High Latitude Surface Fluxes: Requirements and Challenges for Climate Research

authorship: U.S. CLIVAR High Latitude Flux Working Group plus other contributors

Abstract: to be done last.

1. Introduction

High latitude regions have been marked by rapid climate change in recent years. Perennial sea ice in the Arctic has decreased by at least 20% since the mid-1970s (reference?), the Southern Ocean has warmed (e.g. Gille, 2002, 2008; Boning, 2008), and grounded ice has melted and broken away from the Antarctic continent (e.g. Rignot and Jacobs, 2002; Shepherd et al, 2004; Thoma et al., 2008.) Most Intergovernmental Panel on Climate Change (IPCC) climate models forecast that these high latitude warming patterns are likely to persist at least through the coming two centuries (??, ??). However, the harsh environment of high latitude regions makes in situ monitoring of these changes challenging. This is particularly true of surface fluxes, which are crucial, because they determine how heat, momentum, fresh water, and gases such as CO2 are exchanged between the atmosphere, ocean, and ice. Knowledge of these fluxes must be improved to better understand high latitude surface processes (i.e., the physics behind the changes), and to better parameterize these processes in climate models. This report was coordinated by the US CLIVAR Working Group on High Latitude Fluxes, and it is intended to starting point for community discussion focused on how best to improve surface fluxes at high latitudes.

The observational challenges in measuring fluxes are myriad. High latitude regions are remote, far from ports or major airports, so field programs require daunting logistics, and autonomous instruments cannot easily be serviced. Moreover, winds are among the strongest in the world (e.g. Liu et al. 2008; Renfrew, 2008), so oceanographic instruments must be able to withstand high winds and rough seas, as well as cold temperatures and icing conditions. Flux observations were successfully collected from the Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp, but SHEBA was most successful at characterizing fluxes over year-round sea ice without leads (Perrson et al. 2002), and these are the conditions that appear to be disappearing most rapidly in the Arctic.

We expect that fluxes through an ice-free Arctic Ocean should be distinctly different from fluxes through a high-albedo, ice-covered Arctic Ocean. Although new measurement technologies for the ice-covered ocean have evolved, in part stimulated by the International Polar Year, most of those sensors either stay fixed in the ice and thus fail when the ice melts, or they operate under the ice with instruction to stay well below the surface when they detect ice. Because of their constantly changing ice conditions, marginal sea ice zones that contain ice/water mixes are among the most difficult regions in the world to instrument for year-round flux observations, and fluxes through these regions (Fig. 1) have proved difficult to characterize (reference ??). Moreover, the rapid warming in high latitude regions is amplified by feedbacks associated with (1) the high albedo of polar snow and ice (xxref), and (2) feedbacks between snow melt, temperature, and longwave emission (xx ref). If ice is lost, extra heat can be stored in these regions and remain through winter and reduce ice thickness the following spring, further accelerating the loss of ice.

Even over land there is no practical high latitude flux observing system. In the Arctic, few flux-quality surface instrumentation sites have been established (confirm, reference ??) In the Antarctic, surface meteorological data are used primarily for aviation, and data relevant for assessing fluxes or climate variability are not routinely collected.

In some parts of the world, satellite data and numerical weather prediction (NWP) can provide reasonable estimates of surface fluxes even in the absence of in situ observations, but these products are less successful in high latitude regions because the overwhelming lack of in situ observations means that the satellites are not well calibrated, particularly at high wind speeds. Moreover, few in situ data are available for assimilation into NWP fields. Additionally, parameterizations used in NWP models are rarely validated for polar conditions. As a result, flux products can differ substantially at high latitudes, even in their climatological average, as illustrated in Figure 2.

While a number of new high latitude programs were initiated as part of the 2007-09 International Polar Year, these programs for the most part did not focus on surface fluxes (Southern Ocean GasEx was one notable exception). The objective of this report is two-fold. We describe the current accuracy of flux estimates for momentum, energy, freshwater, and gas fluxes for individual observations. Then we evaluate how these current accuracies compare with the requirements for high latitude fluxes for a range of applications.

2. Approaches to Determining Fields of Fluxes

Surface fluxes fall into three general categories. Radiative fluxes include the shortwave electromagnetic radiation from the sun impinging on the ocean (or ice) surface and the longwave electromagnetic radiation emitted from the surface and from within the atmosphere. Freshwater fluxes measure precipitation and evaporation (i.e., latent heat). And turbulent fluxes measure just about everything else, including momentum, sensible heat, latent heat, and gas exchange.

The surface energy budget is:Rate of Energy Input = Ldown – Lup + (1 – ) Sdown + HS + HL + G (1)

where, Ldown and Lup are the downward and upward long wave fluxes, Sdown is the downward shortwave flux, a is the surface albedo, is the HS sensible heat flux, HL is the latent heat flux, and G is the conductive flux (though snow and ice) or advective transport through water (King and Connolley 1997). For the purposes of determining surface fluxes (from air into water or ice) the transport term is ignored.

Fluxes are usually only observed directly in geographically-limited field programs. The gridded flux fields used for climate research or to force ocean models are inferred by applying flux parameterizations to observations of basic physical variables, such as air-sea temperature difference and surface wind speed. Fields of fluxes have been determined from in situ observa-tions (DaSilva 1994; Legler et al. 19xx; Josey et al. 1998; Bourassa et al. 2005), satellite obser-vations (Grassl et al. 20xx), Numerical weather prediction models (xxrefs), and hybrid combina-tions of these three approaches (Chu et al. 19xx; Bentamy et al. 20xx; Yu and Weller 20xx; Large and Yeager 2008). Each of these products has its own strengths and weaknesses (see Smith et al., 2009). High latitudes are particularly adverse regions for all these products.

a. Basic definitions of fluxes for the purpose of this paper Surface fluxes are, by definition, rates of exchange, per unit surface area, between the

ocean and the atmosphere. Stress is the flux of horizontal momentum. The evaporative moisture flux would be the rate, per unit area, at which moisture is transferred from the ocean to the air.

The latent heat flux (HL) is related to the moisture flux: it is the rate (per unit area) at which energy associated with the phase change of water is transferred from the ocean to the atmosphere. Similarly, the sensible heat flux (HS) is the rate at which thermal energy (associated with heating, but without a phase change) is transferred from the ocean to the atmosphere. Many people are uncomfortable with the interpretation of an energy flux (commonly called heat flux); however, the following example has worked remarkably well with students. A 60Wm-2 flux is the equivalent of the rate of energy release of a 60W light bulb for each square meter of the surface. This is well over ten times the amount of power required to heat or cool a typical large home in an adverse environment (<5Wm-2).

Solar radiation (or shortwave radiation) from the sun in the spectral interval of (0.3-4.0 µm) that reaches the Earth’s surface is the major source of energy for heating our planet. At the mean distance of the earth from the sun (1.50 × 108 km) the incident energy flux density (energy, per unit time, per unit area) on a plane perpendicular to the solar beam is known as the Solar Constant (about 1366 Wm2). At various locations of the earth, the incoming solar radiation is determined by the earth–sun distance, and the angle between the surface and the incoming solar radiation (i.e., the season, latitude and slope of the surface), and the absorption or reflection in the atmosphere. It arrives at the surface as direct solar and diffuse sky radiation due to scattering in the atmosphere. Absorption of solar radiation in the atmosphere is mostly by ozone, water vapor, carbon dioxide, oxygen and clouds.

