Outline: High Latitude Surface Fluxes: Requirements and Challenges for Climate Research

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

[Some suggested changes by Ross; comments questions in brackets like this.]

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 lati-tude warming patterns are likely to persist at least through the coming two centuries (??, ??). How-ever, the harsh environment of high latitude regions makes in situ monitoring of these changes chal-lenging. 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.

[We are not explicit about the motivation for this article in the introduction. Fluxes are critical for accurate climate projections; trustworthy projections are required to target adaptation strategies; etc. ]

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 instru-ments cannot easily be serviced. Moreover, winds are among the strongest in the world (e.g. ??; Ren-frew, 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 char -acterizing fluxes over year-round sea ice without leads (reference ??), 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 operate under the ice with instruc-tion to stay well below the surface when they detect ice. Because of their constantly changing ice con-ditions, 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 have proved dif-ficult 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 be-tween 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 accel-erating the loss of ice.

Even over land there is no real 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 1.

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 the space and time scales dictated by these applications [what applications]. Then we evaluate how these current accuracies compare with the requirements for high latitude fluxes for a range of applications. [Why don’t requirements come first?] In this paper Section 2 summarizes methods used to determine fluxes. Section 3 reviews the methods used to measure and parameterize fluxes using in situ data. Section 4 discusses gridded fields from satellite and numerical weather prediction products. Section 5 considers applications requirements, and section 6 summarizes the results. [The following sentence could go earlier in the introduction as part of the motivation.] 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.

2. Approaches to Determining Fields of Fluxes

Surface fluxes fall into three general categories. Radiative fluxes measure 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 and latent heat, and gas exchange.

The surface energy budget includes the following components (King and Connolley 1997):0)1( =+++−+− GHHSLL Lsdownupdown α

where, downL and upL are the downward and upward long wave fluxes, downS is the downward shortwave flux, α is the surface albedo, sH is the sensible heat flux, LH is the latent heat flux, and G is the conductive flux through the snow/ice pack (Pavolonis, et al., 2003).

1. Basics of Fluxes A. Basic definitions of fluxes for the purpose of this paper Bourassa, Pinker

radiative versus turbulent fluxes.B. Differences among easily available products Bourassa, Speer (sea ice zone)

types of fluxes, basic heat balance, comparison of different product, definition of accuracy and uncertainty

3. In situ methods and their parameterization of surface fluxes Fairall

[Way too many equations that I can not even see; but no matter we probably shouldn’t have any equations in this paper.]

Turbulent 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 advance calibrate and validate indirect methods so that fields of fluxes can be determined 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). Advances 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 algorithms.

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:

XSCXSccxw xdx Δ=Δ=′′ 2/12/1 (1)

where 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), which is composed of a magnitude of the mean wind vector part U and a gustiness part Ug:

GUUUSzXXX gsea ≡+=−=Δ 22);( . (2)In [2] z is the height of measurements of the mean quantity X(z) above the sea surface (usually 10 m) and 2)/(1 UUG g+= is the gustiness factor. The gustiness term in [2] represents the near-surface wind speed induced by the BL-scale; it prevents the transfer coefficients from becoming singular at low wind speeds. Properly scaled dimensionless characteristics of the turbulence at reference height z are universal functions of a stability parameter, Lz /≡ζ , defined as the ratio of the reference height z and the Obukhov length scale, L. Thus, the transfer coefficients in [1] have a dependence on surface stability prescribed by Monin-Obukhov similarity theory:

)/ln(,

)()/(1)( 2/1

2/1

2/12/1

oxxn

xxn

xnx zz

cc

cc κ

ζκζ =

Ψ−= , (3)

where the subscript n refers to neutral (ζ = 0) stability, xΨ is an empirical function describing the stability dependence of the mean profile, and zox is a parameter called the roughness length that characterizes the neutral transfer properties of the surface for the quantity, x (see also Fairall et al. [2003] for details).

i. Instrumentation

The neutral transfer coefficients (or, equivalently, the roughness lengths) are determined by direct observations of the fluxes and associated mean bulk variables required in [1]. 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 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 motion 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 velocity (Edson et al., 1998). [Could we show examples of actual data used to make these measurements---a correlation plot and a spectrum showing whatever fitting is needed to estimate the flux.] An example of a cluster of sensors for a ship-based flux observing field program is shown in Fig. 1.

ii. Computation of Transfer Coefficients

The reduction of an ensemble of observations of turbulent fluxes and near-surface bulk meteorological variables to estimates of the mean 10-m neutral transfer coefficient is a problem of some subtlety. The straightforward approach is to convert each observation to Cx10n

,)/10ln()/10ln(

''

101010

oxonnnx zzGXU

xwC κκ=

Δ= (4)

then average to obtain

.''

