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Surface turbulent heat fluxes
Carol Anne Clayson Physical Oceanography Department
with significant thanks to Carlos Jimenez and Matt McCabe
CERES Science Team MeetingNASA Langley
7 May 2013
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Global energy cycle
Trenberth et al. 2011
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The global water cycle
1.1!
1017 kg/yr!
0.6! 3.8!
4.4!
0.36!
0.36!
• Evaporative processes play a critical role in the climate system, coupling the surface and the atmosphere and linking the water, energy, and carbon cycles.
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The global water cycle
• Primarily an ocean-atmosphere cycle: terrestrial component less than 25% of the mean cycle
Schmitt 2010
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In-situ measured fluxes: not enough
• Turbulent fluxes over the oceans are almost entirely unmonitored by in situ sensors
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In-situ measured fluxes: not enough
• Similarly for land
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Turbulent fluxes
• Fluxes are defined by:
• w’: turbulent fluctuations of upward wind velocity• q’: turbulent fluctuations of humidity• ‘: turbulent fluctuations of temperature
QH = ρCpw 'θ '
QLE = ρLvw 'q '
θ
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Go out and measure . . . • Need: fast response
instrument• For winds: a sonic
anemometer
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Go out and measure . . .
• Sonic anemometer recording at 25 Hz
30 minutes
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Go out and measure . . .
• Sonic anemometer recording at 25 Hz
w w w
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Go out and measure . . .
w ' = w−w
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Why we don’t do this often
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On the land side
• Surface not moving• But: turbulence not stationary; largest eddies may
be affected by varying surface characteristics
• What this means: for ocean turbulence fluxes, eddy covariance measurements are “gold standard”. This is not true for land turbulence fluxes. Also: how to upscale for comparisons?
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Estimating the ocean fluxes
• Subscripts a and s denote values pertaining to the atmosphere at height zh and at surface
• CE, CH: bulk transfer coefficients: coefficients of water vapor or heat exchange (also called Dalton or Stanton number)
• Thus to calculate evaporation correctly we need: Ua, Us, qs, qa Ts, Ta and an appropriate model of CE and CH
• From satellite we estimate the fluxes from the bulk equations:QLE = ρLvw 'q ' = ρLv (Ua −Us )CE (qa − qs )QH = ρCpw 'θ ' = ρCp (Ua −Us )CH (Ta −Ts )
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“Transfer coefficient”
• Depends on:• Wave breaking• Roughness of sea
surface• Air-sea
temperature difference
• Sea spray• ?
Garfinkel et al. 2011
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Estimating the fluxes• How well we estimate is now both a function of how
well we MEASURE and how good our MODEL is
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!
Hurricanes
!
!
SeaFlux
Liu et al. 2011
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• Plateau of CD at wind speeds above 30 m/s in both laboratory and field measurements – Spray generation? – Flattening of wave crests
accompanied by flow separation?
“Transfer coefficient”
Sullivan and McWilliams 2010
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Flux parameterization effects
!
Wave effects
Sea sprayeffects
5%
15%
Liu et al. 2011
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Additional land concerns
Developing*a*long,term*record*of*global*heat*fluxes*• Previously+examined+the+global+scale+(space+and+8me)+• Have+studied+the+regional+scale+(Princeton+++UNSW)+• Need+to+evaluate+tower+scale+(forcing+and+fluxes)+
! Run+models+using+common+forcing+data+! Examine+variability+of+model+response+! Impact+of+forcing,+biome+and+climate++
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• Near-surface air temperature and humidity– Roberts et al. (2010) neural net technique– SSM/I only from CSU brightness temperatures
(thus only covers 1997 - 2006)– Gap-filling methodology -- use of MERRA
variability – 3 hour
• Winds• Uses CCMP winds (cross-calibrated SSM/I,
AMSR-E, TMI, QuikSCAT, SeaWinds)
• Gap-filling methodology -- use of MERRA variability – 3 hour
• SST• Pre-dawn based on Reynolds OISST
• Diurnal curve from new parameterization
• Needs peak solar, precip
• Uses neural net version of COARE• Available at http://seaflux.org
1999 Latent Heat Flux
1999 Sensible Heat Flux
SeaFlux Climatological Data Set Version 1.0
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Ta, qa• Use of neural net
technique from SSM/I fields (Roberts et al. 2010)
• Gridding into equal-angle grids
• Interpolation using model gradients
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Retrievals of qa
Roberts et al. 2010
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Some comparisons
Bias: 2.1 W m-2
Std Error: 38 W m-2Bias: -3.1 W m-2
Std Error: 13.2 W m-2
Here we do comparison with eddy covariance fluxes from research vessels -- they are our “ground truth” Clayson et al. 2013
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Climatological Datasets
• Goddard product: GSSTF/2b– Daily, 1°, input variables and turbulent fluxes
