt-related 2% vapor pressure reduction in the estimate of qs in experiments with an AGCM. Zeng et al. (1998) and Brunke et al. (2002, 2003), using direct TASF measurements, concluded that omitting this correction leads to substantial errors in evaporation under strong wind conditions.
Mean relative turbulent heat flux discrepancy, with respect to the control experiment CtrlE, for experiments (a) SaltE, (b) NoSkinE, (c) SimplE, and (d) NCE (Table 1). Negative discrepancies mean a decrease of the heat flux to the ocean. White contours show the mean relative discrepancy in multiples of 5%.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
In SaltE, the regions that undergo the strongest increase in evaporation (larger than 10%) are strongly localized and are collocated with the regions with a propensity for stably stratified ASLs, such as the eastern Pacific equatorial upwelling, the central midlatitude North Atlantic, and the Malvinas Current retroflection (Fig. 6a). Figure 7a shows that the error is the largest under weakly stratified conditions (ΔΘ = +1°C), which leads to an overestimation of evaporation by +40%. Note, however, that under such conditions, E is small, so the large relative error may not necessarily imply large absolute errors. Therefore, it is mainly the regions with strong evaporation, and not necessarily those that exhibit large relative errors, that are affected the most, like the tropics (equatorial upwelling regions aside; Figs. 5b,f). Nevertheless, the mean increase of |QT| in SaltE is substantially larger than 2% over the whole global ocean (Fig. 6a). This highlights the outsized response of evaporation to a small change in qs [Eq. (5)], particularly in marine conditions as qz is generally close to qs. Thus, for the global ocean, omitting the 2% reduction in qs leads to the relatively large excess of evaporation of 1.12 Sv, associated with a deficit of heat of 7.5 W m−2 (Table 1). The most likely error in QT in the tropics is between −10 and −12 W m−2, while it is twice as small (−6 W m−2) in the extratropics of both hemispheres (Fig. 5f). The excess of evaporation is the highest at ±15° of latitude, where it leads to a deficit of freshwater and heat of 0.5 mm day−1 and 11 W m−2, respectively, that decreases to zero toward the poles.
(a) Scatterplot, in the U10, ΔΘ space, of the relative error in QT due to the omission of the salt-related 2% decrease in qs (experiment SaltE compared to CtrlE, a positive ΔΘ means a stable ASL); only points for which the relative error is larger than 8% are shown, based on 6296 points in space that evenly spans the global ocean from 60°S to 60°N for years 2002–14 at a frequency of 3 h. (b) Relative error in qs, emerging from the use of the simpler formula 
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
2) Accuracy of formula for qsat
In QsatE, the largest errors in QT are confined to the tropics, particularly over the equatorial upwelling region of the eastern Pacific where enhanced evaporation leads to a deficit of freshwater and heat flux for the ocean of 4% on average (not shown). This results in a zonally averaged heat flux deficit of about 4 W m−2 (Fig. 5c). In the extratropics, the underestimation of qs results in a small gain of heat for the ocean of less than 2 W m−2 on average (Fig. 5c). In the NH extratropics, the amplitude of the seasonal cycle of QT decreases by about 5 W m−2 due to the decrease and increase of evaporation in winter and summer, respectively. Globally, the enhanced evaporation in the tropics dominates over the extratropical evaporation reduction (Figs. 5c,g), leading to a global surplus of evaporation of 0.18 Sv, equivalent to a heat flux deficit of 1.2 W m−2 for the ocean (Table 1).
The use of 
c. SLP: Prescribed versus fixed value
The SLP is needed to compute ρ [Eq. (4)] and qsat [Eq. (3)]. In many applications, such as ocean-only experiments, the use of a fixed SLP of 1010 hPa is common practice. Cyclone activity climatologies report the depth of intense cyclone events being deeper than 960 hPa, with extreme cyclones approaching a central pressure of 920–930 hPa (Tilinina et al. 2013). The use of the fixed SLP assumption in such low pressure conditions has two effects: 1) qs is underestimated (Table 3), which implies a reduction of E and QL with no significant impact on other TASFs (section 3b), and 2) ρ is overestimated, which enhances all TASFs. Under anticyclonic circulation conditions, the opposite scenario occurs. In the SLP range from 950 to 1040 hPa, both ρ and qs vary by roughly ±5% (Table 3).
Sensitivity of the (second and third rows) sea surface saturation–specific humidity and (fourth and fifth rows) the density of moist air to the SLP. The density is calculated for a relative humidity of 80%. The relative errors are calculated as the difference between values obtained with a SLP of 1040 and 950 hPa divided by the reference value obtained with a SLP of 1010 hPa.
In sensitivity experiment SLPE, the SLP was fixed to 1010 hPa (Table 1). The fact that the ρ- and the qs-induced errors have an opposite impact on E suggests a potential compensating effect. To quantify the individual contributions of these errors, an additional sensitivity experiment was run with the fixed SLP assumption implemented only in the calculation of ρ.
Over the Southern Ocean, strong cyclonic activity leads to an overestimation of ρ in SLPE, which enhances the wind stress by up to 5 mN m−2 over the Antarctic Circumpolar Current (ACC; Fig. 4b). North of 45°N, periods of cyclonic activity alternate with blocking episodes more frequently, resulting in a smaller effect (+2 mN m−2). No impact on the wind stress is found in the tropics and subtropics.
In SLPE, E and QT are mainly affected by the qs-induced error in E (not shown), which is 3 times as large as the ρ-induced error on average and of opposite sign. The mean error in E and QT does not exceed ±0.1 mm day−1 and ±2 W m−2, respectively (Fig. 5i). Significant increases in QT only occur over the ACC due to reduced evaporation (+2.5 W m−2 is equivalent to −0.1 mm day−1). In the subtropics of both hemispheres, persistent anticyclonic conditions result in an overestimated qs, which enhances E and decreases QT (−0.1 mm day−1 and −2 W m−2 on average). In the NH, the decrease in QT caused by high pressure is never less than −10 W m−2, while in the Southern Hemisphere (SH), extreme low pressure episodes can increase QT by 15 W m−2 (Fig. 5m).