The absorbed energy in the atmosphere re-radiates back to the surface and to outer space in the form of long-wave (terrestrial) radiation (4.0-100.0 µm). Similarly, energy absorbed at the surface is largely emitted as similar long wave radiation. A small portion of the energy emitted from the surface is radiated into space, but most is absorbed by the atmosphere. The earth climate at the planet surface is a result of the balance the terms in Eq. (2).

b. Differences among easily available products Bourassa, Speer (sea ice zone)There are a handful of freely available flux products which are more commonly used in

studies; however, there are remarkable inconsistencies between the fluxes in these products. On sub-synoptic time scales differences are understandable because of the different representations of storms. However, differences on monthly time scales are also large (Fig. 2) and these are due to differences in parameterization and biases in the input data to these parameterizations (Smith et al. 2009). The differences in fluxes are not simply biases, but different distributions. Finer resolution products combined with more accurate flux parameterizations are likely to be helpful in future analyses.

3. Flux Calculations

High latitude regions have unique challenges for determining fluxes and for gridded fields of fluxes. One problem is a lack of surface and upper air in situ data (Fig. 3). Another is the wide dynamic range in conditions, particularly the wind speed (Fig. 4). Particularly important is the large variability in conditions and rapid changes in conditions. This enormous variability means that much finer temporal sampling is required at high latitudes. Another issue is mixed phase sur-faces with highly heterogeneous characteristics over sea ice, marginal ice zones, and polynas.

a. Radiative FluxesIn polar regions, long wave radiation is a very important component of the surface energy

budget. It is the primary mechanism through solid matter (e.g., ice and snow) lose energy, often resulting in strong atmospheric temperature inversions. Energy emitted from water vapor plays a large role in heating the Earth; however, at high latitudes water vapor amounts are small and not well known. The high albedo of the snow- and ice-covered polar regions together with the large loss of long-wave radiation through the clear and dry atmosphere, result in a net loss of radiation in most months of the year.

1) SPECIFIC ISSUES TO HIGH LATITUDES

Most of the information on LW fluxes at high latitudes comes from measurements over the Arctic and Antarctic. There are almost no observations of radiative fluxes (SW or LW) over high latitude water bodies. Therefore, this section will highlight issues unique to high latitudes as identified from the available observations. Not all of these findings would be applicable over wa-ter; yet, issues related to LW radiation at high latitudes over all type of surfaces have some com-monality.

Less is known about high latitudes clouds than clouds at lower latitudes. Most high latitude clouds are composed of ice and have low optical thickness. It is difficult to quantify their radia-tive properties from space due to issues related to low solar zenith angles and lack of contrast over snow and ice. Even less is known on aerosol effects on high latitude clouds. Recent satellite measurements on aerosol changes in clouds of the Northern Slope of Alaska reveal that enhanced aerosol concentrations alter the micro-physical properties of Arctic clouds in a process known as the "first indirect effect" (Lubin and Vogelmann, 2006). The Arctic region experiences significant influx of anthropogenic aerosols from industrial regions of lower latitudes. The study of Lubin and Vogelmann (2006) found that anthropogenic aerosols lead to an increase of about 3.4 W/m2

in the earth surface long-wave radiation. Therefore, schemes to derive radiative fluxes from satellite observations should account for aerosol effects.

2) SELECTED RESULTS FROM OBSERVATIONS ON LW FLUXES AT HIGH LATITUDES

Pietroni et al. (2008) analyzed sets of long-wave radiation data (incoming and outgoing) from two Antarctic sites at same latitude but at different altitudes. At a coastal site (Halley) for years 2003 and 2004 and at a continental site (Concordia) for 2005. At Halley, during the summer, the net long-wave radiation ranges between -100 W/m2 and 40 W/m2, while in winter it ranges between -40 W/m2 and 30 W/m2. In both cases the peak of the occurrence distribution is around 0 W/m2. At Concordia the long wave radiation distribution of the fluxes shows a plateau between -75 W/m2 and -50 W/m2 in summer and a peak in the occurrence distribution at -25 W/m2 in winters. They conclude that the differences in long wave radiation and long wave occurrence distribution is strongly related to the type and amount of clouds cover observed at each station both in summer and winter.

b. In situ methods and their parameterization of turbulent surface fluxes FairallTurbulent fluxes characterize a major part of ocean-atmosphere exchange. While they may

be measured directly with appropriate sensors placed on a suitable platform over the ocean, most applications require estimates distributed over space and time. The principal use of direct in situ flux observations is to calibrate and validate indirect methods so that fields of fluxes can be de-termined from variables, such as wind speed and sea-surface temperature, that are available on

the required space/time scales. The indirect methods are known as bulk flux algorithms. Bulk flux algorithms have been in use for nearly a century. They now form the basis for the ocean-surface boundary condition in virtually all climate and NWP models, retrieval of turbulent fluxes from satellite observations, and have been used extensively to estimate the heat balance of the oceans from historic weather observations from volunteer observing ships (WCRP 2000). Ad-vances in understanding of the physical processes involved in air-sea exchange and in observing technologies has promoted steady improvements in the sophistication and accuracy of these algo-rithms.

In bulk algorithms the turbulent fluxes are represented in terms of the bulk meteorological variables of mean wind speed, air and sea surface temperature, and air humidity:

1/ 2 1/ 2x d xw x c c S X C S X¢¢= D = D (3)

Where w is the vertical velocity, and the prime indicated a perturbation from the mean; x can be u, v wind components, the potential temperature, , the water vapor specific humidity, q, or some atmospheric trace species mixing ratio. Here cx is the bulk transfer coefficient for the variable x (d being used for wind speed) and Cx is the total transfer coefficient; ΔX is the sea-air difference in the mean value of x, and S is the mean wind speed (relative to the ocean surface). The this rel-ative speed is modified (increased) by including a gustiness term that represents the near-surface wind speed induced by the BL-scale (Schumann, 1988; Godfrey and Beljaars 1991; Fairall et al. 1996a,b); it prevents the transfer coefficients from becoming singular at low wind speeds.

Properly scaled dimensionless characteristics of the turbulence at reference height z are uni-versal functions of a stability parameter, = z / L, defined as the ratio of the reference height z and the Obukhov length scale, L. Thus, the transfer coefficients in (4) have a dependence on sur-face stability prescribed by Monin-Obukhov similarity theory (see Fairall et al. 2003 for details).

1) InstrumentationThe neutral transfer coefficients (or, equivalently, the roughness lengths) are determined by

direct observations of the fluxes and associated mean bulk variables required in (5). Mean bulk variables may be measured with relatively slow response instruments optimized for accurate means. Turbulence variables require a flat frequency response to fluctuations out to about 10 Hz (somewhat dependent on the height of the sensor and the conditions). A variety of techniques have been used to estimate the fluxes (Smith et al. 1996) but the eddy-correlation method is cur-rently considered the standard. In the eddy-correlation method u’ and x’ are measured and the flux is estimated as a simple time average of their products (i.e., '''' xwxw ). The inertial-dissipation method (IDM) has also seen broad application. IDM is based on the high-frequency part of the variance spectrum. Its principal advantage is insensitivity to motion (hence, no mo-tion corrections). However, an empirical stability function is required to obtain the flux so it is not considered an unbiased standard. Velocity turbulence is typically measured with a sonic anemometer or a multiport pressure system; humidity turbulence is usually measured with a fast infrared absorption hygrometer; temperature turbulence is usually obtained from sonic anemometer speed-of-sound or from micro-thermal wires. Aircraft and ship platforms dominate the transfer coefficient database, and these require motion corrections to obtain the true air veloc-ity (Edson et al., 1998). [Could we show examples of actual data used to make these measure-ments---a correlation plot and a spectrum showing whatever fitting is needed to estimate the flux.]

