Ty pCDUyU2 Uy12

where ux and uy are the zonal and meridional components of the wind speed respectively, and CD is the drag coefficient which depends upon the height of the wind measurement and the atmospheric stability as well as wave characteristics (e.g. Smith 1988; Taylor and Yelland 2001).

Climatological analyses of the wind stress using these formulae with ship meteorological reports have been carried out in a number of studies (e.g. Hellerman and Rosenstein 1983; Harrison 1989; Josey et al. 2002). More recently various satellite products have become available which avoid the sampling issues inherent with ship observations but are restricted to the past decade or so, for example microwave scat-terometer measurements made by QuikSCAT (http://winds.jpl.nasa.gov/).

The climatological annual mean wind stress field from the NOC1.1 flux dataset is shown in Fig. 6.4. The figure reveals patterns associated with subtropical and subpolar gyres, the ITCZ and the band of intense westerly wind stress in the Southern

NOC1.1 Wind Stress - Annual Mean (N m2)

NOC1.1 Wind Stress - Annual Mean (N m2)

80 70 60 50 40 30

Longitude

Fig. 6.4 Climatological annual mean wind stress, source NOC1.1 climatology, units N m-2, Josey et al. 2002. Colours show the magnitude of the wind stress vectors. (Modified version of figure in Josey et al. (2002), copyright American Meteorological Society)

80 70 60 50 40 30

Longitude

Fig. 6.4 Climatological annual mean wind stress, source NOC1.1 climatology, units N m-2, Josey et al. 2002. Colours show the magnitude of the wind stress vectors. (Modified version of figure in Josey et al. (2002), copyright American Meteorological Society)

Ocean. The curvature of the wind stress field is a measure of local upwelling and downwelling and its integral at a given latitude provides a measure of the strength of the wind driven circulation via the Sverdrup transport (for further discussion of these fields with reference to the NOC1.1 climatology see Josey et al. 2002).

6.2.5 Freshwater Flux

The air-sea freshwater flux is simply the difference between evaporation lost from the ocean surface and precipitation gained by the ocean from the atmosphere, often written E-P (i.e. evaporation-precipitation). It is linked to the net heat flux as the evaporation term corresponds to the latent heat flux component of the net heat exchange discussed above. Estimates of the evaporation are available from ship-based flux datasets, atmospheric model reanalyses and satellite measurements. Various precipitation products are available from satellites (Gulev et al. 2009), for example as a result of the Global Precipitation Climatology Project Version 2 (GPCPv2, Adler et al. 2003). However, there are significant regional differences between the various products and as a consequence precipitation is the least well determined surface exchange field. Atmospheric model reanalyses also provide precipitation but here care must be taken as unphysical trends have been observed in some areas, particularly for the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis in the Tropics. Precipitation is difficult to measure directly at sea (Weller et al. 2008) but may be estimated from present weather codes in voluntary observing ship meteorological reports (via limited historical calibration against island station rain measurements) and was included in the NOC1.1 flux dataset (Josey et al. 1999). However, further work is needed before this method can be reliably used for climate studies.

6.2.6 Density Flux

The combined impact of the net heat flux and evaporation on the buoyancy of water in the sea surface layer may be expressed in terms of the density flux. The total density flux, Fp, into the ocean surface is given by the following equation,

where P is the density of water at the sea surface; cp, the specific heat capacity of water; S, the sea surface salinity and a and p, the thermal expansion and haline contraction coefficients which are defined as follows,

The density flux is frequently split into thermal, FT, and haline, FS, contributions defined as follows,

where,

Heat loss from the ocean (QNet < 0) and net evaporation (E > P) then result in positive values for FT and FS respectively and an increase in the density of the near surface layer. The thermal term usually dominates the density flux with the haline term playing only a minor role (e.g. Josey 2003; Grist et al. 2007) except at high latitudes.

6.3 Air-Sea Flux Datasets

The three primary sources of information regarding air-sea fluxes are surface meteorology reports (mainly from Voluntary Observing Ships), satellite observations and atmospheric model reanalyses which assimilate various data types. All three sources have been employed with the bulk formulae (Eqs. 6.1 and 6.2) to estimate the latent and sensible heat fluxes given a knowledge of the surface meteorology. The radiative fluxes have been determined either from empirical formulae, of the type described in the previous section, or from radiative transfer models.