101010 >

Δ>=<<

GXUxwC

nnnx (5)

The 10-m neutral values of the mean profile are computed as

)]/()10

[ln()()10ln( **10 Lzzu

zUz

uU u

on Ψ−−==

κκ (6a)

)]/()10

[ln()()10ln( **10 Lzzx

zXz

xX q

oxn Ψ−+Δ=−=Δ

κκ , (6b)

where ∗x can be ∗ or ∗q . Note, the sign difference between [6a] and [6b] follows from XΔ being defined as )(zXX s − in Equation [1].

Because artificial correlation may confuse attempts to compute the mean transfer coefficients via [5], Fairall et al. [2003] computed estimates of mean transfer coefficients as a function of wind speed. Here the fluxes are averaged in wind speed bins and the mean transfer coefficient is the one that correctly returns the mean or median flux

,''''

1010 ><><><

>=< nbxb

nx CxwxwC (7)

where the subscript b refers to values computed with the bulk algorithm. 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 coefficient is the combined result of the flux and bulk mean variable inaccuracies. Sampling uncertainty, sensor bias, frequency attenuations, platform flow distortion, and inadequate motion corrections all degrade the results (Fairall et al. 1996; 2000; McGillis et al. 2001). The actual computation of the transfer coefficient also relies on the application of various 2nd-order physical corrections (Fairall et al. 2003): choice of Ψ functions, a cool-skin correction SST, reduction in the water vapor pressure of seawater by salinity, use of a gustiness parameter, the dilution effect (Webb et al. 1980), or flow distortion corrections for mean wind speed (Yelland et al. 1998). High-quality estimates of the scalar transfer coefficients require conditions where XΔ is large. This both improves the signal-to-noise ratio of the flux observation and reduces the measurement fractional error in XΔ .

iii. 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 computation of fluxes should also account for the 2nd-order effects mentioned above, but these are often ignored or assumed to be imbedded in the transfer coefficient. The observing technologies have advance sufficiently in recent years that ignoring the 2nd-order effects makes a noticeable difference. The drag coefficient is often represented as a simple wind speed dependent formula,

)( 1010 nnd UFC = (8)

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

*

2*

ugu

zouubα += (9)

Where u* is the friction velocity, ν the kinematic viscosity of air, α the Charnock parameter, and β 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 velocity roughness length Reynolds number,

u*uz

R our =

)(0 rx RFz = (10)

A bewildering variety of algorithms and approaches are available (see Brunke et al., 2003 for twelve examples). 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 1 to 10 E-3 out of 65 E-3 Ntm-2 for stress and 0.5 to 20 out of 102 Wm-2 for the sum of latent and sensible heat flux. In Fig. 2 we show the wind speed dependence of momentum and moisture transfer coefficients for five sample 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 climate and weather models).

iv. Issues

Fig. 2 illustrates a few points about bulk flux algorithms. 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). Despite decades of work, wave effects on surface fluxes remain

the single most important and confounding problem. Much of the work on wave-flux interactions has keyed on supplementing mean wind speed with wave characterizations such as significant wave height, hs, and/or wave phase speed, cp (Drennan et al. 2005). One approach is to allow the Charnock parameter to be a function of wave age

)/( *ucF p=α (11)

Another assumes roughness length scales with wave height

)/()/(/ 2* pspsou cghForucFhz = (12)

To date these approaches have been ‘promising’ on limited datasets but a simple, universal form that reconciles all conditions has not been found.

Differences between Another interesting topic is the possible difference in sensible heat and moisture transfers may be critical. While scalar transfer in the atmospheric is dominated by turbulent flux, molecular diffusion must contribute heavily 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 [1] the effects cannot be accounted for simply by adding wind speed dependence to the bulk transfer coefficients (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. In situ methods. FairallChallenges:1. Lack of in situ data2. Very high wind speeds3. Very variable atmospheric conditions, so adequate temporal resolution required.4. Mixed phase surfaces and highly heterogeneous over sea ice, marginal ice zones and

polynyas .Renfrew, SpeerD. in situ observations should directly measure fluxes of mass, heat, momentum, etc, not

implemented at high latitudes for a range of reasons including sea spray, moving platforms, cold conditions. This drives us to use parameterizations.

E. Turbulent Fluxes FairallBulk formulae used to compute surface fluxes have been developed using measurements collected largely in the tropics. For extreme conditions found at high latitudes, these models are poorly verified, and there are considerable differences between parameterizations.

1.Momentum2. Sensible and latent

F. Mass fluxes Drennan1. A paragraph or two defining the concepts (i.e. Piston velocity, Schmidt number, etc),

describing the of methods used (tracer and micromet), and showing a single k660 vs U10 plot. Here we will also look at the role of the high latitude regions in the global CO2 balance, and discuss the paucity of bulk data. This will focus on CO2, but mention DMS, O3 etc in the context of Schmidt number scaling.