i) Computation of Transfer Coefficients

The intricacies of estimating transfer coefficients from observations of fluxes and bulk pa-rameters is described by Fairall et al. (2003). The accuracy of a set of transfer coefficients from a particular field program is extremely difficult to assess. It is clear that uncertainty in the coeffi-cient is the combined result of the flux and bulk mean variable inaccuracies. Sampling uncer-tainty, sensor bias, frequency attenuations, platform flow distortion, and inadequate motion cor-rections all degrade the results (Fairall et al. 1996, 2000; McGillis et al. 2001). The actual com-putation of the transfer coefficient also relies on the application of various 2nd-order physical cor-rections (Fairall et al. 2003): choice of stability functions, a cool-skin correction SST, reduction in the water vapor pressure of seawater by salinity, use of a gustiness parameter, the dilution ef-fect (Webb et al. 1980), and flow distortion corrections for mean wind speed (Yelland et al. 1998). High-quality estimates of the scalar transfer coefficients require conditions where X is large. This both improves the signal-to-noise ratio of the flux observation and reduces the mea-surement fractional error in X.

ii) TURBULENT FLUX PARAMETERIZATIONS

Turbulent fluxes may be estimated from specifications (observations) of the basic mean bulk variables in [1] by specifying the height and stability-dependent transfer coefficients, Cx , or the roughness lengths, zox. The momentum (drag) coefficient is known to vary significantly with mean wind speed while the scalar coefficients have weak wind speed dependence. The computa-tion of fluxes should also account for the 2nd-order effects mentioned above, but these are often ignored or assumed to be embedded in the transfer coefficient. The observing technologies have advance sufficiently in recent years that ignoring the 2nd-order effects makes a noticeable differ-ence. The drag coefficient is often represented as a simple wind speed dependent formula,

)( 1010 nnd UFC (6)

or the velocity roughness length is represented as a Charnock plus smooth flow form (Smith 1988)

*

2*

ugu

zou (7)

where u* is the friction velocity, ν the kinematic viscosity of air, α the Charnock parameter, and β = 0.011 the smooth flow parameter. The sensible heat and latent heat transfer coefficients may be given as a constant or the roughness length parameterized by the friction velocity or velocity roughness length Reynolds number (Rr = zou u/ ν). A variety of algorithms and approaches are available (see Brunke et al., 2003 for twelve ex-amples). Brunke et al. (2003) compared the algorithms to a ship-based data set with about 7200 hours of observations and found mean bias magnitudes of 0.001 to 0.01 out of 0.065 Nm-2 for stress and 0.5 to 20 out of 102 Wm-2 for the sum of latent and sensible heat flux. In Fig. 6 we show the wind speed dependence of momentum and moisture transfer coefficients for five sam-ple algorithms; mean estimates from the database used by Brunke et al. (2003) are included. This same basic approach, with a few modifications to account for the frozen surface, can be applied to turbulent flux parameterization over ice (see Brunke et al. 2008 for examples from four cli-mate and weather models).

2) ISSUES WITH BULK PARAMETERIZATIONS OF SURFACE TURBULENT FLUXES

A few points about bulk flux algorithms are illustrated in Fig. 6. Given a sufficiently large data base of direct observations, the coefficients in the algorithm can be adjusted to fit the mean of the observations at each wind speed. We see that the algorithms, with one or two exceptions, tend to agree for wind speeds between 2-14 ms -1 where (not coincidently) there is a lot of data. Clearly, more observations are needed at wind speeds greater than 14 ms-1. Another major issue is hidden by Fig. 2, namely the real physical variability of fluxes in space and time at a given mean wind speed. It is known that the sea state affects the fluxes, principally the stress. Clearly, a steady wind blowing straight into large swells will generate more surface stress than the same wind going with the swells (observations suggest the difference is about a factor of two). De-spite decades of work, wave effects on surface fluxes remain the single most important and con-founding problem. Differences between sensible heat and moisture transfers may be critical. While scalar trans-fer in the atmospheric is dominated by turbulent flux, molecular diffusion must contribute heav-ily to the transfer at mm-scales near the interface. Because the molecular diffusion coefficients for water vapor and heat are 20% different, we expect their bulk transfer coefficients to be slightly different (order of 5%) but not sufficiently different to be unambiguously detectable by today’s observations. At very high wind speeds evaporation of sea spray will effectively enhance moisture flux and reduce sensible heat flux. Because the spray effects do not scale as (8) the ef-fects cannot be accounted for simply by adding wind speed dependence to the bulk transfer coef-ficients (Fairall et al. 1995). Progress on developing simple bulk algorithms to characterize spray contributions continues (e.g., Andreas et al. 2008). However, direct measurement of this effect is even more difficult than wave effects.

c. Mass fluxes DrennanMass fluxes are often considered separately from momentum and heat fluxes for historical

reasons. Mass fluxes were first carried out by the chemical engineering community some 50 years ago (xxref), and are reported in terms of piston velocities, k = CM S in the notation of section 3b. Early laboratory experiments assumed the validity of ‘stagnant film’ models (i.e., that molecular diffusion in the aqueous diffusive sublayer is the rate limiting step), and calculated the transfer velocity as k = Dx/h, where Dx is the diffusivity of the species concerned, and h the thickness of the diffusive sublayer. The first field measurements followed the same ideas (Broecker and Peng, 1973).

Subsequently, Jähne et al (1979, 1985), proposed k = f(uw, Dx, ν), where ν is the kinematic viscosity of water, and uw is the friction velocity in water. Dimensional analysis yields: k/ uw = f(ν/Dx) = f(Sc), where Sc = ν/Dx, the Schmidt number, is a function of temperature and type of gas. Based on laboratory experiments, Jähne et al. (1979, 1985) proposed, k = f2(uw )Scn, with n = 2/3 for very light winds to n=1/2 for most conditions. Such a relationship allows data from different gases and temperatures to be compared. Data are normalized to a common Schmidt number, Sc =660 that of CO2 in 20oC sea water. For 1oC water, Sc = 1950.

For the past decade, two approaches have been used to measure k: deliberate tracer (dual, usually SF6/3He or multiple, e.g. Nightingale et al 2000) and micrometeorological (e.g. McGillis et al. 2001). Data from both the laboratory and field show large scatter, but general support for an increase of k660 with wind speed. Most data analyses support a relationship between linear (Liss and Merlivat), and quadratic (Wanninkhof). At high winds, wave breaking is thought to signifi-cantly enhance gas transfer rates through bubble-mediated transfer (Woolf 1997). Recent efforts

to incorporate bubble physics into bulk models include those of McNeil and D’Asaro (2007) and Woolf (2005), the latter using a whitecap dependent term (Woolf 2005). Alternatively, Lamont and Scott (1970) proposed a k-scaling based on TKE dissipation rates (ε) in the water instead of S. Following this approach, Zappa et al (2007) find k660 ~ ε1/4.

At low winds, the presence of surface-active films, or surfactants, has been seen to signifi-cantly reduce gas transfer rates by suppressing the small scale waves whose microbreaking dis-rupts the aqueous diffusive sublayer and would otherwise bring bulk water to the surface. Frew et al. (2004) propose a k vs mss relation, where mss refers to the mean square slope of cm-scale waves. This approach has particular promise as a means of estimating k remotely using satellite altimetry (Frew et al 2007) or scatterometry (Bogucki et al 2009). Given the paucity of in situ observations of gas transfer (or the necessary bulk variables) remote sensing will remain as the primary source of global data for some time.