Many air-sea flux datasets have been developed over the past four decades. For example, the pioneering effort of Bunker (1976) relied on merchant ship meteorological reports, while in recent years satellite observations and output from numerical weather prediction models have been combined in new hybrid products (e.g. Yu and Weller 2007). The first flux datasets comprised climatological monthly fields of ether the full set or a subset of the heat, momentum and freshwater fluxes typically based on observations spanning many decades. In the 1990s, several analysis efforts continued to focus on producing climatological fields and addressing specific scientific problems—principally achieving closure of the global ocean heat budget—but in addition provided the individual monthly fields on which the climatologies were based (da Silva et al. 1994; Josey et al. 1998). In recent years, climatological fields have taken a back seat and several new flux products contain fields at daily times-cales as well as monthly. This tendency has been driven, in part, by the high time resolution possible with the atmospheric reanalyses and the need to include high frequency variability in forcing fields for ocean model runs. A full survey of the wide range of methods used to produce flux datasets and the details of the underlying observing system is beyond the scope of the current paper. Instead an overview of the main classes of flux datasets is presented and the interested reader is referred to WGASF (2000) and Gulev et al. (2009) for further details.

6.3.1 In Situ Observation Based Fields

The only source of information regarding air-sea fluxes for many years were routine merchant ship meteorological reports collected under the Voluntary Observing Ships (VOS) programme and collated to form the Comprehensive Ocean-Atmosphere Dataset (COADS, Woodruff et al. 1987) which has now become International COADS (Worley et al. 2005, 2009). Estimates of the various surface heat flux components were obtained either from individual surface meteorology reports, or from monthly averaged values of the key variable such as wind speed (although this has the potential to lead to biases as a result of neglected correlations between the different variables, Josey et al. 1995) using formulae of the type discussed in Sect. 6.2. The resulting flux estimates are then combined using various averaging and interpolation techniques to form gridded fields. Two widely used flux products developed using this approach have been the UWM/COADS dataset of da Silva et al. (1994) and the National Oceanography Centre 1.1 (NOC1.1) flux dataset (Josey et al. 1998, 1999—formerly termed the Southampton Oceanography Centre (SOC) flux climatology), recently revised using optimal interpolation (NOC2, Berry and Kent 2009) to include error estimates (Kent and Berry 2005).

The major problem with ship based flux datasets is the uneven distribution of meteorological reports, which are heavily concentrated along the major shipping routes, leading to significant undersampling of the required fields in many regions—including much of the Southern Hemisphere (for example see Fig. 6.2 of Josey et al. 1999). This is likely to have played a major role in the ocean heat budget closure problem which has affected to a certain extent all flux datasets produced to date and is manifest as a 20-30 W m-2 global mean net ocean heat gain while in reality the budget should be closed to of order 1 W m-2 at decadal and longer timescales. We will return to this issue in Sect. 6.5.1 but note here that several flux datasets have achieved closure by applying inverse analysis techniques with hydrographic observations of ocean heat transport as constraints (e.g. the NOC1.1a fields described in Grist and Josey (2003) which are an adjusted, globally balanced version of the original NOC1.1 climatology).

A further issue with ship based fluxes is the diverse range of instrumentation types used for making the routine meteorological measurements (e.g. air temperature, specific humidity) under the VOS programme. Each sensor type has its own error characteristics that need to be determined in order to correct for biases prior to determining the fluxes (e.g. Josey et al. 1999). A recent development, targeted at reducing these errors is the VOS Climate Project (VOSCLIM) originally suggested by Taylor et al. (2001). One of the goals of this project is to provide a high-quality VOS data subset that can be used to better calibrate the VOS fleet as a whole. A further initiative, the Shipboard Automated Meteorological and Oceanographic System (SAMOS, Smith et al. 2010), seeks to collect high quality meteorological and flux measurements from research ships and provide these as a resource which may be used for better determination of biases in both the VOS measurements and other flux products (e.g. the reanalyses). SAMOS has focused on data obtained from the

US research ships but provides an example which, if applied internationally, would create an even more valuable resource.

6.3.2 Remotely Sensedfluxes

Remote sensing is now capable of providing observations of some of the key air-sea flux terms and has the major advantage over ship based estimates of essentially complete global coverage. However, satellite estimates suffer because it is not yet possible to reliably measure near surface air temperature and humidity directly from space. Indirect techniques must be used instead and this leads to a major source of uncertainty in the turbulent heat flux terms which are critically dependent on the sea-air temperature and humidity difference. Estimates of the radiative flux terms are available from various sources, most recently from the Moderate Resolution Imaging Spectro-radiometer (MODIS, e.g. Pinker et al. 2009) and have been combined with indirect estimates of the turbulent fluxes to form net heat flux products; a recent example is the Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite Data version 3 (HOAPS3, Andersson et al. 2010). However, significant uncertainties remain in such net heat flux fields because of problems with determining the latent and sensible heat flux.