In SLPE, the enhanced subtropical evaporation dominates over the reduced evaporation over the ACC. Consequently, the fixed SLP assumption enhances global evaporation by 0.1 Sv and leads to a global deficit of heat of 0.7 W m−2 (Table 1).
Implementation of the fixed SLP and fixed air density assumptions together, tested in experiment SLPRhoE, acts to increase the magnitude of the errors in E and QT with respect to RhoE and SLPE (cf. Figs. 5a,i,j). In SLPRhoE, the fixed SLP assumption acts only through qs, and the compensating effect of the ρ-induced error that acts in SLPE vanishes. This amplifies the excess of evaporation in the tropics and subtropics and decreases QT by 4–5 W m−2 between 30°S and 30°N (+0.15 and +0.2 mm day−1). In the extratropics, the opposite effect occurs: an underestimation of E and an overestimation of QT, particularly in subpolar regions (−0.2 mm day−1 and +6 W m−2 at 60°S). In northern mid- and subpolar latitudes, the substantial decrease in the amplitude of the seasonal cycle of QT observed in RhoE is further decreased in SLPRhoE and reaches −10 W m−2 at 60°N (cf. Figs. 5a and 5j).
d. Cool skin and warm layer
Similar to COARE, the ECMWF algorithm is meant to be used with the SSST; it includes the same cool skin scheme as in COARE (Fairall et al. 1996a) and the warm layer scheme of Zeng and Beljaars (2005). In contrast to COARE and ECMWF, the NCAR algorithm is calibrated and developed for the (bulk) SST.
The warm layer scheme of the ECMWF algorithm is more advanced than its COARE counterpart, which tends to promote the diurnal warming of the surface layer induced by Qs, mostly under relatively calm conditions (Fig. 8a). Therefore, in regions with significant insolation and relatively weak winds, like the tropical Indian Ocean, the warm pool, and eastern boundaries, COARE tends to underestimate the mean effect of the diurnal warming of the surface layer (and therefore Ts) with respect to ECMWF, by about 0.1°C (cf. Fig. 8b and Fig. 8c). Over the warm pool Ts is 0.3°C colder than the SST on average according to COARE, while it is 0.2°C colder according to ECMWF.
Temperature difference between the surface skin temperature Ts and the bulk SST, calculated using the cool skin and warm layer schemes of the COARE and ECMWF algorithms. (a) A function of the wind speed for two values of the surface downwelling solar radiative flux 
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
In sensitivity experiment NoSkinE (Table 1), we suppress the COARE CSWL correction used in CtrlE, which implies that Ts is set equal to the daily SST of ERA-Interim. In CtrlE, the SSST is colder than the SST by about 0.1° to 0.3°C on average (Fig. 8b). In NoSkinE, the slightly warmer Ts substantially enhances E and |QT| (Fig. 6b); the resulting deficit of heat (and freshwater) for the ocean varies from roughly 10 W m−2 (0.3 mm day−1) in low latitudes to 2–3 W m−2 (0.03–0.06 mm day−1) in the subpolar regions (Fig. 5k). The contribution of QH is not negligible and amounts to roughly 2 W m−2 from the equator to midlatitudes. The amplitude of the seasonal cycle of QT is increased by up to 6 W m−2 in the NH extratropics (Fig. 5k) on account of calm and sunny conditions being more frequent than in the SH during summer.
Interestingly, the global distribution of ΔQT is bimodal (Fig. 5o) due to two distinct responses to the skin correction between the tropics and extratropics. In the tropics, a low wind/high evaporation mode dominates, with a Gaussian distribution of the errors in QT centered about −11 W m−2. In midlatitudes, however, the magnitude of Ts − SST is limited by the impact of the stronger winds (Fig. 8a) and also because the diurnal solar warming becomes more efficient in compensating the weaker |QL|.
In summary, the omission of the skin correction in the COARE algorithm leads to an increase of global ocean evaporation by 0.74 Sv, associated with a deficit of heat for the ocean of 7 W m−2 (Table 1).
e. Wind vector referenced to the surface current
In many applications of bulk formulas, TASFs are determined from the apparent wind speed relative to the sea surface current vector u0, so that in Eq. (1), Uz is replaced by Uz − u0.
In sensitivity experiment CurrE (Table 1), we use the apparent 10-m wind vector of ERA-Interim relative to the prescribed daily u0 from the Global Ocean Reanalysis and Simulation (GLORYS),4 mesoscale, eddy-permitting (¼° resolution) ocean reanalysis (Ferry et al. 2012; Balmaseda et al. 2015). Note that CurrE is limited to the 1993–2013 period, which corresponds with the longest overlap in the GLORYS and ERA-Interim datasets.
The bulk formulas imply that the relative error in E, QL, and QH is of the order of u0/U, while it is at least twice as large for the wind stress on account of the quadratic dependence with wind speed. This suggests that the error is only significant if ocean currents are sufficiently strong with respect to the wind speed. This configuration predominates near the equator where CurrE shows significant relative errors in wind stress (Fig. 9a), particularly over the South Equatorial Current (SEC), the North Equatorial Countercurrent (NECC), and the North Equatorial Current (NEC). These results are consistent with previous studies reporting this effect to be the largest in the tropics (Luo et al. 2005; Dawe and Thompson 2006).
