Schmid, H.P.

Su, H.B.

Schmid, H. P.

Su, H. B.

KITopen

Out of Kansas: Meaningful Turbulence Measurements in Non-Ideal ConditionsHaPe Schmid Research Center Karlsruhe, IMK-IFU, Garmisch-Partenkirchen (D)Tech. U. Munich (D)Indiana University (USA)Hong-Bing Su East Carolina University (USA)MMSF & UMBS1998-2001Kansas1968• short stubble (20 cm)• flat, smooth terrain• 15 hours data(3 levels)• tower: 32 m• z/h > 20-200• tall forest (23-28 m)• ridge-ravine terrain;gentle slope• ~ 40‘000 hours data(2+2 levels)• tower: 47 m• z/h < 2.1Haugen et al. 1971 (QJRMS, 97, 168-180)Atmosphere – Biosphere ExchangeWhy is it relevant ?From: Sarmiento and Gruber, 2002 (Phys. Today)For Example: CO2(from Sarmiento and Gruber, 2002)CO2,Atm Accumulation = CO2 Source - Land & Ocean SinksBackground: Global Carbon BudgetFLUXNETIntegrating Worldwide CO2 Flux Measurements(currently ~ 300 stations)IndianaMMSF~FluxMichiganUMBS~FluxAmeriFlux NetworkProblem: Complex Terrain Biosphere-Atmosphere Exchange Measurements in “Difficult Conditions”“Difficult Conditions” ???⇒ deviations from micrometeorological ideal:• flat terrain• homogeneous fetch• low, homogeneous vegetation (if any)• stationarity• well-developed turbulence (MOST)• topography• patchy land-cover• deep, multy-layer  vegetation canopy • instationarity• weak turbulence; free convectionHeterogeneousScalar Field(∆LAI, ∆Bowen-Ratio)HeterogeneousFlow/Turbulence(disturbance, forest edges)Difficult Conditions: Patchy Land CoverTall TreesDifficult Conditions: Deep CanopiesMulti-Layer UnderstoreyLarge Scale TopographySmall Scale, Gentle TopographyDifficult Conditions: TopographyEddy-Covariance: Closed Path SystemUMBS~Flux Tower: InstrumentationQ*, PARCO2, H2Oeddy fluxPrecip.T/RHSoilUnderstorypineCanopyaspen, oak,mapleN5.1 m15045.7 13039.611033.59027.45015.27021.3103.0309.1Height (feet & meters)Trunk SpaceIRGAEddy-Covariance: ' 'w c = cov(wt,ct)Lagged E-C:  cov(wt,ct-τ)• τ: determined so that covarianceis maximizedcov(wt,ct-τ)Lag, ττmaxFluxes are determined in post-processing of 10 Hz data-stream (> 1GByte/week)Turbulent Flux: the correlation of eddiesSonic Anemometer• measures transit time of ultrasonic pulse →depends on air velocity• fast sampling rate (~10-60 Hz)• three velocity components• sonic temperature• at ≥ 10 Hz: resolves most fluctuations in turbulence•ultrasonicpulseScalar Concentration• sample-air intake• synchronized analysis with sonic signals•′= +w w w′= +C C C′ ′= +wC wC w Ceddy covarianceUMBS, 46 m, foliatedSu et al. 2004 (BLM 110, 213–253)Kaimal et al. 1972 (QJRMS 98, 563–589)Kansasfmax ≈ 0.05fmax ≈ 1.0fmax ≈ 0.05fmax ≈ 0.2Su et al. 2004 (Boundary-Layer Meteorol. 110, 213–253)UMBS, 46 m, foliatedneutral & unstable:Su et al. 2004 (Boundary-Layer Meteorol. 110, 213–253)• co-spectra appear to match well with „smooth“ terrainJun-Aug, daytimeSu et al. 2004 (Boundary-Layer Meteorol. 110, 213–253)Energy-Balance Closure(after spectral correction)Hourly Fluxes of CO2 over 8 Years (MMSF)NEE: Net Ecosystem Exchange = Respiration - Assimilationforest toatmosphereatmosphereto forest1998 1999 2000 2001 2002 2003 2004 2005 2006 2007-35-30-25-20-15-10-505YearCumulative NEE (ton C ha-1 )30 tons C ha-1 = 3 kg C m-2Cumulative Exchange of CO2 over 9 Years (MMSF)NEE: Net Ecosystem Exchange = Respiration - Assimilation30 tons C ha-1367[g(C)m-2]267[g(C)m-2]304[g(C)m-2]366[g(C)m-2]274[g(C)m-2]418[g(C)m-2]386[g(C)m-2] 360[g(C)m-2]236[g(C)m-2]Examine CO2 Conservation Equation!                     0CNEEmzmCxxmCz dz dxVFzCxtzCu w∆ C-StorageNEEChoriz.Adv.vert.Adv.FCO2 What do we want?    Are fluxes capturing the right processes ?NEE !What do we have?    FC (+ storage)!Potential problems:• location, shape of the box• “leaking” out of the box 0.0 0.2 0.4 0.6 0.8 1.0-0.20.00.20.40.60.81.01.2 Eddy-covariance flux FCO2 Canopy-air-layer storage flux FCO2 + storage flux  Fraction of Expected Ecosystem Respirationu* ( m s-1 )UMBS: Eddy Flux and Storage Term• lack of closure indicates advection important at low u* valuesSchmid et al. 2003 (JGR 108, 4417)Mead rain-fed: land use    Micrometeorological Flux Measurements: at what scale?Schmid 2002 (Agric. For. Meteorol. 113, 159-184)Flux FootprintSensorCO2SourceCO2The Flux Footprint:• What Part of the Ecosystem does the Flux Sensor ‘see’  ?