In contrast, to the net heat flux, the wind stress is now well determined as a result of QuikSCAT although there are concerns as to whether this will remain the case in the near future given the likely imminent demise of this mission. Precipitation has also been determined using various techniques including infrared measurements of cloud top brightness temperature, which acts as a proxy for rain rate, and passive microwave measurements. Such estimates have been combined under the Global Precipitation Climatology project (GPCP) to form best estimates of the rainfall (CPCPv2, Adler et al. 2003). However, validation of these fields over the ocean is challenging due to the lack of high quality measurements from rain sensors and the difficulty with making this measurement (e.g. Weller et al. 2008). As a consequence, major uncertainty remains in the precipitation fields with knock-on effects for attempts to estimate the air-sea freshwater flux (E-P).

6.3.3 Atmospheric Model Reanalyses

Numerical weather prediction models assimilate a wide range of observations including surface meteorological reports, radiosonde profiles and remote sensing measurements. In recent decades, these models have had the potential to provide the complete set of air-sea flux fields at high (6 hourly) resolution with full spatial coverage. However, they are of course dependent on the model physics which, although constrained to some extent by the assimilated observations, has the potential to produce large biases, particularly in the radiative flux fields and precipitation (e.g. Trenberth et al. 2009). Fixed versions of the models run over multidecadal pe riods are commonly referred to as atmospheric reanalyses—the two major products being those from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) and ECMWF.

For the reanalyses, the turbulent flux terms are again estimated from the model surface meteorology fields while the shortwave and longwave flux are output from the radiative transfer component of the atmospheric model. To date, available reanalyses have been on a relatively coarse grid on the order of 1.5-2°. However, higher resolution reanalyses are anticipated in the near-term which will, for the first time, assimilate radiance measurements from satellites. There are hopes that these new products will contain smaller biases than those currently available (Trenberth et al. 2009).

6.3.4 Other Flux Products

In addition to the three primary classes of flux dataset described above, flux fields are available from several other types of products. The leading example here is the Objectively Analyzed air-sea Fluxes (OAFLUX) dataset (Yu and Weller 2007) which blends reanalysis and satellite surface meteorology fields prior to estimation of the fluxes, but still suffers from being unable to close the global ocean heat budget. A further product, combining reanalysis and satellite measurements, is the Common Ocean Reference Experiment (CORE) flux dataset (Large and Yeager 2009) which has been designed to provide forcing fields for ocean models. This requires closure of the ocean heat budget and this has been achieved via adjustments to several of the underlying fields which, although plausible, are not the result of comprehensive analysis. Thus this product must be regarded as a possible solution to the closure problem rather than necessarily being the correct solution.

The climatological annual mean net air-sea heat flux field for the mid-latitude North Atlantic from four different flux products (including OAFLUX) is illustrated in Fig. 6.5. The same broad scale pattern is observed for each dataset with strong heat loss over the Gulf Stream and a transition towards ocean heat gain from west to east. The NCEP/NCAR fields tend to have stronger heat loss than the other 3 datasets considered and this is partly due to use of a transfer coefficient scheme which results in high values that are not supported by observational analyses. NOC1.1, NOC2 and OAFLUX all show similar results for the location of the zero net heat flux line which extends from south-west to north-east across the basin.

Surface fluxes are also available from various ocean synthesis efforts, that is ocean models with data assimilation such as the Estimating the Circulation and Climate of the Ocean (ECCO) model. These are typically forced by NCEP or ECMWF reanalysis fields which are then adjusted as a result of the assimilation process. For the ECCO model, in some regions, comparisons against independent measurements suggest the resulting fields may be an improvement over the original forcing data (Stammer et al. 2004). However, there remains a high degree of divergence between the different ocean model syntheses, and although this method holds some promise, it is not yet at the stage where it can provide reliable estimates of the surface

NCEP/NCAR NOC1.1

NCEP/NCAR NOC1.1

- 80 - 60 -40 - 20 0 20 40 - 80 - 60 -40 - 20 0 20 40

NOC2 OAFLUX

- 80 - 60 -40 - 20 0 20 40 - 80 - 60 -40 - 20 0 20 40

NOC2 OAFLUX

- 80 - 60 - 40 - 20 0 20 40 - 80 - 60 - 40 - 20 0 20 40

Fig. 6.5 Annual mean net air-sea heat flux from (a) NCEP/NCAR, (b) NOC1.1, (c) NOC2 and (d) OAFLUX for the common period 1984-2004, units W m-2. Blue colours indicate ocean heat loss to the atmosphere, red indicate ocean heat gain

- 80 - 60 - 40 - 20 0 20 40 - 80 - 60 - 40 - 20 0 20 40

Fig. 6.5 Annual mean net air-sea heat flux from (a) NCEP/NCAR, (b) NOC1.1, (c) NOC2 and (d) OAFLUX for the common period 1984-2004, units W m-2. Blue colours indicate ocean heat loss to the atmosphere, red indicate ocean heat gain exchanges. Finally, the so-called residual method obtains the net surface heat flux as the residual of top of the atmosphere heating, measured by satellites, and the atmospheric heat divergence obtained from reanalysis (e.g. Trenberth and Caron 2001). This method has the potential to provide a valuable complementary estimate of the net heat exchange (but not of course the individual components). However, it is dependent on the accuracy of the atmospheric reanalysis which as noted above requires improvement.