2. A paragraph summarizing the fundamental physics (including the roles of surfactants and

bubbles) , ending with other ideas for parameterizations (k - TKE diss, k - mss)3. A (brief) summary of high latitude measurements, followed by a longer discussions of high

latitude issues (high winds, and the role of ice, including recent (and tentative) results on fluxes through sea ice, and the role of brine channels.

4. Remote sensing early results. (Frew et al 2007, others).

Although bulk algorithms have limitations, they nonetheless serve as a cornerstone in satellite and NWP estimates of fluxes.

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

Overview: Since ground truth data are only point measurements, satellites and NWP offer prospects for extending to global fields. However, there are lurking concerns, most notably that limited in situ data also mean limited ground truth for models and satellite observations.G. Satellite Wick, Bourassa

1. Radiative (Shortwave and longwave) Pinker

At present, large scale satellite estimates of radiative fluxes from satellite observations disagree most in Polar Regions (Figure 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 estimates 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 coupled mod-els for the IPCC fourth Assessment with 5 observationally based estimates and reanalysis shows that the simulation of the Arctic surface energy budget has large bias in climate models and the largest dif-ferences are located over the marginal ice zones. Recent studies (Liu et al., 2005) indicate that the sur-face downward shortwave radiative fluxes derived from satellites are more accurate than the two main reanalysis dataset (NCEP and ECMWF), due to the better information on cloud properties in the satel-lite products. The SHEBA project showed that satellite-based analysis may provide downward short-wave (long wave) radiative fluxes to within ~ 10-40 (~10-30) W/m2 compared with ground observa-tions (Perovich et al., 1999). [Can’t make sense of following sentence:] Present-day Arctic and Antarc-tic radiation budgets of the National Center for Atmospheric Research Community Climate Model ver-sion 3 (CCM3) (Briegleb, 1998) show that the summer Top-of-Atmosphere (TOA) absorbed shortwave radiation estimates in Arctic and Antarctic from 1985 to 1989 are less than 20 W/m2 less than ERBE (Earth Radiation Budget Experiment) data and the surface downward shortwave radiation estimates are too small by 50-70 W/m2 compared with the selected model and observational surface radiative fluxes data.

Despite the challenges, the accuracy of the radiative fluxes in these regions are likely to improve 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, improved 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 development of a new inference scheme to estimate shortwave radiative fluxes (Wang and Pinker, 2009). The daily average values are constructed from the

combination of Terra and Aqua and agree well with ground measurements as shown in Figure 6 over oceanic sites (agreement is better over land). The improvement 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. Turbulent Heat Wickwhat SSMI/AMU measure, how that becomes heat flux, lurking deficienciesChallenges:

a) Relatively small spatial scale of atmospheric features, e.g. common occurrence of polar mesoscale cyclones or polar lows (100-1000 km); topographic jets; katabatic flows; fronts & other local features associated with sea-ice edge; etc

b) Problems causes by similarity in albedo/brightness temperatures between ice-covered surfaces and clouds.

c) Often large sea-air temperature differences; and near-surface air temperature relatively uncertain for NWP and satellite over and close to sea ice.

3. Turbulent Momentum Bourassaa) what scatterometer measures, how that becomes wind stress, deficienciesb) Inadequate observations at high wind speed for bulk formula development (and also

for QuikSCAT retrieval algorithm refinement); also limited observations for conditions of large sea-air temperature differences

c) Stable boundary-layer stratification and rapid transitions from stable to unstable stratifications versus usual assumptions.

4. Precipitation BourassaH. Assimilation Systems

1. 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 updated 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 resolution 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 variational technique for the T159L60 version of the Integrated Forecasting System to produce the analyses every six hours. NWP products, including flux estimates, are necessarily limited by the model resolution, parameterizations (including some of the bulk transfer formulae described earlier), data assimilation methodology, and observational database. Future NWP products, including reanalysis products, will certainly improve on the all of these items listed. The observational database improves as new observing systems are fielded. On-going and future data recovery projects will even promise to improve the historical data sets.

Issuesa) Inconsistencies between NWP products vastly exceed desired accuracy

Bourassa, Bitzb) NWP analyses and reanalyses reporting at relatively coarse resolution relative to

physical length scales and with a wide variety of PBL parameterisation schemes, Surface-Layer schemes and bulk flux algorithms, some of which are (probably) out-of-date but have not been changed due to operational priorities. Renfrew

2. Ocean assimilation (Talley)SOSE, Reanalysis mode only assimilation of ocean data, adjust fluxes to get circulation that best matches observations, results in extensive small scale structure in fields.