There remains the need for validation of both the bulk relations and satellite algorithms at high latitudes. To date, only two campaigns have measured gas fluxes at high latitudes: the Southern Ocean Gas Exchange experiment (2008), and the Circumpolar Flaw Lead experiment (2007-2008), both part of IPY. Other efforts, notably those of Miller et al (1999, 2002) and Skjel-van et al (1999), looked at the annual carbon budget in the Greenland and Norwegian Seas and Baffin Bay. An important question in high latitudes is the role of ice. While these studies as-sumed ice to be a barrier to air-sea transfer, subsequent work has indicated that brine channels in sea ice may act as a pathway for carbon (Semiletov et al 2004). This is currently an active area of research (e.g. Delille et al 2007).

d. Precipitation (Serreze)Precipitation is notoriously difficult to measure in high latitudes. Turbulence around gauge orifices reduces catch efficiency, especially for solid precipitation. Snow can also be blown into gauges Measurement errors may reach 100% in winter Different countries in turn use different gauge and shield types (Goodison et al., 1998; Yang et al., 2001). While gauge data can be adjusted (e.g., Groisman et al., 1991), techniques are far from perfect. The station network is also quite sparse. Serreze et al. (2005) estimate that even at a coarse grid cell resolution of 175 km, obtaining an accurate assessment of the grid cell precipitation requires typically 3-5 station within the cell, more in topographically complex areas. However, for the Arctic terrestrial drainage, only 38% of175 km grid wells contain even a single station. The situation is much worse over Antarctic and the Arctic Ocean. Alternative sources of high latitude precipitation include satellite retrievals, output from atmospheric reanalyses, and various blends of satellite, reanalysis and station data (Huffman et al., 1997; Serreze et al., 2005; Xie and Arkin, 1997). Satellite retrievals are uncertain over the cloudy, snow and ice-cover polar regions and it appears that better estimates can be obtained from atmospheric reanalyses (Serreze et al., 2005). However precipitation biases in reanalysis field can be very large (Serreze aand Hurst, 2000).

4. Gridded Flux Products: Development and Verification of Satellite and Model-based

Fluxes

In situ observations are inherently limited in resolution; for example, observing only in one location in the case of ship or buoy measurements or for only a short time in the case of airborne measurements. Therefore to generate comprehensive air-sea flux products with adequate spatial and temporal resolution for most applications, satellite or numerical weather prediction (NWP)

products must be used. Unfortunately the generation of such products for the high-latitude re-gions brings with it a set of significant challenges, not least of which is a scarcity of in situ obser-vations to use as ground truth for these products. Aside from this observational constraint, there are a number of notable features of the high latitudes that make satellite retrieval and numerical simulation challenging:

The high latitudes are prone to a greater incidence of very high wind speed conditions (see Fig. 4) and there is much greater uncertainty in bulk flux parameterizations during such con-ditions. Towards higher latitudes the Coriolis force increases, the height of the troposphere de-creases and atmospheric stability is generally higher: the consequences are a smaller Rossby number and a reduction in scale of circulations. Polar mesoscale cyclones, or polar lows, are typically 100-500 km in scale and are common in the high latitude seas. Despite their modest size they can be associated with gale (or hurricane) force winds. They tend to occur in cold-air outbreak conditions; consequently, they are associated with very high turbulent heat fluxes (e.g., up to 1000 W m-2 in the case observed by Shapiro et al. 1987). However these mesoscale features are not well resolved by global NWP systems (e.g., Condron et al. 2006). It seems that grid sizes of order 10 km or smaller are required for accurate NWP simulation of such features.Mesoscale features are usually detected by scatterometers (active microwave systems) such as QuikSCAT, and possibly other satellite sensors such as passive microwave sensors and in-fra-red sounders, but this information appears not to be retained through the data assimilation cycle of typical global models; for example, much sub-1000 km scale variability in the sur-face wind field is lost when comparing ECMWF and NCEP global analyses to QuikSCAT winds (e.g., Chelton et al. 2006).This resolution-related limitation also affects a number of other high wind speed (and often high heat flux) mesoscale weather systems that commonly occur over the high latitude seas and which are also likely to be poorly resolved in global products; for example orographic tip jets and barrier winds (e.g. Doyle and Shapiro 1999, Moore and Renfrew 2005) and bound-ary-layer or sea-ice fronts (e.g. Drüe and Heinemann 2001). The rapid translational motion and evolution of high latitude weather systems results in poor temporal sampling, even from a wide swath polar orbiting satellite (e.g., QuikSCAT).

The high latitude seas also have challenging thermodynamic characteristics related to the proximity of the sea-ice pack, cold continents and tight SST gradients:Cold-air outbreaks, associated with flows off the sea-ice or off a cold wintertime continent are prevalent and these tend to be associated with large air-sea temperature differences and so large turbulent sensible heat fluxes (e.g. Fig. 7; Brümmer 1996; Renfrew and Moore 1999). Interest-ingly this is one area of the global ocean where the surface sensible heat flux is consistently higher than the surface latent heat flux. Indeed in winter at high latitudes the surface sensible heat flux is often the dominant term in the surface energy budget.

Cold-air outbreaks result in sharp spatial gradients in boundary-layer temperature, humidity, atmospheric stability, and wind, as well as in surface heat fluxes (see Fig. 7). These sharp gradi-ents are difficult to capture in gridded products: they are a challenge to simulate accurately in NWP models and near-surface temperature is one of the more difficult quantities to measure from satellite. For example, in Fig. 7 over the sea-ice (0-30 km) the SST and wind speed are too high in the NCEP analyses, leading to an overestimate in the heat fluxes; while just off the ice-

edge (40-120 km) the 2-m temperature and the 10-m wind speed are both too low in the ECMWF analyses, leading to an underestimate in the heat fluxes there. Using higher-resolution NWP products does not necessarily fix these problems (e.g. Renfrew et al. 2009). For example NWP models often have difficulty with rapid transitions from stable to unstable boundary layers, such as occur during such cold-air outbreaks.

a. Satellite Products1) Radiative Fluxes

At low latitudes, many attempts have been made to propose simple parameterization of LW in terms of readily observable parameters (Idso and Jackson 1969; Brutsaert 1975; Prata 1996). This is feasible since most of the atmospheric moisture is concentrated close to the ground and surface temperature is a good indicator of moisture conditions. Such an approach is not valid at high latitudes. Pirazzini et al. (2000) used ground-based data collected at Ny-Ålesund (Svalbard islands) during the ARTIST (Arctic Radiation and Turbulence Interaction Study) experiment to study the effect of clouds on the downward long-wave radiation, carried out during the spring of 1998 around the Spitsbergen Island (78o-79o N). Various parameterizations including screen-level air temperature, water vapor pressure and cloud cover index as independent variables were tested for clear sky and all-sky conditions. Two parameterization schemes of downward longwave (DLW) radiation have been derived (xxrefs?) and their performances were found to be equally good (xxref).

Several authors have recently evaluated the accuracy of the most used parameterizations of DLW radiation data in Polar Regions. The focus of their studies was the verification of the for-mulas with year-round data (König-Langlo and Augstein, 1994), polar summer data (Key et al., 1996) or polar winter/late-autumn data (Guest, 1998, Makshtas et al., 1999). The parameteriza-tion of König-Langlo and Augstein (1994) reproduced the observations with root mean square (RMS) deviations of less than 16 W/m2

At present, large scale satellite estimates of radiative fluxes from satellite observations dis-agree most in Polar Regions (Fig. 5). None of the current satellite inference schemes accounts for the variability in the extent of sea ice and as such, do not correctly represent the boundary conditions in the radiative transfer computations. Consequently, errors are introduced in the esti-mates of the surface heating, which in turn, affect the ice melt computations. [I don’t follow. Don’t we map sea ice from satellites operationally with microwave instruments which work well in cloudy conditions?]