Each of these classes of flux product has its own advantages and disadvantages and it is not possible to recommend a best flux product; rather, the choice of flux dataset must be guided by the scientific issue which is to be addressed.

6.4 Methodology for Evaluating Surface Fluxes

The discussion above has provided some indication of the diverse range of air-sea flux datasets that are now available for the community to use. All of these are limited in some manner by spatially and temporally dependent biases and it is therefore vital that each new flux dataset is properly evaluated against a range of independent measures in order to quantify these biases and understand their causes. Historically, this has not been the case, partly because of a lack of reference data. This issue has been recognised for some time, in particular Josey and Smith (2006) developed a methodology for evaluation of air-sea heat, freshwater and momentum flux datasets in response to a recommendation of the CLIVAR Global Synthesis and Observation Panel (GSOP). The panel recognised the need for such guidelines in order to facilitate consistent evaluation and intercomparison of the many new flux datasets being developed (particularly those from ocean reanalyses). The methodology makes use of both research quality data from flux buoys and research vessels (local evaluation) and large scale constraints (regional and global evaluations).

For clarification of terminology, Josey and Smith (2006) defined two main classes of flux dataset. The first consists of the large scale 'gridded flux datasets' (typically at spatial resolutions of order 1° and timescales from 6 hourly to monthly) produced from in situ, model or remote sensing sources, or some combination thereof. The second class of datasets was termed 'research quality data', most of which are in-situ point measurements (for example radiative fluxes and meteorological variables from research buoys/vessels) at high temporal resolution (typically available as averages on timescales of order minutes).

In summary, their key evaluation points are as follows:

a. Local evaluation of time averaged fluxes and meteorological variables at specific grid locations with corresponding research quality data from surface flux reference moorings and vessels.

b. Regional evaluation of either gridded flux product ocean transports or, preferably, area averaged fluxes with corresponding research quality data from hydro-graphic sections.

c. Global evaluation of gridded flux product area weighted mean fluxes through closure of the appropriate property budget within observational constraints.

They noted several difficulties in implementing this method including the lack of a central archive of heat and freshwater transports required for point b. This remains a problem at present and the creation of such an archive would be highly desirable for flux evaluation studies. Despite these problems this methodology has been adopted to some extent in recent studies particularly for the OAFlux and CORE products (Yu and Weller 2007; Large and Yeager 2009). Evaluations of flux products in specific air-sea interaction regimes using flux reference buoys are becoming more common practice as the global distribution of such buoys increases, fostered through the OceanSITES programme (Send et al. 2009). A recent example is an evaluation of the new satellite based J-OFURO2 flux dataset using two moorings in the Kuroshio region of the north-west Pacific Ocean (Tomita et al. 2010).

6.5 Surface Fluxes in the Global Climate System

6.5.1 The Implied Ocean Heat Transport and the Closure Problem

The excess of heat gain over heat loss in the Tropics, as revealed in the net heat flux spatial field (Fig. 6.2), requires that the oceans transport energy away from the equator and towards the poles. Evidence for this latitudinal variation is provided by direct estimates of the ocean heat transport from hydrographic sections, which were collected in significant numbers for the first time as part of the World Ocean Circulation Experiment (WOCE); this variation is illustrated by the crosses in Fig. 6.6. In addition to the direct estimates of the heat transport, indirect estimates, H^, may be obtained by integrating the net heat flux, QN, across successive latitude bands from a reference latitude yo which has a known value of the heat transport, Ho, from hydrography,

9o A2

9 Al where M and k2 are the longitude limits at the western and eastern continental boundaries respectively of a given latitude band. The general form for this equation includes a term that accounts for heat storage by the ocean. However, as heat storage is relatively small at multi-decadal timescales, the storage term may be set equal to zero for calculating the implied climatological transport.