Mean relative wind stress discrepancy, with respect to the control experiment CtrlE, for experiments (a) CurrE, (b) C35E, (c) ECE, and (d) NCE (Table 1). Positive discrepancies mean an increase of the magnitude of the wind stress. White contours show the mean relative discrepancy in multiples of 5%.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
In CurrE, the wind stress decreases by about 2%–3% over the ACC (−6 mN m−2), by 10% at the equator over the NEC (−4 mN m−2), and by 2% in the NH extratropics (−2 mN m−2; Fig. 4c). Globally, the decrease of wind stress ranges between −1 and −5 mN m−2, with the strongest biases reaching −25 mN m−2 (Fig. 4g). The contribution from counterflow situations, in which the wind stress is increased, is significant and limits the overall decrease in wind stress by about +1%. Thus, the mean zonal error is entirely canceled over the NECC, on account of the ocean counterflow relative to the trade winds.
In CurrE, E and |QT| are decreased in the tropics, which leads to a mean surplus of heat (and freshwater) flux of about +2 W m−2 (+0.08 mm day−1) with a maximum over the SEC at the equator (+5 W m−2; +1.9 mm day−1) and zero mean error over the narrow NECC band (Fig. 5l). The error in QT diminishes outside of the tropics on account of the error being related to u0/U (Figs. 5l,p), meaning that the surplus of heat flux is less than 1 W m−2 over the ACC as well as in the NH extratropics. Over the global ocean, errors in the surface heat flux very rarely exceed 5 W m−2. The distribution is skewed toward positive error values (heat input to the ocean) but also displays significant contributions from the negative errors related to counterflow situations.
In summary, the correction of the wind speed with the daily u0 leads to a global deficit of evaporation of 0.17 Sv and a global surplus of heat flux to the ocean of 1.2 W m−2 (Table 1).
f. CP and Lυ
The specific heat capacity of moist air CP [Eq. (1b)] is slightly larger than its dry air counterpart CPd (appendix B, section c). In typical marine conditions, CP is close to 1020 J kg−1 K−1, with a range of +2.3%/−1.2% [see Table 2 and Eq. (B4)]. Therefore, on a global scale, the errors in QT due to the use of a fixed CP are negligible, given the small contribution of QH to QT (Fig. 2d). However, such an approximation could have a nonnegligible impact on episodes of extreme fluxes (Gulev and Belyaev 2012) associated with oceanic deep convection in subpolar and high latitudes, when most of the buoyancy loss to the atmosphere is due to QH. In CtrlE, QH can locally reach −500 W m−2 in winter over the western branches of NH west boundary currents, the Labrador Sea, the Nordic Seas, and the southern ACC.
The latent heat of vaporization of water Lυ depends only on the temperature. Based on the widest spread of realistic marine conditions, the possible error in Lυ does not exceed 1.5% (Table 4).
Impact of sea surface temperature on the latent heat of vaporization of seawater. The relative errors are calculated as the difference between values obtained with a SST of 30° or −1°C and the reference value taken at 15°C, divided by the same reference value.
Two separate experiments, using a fixed Lυ (2.46 106 J kg−1) and CP (1005 J K−1 kg−1), respectively, confirm the negligible impact of these two approximations on the climatologies of QT and E (not shown). The largest errors are observed in the fixed Lυ experiment, in particular in the tropics where enhanced evaporation leads to a mean decrease of QT by −1.1 W m−2 (not shown). The QT errors due to the fixed Lυ and CP approximations are of opposite sign in regions where they are the largest (tropics and extratropics, not shown). Consequently, experiment CpLvE, which combines both approximations, shows even smaller QT errors (Table 1).
Therefore, both CP and Lυ can safely be assumed as constants.
g. Combined effects of common assumptions
We investigate to what extent multiple simplifications and approximations, implemented together, lead to accumulation or cancellation of errors in the time mean of computed TASFs. We address this by computing TASFs in experiment SimplE, which implements what we consider to be the most common choice of parameterization implemented in OGCMs. As such, SimplE differs from CtrlE in the following ways:
no CSWL correction of the SST (Ts = SST),
assumed constant ρ (1.2 kg m−3),
estimation of qs using
assumed constant Lυ (2.46 J kg−1) and constant Cp of dry air (1005 J kg−1 K−1).
Our results indicate that for the wind stress, only the impact of the fixed density assumption is expressed in SimplE (cf. Fig. 4a and Fig. 4d).
In SimplE, the errors in Q (Figs. 5q, 6c) are roughly equivalent to the addition of the errors from QsatRhoE and NoSkinE, respectively (Figs. 5d,k). Thus, most of the tropical decrease in Q associated with the omission of the skin correction (NoSkinE) is compensated by the increase in Q due to the fixed density assumption used in conjunction with 
The globally averaged errors in Q and evaporation in SimplE are −5 W m−2 and +0.54 Sv, respectively (Table 1).
4. Algorithm-related flux discrepancies
The bulk algorithm determines the BTCs and adjusts the temperature and humidity of air to the standard height (z = 10 m). The algorithm may also include a convective gustiness contribution to the wind speed under calm and unstable conditions.
Zeng et al. (1998) and Brunke et al. (2002, 2003) carried out an extensive intercomparison of algorithms, including COARE 3.0 and the predecessors of ECMWF and NCAR algorithms, against direct flux measurements in different regions over the ocean. They reported a very large spread in the results and concluded that COARE and ECMWF were the least problematic of all. Since then, COARE, NCAR, and ECMWF have undergone updates and improvements, often taking advantage of direct flux measurements from recent observations.
Bulk empirical closures as a function of the wind speed at 10 m for COARE (red), NCAR (blue), and ECMWF (green) bulk algorithms. (a) Charnock parameter α; for NCAR, α is calculated according to Eq. (B10) (appendix B, section g). (b) Neutral drag and moisture transfer coefficients (thick and thin lines, respectively), as functions of the neutral wind speed at 10 m.