Similar discrepancies have been noted in numerical model outputs as shown by Sorteberg et al. (2007). The comparison of the surface energy budget over the Arctic (70-90°N) from 20 cou-pled models for the IPCC fourth Assessment with 5 observationally based estimates and reanaly-sis shows that the simulation of the Arctic surface energy budget has large bias in climate models and the largest differences are located over the marginal ice zones. Recent studies (Liu et al., 2005) indicate that the surface downward shortwave radiative fluxes derived from satellites are more accurate than the two main reanalysis dataset (NCEP and ECMWF), due to the better infor-mation on cloud properties in the satellite products. The SHEBA project showed that satellite-based analysis may provide downward shortwave (long wave) radiative fluxes to within ~ 10-40 (~10-30) W/m2 compared with ground observations (Perovich et al., 1999), and some of these differences are simply due to differences in scale between satellite and in situ data.

Despite the challenges, the accuracy of the radiative fluxes in these regions are likely to im-prove with the utilization of newly available satellite observations, improved inference schemes, and improved in situ observations as ground truth. In particular, more accurate data on surface condition, such as ice extent, atmospheric information, such as aerosol optical properties, im-proved models of narrow to broadband transformations with realistic surface models and newly available bi-directional distribution functions (BRDF) models (e.g., from CERES or MISER) need to be utilized. Data from the Moderate Resolution Imaging Spectro-radiometer (MODIS) instrument onboard the Terra and Aqua satellites (King et al., 1992) have allowed the develop-ment of a new inference scheme to estimate shortwave radiative fluxes (Wang and Pinker, 2009). Daily average values are constructed from the combination of Terra and Aqua and agree well with ground measurements (Fig. 6) over oceanic sites (agreement is better over land). The im-provement is very significant at problematic areas for most inference schemes such as the Tibet Plateau and Antarctica. problems in polar regions, discrepancies among models, promise that satellite data will actually provide better fluxes given adequate ground truth, etc.

2) SATELITE-DERIVED TURBULENT HEAT

Satellite-derived turbulent sensible and latent heat flux products are based primarily on appli-cation of the bulk aerodynamic flux formulations (see section 3) using mean input quantities re-trieved from satellite observations. Key input quantities include the wind speed, sea surface tem-perature (SST), and near-surface air temperature and specific humidity. The SST and wind speed retrievals are relatively direct but face additional challenges in the high latitudes. Retrieval of the near-surface air temperature and specific humidity are indirect and are significant sources of errors in the fluxes even in better characterized tropical and midlatitude conditions. Current satellite observations are unable to resolve the atmospheric conditions just above the surface and retrievals are based on relations to broad layer and total atmospheric column properties (see e.g., Liu 1986; Jackson et al. 2006). Evaluation and refinement of satellite-based turbulent heat flux products over the oceans are subjects of the ongoing SEAFLUX project (Curry et al. 2004). Fur-ther recent descriptions of intercomparisons between the various products in existence can be found in Bourras (2006), Chou et al. (2004), and Kubota et al. (2003).

The high latitude challenges to the satellite-based turbulent heat flux products are largely due to limited numbers of direct observations with which to derive and validate the methods and products in these regions. Atmospheric stability and air-sea temperature differences can be very different near the ice edge and cold land masses than in other parts of the globe where the major-ity of observations used in the derivation of the satellite retrieval algorithms are obtained. Little data exists to confirm the accuracy of the retrievals under these conditions. SST retrievals are also subject to increased uncertainties in the high latitudes due to difficulties in discriminating open ocean, clouds, and ice in infrared brightness temperatures, and proximity to ice in mi-crowave retrievals. Additional challenges result from the relatively small spatial scale of atmo-spheric features near the ice and land edge compared to those over the open ocean.

3) TURBULENT MOMENTUM FLUX (STRESS)The momentum flux (stress) is typically derived from observations of surface wind. Satellites

winds are actually equivalent neutral winds (U10EN; Kara et al. 2008; and references therein), which accounts for dependence on stability, in a manner that allows a neutral drag coefficient to be used in the conversion from winds to stress: = CDN |U10EN| U10EN. There are three shortcom-ings of this approach. One is that the density is usually unknown; however, that results in only a

local bias of a few percent. The other problem is that most products average speed, rather than speed squared, which also results in a small bias. The third shortcoming is that rain influences scatterometer retrievals, preferentially causing areas with cyclonic vorticity to be flagged as sus-pect. The problems with rain are substantial for ku-band scatterometers (e.g., NSCAT and QuikSCAT), and are much smaller but not negligible for C-band scattererometer (e.g., ERS1/2 and ASCAT).

Another issue with scatterometers is calibration for very high wind speeds. There are sub-stantial differences between QuikSCAT and ASCAT for wind speeds greater than 16 ms-1. These differences will likely be resolved within the next year or two; however, the reason this problem exists is the paucity of good comparison data for wind speeds greater than roughly 20ms-1. Such wind speeds are often associated with rain, which modifies retrievals (Draper and Long 20xx; Weissman et al. 20xx; Weissman and Bourassa 2008). More high quality comparison data for high wind speeds, with and without rain contamination, are needed to greatly improve the cali-brations.

The sampling from a single wide swath scatterometer (e.g., QuikSCAT) is approximately suf-ficient to determine monthly average stresses with an accuracy of better than 0.01Nm-2. The dif-ferences between monthly scatterometer observations and monthly merchant marine (xxref) ob-servations are remarkably small for regions with good ship traffic; however, the scatterometer sampling is much better at high latitudes. For high latitudes, at least two wide swath scatterome-ters are desired for sampling typical mid-latitude storms and polar lows.

4) PRECIPITATION

I will contact someone to do this part.

b. Numerical Weather Prediction Hoffman NWP products come from both operational forecast systems and reanalysis projects. Fluxes

from operational systems are available in near real time. These systems use the most up-to-date parameterizations and highest possible resolution. The downside is that such systems may be up-dated every few months. Reanalyses provide the most complete (space-time), uniform (gridded) estimates of fluxes. A reanalysis system uses a fixed data assimilation system with lower resolu-tion than the current operational system to process all available past data (after QC, and thinning or super-obbing). For example the ERA-40 reanalysis project uses a three dimensional varia-tional technique for the T159L60 version of the Integrated Forecasting System to produce analy-ses every six hours. NWP products, including flux estimates, are necessarily limited by the model resolution, parameterizations (including some of the bulk transfer formulae described ear-lier), data assimilation methodology, and observational database. Future NWP products, includ-ing reanalysis products, will certainly improve on the all of these items listed. The observational database improves as new observing systems are fielded. On-going data recovery projects even promise to improve the historical data sets.

Inconsistencies between NWP products vastly exceed desired accuracy (Fig.8). Due to the course resolution of most current reanalyses, data tend to be blurred across land and sea bound-aries (Kara et al. 2008) causing errors in fluxes and flux related variables.