Taking this approach, the implied ocean heat transports obtained with a range of surface flux datasets for the Atlantic, Pacific and Global Oceans are shown in Fig. 6.6. These reveal a peak in the transport values at about 20°N although the details differ between the datasets. In some cases, the hydrography can be used to indicate problems with the surface forcing fields, for example the ECMWF product diverges from hydrography in the southern hemisphere. It should, however,

Fig. 6.6 Climatologically implied ocean heat transport derived by integrating the net heat surface flux southwards from 65°N. Key: ECMWF—Dash-dot red; Large and Yeager (2009)—Dashed blue; NCEP—Dashed magenta; NOC1.1a—Solid black; Trenberth residual—Dashed black; UWM/COADS—Solid grey. The crosses with error bars represent direct hydrographic estimates of the heat transport—updated version of Fig. 6.9. (In Grist and Josey (2003), copyright American Meteorological Society)

Fig. 6.6 Climatologically implied ocean heat transport derived by integrating the net heat surface flux southwards from 65°N. Key: ECMWF—Dash-dot red; Large and Yeager (2009)—Dashed blue; NCEP—Dashed magenta; NOC1.1a—Solid black; Trenberth residual—Dashed black; UWM/COADS—Solid grey. The crosses with error bars represent direct hydrographic estimates of the heat transport—updated version of Fig. 6.9. (In Grist and Josey (2003), copyright American Meteorological Society)

be noted that all of the flux products shown have been adjusted either directly or indirectly to achieve global closure and this to some extent ensures agreement with the hydrography. In the case of the reanalyses, the values for the transfer coefficients in the turbulent flux formulae are higher than can be supported by observations (e.g. Renfrew et al. 2002). NOC1.1a and UWM/COADS have been made to agree with at least some of the hydrographic values using the technique of inverse analysis, first applied by Isemer et al. (1989). Most recently, the Large and Yeager (2009) fields have been modified using various plausible adjustments as noted earlier. Without such adjustments, the implied ocean heat transport would diverge rapidly from the hydrographic values and this is a manifestation of the more general ocean heat budget problem i.e. the inability to close the global ocean heat budget at decadal timescales to within the 1 W m-2 required to avoid unrealistically large warming signals.

The budget closure problem has been recognised for many years and, despite various advances in our understanding of air-sea interaction, it remains a major issue for both ship based (e.g. NOC1.1 and NOC2) and remote sensing/reanalysis hybrid products (OAFLUX) all of which have global mean net heat flux values in the range 20-30 W m-2. Progress towards resolution of this problem has been limited and it is likely to be the result of the combination of various small biases which amount to 3-5 W m-2 in the global mean. These are likely to include (i) sampling issues revolving around the gross deficit of information on air-sea exchange in the Southern Hemisphere, (ii) missing physics in the high and low wind speed regime applications of the turbulent bulk flux formulae, (iii) a potential fair weather bias i.e. avoidance of high wind regions in merchant ship reports which will affect both in situ climatologies (directly) and reanalyses (indirectly as they rely on surface observations in the data assimilation), (iv) residual biases in ship meteorological reports which have yet to be determined, (v) uncertainty in the empirical formulae used to estimate the radiative fluxes (in situ based fields) and problems with representation of clouds (reanalyses). Only by a careful examination of each of these issues will progress be made towards obtaining an accurate picture of the global ocean-atmosphere heat exchange field. At a time when it is possible to calculate the climate change related signal in the global mean net heat flux to be of order 0.5 W m-2 from observed variations in ocean heat content, it remains a major problem that it is not possible to reliably close the global mean ocean heat budget to better than 20 W m-2.

6.5.2 Climate Change Related Trends in Surface Fluxes

Both observation and model based analyses of changes in the surface air-sea heat flux associated with increasing global ocean heat content have revealed that the anthropogenic climate signal is small compared to natural variability (Pierce et al. 2006; Levitus et al. 2009). Changes in the net surface heat flux over the past 50 years at global and basin scales are expected to be about 0.5 W m-2 with corresponding changes in the individual heat flux components of less than 2 W m-2. Lozier et al. (2008) have examined the spatial pattern of heat-content change in the North Atlantic using historical hydrographic station data from the National Oceanic Data Center World Ocean Database from 1950 to 2000. They find that the total heat gained by the North Atlantic Ocean is equivalent to a basin wide increase in the flux of heat across the ocean surface of 0.4 W m-2. However, they note that it is not possible to say whether this gain is due to anthropogenic warming because natural variability may be masking this signal.

An example of the total net heat flux variability since 1949 from a region in the mid-latitude North Atlantic is given in Fig. 6.7. The figure shows a time series of

1950 1960 1970 1980 1990 2000 2010

Year

Fig. 6.7 Monthly mean net air-sea heat flux anomaly for the box (40-55°N, 20-40°W) from NCEP/NCAR (red), NOC1.1 (green), NOC2 (blue) and OAFLUX (black), units W m-2

the monthly net heat flux anomaly (i.e. with seasonal cycle removed) averaged over an example box (40-55°N, 20-40°W) in the mid-latitude North Atlantic for each of the four flux datasets. Strong month to month variability is evident in the figure with box averaged anomalies often exceeding 50 W m-2. Similar variations are observed in each of the datasets for the periods in which they overlap. To some extent this is to be expected as, despite major differences in analysis methods, observations from Voluntary Observing Ships are a primary source of data for each of the flux products considered. The advent of Argo float data has enabled the study of the role of surface heat flux variability in causing interannual variability in ocean heat content in the North Atlantic in recent years (e.g. Hadfield et al. 2007; Wells et al. 2009). At decadal timescales, the relative roles of ocean heat transport and surface heat flux variations in North Atlantic temperature variability have been examined from an ocean model perspective by Marsh et al. (2008) and Grist et al. (2010).