– July 1987 - December 2007; global oceans
• IFREMER product – Weekly, 0.25°, input variables, turbulent fluxes
– Currently available: 1992 – 2006; global oceans
• Japanese Ocean Flux datasets: J-OFURO2v2 – Input variables, fluxes, radiation
• Daily, 1°, 1988 – 2005; global oceans
• Satellites, JMA model analyses
• HOAPS3– Twice-daily, 0.5°, 1987 - 2005, global oceans
– July 1987 - December 2005
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W m-2
Latent Heat Flux: 1999-2005
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W m-2
Sensible Heat Flux: 1999 - 2005
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• Can not measure what is needed from satellite to calculate the fluxes with the bulk algorithms
• Satellite products use a neural net or other statistical technique or model and generally a combination of the following inputs:•Backscatter (ERS among others) -- provides information on soil and
vegetation moisture, vegetation coverage, and surface roughness•AVHRR visible and infrared reflectances (vegetation information)•SSM/I emissivities at 19v and 19h• skin temperature (ISCCP), it’s diurnal cycle (also ISCCP)•net radiation (often ISCCP)
On the land side
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On the land side
• Satellite-based products (process-based, empirical)
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Latent Heat Flux
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Some ocean comparisons
Brunke et al. 2011
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Some sample comparisons
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Some land comparisons (LHF)
Varia%on(via(biome(type(at(GRID(and(TOWER(scales(• Filtered(to(45(“common”(towers(to(simulate(ALL(models(
Flux(Tower:(3hr( LandFLUX:(3hr(
McCabe 2013
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Some land comparisons (LHF)
• Evaluation using La Thuile FLUXNET Synthesis Dataset
78.908 daily observations
[D. Miralles, work in progress]
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Bias: -0.21 m s-1
Std Error: 2.17 m s-1
Bias: 0.37 o CStd Error: 1.67 o C
Bias: 0.09 o CStd Error: 1.43 o C
Bias: 0.33 g kg-1
Std Error: 1.69 g kg-1
Comparisons with IVAD data (courtesy of E. Kent)
Clayson et al. 2013
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Evaluating uncertainty using IVAD data Qs-Qa Ts-Ta
Windspeed
Clayson et al. 2013
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Instantaneous fields
Clayson et al. 2013
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LHF monthly uncertaintyMean LHF June 1998
Total uncertainty
Clayson et al. 2013
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Uncertainty estimates
Clayson et al. 2013
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Trends in evaporation
From Axel Andersson, CM-SAF
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From Axel Andersson, CM-SAF
HOAPS-3 Wind Speed
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Strategies for Improving Ocean Turbulent Fluxes
• More routine observations: Moorings, or routine ship-board observations of momentum and turbulent heat fluxes
• Extremes: cold air outbreaks, hurricanes• High-latitudes
• More process studies: put together evap, precip, latent heating profiles into some case studies
• Coastal/MIZ processes: how do we resolve coastal processes
• Better calibration: we need to resolve what are appropriate calibrations for multiple uses
• New satellites: Prospect of obtaining momentum, latent heat, sensible heat, radiative fluxes through a well-defined set of sensors, possibly in multi-satellite formation (“Flux Train”)
• More global coverage: “missing” events significant problem
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New results: AMSR-E/AMSU
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We can look to ocean salinity
Fig. 1 Observed and selected CMIP3 20C3M simulations of surface salinity and water fluxes.
P J Durack et al. Science 2012;336:455-458
Published by AAAS
MeanE - P
Mean Surface Salinity
Surface Salinity Change
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Strategies for Improving Land Turbulent Fluxes
• Conducting first assessments characterising the uncertainty in the existing global estimates of land surface heat fluxes
• Global annual Qle are in a range of 15 W/m2 for an ensemble average of ~ 45 W/m2, a bit larger for Qh
• Progress has been made (a growing number of global satellite-based (diagnostic) estimates) but significant differences can still be observed
• Moving to dataset production: based on experienced gained during the assessments
• Merged/synthesis dataset [ETHZ]
• Satellite-based V0 products [PU, UoB, UoNSW, PO]
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Strategies for Improving Land Turbulent Fluxes
KING%ABDULLAH%UNIVERSITY%OF%SCIENCE%AND%TECHNOLOGY,%%SAUDI%ARABIA%
Global%products%of%evapotranspiraFon:%the%GEWEX%LandFLUX%IniFaFve%
MaLhew%McCabe1,%Ali%Ershadi2,%Eric%Wood3,%Nathaniel%Chaney3,%Carlos%Jimenez4,%%Diego%Miralles5,%BrigiLe%Mueller6%and%Sonia%Seneviratne6%%
1%King%Abdullah%University%of%Science%and%Technology,%Saudi%Arabia%2%University%of%New%South%Wales,%Australia%3%Princeton%University,%United%States%of%America%4%Observatoire%de%Paris,%France,%%5%University%of%Bristol,%United%Kingdom%
6%ETH%Zurich,%Switzerland%
%
2013%European%Geophysical%Union%!%Vienna,%Austria%!%EGU2013\6439%
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Strategies for Improving Land Turbulent Fluxes