1) RESULTS FROM NUMERICAL MODELS

Observations of LW fluxes at high latitudes are also needed to verify climate and NWP mod-els. As shown in Figure 1 (Wild et al. 2005), there is a large discrepancy between model esti-

mates of DLW and ground observations over the poles. Downward longwave radiation (DLW) climatologies have been assessed against ground observations in four GCMs with four indepen-dent radiation schemes and in the ERA using direct observations. Based on a total of 45 world-wide distributed observation sites from the Global Energy Budget Archive (GEBA) and Baseline Surface Radiation Network (BSRN) databases, a tendency was found in the GCMs to underesti-mate the DLW. The best estimate given for global mean DLW is 344 W/m2, a value higher than typically found in GCMs. However, it has been shown that this underestimation is not uniform over the globe, but depends systematically on the prevailing climatic conditions. Significant un-derestimates are found at observation sites in cold and dry climates with low DLW emission. This underestimation gradually diminishes at sites with more moderate climates, while at sites with warm and humid atmospheres and high DLW emission the biases are small or even reversed in some of the models. This implies that the meridional gradient of DLW in the GCMs is exces-sively strong. There is some evidence that the underestimate of DLR for cold, dry climates is less serious for the more recent radiation codes because the Edwards and Slingo (1996) radiation scheme includes the CKD formulation of the water vapor continuum, which leads to an increase in the DLR for such conditions. This result is consistent with Iacono et al. (2000), who found a substantial increase in the DLR at high latitudes when they included the Rapid Radiative Trans-fer Model radiation code, which uses the CKD continuum, in the National Center for Atmo-spheric Research Community Climate Model. Further comparisons between models and observa-tions, particularly at high latitudes, are needed in order to determine the source of the errors dis-cussed here.

2) OCEAN ASSIMILATION

The Southern Ocean State Estimate (SOSE), a reanalysis mode only assimilation of ocean data, adjust fluxes to get circulation that best matches observations, results in extensive small scale structure in fields. SOSE air-sea heat and freshwater flux estimates (Mazloff, 2008) are ob-tained in the same way as Stammer et al. (2004) obtained global air-sea heat and freshwater fluxes in the course of the construction of the ECCO (Estimating the Circulation and Climate of the Ocean) global ocean state estimate. The model used is developed from the ECCO model, which is in turn a modified version of the MIT General Circulation Model (MITgcm; Marshall et al., 1997). SOSE's initial estimate of the atmospheric state is obtained from NCEP-NCAR Re-analysis 1 (Kalnay et al., 1996) for air temperature, specific humidity, zonal and meridional wind speed, precipitation, short wave radiative flux, and long wave radiative flux. The misfit between ocean observations and the model results is reduced through an iterative process of adjusting the control variables (prescribed atmospheric state and the initial condition), but not the dynamical parameters of the model. SOSE (Mazloff, 2008) is novel in that it is the first eddy-permitting data assimilating system constrained to fit observations in a least square sense.

SOSE Southern Ocean (south of 25S) air-sea heat and freshwater flux fields are provided at much higher horizontal resolution (1/6) than readily available alternatives (primarily numerical weather prediction models, both operational and reanalysis), and much shorter temporal resolu-tion (five day averages) than the recently devloped air-sea flux estimate by Large and Yeager (2008) which was developed as an improvement of NCEP-NCAR Reanalysis (monthly aver-ages).

Cerovecki et al. (2009) show that the adjustments made by SOSE to the control variables tend to reduce the known biases in heat and freshwater flux estimates (e.g. Taylor et al., 2000). SOSE flux estimates moreover have the advantage over alternative flux estimates that they are

mutually consistent with one another and with the oceanic variables in that they fit into closed model budgets of heat, freshwater, momentum, and particularly salinity, which is especially im-portant for an accurate estimate of the SO surface buoyancy fluxes where very cold temperatures in the southern part of the southern ocean can make the fresh water contribution to the total buoyancy flux more important then the heat flux contribution (Warren et al., 1996).

c. Hybrid ProductsHybrid products combine one or more sources of in situ and satellite data with an existing

NWP or ocean data assimilation product. No forecast model is involved. The existing NWP or ocean data assimilation product acts as a background for a specialized data analysis step. Com-pared to the data assimilation system that produced the background, hybrid analyses make use of a data sets not used or create analyses at higher spatial or temporal resolution or directly analyze additional quantities, e.g., fluxes. Compared to satellite data products, hybrid analyses fill in data voids, adjust for time differences between data and analysis times, can act as a multi-dimensional retrieval system, resolve ambiguity, and provide for sophisticated QC.

The variational analysis method (VAM) has been a favored approach for producing fields of ocean surface wind and various ocean fluxes. The VAM solution simultaneously minimizes the misfit to all available data and a priori constraints. This total misfit is termed the objective func-tion. For example, if the difference between the analysis and the background should be smooth, a measure of the roughness of this difference is added to the objective function.

5. Applications: Desired and Current Accuracies

Differences shown in Figure. ?? suggest large uncertainties in the existing gridded flux prod-ucts. Although these widely varying gridded flux products are all widely used in climate re-search, their uncertainties constrain they ways in which they can be applied. What new high lati-tude applications could be considered if we had more reliable flux products? Examples of the space and time scales of processes, and the desired accuracy for studying these processes, are shown in Fig. 13.

a. Global Warming Signal Long-term warming in the climate system occurs as a result of small changes in heat flux.

For example, observed long-term warming trends in the ocean from 1993-2003 can be explained by a ocean heat gain of 0.86 ± 0.12 W/m2 (Hansen et al., 2005). For sea ice, a 1 W/m2 flux im-balance can melt 10 cm of ice in a year. However, the climate change signal of O(1 W/m2) is, at present an unachievable standard for directly measured surface fluxes, and long-term changes in fluxes are more effectively diagnosed through other means. While gridded flux products with 1 W/m2 accuracy may be unachievable, significant scientific gains could be achieved if we could improve the accuracy of heat flux estimates by an order of magnitude.

FRESHWATER BALANCE The freshwater flux anomalies that have been documented in the North Atlantic are related to

changes in upper ocean stratification, and consequently, they are sufficient to inhibit or slow deep convection are on the order of 0.05 Sv (Curry and Mauritzen 2005). Although the principal source of observed freshwater anomalies is river runoff and ice melt, it is useful to convert this to

an equivalent precipitation/evaporation, since variations in P-E can also impact the surface salin-ity. The area of the Arctic and North Atlantic north of 60°N is approximately 1.8 x 10 13 m2. A variation of 0.05 Sv over this area converts to almost 1 cm/yr.

Basin-scale changes in salinity associated with global change are associated with much smaller changes in air-sea freshwater flux. Trends in basin-scale salinity are on the order of 0.05 to 0.1 psu/decade (Boyer et al., 2005). This change is concentrated in the top 200 m. (The net change for the globe is essentially zero, which it must be since the reservoirs of salt and freshwater are essentially constant in the absence of widespread ice melt.) This trend is equiva-lent to a change in precipitation-evaporation of 0.003 cm/yr, which is far below any expected ac-curacy. (xx better check this number before publishing xx) Thus, as for heat fluxes, for which observing ocean temperature change is the best approach, the best approach for monitoring basin-scale freshwater changes associated with climate change is to continue measuring salinity.

At high latitudes, salinity is a major or even the dominant factor in upper ocean vertical sta-bility since seawater is close to the freezing point there. The transition to temperature-dominated stabilization occurs within the region we consider to be high latitudes. Thus surface freshwater fluxes, ice formation/export/melt are critical to high latitude ocean processes. Freshwater trans-port divergences are on the order of 0.1 Sv over regions with thousand km lengthscales. Trans-lating this to a desired accuracy in P-E, a net divergence of 0.1 Sv over a box that is 5000 x 1000 km corresponds to a new P-E of 1 cm/yr. Therefore the required accuracy must be smaller than this, on the order of 0.1-0.5cm/yr or much better.

The P-E error reported by Taylor et al. (2000) appears to be on the order of 0.3 cm/yr, so within the ballpark, but the adjustment to NCEP created by SOSE is on the order of 1 cm/yr, which is the size of the required signal. Thus, improvement in accuracy of NWP products is clearly required.