An intensification of the hydrological cycle is also expected as a result of anthropogenic climate change (e.g. IPCC 2007) with regional impacts on E-P as spatial patterns and the relative intensity of the evaporation and precipitation shift. It is worth noting that changes in evaporation imply a corresponding change in the latent heat flux, the two being related by the following simple equation,

Qe = pole where po is the fresh water density as a function of temperature. Thus, analysis of changes to the evaporation rate using observational datasets also need to take into account the implied change in latent heat flux and use the value obtained as a check on whether the changes in E are physically plausible. This is particularly important as spurious trends in E have the potential arise from time dependent biases in the wind speed.

6.5.3 Relationship to Major Modes of Atmospheric Variability

It is now well recognised that atmospheric variability on a range of timescales may be characterised to a certain extent by various spatial patterns or modes typically expressed in terms of pressure on a given level. These modes have been determined primarily using statistical techniques, such as principal component analysis (Barnston and Livezey 1987) but have also been indexed in some cases via their expres

1950 1960 1970 1980 1990 2000 2010

Year

Fig. 6.7 Monthly mean net air-sea heat flux anomaly for the box (40-55°N, 20-40°W) from NCEP/NCAR (red), NOC1.1 (green), NOC2 (blue) and OAFLUX (black), units W m-2

sion in the surface pressure fields as the difference in pressure anomaly (i.e. actual value-long term mean) between two points (e.g. Hurrell 1995). The leading mode in the Atlantic is the North Atlantic Oscillation (NAO), characterised by variations in the pressure difference between the Azores High and Iceland Low. The NAO has been the subject of numerous studies documenting its influence on a range of oceanic, land and atmospheric physical processes, as well as its influence on ecosystems (see the comprehensive review of Hurrell et al. 2003). Likewise, in the Tropical Pacific the El Nino-Southern Oscillation (ENSO) east-west pattern associated with variations in the strength of the Walker Cell has profound consequences for the ocean and neighbouring land masses. It too has been the subject of intensive research over many decades and received significant attention prior to the discovery of the NAO (Philander 1990). More recently, a north-south variation in the pressure difference between the Southern Ocean and Antarctic landmass has been dubbed the Southern Annular Mode (SAM). Attention here has focused on the strengthening of the SAM index over the past several decades and consequences of the associated southwards displacement of the main westerly wind belt over the Southern Ocean (e.g. Ciasto and Thompson 2008; Boning et al. 2008).

Mode-associated variations in the surface pressure gradient naturally lead to changes in the strength and direction of the wind field, and the source region for the air mass advected over a particular region of ocean (and thus its temperature and humidity characteristics). As discussed previously (Sect. 6.2.2, Eqs. 6.1 and 6.2), the wind speed and near surface air temperature and humidity are the primary variables which establish the strength of the latent and sensible heat loss, hence the leading modes of variability have a clear signature in the surface heat flux (e.g. Josey et al. 2001 for the NAO). The air temperature and humidity also impact the longwave flux (Eq. 6.6), and the change in air mass characteristics can also lead to variations in cloud amount, thus the modes may also impact on both radiative flux terms.

As an example of mode impacts on the wind speed and net surface heat flux, these fields are shown for the two leading modes of variability in the North Atlantic, the aforementioned NAO, and the second mode which is widely termed the East Atlantic Pattern (EAP), in Fig. 6.8. The NAO exhibits the well known north-south dipole in sea level pressure which results in stronger than normal winds from the north-west over the Labrador Sea and heat flux anomalies of up to -80 W m-2 in this region for a unit positive value of the NAO index. Other notable features include enhanced flow of air from the south-east over the Gulf Steam which additional analysis shows to be anomalously warm, reducing the heat loss in this region. The EAP is characterised by a monopole structure in sea level pressure with lower than normal values in the East Atlantic at about 50-55°N. This gives rise to anomalously strong northerly winds in the mid-high latitude western Atlantic and strong heat loss at 45-50°N. Other features may be identified in both plots, and in general these are consistent with the increase in wind speed and change in air temperature expected from the anomalous wind direction. Note that in addition to the leading mode, there may be a further 3 or 4 modes which can be identified as being of importance for understanding the atmospheric variability and its impacts depending on the region considered.