I. Hybrid Products (Bourassa)What is a hybrid product, what do they combine, strengths, pitfalls.

Hybrid 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 assim-ilation product acts as a background for a specialized data analysis step. Compared to the data assimila-tion 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 be-tween 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 sur-face 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 function. 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.[Add example here.]Product summary (Hoffman, Bourassa, Wick, others)

2. Applications: Desired (Talley, Gille, Magnusdottir, Bourassa, Serreze) and Current Accuracies (Bourassa, Fairall, Serreze, Gille, Drennan, Wick)A. Ocean applications

1. Global warming signal GilleSurface fluxes determine how heat, freshwater, momentum, CO2, and other gases are trans-

ferred between the atmosphere, ocean, ice, and land. Knowing fluxes is critical for understanding cli-mate change, and small changes in fluxes can significantly alter the climate system. For example, Hansen et al. (2005) calculated that from 1993 to 2003, the ocean gained heat at a rate consistent with a global heat flux into the ocean of 0.86 ± 0.12 W/m2. This implies that if we wanted to identify the spe-cific locations where excess heat might be entering the ocean, we would need to be able to identify lo-cal changes in decadally-averaged net surface heat fluxes on the order of 1 W/m2. This ideal standard of 1 W/m2 flux accuracy stands in stark contrast with our present reality. 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.

2. Freshwater balance Talley[I don’t understand the next sentence:] The freshwater flux anomalies that have been docu-

mented in the North Atlantic and that are related to changes in upper ocean stratification 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 varia-tion 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 con-stant in the absence of widespread ice melt.) This trend is equivalent to a change in precipitation-evap-oration of 0.003 cm/yr, which is far below any expected accuracy. (xx better check this number before publishing xx) Thus, as for heat fluxes, for which observing ocean temperature change is the best ap-proach, 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 stability 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 transport divergences are on the order of 0.1 Sv over regions with thousand km lengthscales. Translating 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.

3. Mixed-layer (as side bar) TalleyB. Atmospheric applications

1. Rossby wave breaking MagnusdottirC. Ice applications

1. Modeling Bitz2. Ice budgets Serreze

D. Summary Table/Figure Bourassa3. Summary all

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.

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.

100m, daily or tidal (accuracy from Ed and Mark S.)Leads

10km and 1 hour 50Wm-2 (must be worked on)NWP high impact weather events (cyclones), precipitation, cloud formation, diurnal variability

10km weekly 10Wm-2Polynyas, Eddies/ocean fronts, Coastal, Dense water formation, shelf processes/upwelling, ice breakup,

100km daily to weekly 10Wm-2Ocean atmosphere feedback: Rossby wave breaking, seasonal NH hurricane activity, cloud decks

100km Weekly 10Wm-2Dense water formation, mixed layer & mode water, Eddies/ocean fronts

100 to 1000km monthly 0.01Nm-2CO2 fluxes

1000km Seasonal 10Wm-2Upper ocean heat content, Seasonal forecasts ????Ice sheet evolution 1Wm-2open ocean upwelling xxNm-2 & Wm-2

1000km annual 10Wm-2heat flux out of ocean

10000km 10 year 0.1Wm-2Climate change

Consider color coding ovals for accuracyNeed current accuracies for each type of errors

SIDEBAR: OVERTURNING CIRCULATION

The ocean mixed layer mediates fluxes between the deeper ocean and connects to the atmos-phere through the ocean’s mixed layer. 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 layers can be especially thick, as they can be cooled, producing when surface cooling produces deeper mixing.

In the Southern Ocean, there are large variations in mixed layer depth (Figure from Dong et al.). One especially interesting feature is the axis of very thick mixed layers stretching from the central In-dian Ocean across the Pacific to South America. These thick mixed layers (“mode water”) lie just north of the Antarctic Circumpolar Current. Understanding the onset of (There are relatively thick mixed layers across the South Atlantic and western Indian Ocean, but they are much less dramatic.) These mixed layers have 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 up -welling. Air-sea buoyancy fluxes and winds are important inputs constraints on for 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 wa-ters, can be carried out quantitatively using “Walin analysis” (Fig. Walin). Air-sea buoyancy fluxes are summed for each isopycnal layer outcrop. 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 surface), 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, which requires loss of that mass into the interior ocean; this is the formal definition of “formation” in the Walin analyses. 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 available flux products are biased, largely because of lack of observations. Recent adjusted flux product (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 isopy-cnals 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, TalleyThe 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 northern northward flowing limb of the MOC carries water northward 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 southern 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 Subantarctic Mode Water. This water carries with it the signature of its contact with the atmosphere. Both the northern and southernThe limbs of the MOC help to determine howare key determinants of heat and other water properties are stored 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 at , and 130 W/m^2 in a globally 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 w/m^2,

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