Citation: Journal of Physical Oceanography 47, 1; 10.1175/JPO-D-16-0169.1
The NCAR algorithm is based on a closure proposed by Large and Pond (1981, 1982), in which the three neutral BTCs are empirical functions of UN10. As noted by Beljaars (1997, p. 35), “neutral transfer coefficients and surface roughness lengths are compatible concepts,” so that the 
a. Influence of the Charnock parameter
The value of α directly influences the growth of CD with the wind speed (Fig. 10). We assess the sensitivity of the computed wind stress to the value of α in two sensitivity experiments, C35E and ECE, in which CD is computed using COARE 3.5 and ECMWF algorithms, respectively.
Significant wind stress discrepancies between C35E and CtrlE are a result of the larger CD of COARE 3.5 under stormy conditions (typically U10 > 15 m s−1; Fig. 10b). Thus, in C35E, the wind stress is increased by about 15 mN m−2 over the ACC (Figs. 9b, 4i) and 7.5 mN m−2 in the northern subpolar latitudes. Under extreme storm conditions, the local wind stress computed with COARE 3.5 can deviate from COARE 3.0 by more than 50 mN m−2 (Fig. 4m; not shown). The discrepancies between C35E and CtrlE are smaller in the North Atlantic midlatitudes on account of the lower probabilities of such high wind speeds. We also observe a slight decrease of the mean wind stress in the tropics due to the smaller CD in COARE 3.5 under calm and moderate winds. The associated deficit of momentum flux to the ocean amounts to typically 2 to 3 mN m−2 (Fig. 4i).
In ECE, the larger α under calm to moderate gale conditions (0.018 against 0.011 in CtrlE) leads to a globally uniform increase of the wind stress by 8%–9% on average (Fig. 9b). This results in an increase of the wind stress by +15 mN m−2 over the ACC, decreasing to about 3 mN m−2 at the equator (Fig. 4k).
b. Uncertainties related to the choice of the bulk algorithms
BTCs typically differ by about 10% between the bulk algorithms (Fig. 10). We note here that the COARE and ECMWF algorithms have similar behaviors, so we focus mainly on the discrepancies between the COARE and NCAR algorithms. We address this by performing sensitivity experiment NCE (Table 1), in which the NCAR algorithm (Large and Yeager 2009) is used to compute the BTCs and UB and adjust θ and q from 2 to 10 m. Since the NCAR algorithm is intended to be used with a SST rather than a SSST (section 3d), TASFs are computed with Ts = SST in NCE.
For wind speeds above 5 m s−1, the CD of NCAR is smaller than that of COARE (Fig. 10b). This leads to a substantial reduction of the wind stress in NCE (Fig. 9c): from zero, on average, at the equator down to −30 and −17 mN m−2 over the ACC and northern midlatitudes, respectively (Fig. 4j). Under gale conditions and above (18–28 m s−1), the magnitude of the wind stress computed with COARE is typically 15% larger than that computed with NCAR (Fig. 10b). Therefore, the disagreement between NCE and CtrlE is the highest in the regions with the highest wind speeds, such as the ACC and the North Atlantic storm track (Fig. 9d). Under hurricane force winds, the latest version of the NCAR algorithm (Large and Yeager 2009) constrains CD to remain below 2.4 × 10−3, while in COARE CD increases linearly with the wind speed (Fig. 10b). This can lead to large disagreements between the two algorithms under extreme wind conditions, which are illustrated by the heavy tail in the negative flux discrepancy range of the distribution (Fig. 4n). From calm up to light breeze conditions (U10 < 5 m s−1), the CD of NCAR is larger than that of COARE (Fig. 10b). These conditions occur quite frequently (positive region of the distribution in Fig. 4n), but they have a limited and localized impact on the global wind stress disagreement in the time mean due to the small wind stress magnitudes involved, particularly outside of the tropical band (Fig. 9c).
Differences in evaporation and QT emerge under all but gentle and moderate breeze conditions (3 < U10 < 8 m s−1, depending on the stability) on account of the larger CE (Fig. 10b) and generally larger CH (not shown) in NCAR compared to COARE (Fig. 10b). Under calm conditions (U10 < 3 m s−1), CE and CH are also larger in NCAR than COARE.
The large disagreement in E and QT between NCE and CtrlE (Figs. 5r, 6d) is the consequence of two effects: the enhanced E and |QT| in NCE due to the larger CE and CH of NCAR and the decreased E and |QT| in CtrlE due to the corrected (colder) Ts. Thus, in NCE, QT is decreased by about 12 W m−2 at the equator and 17 W m−2 in the subtropics (E is increased by 0.4 and 0.6 mm day−1, respectively). In the extratropics, the contribution from QH to this difference is not negligible compared to that from QL and accounts for roughly one-fourth of the difference (Fig. 5r). The mean relative differences in QT are about −20% over the ACC and −15% in the northern midlatitudes (Fig. 6d). The absolute differences (not shown) are the largest over the west boundary currents (Gulf Stream and Kuroshio) where the magnitude of QT is large (Fig. 2b). Over the Gulf Stream, daily discrepancies in QT between NCE and CtrlE can locally amount to more than 300 W m−2 in winter. In NCE, the amplitude of the NH seasonal cycle of QT strongly increases compared to CtrlE, with a maximum increase of +18 W m−2 at 40°N (Fig. 5r).