Sep-Mar NAO

Sep-Mar EAP

Sep-Mar NAO

80 80

80 80

Sep-Mar EAP

Fig. 6.8 Composites of the NCEP/NCAR reanalysis net heat flux (coloured field, units W m-2), sea level pressure (contours, intervals 1 mb, negative values solid, zero and positive values dashed) and wind speed (arrows) on winter-centred Climate Prediction Center NAO and EAP values for the period 1958-2006

In addition to the net heat flux, the main modes of variability also have a direct impact on the freshwater flux as the change in the latent heat flux has an equivalent signature in the evaporation field, and variations in the evaporation result in modified precipitation downstream. For example, such variations in E-P associated with the NAO and EAP have been identified by Josey and Marsh (2005) and linked by them to changes in ocean surface salinity. These authors find that much of the multidecadal freshening in the eastern subpolar gyre region of the North Atlantic from the 1960s through to the 1990s can be attributed to a change in the strength of the East Atlantic Pattern (see also Myers et al. 2007 for an extension of this work to the Labrador Sea). Variations in full depth ocean salinity are more difficult to relate to changes in the surface exchanges and this implies a leading role for advective effects (Boyer et al. 2007). The combined effects of heat and freshwater flux anomalies lead to mode-related changes in the surface density flux field (via Eq. 6.10). Such changes have their greatest impact in dense water formation regions, for example at high latitudes in the North Atlantic. Here changes in the surface buoyancy loss associated with the NAO have lead to a multidecadal variation in the location of the dominant site for deep water formation from the Greenland Sea to the Labrador Sea as the NAO shifted from a primarily negative state in the 1960s to a positive state in the 1990s (Dickson et al. 1996, 2008). Finally, as regards mode impacts on the surface exchanges, changes in the wind field have a direct impact on the wind stress (via Eq. 6.9) and thus the wind driven response of the ocean. See for example Josey et al. (2002) who include an analysis of variations in the Ekman transport and wind driven upwelling associated with the NAO as part of a wider study of the wind stress forcing of the ocean.

The brief discussion of mode impacts on high latitude buoyancy loss in the previous section, opens up a wider area, which will be only briefly touched on here, namely the dominant processes controlling dense water formation. Recent work has focused on both the wind- driven preconditioning for such events in the Nordic Seas (Gamiz-Fortis and Sutton 2007) and the role of heat loss (Grist et al. 2007, 2008). Gamiz-Fortis and Sutton (2007) find that doming of isopycnals in response to wind stress curl anomalies and the consequent increase in surface density due to upwelling play a role in dense water formation. Grist et al. (2007, 2008) have studied the impacts of heat flux extrema on Nordic Seas dense water formation and transport through the Denmark Strait in a range of coupled models. They find that heat flux extrema alone are sufficient to trigger new dense water production and find a consistent response across the model considered in terms of the response at the Denmark Strait. An increase in heat loss from -80 to -250 W m-2 results in a strengthening of the dense water transport through the Strait of 1-2 Sv depending on the model considered. Other processes are also expected to play a significant role in the dense water formation, for example exchanges of water with fresher coastal boundary currents which are strongly influenced by Arctic outflows (for a full overview of this complex region see Dickson et al. 2008).

6.6 Unresolved Issues and Conclusion

There are many unresolved issues and areas for future improvement in the field of ocean-atmosphere interaction, including those surrounding the global heat budget closure problem, two particular examples follow.

6.6.1 The Southern Ocean Sampling Problem

Observations that can be used to provide surface latent/sensible heat flux estimates are extremely sparse at high latitudes resulting in large uncertainties in the various flux products in the Southern Ocean. A primary factor here is the lack of the combined set of observations (wind speed, air temperature, surface humidity, sea surface temperature) necessary to estimate these flux terms. This is illustrated in Fig. 6.9 which shows all available surface meteorological reports from the COADS dataset with sufficient information to estimate the latent heat flux over the 5 year period from 2000-2004 in January and July. The situation is most severe in winter when we have essentially no information on this key field for assimilation into reanalyses or generation of in situ flux datasets.

Fig. 6.9 All available surface meteorological reports from the COADS dataset with sufficient information to estimate the latent heat flux over the 5 year period from 2000-2004 in July (leftpanel) and January (rightpanel)
Fig. 6.10 Annual mean net heat flux (units W m 2) from the ECMWF reanalysis for the period 1979-1993

There is a tendency to think of heat exchange in the Southern Ocean as being relatively uniform in a zonal sense when, at least according to the available reanalysis datasets, there is quite a significant amount of zonally asymmetric structure in the surface forcing. For example, the ECMWF annual mean net heat flux (Fig. 6.10) shows heat loss in the SE Pacific at 50-60°S of -20 W m-2, which contrasts with a heat gain of 10-40 W m-2 at the same latitudes in the Atlantic and Indian sectors of the Southern Ocean. How do we go about determining whether this zonal asymmetry is real with existing/future observing systems?