Subantarctic Mode Waters (SAMW) are thick subsurface mixed layers that form in the winter on the equatorward side of Subantarctic Front (SAF). Cerovecki et al. (2009) used SOSE fluxes as input to a Walin (1982) analysis to estimate Subantarctic Mode Water (SAMW) formation rates. The air-sea buoyancy flux which leads to SAMW formation is a result of competing effects of buoyancy gain by excess precipitation and buoyancy loss by ocean heat loss; Cerovecki et al. (2009) show that both are of comparable importance in estimating SAMW formation rates so that highly accurate estimates of both forcing fields are required.

b. Atmospheric applicationsThe extratropical storm tracks reach into high latitudes of both hemispheres. A poleward

trend in storm-track location has already been detected in the southern hemispheric summer (ref-erence). Hakkinen et al (2008) show a positive trend in storm activity in the high Arctic both in summer and winter over the past 50 years. The increased storm activity results in increased mo-mentum forcing of sea ice and can lead to more intense mixing beneath the sea ice cover (possi-bly drastically affecting the halocline), but not necessarily leading to increased ice export out of the Arctic. The latter appears to depend on how robustly the internal atmospheric variability (storm activity) can organize itself into a dominant climate pattern. This is an area of active re-search where turbulent surface fluxes can feed back on the large scale atmospheric flow.

Turbulent energy fluxes resulting from the opening up of previously ice covered areas of the Arctic are especially large (area average of O(50-70 Wm-2) in winter. Even though more exten-sive areas are opened up in summer the results in terms of the atmospheric response are minor,

because of the small temperature difference between ocean/atmosphere (and thus small turbulent flux) in summer compared to winter (C. Deser personal communications, 20xx).

c. Ice applications

1) MODELING

When the sea ice top surface is below freezing: net surface flux - conductive flux = 0and when it is at the melting point net surface flux - conductive flux = rho L dh/dtwhere rho is the density, L is the latent heat of fusion, and here dh/dt is the top surface melt rate. The bottom temperature of the ice is always at the freezing temperature of sea water and the flux balance is conductive flux - ice-ocean heat flux = rho L dh/dtwhere here dh/dt is the basal growth or melt rate.

Experiments that lower the sea ice albedo in a global climate model serves to show the sensitiv-ity of sea ice to an imbalance in the incoming fluxes. For example, Bitz et al 2006 lowered the sea ice albedo by about 8%, which resulted in a change in the net surface shortwave flux by about 5-10 W/m2 on the annual mean. After a few decades the sea ice reached a new equilibrium thickness so that the net flux into the sea ice was zero. The sea ice was thinner by 1-2 m in the central Arctic and about 1/4 m in the Antarctic. The thinner ice resulted in changes to the the con-ductive flux through the ice as well as the sensible and upward surface longwave fluxes.

2) ICE BUDGETS

A key need is an improved assessment of the Arctic sea ice mass budget. Hindcasts from es-sentially all of the current generation of coupled global climate models point to declining Sep-tember sea ice extent over the period of observations, but with few exceptions simulated trends are smaller than observed [Stroeve et al., 2007]. The range in recent and projected future ice ex-tent and volume trends from different models is also large, reflected in both initial (20th century) and evolving surface energy fluxes. Inter-model scatter in absorbed solar radiation, due in part to differences in the surface albedo simulation, is a particular concern [Holland et al., 2009]. Esti-mates of surface energy fluxes over the Arctic Ocean from atmospheric reanalyses, which could provide a provide basis for model evaluation, are of questionable accuracy, both in terms of indi-vidual components and the net surface flux. A net surface flux of 1 W m -2 over a year is the ener-getic equivalent of melting 0.1 m of ice. This places a very stern accuracy requirement in order to get the mean ice thickness correct. A random error from year to year of only 1-2 W m -2 could easily swamp out any real trend in thickness. To place these accuracy requirements in context, the annual net surface flux averaged over the Arctic Ocean from ERA-40 is 11 W m -2 [Serreze et al. 2007], compared to 6 W m-2 from JRA-25, representing an effective annual ice thickness dif-ference of 0.5 m.

6. Summary

a. Conclusion on current accuracy of spatial fields in Hilat1. Flux products do not agree at present2. Choice of flux products depends on application; no one size fits all solution3. Flux accuracies at present do not match requirements outlined above.4. high latitudes are region of climatologically frequent high wind speed events5. sensible heat fluxes often greater than latent heat fluxes, due to frequency of cold-

air outbreaks.6. region of strong gradients in surface-layer meteorology, SST and surface

turbulent fluxes.

b. Research issuesNumerous challenges lie ahead. What we know best (fluxes over perennial Arctic ice from SHEBA) is what is least likely to persist. Understanding fluxes through leads and in high wind conditions is critical. Will require observations, but observations cannot be made everywhere, so synthesis of observations into global systems is also essential. Enormous challenges for assessing albedo, gas flux, heat exchange in changing climate system.

c. Observing system issues Carlson1. Concerns about reduced ship availability and rising ship costs making things

worse rather than better2. Speculation: Prospects for making use of tourist vessels as volunteer observing

ships, better use of autonomous systems, airborne AUVs, improved NWP with ocean assimilation(?), etc.

As funding shrinks, and fuel costs rise, the number of research or logistics ships operating at high latitudes will decrease, certainly from the peak IPY years and possibly (although one hopes not) from pre-IPY levels. If, as I suspect, we had almost no flux-quality measurements from these ships in any case, then a trend towards fewer days and fewer transects can hardly represent a loss to the flux community; it does mean we need to make better use of fewer opportunities. Tourist ship trips will increase, albeit to only a small portion of the Arctic and a minute portion of the Antarctic; the tourist ships do cross some interesting straits, however. Think VOS XBT in the days of smart sensor arrays, Iridium, and Google Earth.

SIDEBAR: SEA ICE

Mark Serreze has draft to be reviewed by Cecilia Bitz....

SIDEBAR: OVERTURNING CIRCULATION

[replace with sea ice case?]The ocean mixed layer mediates fluxes between the deeper ocean and the atmosphere. Air-

sea buoyancy fluxes change water properties, the waters then circulate and are subjected to other forcings including diffusion, and overall they accomplish a portion of the required planetary transports of heat and freshwater between sources and sinks. Within the ocean’s surface layer there are large spatial and seasonal variations in thickness of the mixed layer, and longer-term variations associated with natural and forced climate variability. At high latitudes, mixed lay-ers can be especially thick when surface cooling produces deeper mixing.

In the Southern Ocean, there are large variations in mixed layer depth (Figure xx from Dong et al. 20xx). One especially interesting feature is the axis of very thick mixed layers stretching from the central Indian Ocean across the Pacific to South America (there are relatively thick mixed layers across the South Atlantic and western Indian Ocean, but they are much less dra-matic.). These thick mixed layers (“mode water”) lie just north of the Antarctic Circumpolar Current. Understanding the onset of these mixed layers has global significance as they contain a large anthropogenic carbon inventory (Sabine et al., 200xx). To the south of the axis of thick mixed layers, there is a broad region of upwelling. Air-sea heat and moisture fluxes and winds are important constraints on the budgets that describe these mode waters.