The globally averaged differences in QT and E between NCE and CtrlE are −13 W m−2 and +1.5 Sv. For reference, the total continental freshwater runoff into the ocean is estimated to be about 1.2 Sv (Lagerloef et al. 2010). When the COARE algorithm is used without the skin temperature correction, the agreement with NCAR is better, with the globally averaged differences between NCE and NoSkinE being −6.3 W m−2 and +0.8 Sv.
c. Treatment of calm conditions
In the unstable ASL, as the wind speed approaches zero, the production of TKE is dominated by buoyancy. Under such convective and calm conditions, standard bulk formulas are not suitable as they suggest that evaporation and turbulent heat fluxes tend to zero. These conditions were investigated in laboratory and model studies (Liu et al. 1979; Golitsyn and Grachev 1986; Godfrey and Beljaars 1991; Beljaars 1995), which led to the addition of a parameterized convective gustiness contribution to the wind speed in the bulk algorithm. The COARE and ECMWF algorithms both implement the same convective gustiness scheme. In the NCAR algorithm; however, the zero wind singularity is avoided by simply setting a minimum value for the scalar wind speed of 0.5 m s−1.
To assess the impact of this simpler approach, we perform sensitivity experiment NoGustE (Table 1), similar to CtrlE, but in which very low wind speeds are dealt with using the same approach as in the NCAR algorithm.
The different treatment of extremely weak wind conditions has no significant impacts on the computed wind stress (Figs. 4l,p).
In NoGustE, evaporation is slightly reduced over the global ocean (Figs. 5t,x), especially in the tropics where the associated mean surplus of heat flux is about 1 W m−2 on average. The gustiness correction can significantly enhance UB with respect to |Uz| in the range 0 to a few meters per second (not shown). As a consequence, E is decreased by about 4% over the warm pool and in Indonesia in NoGustE (not shown). Over the global ocean, the implementation of the simple minimum wind speed approach instead of the convective gustiness correction leads to a net warming of +1.1 W m−2 and a deficit of evaporation of 0.14 Sv (Table 1).
5. Summary and concluding remarks
We have quantified the impacts of some common simplifying assumptions and approximations in the parameterization of bulk formulas on climatologies of computed turbulent air–sea fluxes over a series of sensitivity experiments. Estimates of the 3-hourly wind stress, evaporation, and turbulent heat flux were calculated for the period 1982–2014 using the SST and near-surface atmospheric state variables from ERA-Interim. Each sensitivity experiment is evaluated with respect to our control setup, utilizing what we consider as the most consistent and appropriate parameterizations of bulk formulas.
The majority of approximations used in practice have limited impacts on computed flux errors. When tested individually, these errors only amount to about one-quarter of the discrepancies related to the computation of the transfer coefficients themselves. However, when several approximations are implemented together, their summed contributions can lead to significantly larger errors, up to about 20% in the time and zonal mean. For example, the use of constant values for air density and sea level pressure (rather than those instantaneously computed) lead to flux errors of about 5% in subpolar regions when tested individually. These impacts combine when the approximations are implemented together, leading to a 12% increase of the heat flux over the ACC.
The accuracy of the estimate of the saturation-specific humidity (and consequently the temperature) at the air–sea interface is found to be a major source of uncertainty in the computation of the surface turbulent heat flux and evaporation. The air above the sea is generally close to saturation, and, as such, a small error in the estimate of the sea surface saturation–specific humidity was demonstrated to have an outsized and amplifying effect on the evaporation errors. In particular, implementation of the salt-related 2% reduction of the saturation humidity and the consideration of the cool skin and warm layer are each shown to produce uncertainties of about 10 W m−2 and 0.3–0.4 mm day−1 in the tropics, respectively. The use of a simplified formula for the saturation humidity is found to have weaker impacts, leading to errors no larger than 4%. However, large errors are possible when this simplified formula is used together with the constant air density assumption, leading to heat flux errors of +8 W m−2 in the tropics and −5 W m−2 in midlatitudes.
We note that bulk estimates of the wind stress are much less affected by approximations or uncertainties in state parameters than are the evaporation and turbulent heat fluxes. This is due to the more limited dependency of the wind stress on the stability of the atmospheric surface layer.
The largest uncertainties in the estimate of the turbulent air–sea fluxes are related to the computation of the transfer coefficient and therefore to the choice of the bulk algorithm. The comparison between the COARE 3.0 and NCAR algorithms, two widely used algorithms within the ocean community, shows large disagreements. NCAR is found to systematically enhance evaporation with respect to COARE by 12%–15% on average. This results in a mean extra cooling of 13 W m−2 and freshwater loss of 1.5 Sv over the global ocean. This cooling can reach up to 20 to 40 W m−2 over parts of the global ocean, especially in the ACC and in the large upwelling areas of the eastern basins.
These algorithm-related disagreements are found to be primarily linked to the empirical closure used to relate the transfer coefficients to the wind speed in neutral conditions. In particular, it is the value of the Charnock parameter, and more generally it is the value of the parameter that constrains the linear growth of the drag coefficient with the wind speed that matters. Disagreements related to these latter aspects amounts to about 10%, with the NCAR wind stress being notably weaker at latitudes higher than 30° in both hemispheres, especially over the ACC where it reaches −30 mN m−2 in the time and zonal mean. The latest improvements of the COARE algorithm (version 3.5) suggests that the drag coefficient of COARE 3.0 is likely too small in strong wind conditions, which in turn suggests an even greater tendency for NCAR to underestimate the wind stress, typically under gale force winds.
In the absence of surface gravity wave information from observations or wave model output data, such as spectral density, age, height, and steepness, the bulk, empirical, wind speed–dependent closures studied in this paper still represent the best choice for estimating the transfer coefficients. In that sense, the large disagreements between COARE and NCAR highlight the crucial need of further validation of these algorithms with respect to observations and new developments in their formulation. Therefore, efforts to converge on a more universally accepted dependence of the Charnock parameter or the neutral drag coefficient on the wind speed remain key priorities.
