Outline: High Latitude Surface Fluxes: Requirements and Challenges for Climate Research
authorship: U.S. CLIVAR High Latitude Flux Working Group plus other contributors
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.
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. ??;
Renfrew, 2008), so oceanographic instruments must be able to withstand high winds and rough seas, as
well as cold temperatures. 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 (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
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 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 real high latitude flux observing system. In the Arctic, few fluxquality 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. 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. Then we evaluate how these current accuracies compare
with the requirements for high latitude fluxes for a range of applications. 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. 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 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. 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):
L

L

(
1

)
S

H

H

G

0
down
up
down
s
L

where, Ldown and Lup are the downward and upward long wave fluxes, S down is the downward
shortwave flux,  is the surface albedo, H s is the sensible heat flux, H L 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
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 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:
1
/
21
/
2


w
x
c

X

C
S

X
xc
dS
x
(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:
2 2

X

X

X
(
z
);
S

U

U

U
G
.
sea
g
(2)
In [2] z is the height of measurements of the mean quantity X(z) above the sea surface (usually 10 m)
1

(
U
U
) is the gustiness factor. The gustiness term in [2] represents the near-surface
and G
g/
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,   z / L , 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:
1
/
2
c
1
/
2
1
/
2
xn
c
(
)

, c
 ,
(3)
x
xn
1
/
2
ln(
z
/
z
)
1

(
c
/
)

(
)
ox
xnx
where the subscript n refers to neutral ( = 0) stability, x 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).
2

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 eddycorrelation method u’ and x’ are measured and the flux is estimated as a simple time average of their
'x' 
w
'x'). The inertial-dissipation method (IDM) has also seen broad application.
products (i.e., w
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). 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
w
'
x
'
C


,
(4)
x
10
n
U

X
G
ln(
10
/
z
)
ln(
10
/
z
)
10
n
10
n
o
ox
then average to obtain
w
'x
'

C
 
.
(5)
x
10
n
U

X
G
10
n
10
n
The 10-m neutral values of the mean profile are computed as
u
u
*10
* z
U

ln(
)

U
(
z
)

[ln(
)


(
z
/
L
)]
10
n
u
(6a)
z
10
o
x
x
*10
* z

X


ln(
)


X
(
z
)

[ln(
)


(
z
/
L
)] (6b)
10
n
q
z
10,
ox
where x can be   or q . Note, the sign difference between [6a] and [6b] follows from X being
defined as Xs  X(z) 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

w
'x
'

C
 
C

,
x
10
n
x
10
nb
(7)

w
'x
'
b
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 X is large. This
both improves the signal-to-noise ratio of the flux observation and reduces the measurement fractional
error in X .
 




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,
C
F
(U
d10
n
10
n)
(8)
or the velocity roughness length is represented as a Charnock plus smooth flow form (Smith 1988)
2
u
zou
* 
g
u
*
(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,
z u
Rr  ou *

z0x F(Rr )
(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
F(cp /u*)
(11)
Another assumes roughness length scales with wave height
2
z
/
h

F
(
c
/
u
)
or
F
(
h
g
/
c
)
ou
s
p
*
s
p
(12)
To date these approaches have been ‘promising’ on limited datasets but a simple, universal form that
reconciles all conditions has not been found.
Another interesting topic is the possible difference in sensible heat and moisture transfers.
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. Fairall
Challenges:
1. Lack of in situ data
2. Very high wind speeds
3. 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, Speer
D. 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 Fairall
F.
1.
2.
3.
Bulk 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.Momentum
2. Sensible and latent
Mass fluxes Drennan
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.
A paragraph summarizing the fundamental physics (including the roles of surfactants and
bubbles) , ending with other ideas for parameterizations (k - TKE diss, k - mss)
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.
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
models 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
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 information 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). Present-day Arctic and Antarctic radiation budgets of the National
Center for Atmospheric Research Community Climate Model version 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 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 Wick
what SSMI/AMU measure, how that becomes heat flux, lurking deficiencies
Challenges:
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 icecovered 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 Bourassa
a) what scatterometer measures, how that becomes wind stress, deficiencies
b) 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 Bourassa
H. 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, 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 and future data recovery projects will even improve the historical
data sets.
Issues
a) Inconsistencies between NWP products vastly exceed desired accuracy
Bourassa, Bitz
b) 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.
J. 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 Gille
Surface fluxes determine how heat, freshwater, momentum, CO2, and other gases are
transferred between the atmosphere, ocean, ice, and land. Knowing fluxes is critical for understanding
climate 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
specific locations where excess heat might be entering the ocean, we would need to be able to identify
local 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
The freshwater flux anomalies that have been documented 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 salinity. The area of the
Arctic and North Atlantic north of 60°N is approximately 1.8 x 1013 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 equivalent to a change in precipitationevaporation 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 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 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 0.10.5cm/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) Talley
B. Atmospheric applications
1. Rossby wave breaking Magnusdottir
C. Ice applications
1. Modeling Bitz
2. Ice budgets Serreze
D. Summary Table/Figure Bourassa
3. Summary all
A. Conclusion on current accuracy of spatial fields in Hilat
1. Flux products do not agree at present
2. Choice of flux products depends on application; no one size fits all solution
3. Flux accuracies at present do not match requirements outlined above.
B. Research issues
Numerous 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 Carlson
1. Concerns about reduced ship availability and rising ship costs making things worse
rather than better
2. 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-2
Polynyas, Eddies/ocean fronts, Coastal, Dense water formation, shelf processes/upwelling, ice breakup,
100km
daily to weekly
10Wm-2
Ocean atmosphere feedback: Rossby wave breaking, seasonal NH hurricane activity, cloud decks
100km
Weekly
10Wm-2
Dense water formation, mixed layer & mode water,
Eddies/ocean fronts
100 to 1000km
CO2 fluxes
monthly
0.01Nm-2
1000km
Seasonal
10Wm-2
Upper ocean heat content, Seasonal forecasts
Ice sheet evolution
1Wm-2
open ocean upwelling
xxNm-2 & Wm-2
1000km
annual
heat flux out of ocean
10000km
10 year
Climate change
10Wm-2
0.1Wm-2
Consider color coding ovals for accuracy
Need current accuracies for each type of errors
????
SIDEBAR: OVERTURNING CIRCULATION
The ocean connects to the atmosphere 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 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
Indian 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
upwelling. Air-sea buoyancy fluxes and winds are important inputs 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
waters, 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
isopycnals do form mode waters of significant volume.
Xx more on mixed layer budgets from Sarah, also need to reduce the above to about 1/3 the
length or more!xx
Talley 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 northern 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 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 southern limbs of the MOC help to determine how heat and other water
properties are stored in the deep ocean, and both 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 130 W/m^2 in a globally 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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