The mode waters circulate northward into the subtropics of the southern hemisphere. They are thus part of the upper ocean gyres, and carry relatively cold, fresh water northward into the gyres, which is returned as warmer, saltier water; therefore the mode waters are part of the upper ocean heat and freshwater transport systems. Calculating the formation rate of these waters, as well as of all waters, can be carried out quantitatively using “Walin analysis” (Fig. xx Walin xx). If it is assumed that all diabatic processes are confined to the surface diabatic layer directly forced by air-sea buoyancy fluxes, water mass formation can be calculated using only sea surface density and air-sea buoyancy fluxes. Air-sea buoyancy fluxes are summed for each isopycnal layer outcrop from one coast to the other. These indicate the rate at which water is moving from that outcropping layer into either a denser or lighter layer (which would clearly also be at the sur-face), which can be written in terms of a volume transport from one density class to the next. If more volume transport moves from one class to another than moves from that second one to a third one, there must be a convergence of mass into that second class, resulting in subduction of that mass into the interior ocean; this is the formal definition of “formation” in the Walin analy-ses. Therefore the formation rate of a water mass can be computed without any information about the velocities within the ocean, which is the other approach (volume transport balances within the ocean).

Thus accurate calculation of water mass formation via this method requires accurate fluxes, and is attainable on a global scale. However, it is essential that the surface fluxes be in balance overall, that is, that they not have a net warming or cooling bias.

A new study (Cerovecki et al., 2009) highlights the difficulties with applying NWP reanalysis air-sea fluxes to the Southern Ocean water mass formation problem; the most commonly avail-

able flux products are biased, largely because of lack of observations. Recent adjusted flux prod-uct (Large and Yeager, 2008) and adjusted fluxes from high resolution ocean data assimilation (Mazloff, 2008) remove the bias and product credible water mass formation results, matching what is known about which isopycnals do form mode waters of significant volume. [Refer here to 4.H.2 or vice versa.]

Xx more on mixed layer budgets from Sarah, also need to reduce the above to about 1/3 the length or more!xxTalley comments

------•Driving ocean simulations, input to ocean data assimilation (could talk to Julie about some kind of figure)•Quantifying climate change vs. natural variability –Barnett in units of W/m2–or come up with figure showing difference in surface fluxes between different phases of the SAM or AO or NAO in high latitude N. Atlantic •Running higher latitude mixed layer models – Lab Sea or Greenland Sea or Jamie’s SAMW•Quantifying water mass transformation rates (Walin approach)-------• Dense water formation

•Basics: heat loss, ice cycle (formation, export, melting rates), net precip•Polynya characterization and fluxes within them and within leads, producing brine

rejected waters (Antarctic, Arctic, Okhotsk/Bering)•Open ocean convection with neighboring sea ice (Labrador and Greenland Seas)

•Water mass modification in the ACC: upwelling, buoyancy gain rather than loss• Need to study these regions in much greater detail, requiring excellent air-sea and E-P fluxes to understand water mass processes. Probably the least studied and least-understood part of the ACC water mass modification processes, not that the others have small enough errors yet

•Water mass formation north of the ACC: surface layer, mode water• Thick surface mixed layers with zonal asymmetry, requiring highly accurate air-sea fluxes to compare and balance with other factors that affect the mixed layers: cross-frontal advection including Ekman advection, eddy field, diapycnal diffusion, strong lateral mixing

Mention of Speer et al. Deacon cell

ii. Upper ocean mixed-layer budget (e.g. requirements to close budget). Gille, Talley

The global meridional overturning circulation (MOC) describes the ocean circulation patterns that bring water poleward at one depth, transform its properties at high latitudes, and return it equatorward at a different depth. The northward flowing limb of the MOC carries water north-ward at the ocean surface in the North Atlantic, into the Greenland and Labrador Seas where wintertime atmospheric conditions can induce deep convection, generating cold North Atlantic

Deep Water that returns southward as part of a Deep Western Boundary Current. The southward-flowing limb of the MOC brings mid-depth water southward along constant density surfaces into the Southern Ocean. In the Antarctic Circumpolar Current, density surface tilt steeply up to the ocean surface, and water parcels rise along these density surfaces to the surface, where the water can interact with the atmosphere while it is carried northward via surface Ekman transport. In winter, just to the north of the ACC, surface water cools and becomes dense enough to sink, forming a large layer of homogeneous water known as Antarctic Intermediate Water or Sub-antarctic Mode Water. This water carries with it the signature of its contact with the atmosphere. The limbs of the MOC are key determinants of heat and other water properties in the deep ocean, and both limbs have been the focus of major international field programs (RAPID-MOCCA for the North Atlantic; DIMES and SAMFLOC for the Southern Ocean). Understanding water mass transformation at the ocean surface requires good estimates of the fluxes. Using the best available data products, Dong et al (2007) found that the zonally averaged imbalance can be 50 W/m^2, and the locally, the upper ocean heat balance can have an root mean squared misfit of more than 200 W/m^2 at any given location , and 130 W/m^2 in a global rms averaged sense. Such large errors make it difficult to discern the details of the upper ocean heat storage and meridional overturning circulation. If root mean squared errors could be reduced to 10 Wm-2,

Selected Relevant References

Andreas, E.L., P.O.G. Persson, and J.E. Hare, 2008: A bulk turbulent air–sea flux algorithm for high-wind, spray conditions. J. Phys. Ocean., 38, 1851-1896.Arctic Climate Impact Assess-ment, 2004. Impacts of a Warming Arctic: Arctic Climate Impact Assessment. Cambridge Univ. Press, New York, 139.

Armstrong, R.L., and M.J. Brodzik, 2001. Recent Northern Hemisphere Snow Extent: a Compar-ison of Data Derived from Visible and Microwave Sensors. Geophysical Research Letters, 28(19), 3673-3676.

Bindoff, N.L., J. Willebrand, V. Artale, A, Cazenave, J. Gregory, S. Gulev, K. Hanawa, C. Le Quere, S. Levitus, Y. Nojiri, C.K. Shum, L.D. Talley and A. Unnikrishnan, 2007: Observa-tions: Oceanic Climate Change and Sea Level. In: Climate Change 2007: The Physical Sci-ence Basis. Contribution of Working Group I to the Fourth Assessment Report of the Inter-governmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cam-bridge, United Kingdom and New York, NY, USA.

Boning, C., A. Dispert, M. Visbeck, S. R. Rintoul and F. U. Schwarzkopf, 2008: The response of the Antarctic Circumpolar Current to recent climate change, Nature Geoscience, 1, 864 – 869.

Bourras, D, 2006: Comparison of five satellite-derived latent heat flux products to buoy data. J. Climate, 19, 6291-6313.

Bourassa, M. A., D. M. Legler, J. J. O’Brien and S. R. Smith, 2003: SeaWinds Validation with Research Vessels. J. Geophys. Res., 108, DOI 10.1029/2001JC001081.

Boyer, T.P., J.I. Antonov, S. Levitus, and R. Locarnini, 2005: Linear trends of salinity for the world ocean, 1955-1998. Geophys. Res. Lett., 32(L01604), doi:1029/2004GL021791.

Brümmer, B 1996: Boundary-layer modification in wintertime cold-air outbreaks from the Arctic sea ice, Bound.-Layer Meteorol., 80, 109-125.

Brunke, M. A., C W. Fairall, and X. Zeng, 2003: Which bulk aerodynamic algorithms are least problematic in computing ocean surface turbulent fluxes? J. Clim., 16, 619-635.

Brunke, M. A., M. Zhou, X. Zeng, and E.L. Andreas, 2009: An intercomparison of bulk aerodynamic algorithms used over sea ice with data from the SHEBA experiment J. Geophys. Res., to appear.

Cerovecki, I., L. Talley, M. Mazloff, and G. Maze, 2008. A comparison of Southern Ocean sur-face buoyancy flux estimates. In Preparation.

Cerovecki, I., L. Talley and M. Mazloff, 2008c. Subantarctic Mode Water and Antarctic Interme-diate Water Formation using a Walin framework. In preparation.

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