Acknowledgments
This study has been made possible by support of the Bolin Centre for Climate Research. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre at Linköping University (NSC). All figures were created with Matplotlib (Hunter 2007). Bernard Barnier is supported by Centre National de la Recherche Scientifique (CNRS). Sergey K. Gulev benefited from Grant 14-17-00697 of the Russian Science Foundation.
APPENDIX A
List of Acronyms and Symbols
Tables A1–A4 list the acronyms, physical parameters, meteorological variables, and bulk variables that were used in this research and are described in this paper.
List of acronyms and their expansions.
Physical parameters used, their values, and their units.
Meteorological variables used and their units.
Bulk variables used and their units.
APPENDIX B
Physical and Empirical Relations
a. Goff–Gratch formula for saturation partial pressure of water vapor
b. Virtual temperatures
c. Heat capacity of moist air
d. Latent heat of vaporization of water
e. Relation between q and e
f. Specific humidity from dewpoint temperature and pressure
g. Relation between α and 
REFERENCES
Andersson, A., C. Klepp, K. Fennig, S. Bakan, H. Grassl, and J. Schulz, 2011: Evaluation of HOAPS-3 ocean surface freshwater flux components. J. Appl. Meteor. Climatol., 50, 379–398, doi:10.1175/2010JAMC2341.1.
Balmaseda, M., and Coauthors, 2015: The Ocean Reanalyses Intercomparison Project (ORA-IP). J. Oper. Oceanogr., 8, s80–s97, doi:10.1080/1755876x.2015.1022329.
Beljaars, A. C. M., 1995: The parametrization of surface fluxes in large-scale models under free convection. Quart. J. Roy. Meteor. Soc., 121, 255–270, doi:10.1002/qj.49712152203.
Beljaars, A. C. M., 1997: Air-sea interaction in the ECMWF model. Proc. ECMWF Seminar on Atmosphere Surface Interaction, Shinfield Park, Reading, England, ECMWF, 33–52. [Available online at http://www.ecmwf.int/sites/default/files/elibrary/1998/8036-air-sea-interaction-ecmwf-model.pdf.]
Bentamy, A., K. B. Katsaros, A. M. Mestas-Nuñez, W. M. Drennan, E. B. Forde, and H. Roquet, 2003: Satellite estimates of wind speed and latent heat flux over the global oceans. J. Climate, 16, 637–656, doi:10.1175/1520-0442(2003)016<0637:SEOWSA>2.0.CO;2.
Berry, D. I., and E. C. Kent, 2005: The effect of instrument exposure on marine air temperatures: An assessment using VOSClim data. Int. J. Climatol., 25, 1007–1022, doi:10.1002/joc.1178.
Berry, D. I., and E. C. Kent, 2009: A new air–sea interaction gridded dataset from ICOADS with uncertainty estimates. Bull. Amer. Meteor. Soc., 90, 645–656, doi:10.1175/2008BAMS2639.1.
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, doi:10.1175/1520-0485(1985)015<0650:VOBDSF>2.0.CO;2.
Blanc, T. V., 1986: The effect of inaccuracies in weather-ship data on bulk-derived estimates of flux, stability and sea-surface roughness. J. Atmos. Oceanic Technol., 3, 12–26, doi:10.1175/1520-0426(1986)003<0012:TEOIIW>2.0.CO;2.
Blanc, T. V., 1987: Accuracy of bulk-method-determined flux, stability, and sea surface roughness. J. Geophys. Res., 92, 3867–3876, doi:10.1029/JC092iC04p03867.
Bourras, D., 2006: Comparison of five satellite-derived latent heat flux products to moored buoy data. J. Climate, 19, 6291–6313, doi:10.1175/JCLI3977.1.
Bradley, F., and C. Fairall, 2007: A guide to making climate quality meteorological and flux measurements at sea. NOAA/ESRL/PSD Tech. Memo. OAR PSD-311, 108 pp.
Brodeau, L., B. Barnier, A.-M. Treguier, T. Penduff, and S. Gulev, 2010: An ERA40-based atmospheric forcing for global ocean circulation models. Ocean Modell., 31, 88–104, doi:10.1016/j.ocemod.2009.10.005.
Brunke, M. A., X. Zeng, and S. Anderson, 2002: Uncertainties in sea surface turbulent flux algorithms and data sets. J. Geophys. Res., 107, 3141, doi:10.1029/2001JC000992.
Brunke, M. A., C. W. Fairall, X. Zeng, L. Eymard, and J. A. Curry, 2003: Which bulk aerodynamic algorithms are least problematic in computing ocean surface turbulent fluxes? J. Climate, 16, 619–635, doi:10.1175/1520-0442(2003)016<0619:WBAAAL>2.0.CO;2.
Brunke, M. A., Z. Wang, X. Zeng, M. Bosilovich, and C.-L. Shie, 2011: An assessment of the uncertainties in ocean surface turbulent fluxes in 11 reanalysis, satellite-derived, and combined global datasets. J. Climate, 24, 5469–5493, doi:10.1175/2011JCLI4223.1.
Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639–640, doi:10.1002/qj.49708135027.
Chou, S.-H., E. Nelkin, J. Ardizzone, and R. M. Atlas, 2004: A comparison of latent heat fluxes over global oceans for four flux products. J. Climate, 17, 3973–3989, doi:10.1175/1520-0442(2004)017<3973:ACOLHF>2.0.CO;2.
Dawe, J. T., and L. Thompson, 2006: Effect of ocean surface currents on wind stress, heat flux, and wind power input to the ocean. Geophys. Res. Lett., 33, L09604, doi:10.1029/2006GL025784.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
ECMWF, 2014: IFS documentation—Cy40r1. Operational implementation 22 November 2013. Part IV: Physical processes. ECMWF, accessed April 2016. [Available online at http://www.ecmwf.int/sites/default/files/IFS_CY40R1_Part4.pdf.]