• Is that Part Representative of the Ecosystem? (answer varies over time)• If yes: use data; if not: reject datae.g.: Schmid (2002, Ag. For. Met., 113, 159-184 )Schmid 1994 (Boundary-Layer Meteorol., 67, 293-318)Flux Footprint = spatial filter, “field of view”(convolution of the source distribution, QS, with the footprint, f )      ffs sQF d Q       x x x x xsensorInputs:• zm• z0• h• u*• σw• σvConcentration and Flux Footprint ModelsGoverning equations in Eulerian analysis:** following Finnigan (2004, AgForMet 127, 117-129);neglecting horizontal turbulent fluxes and pressure interactions.F::cadvection diffusion forcingsurface sourcesflux production rate(arises from c-gradient in turbulent flow).surface sources only in boundary conditionsin inhomogeneous flow, may cause complex behavior of flux footprint500 m1000 mLocation and shape of the box ...500 m1000 mLocation and shape of the box ...... is variable (see footprint)Is the tower optimally located ?What kind of location bias can we expect ?Footprint is different for flux and storage (concentration)!HourlyFootprints2001:YD 217-YD 225Aug 5 –Aug 13HourlyFootprints2001:YD 217-YD 225Aug 5 –Aug 138-Day Flux Footprint Composite46 m tower:•Located on ridge-topGully tower:•30 ft tower•~350 m downstream, ~30 m elevation drop from main towerAdvection and Gully Flowsin Complex Forested TerrainN.J. Froelich, H.P. SchmidIndiana UniversityProblem with Nighttime Fluxes in Topography?1 kmTowergully towerhorizontaldivergenceL*L*L*verticalconvergencematerial emited from the ground (CO2) is advected away, before affecting the flux on the tower.Is respired CO2 at night “leaking” out of the box, without a trace detectable by the flux sensor?417Height (m)ΔT (ºC)T av (ºC)0203601800Wind Direction254 255 256 257Day of YearThermotopographic Flow – Leaf-On Night «─» Up-gully flow with lapse conditions Day «─» Down-gully flow with inversion conditions0.5-0.5DOWNUP1m46mHeight (m)417ΔT (ºC)0.5-0.5T av (ºC)1800203600317 318 319 320Wind DirectionThermotopographic Flow – Leaf-Off Night «─» Down-gully flow with inversion conditions Day «─» Up-gully flow with lapse conditionsUPDOWNDay of Year1m46mtemperature inversion «─» down-gully velocitieslapse conditions «─» up-gully velocitiesThermotopographic forcing of gully flows-0.6-0.4-0.200.20.40.60.8-0.15 0 0.15 0.3 0.45Gully Temperature Gradient (oC m-1)Along-gully Velocity (m s-1 ) LeafOffbin-averaged dataIncludes all dataBelow-Canopy Temperature Gradient andAlong-Gully Velocitydown-gully velocities   «─»     stronger convergence / weaker divergence aloft-10-50510-0.5 -0.3 -0.1 0.1 0.3 0.5 0.7Along-gully Velocity (m s-1)Vertical Velocity (cm s-1 )LeafOffbin-averaged dataIncludes all Leaf-Off dataAlong-Gully Velocity and Vertical Velocity Above-canopyFlow Patterns: Leaf-Off NighttimeverticalconvergencehorizontaldivergenceL*L*L*Flow Patterns: Leaf-On Nighttimeverticalconvergence??? horizontal divergencehorizontalconvergence ?L*L*L*Temperature inversionLapse conditionsFlow Patterns: Leaf-On Daytimeverticaldivergence??? horizontal convergencehorizontaldivergence ?Q*Q*Q*Temperature inversionLapse conditionsSummary of ResultsNocturnal vertical convergence above canopy• tendency to downward vertical velocitiesNocturnal below-canopy thermotopographic flows• down-gully (divergence) in Leaf-Off season• up-gully (convergence) in Leaf-On seasonImplicationsAbove-canopy conditions may misrepresent below-canopy conditionsNeed to consider complex 3-D flow patterns at each site, via both measurement campaigns and modelingAcknowledgements:This work is being supported by a grant from the US Dept. of Energy (TCP/Office of Science) through its participation in the joint-agency Carbon Cycle Science Program and by several grants from National Institute for Global Environmental Change (NIGEC/US-DOE).The crew: Gabriella Villani (Italy), Hong-Bing Su (China), Steve Scott (Scotland), Laura Ciasto (USA), Shane Hubbard (USA), Heidi Zutter (USA), Norma Froelich (Canada), HaPe Schmid (Switzerland), Andrew Oliphant (New Zealand), Sue Grimmond (New Zealand), Chris Vogel (USA), Jennifer Hutton (USA). Not present: Ford Cropley (UK), Reiko Toriumi (Japan), Danilo Dragoni (Italy), Bin Deng (China), Jessica Howe, Catherine Wade, Ben Crawford, Jen Klippel, Matt Seavitte, Nate Langwald, Brian Bovard, Craig Wayson (all USA)MMSF UMBS

Out of Kansas: meaningful turbulence measurements in non-ideal conditions. Weg von Kansas - Turbulenzmessungen in nicht-idealem Terrain

https://publikationen.bibliothek.kit.edu/230075873/3815479

Out of Kansas: meaningful turbulence measurements in non-ideal conditions. Weg von Kansas - Turbulenzmessungen in nicht-idealem Terrain

Abstract

Similar works

Full text

Available Versions

KITopen