6.6.2 Estimating Meridional Overturning Circulation (MOC) Variability from Surface Fluxes

The surface fluxes of heat and freshwater each act to modify the density of the ocean surface layer via their impact on temperature and salinity. Cooling of the ocean surface and net freshwater loss serve to increase the density as they result in a reduction in temperature and increase of salinity (the converse holds for ocean warming and freshwater gain). The combined effect of the heat and freshwater exchanges can be expressed in terms of the surface density flux (also referred to as the buoyancy flux). Variations in the density flux at high latitudes have potentially significant implications for European climate as they modify the amount of dense water formed in deep convection regions (Grist et al. 2007, 2008) and consequently the overturning circulation of the North Atlantic.

The impact of the air-sea density flux on the amount of water formed in different density classes can be determined using water mass transformation theory (Walin 1982) and these techniques have been employed in many model studies (e.g. Marsh et al. 2005). A modification of this method has been recently used to estimate surface forced variability in the North Atlantic overturning circulation (Grist et al. 2009; Josey et al. 2009). The method has been shown to provide useful estimates of the MOC variability in the range 35-65°N with the HadCM3 coupled climate model and has been applied using NCEP/NCAR reanalysis flux fields to estimate surface forced variability in the mid-high latitude North Atlantic for the past 45 years. The variability of the MOC at latitude 55°N obtained using this technique is shown in Fig. 6.11.

The figure reveals a tendency for an anomalously high overturning circulation, by about 1-2 Sv, from the late 1970s to the late 1990s. This period coincides with the prolonged positive phase of the North Atlantic Oscillation and may indicate that surface forcing associated with this mode plays a significant role in determining the strength of the circulation at this latitude. From 2000 onwards, there is some indication of weakening of the transport which probably reflects natural variability. Further work is planned to refine the method which has the potential to provide valuable complementary information on circulation variability at mid-high latitudes to that obtained from the Rapid mooring array at 26°N.

Year

Fig. 6.11 Reconstruction of the maximum surface forced North Atlantic overturning circulation anomaly (units Sverdrup, 1 Sv = 106 m3s-1) at 55°N using density fluxes determined from the NCEP/NCAR reanalysis. Details of the method are given in Josey et al. (2009), the different lines are estimates based on surface flux fields integrated over 6 years (dash-dot line), 10 years (solid line) and 15 years (dashed line)

Year

Fig. 6.11 Reconstruction of the maximum surface forced North Atlantic overturning circulation anomaly (units Sverdrup, 1 Sv = 106 m3s-1) at 55°N using density fluxes determined from the NCEP/NCAR reanalysis. Details of the method are given in Josey et al. (2009), the different lines are estimates based on surface flux fields integrated over 6 years (dash-dot line), 10 years (solid line) and 15 years (dashed line)

In conclusion, the main aim of this paper has been to provide an overview of the air-sea fluxes of heat, freshwater and momentum focusing on methods used to determine these fluxes and their role in the wider climate system. The intention being to provide a firm basis for future studies which seek to evaluate the importance of air-sea fluxes for operational oceanography. This is a rapidly developing field as highlighted by the other papers in this volume and at present the relative importance of surface fluxes as opposed to other processes in obtaining short range (i.e. up to 1 week) ocean forecasts is a matter of debate and will depend on the region and particular timescales being considered. It is to be expected that surface fluxes will prove key to obtaining reliable forecasts of, for example, ocean mixed-layer depth or density structure. Significant progress in this area is likely over the next few years and will benefit from evaluations of the accuracy of surface flux datasets (in particular from numerical weather prediction models) being carried out in a wider climate context beyond operational oceanography. Developments in the observing network, in particular the advent of Argo and the increasing number of surface flux reference sites, will enable such evaluations. An exciting recent development has been the deployment, for the first time, in March 2010 of a surface flux buoy in the Southern Ocean (http://imos.org.au/sofs.html). Such deployments in regions previously unsampled with high quality surface flux instrumentation promise major advances in our understanding of air-sea interaction processes and a better picture of how transfers across the ocean-atmosphere interface influence the climate system.

Acknowledgements The research summarised here is the result of efforts by a very broad community and I would like to thank the many people with whom I've discussed ocean-atmosphere interaction over the years. In particular, I would like to express my gratitude to Peter Taylor for guiding my thinking through much of my research career and to the UK Natural Environment Research Council for funding much of my research activity. In addition, I am grateful for many helpful comments on the manuscript by the anonymous reviewer and by members of the GODAE Summer School, in particular Cynthia Bluteau and Stephanie Downes.

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