Edson, J. B., and Coauthors, 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr., 43, 1589–1610, doi:10.1175/JPO-D-12-0173.1.
Eymard, L., and Coauthors, 1999: Surface fluxes in the North Atlantic Current during CATCH/FASTEX. Quart. J. Roy. Meteor. Soc., 125, 3563–3599, doi:10.1002/qj.49712556121.
Fairall, C. W., E. F. Bradley, J. S. Godfrey, G. A. Wick, J. B. Edson, and G. S. Young, 1996a: Cool-skin and warm-layer effects on sea surface temperature. J. Geophys. Res., 101, 1295–1308, doi:10.1029/95JC03190.
Fairall, C. W., E. F. Bradley, D. P. Rogers, J. B. Edson, and G. S. Young, 1996b: Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment. J. Geophys. Res., 101, 3747–3767, doi:10.1029/95JC03205.
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, doi:10.1175/1520-0426(1997)014<0338:ISMOTM>2.0.CO;2.
Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571–591, doi:10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.
Fairall, C. W., and Coauthors, 2011: Implementation of the Coupled Ocean-Atmosphere Response Experiment flux algorithm with CO2, dimethyl sulfide, and O3. J. Geophys. Res., 116, C00F09, doi:10.1029/2010JC006884.
Ferry, N., and Coauthors, 2012: NEMO: The modelling engine of global ocean reanalyses. Mercator Ocean Quarterly Newsletter, No. 46, Mercator Ocean, Ramonville-Saint-Agne, France, 46–59.
Garratt, J. R., 1992: The Atmospheric Boundary Layer. Cambridge University Press, 316 pp.
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, 22 043–22 048, doi:10.1029/91JC02015.
Goff, J. A., 1957: Saturation pressure of water on the new kelvin temperature scale. Transactions of the American Society of Heating and Ventilating Engineers, Murray Bay, Quebec, Canada, American Society of Heating and Ventilating Engineers, 347–354.
Golitsyn, G. S., and A. A. Grachev, 1986: Free convection of multi-component media and parameterization of air-sea interaction at light winds. Ocean–Air Interact., 1, 57–78.
Griffies, S. M., and Coauthors, 2009: Coordinated Ocean-Ice Reference Experiments (COREs). Ocean Modell., 26, 1–46, doi:10.1016/j.ocemod.2008.08.007.
Grodsky, S. A., A. Bentamy, J. A. Carton, and R. T. Pinker, 2009: Intraseasonal latent heat flux based on satellite observations. J. Climate, 22, 4539–4556, doi:10.1175/2009JCLI2901.1.
Gulev, S., and K. Belyaev, 2012: Probability distribution characteristics for surface air–sea turbulent heat fluxes over the global ocean. J. Climate, 25, 184–206, doi:10.1175/2011JCLI4211.1.
Gulev, S., T. Jung, and E. Ruprecht, 2007: Estimation of the impact of sampling errors in the VOS observations on air–sea fluxes. Part I: Uncertainties in climate means. J. Climate, 20, 279–301, doi:10.1175/JCLI4010.1.
Hare, J. E., P. O. G. Persson, C. W. Fairall, and J. B. Edson, 1999: Behavior of Charnock’s relationship for high wind conditions. Proc. 13th Symp. on Boundary Layers and Turbulence, Dallas, TX, Amer. Meteor. Soc., 252–255.
Holdsworth, A. M., and P. G. Myers, 2015: The influence of high-frequency atmospheric forcing on the circulation and deep convection of the Labrador Sea. J. Climate, 28, 4980–4996, doi:10.1175/JCLI-D-14-00564.1.
Hunter, J. D., 2007: Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 9, 90–95, doi:10.1109/MCSE.2007.55.
Josey, S. A., E. C. Kent, and P. K. Taylor, 1999: New insights into the ocean heat budget closure problem from analysis of the SOC air–sea flux climatology. J. Climate, 12, 2856–2880, doi:10.1175/1520-0442(1999)012<2856:NIITOH>2.0.CO;2.
Josey, S. A., S. Gulev, and L. Yu, 2013: Exchanges through the ocean surface. Ocean Circulation and Climate: A 21st Century Perspective, G. Siedler et al., Ed., International Geophysics Series, Vol. 103, Academic Press, 115–140, doi:10.1016/B978-0-12-391851-2.00005-2.
Josey, S. A., L. Yu, S. Gulev, X. Jin, N. Tilinina, B. Barnier, and L. Brodeau, 2014: Unexpected impacts of the tropical Pacific array on reanalysis surface meteorology and heat fluxes. Geophys. Res. Lett., 41, 6213–6220, doi:10.1002/2014GL061302.
Lagerloef, G., R. Schmitt, J. Schanze, and H.-Y. Kao, 2010: The ocean and the global water cycle. Oceanography, 23, 82–93, doi:10.5670/oceanog.2010.07.
Large, W. G., and S. Pond, 1981: Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr., 11, 324–336, doi:10.1175/1520-0485(1981)011<0324:OOMFMI>2.0.CO;2.
Large, W. G., and S. Pond, 1982: Sensible and latent heat flux measurements over the ocean. J. Phys. Oceanogr., 12, 464–482, doi:10.1175/1520-0485(1982)012<0464:SALHFM>2.0.CO;2.
Large, W. G., and S. G. Yeager, 2004: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR Tech. Note NCAR/TN-460+STR, 112 pp. [Available online at http://www.clivar.org/sites/default/files/documents/Large%26Yeager2004.pdf .]
Large, W. G., and S. G. Yeager, 2009: The global climatology of an interannually varying air–sea flux data set. Climate Dyn., 33, 341–364, doi:10.1007/s00382-008-0441-3.
Large, W. G., G. Danabasoglu, S. C. Doney, and J. C. McWilliams, 1997: Sensitivity to surface forcing and boundary layer mixing in a global ocean model. J. Phys. Oceanogr., 27, 2418–2447, doi:10.1175/1520-0485(1997)027<2418:STSFAB>2.0.CO;2.
Liu, W. T., K. B. Katsaros, and J. A. Businger, 1979: Bulk parameterization of air–sea exchanges of heat and water vapor including the molecular constraints at the interface. J. Atmos. Sci., 36, 1722–1735, doi:10.1175/1520-0469(1979)036<1722:BPOASE>2.0.CO;2.
Luo, J.-J., S. Masson, E. Roeckner, G. Madec, and T. Yamagata, 2005: Reducing climatology bias in an ocean–atmosphere CGCM with improved coupling physics. J. Climate, 18, 2344–2360, doi:10.1175/JCLI3404.1.
Ma, X., P. Chang, R. Saravanan, D. Wu, X. Lin, L. Wu, and X. Wan, 2015a: Winter extreme flux events in the Kuroshio and Gulf Stream extension regions and relationship with modes of North Pacific and Atlantic variability. J. Climate, 28, 4950–4970, doi:10.1175/JCLI-D-14-00642.1.
Ma, X., and Coauthors, 2015b: Distant influence of Kuroshio eddies on North Pacific weather patterns? Sci. Rep., 5, 17 785, doi:10.1038/srep17785.
Miller, M. J., A. C. M. Beljaars, and T. N. Palmer, 1992: The sensitivity of the ECMWF model to the parameterization of evaporation from the tropical oceans. J. Climate, 5, 418–434, doi:10.1175/1520-0442(1992)005<0418:TSOTEM>2.0.CO;2.
Minobe, S., A. Kuwano-Yoshida, N. Komori, S.-P. Xie, and R. J. Small, 2008: Influence of the Gulf Stream on the troposphere. Nature, 452, 206–209, doi:10.1038/nature06690.
Moore, G., R. Pickart, I. Renfrew, and K. Våge, 2014: What causes the location of the air-sea turbulent heat flux maximum over the Labrador Sea? Geophys. Res. Lett., 41, 3628–3635, doi:10.1002/2014GL059940.
Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteor., 9, 857–861, doi:10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2.
Small, R., and Coauthors, 2008: Air–sea interaction over ocean fronts and eddies. Dyn. Atmos. Oceans, 45, 274–319, doi:10.1016/j.dynatmoce.2008.01.001.
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, 15 467–15 472, doi:10.1029/JC093iC12p15467.
Sud, Y. C., and G. K. Walker, 1997: Simulation errors associated with the neglect of oceanic salinity in an atmospheric GCM. Earth Interact., 1, 1–19, doi:10.1175/1087-3562(1997)001<0001:SEAWTN>2.3.CO;2.
Sverdrup, H. U., M. W. Johnson, and R. H. Fleming, 1942: The Oceans, Their Physics, Chemistry, and General Biology. Prentice-Hall, 1087 pp.
Tilinina, N., S. K. Gulev, I. Rudeva, and P. Koltermann, 2013: Comparing cyclone life cycle characteristics and their interannual variability in different reanalyses. J. Climate, 26, 6419–6438, doi:10.1175/JCLI-D-12-00777.1.
Tomita, H., and M. Kubota, 2011: Sampling error of daily mean surface wind speed and air specific humidity due to sun-synchronous satellite sampling and its reduction by multi-satellite sampling. Int. J. Remote Sens., 32, 3389–3404, doi:10.1080/01431161003749428.
Visbeck, M., E. Chassignet, R. Curry, T. Delworth, B. Dickson, and G. Krahmann, 2003: The ocean’s response to North Atlantic Oscillation variability. The North Atlantic Oscillation: Climatic Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 113–145.
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, doi:10.1002/qj.49710644707.
Wells, N. C., 1996: Atmosphere–ocean interaction. by E. B. Kraus and J. A. Businger. Oxford University Press. 2nd edition, 1995. Pp. 362. Price £52.50 (hardback) ISBN 0 19 506618 9. Quart. J. Roy. Meteor. Soc., 122, 324–325, doi:10.1002/qj.49712252914.
Yelland, M., and P. K. Taylor, 1996: Wind stress measurements from the open ocean. J. Phys. Oceanogr., 26, 541–558, doi:10.1175/1520-0485(1996)026<0541:WSMFTO>2.0.CO;2.
Yu, L., X. Jin, and R. A. Weller, 2008: Multidecade global flux datasets from the objectively analyzed air-sea fluxes (OAFlux) project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution OAFlux Project Tech. Rep., 64 pp.
Zeng, X., and A. Beljaars, 2005: A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophys. Res. Lett., 32, L14605, doi:10.1029/2005gl023030.
Zeng, X., M. Zhao, and R. E. Dickinson, 1998: Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J. Climate, 11, 2628–2644, doi:10.1175/1520-0442(1998)011<2628:IOBAAF>2.0.CO;2.
Zolina, O., and S. K. Gulev, 2003: Synoptic variability of ocean–atmosphere turbulent fluxes associated with atmospheric cyclones. J. Climate, 16, 2717–2734, doi:10.1175/1520-0442(2003)016<2717:SVOOTF>2.0.CO;2.
Available online at http://aerobulk.sourceforge.net/.
Available online at http://www.nemo-ocean.eu/.
Corresponds to the density of a parcel of air at 20°C, 85% of humidity, and a pressure of 1010 hPa.
Information about GLORYS is available online at https://www.mercator-ocean.fr/en/science-publications/glorys/.