CONSTRUCTING REGIONAL CO 2 FLUXES USING FLUX-TOWER UPSCALING AND ATMOSPHERIC BUDGETS Results from the Chequamegon Ecosystem- Atmosphere Study (ChEAS) and beyond K.J. Davis 1 A.E. Andrews 2, J.A. Berry 3, P.V. Bolstad 4, M.P. Butler 1, J. Chen 5, B.D. Cook 4, A.R. Desai 1, A.S. Denning 6, F.A. Heinsch 7, B.R. Helliker 8, N.L. Miles 1, A. Noormets 5, D.M. Ricciuto 1, S.J. Richardson 1, M. Uliasz 6, W. Wang 9 1 Dept. of Meteorology, The Pennsylvania State University; 2 Global Monitoring and Division, NOAA; 3 Department of Global Ecology, Carnegie Institution of Washington; 4 Dept. of Forest Resources, University of Minnesota; 5 Dept. of Earth, Ecological and Environmental Sci, The University of Toledo; 6 Dept. of Atmospheric Science, Colorado State University; 7 School of Forestry, University of Montana; 8 Department of Biology, University of Pennsylvania; 9 Pacific Northwest National Laboratory. LSCE, Gif-sur-Yvette, 7 March, 2006

outline History and goals Flux tower measurements –Tall tower flux measurements –Regional upscaling –Model-data synthesis with flux measurements Atmospheric boundary layer continuous CO 2 measurements –Atmospheric profiling –Atmospheric budgets Future directions –Enhanced regional upscaling and the ‘ring of towers’ –Prediction and detection –Continental-scale measurements and inversions

Photo credit: UND Citation crew, COBRA WLEF tall tower (447m) CO 2 and H 2 O flux measurements at: 30, 122 and 396 m CO 2 mixing ratio measurements at: 11, 30, 76, 122, 244 and 396 m WLEF flux and mixing ratio observatory

History NOAA tall tower program begins, 1992(?). Pieter Tans and Peter Bakwin. WLEF tower instrumented to measure CO 2 mixing ratios, WLEF tower instrumented to measure CO 2 fluxes, Davis and Bakwin. “You have the perfect site…” Denning. Many complementary studies are initiated in the “footprint” of the WLEF tower, 1997.

Goals of the ChEAS At hourly to multi-year time scales, and regional spatial scales: –Determine ecosystem-atmosphere fluxes of carbon and water; –Determine the processes governing these fluxes; –Develop the capacity to predict how these fluxes will change as climate changes. Characterization of fluxes at regional scales requires advances in methodology. The development of regional flux measurement methodology is a central focus of the ChEAS.

Methods Flux of carbon across this plane = tower or aircraft flux approach - Change in forest biomass over time = forest inventory approach Change in atmospheric concentration of CO 2 over time = inversion or ABL budget approach. Change in CO 2 concentration in a small box over time = chamber flux approach

Atmospheric approaches to observing the terrestrial carbon cycle Time rate of change (e.g. CO 2 ) Mean transport Turbulent transport (flux) Source in the atmosphere Average over the depth of the atmosphere (or the ABL): F 0 C encompasses all surface exchange: Oceans, deforestation, terrestrial uptake, fossil fuel emissions. Inversion study: Observe C, model U, derive F Flux study: Observe F directly

Methodological gap

Complementary nature of atmospheric inversions and flux upscaling Atmospheric inversionFlux upscaling Excellent spatial Intrinsically local integration measurements. Strong constraint on Difficult to upscale flux flux magnitudemagnitudes due to ecosystem complexity. Poor temporal Excellent temporal resolution Limited processMore process understanding.understanding

ChEAS observations Tall tower with F co2, [CO 2 ]Radar and ceilometer ABL profiling [CO 2 ] tower networkAirborne and satellite remote sensing Flux tower networkChamber and sap flux measurements Airborne [CO 2 ] profilesBiometric measurements FTIR column [CO 2 ]

Powered parachute photograph: M. Jensen

View from 396m on the WLEF tower: OK Tower Service

Region: Flat, heterogeneous, forested, managed, rich in wetlands, low in humans 4 meter30 meter 1 kilometer

I.Flux tower results Tall tower flux measurements

Flux measurement method Yi et al, 2000

Eddy-covariance methods summary Sonic axes are rotated into the long-term mean wind direction. Fast-response CO 2 and H 2 O measurements calibrated from slow-response profile measurements. Long tubes are used to sample CO 2 and H 2 O. Lag-time correction applied. Spectral correction for high-frequency loss applied. It is substantial for H 2 O fluxes. Integral of the cospectrum indicates 1 hour averaging time needed for 396 m flux measurement. Hourly random sampling errors are large.

One must capture the large and small eddies Berger et al, 2001

Random errors – a finite number of eddies are counted in one hour Random sampling errors for any one hour can be as large as the magnitude of the measured flux! Berger et al, 2001, following Lenschow and Stankov, 1986.

Radar ABL depth WLEF fluxes CO 2 profile Davis et al, 2003 Daily cycle of ABL depth, and CO 2 fluxes and mixing ratios

“Preferred” NEE Data is taken from 30m at night and 122 or 396m during the day (the highest level where there is turbulent flow) when all data are available. If data are missing, any existing flux measurement is used. Data are screened out when the level of turbulence is very low. CO 2 is probably draining down hill. Early in the morning upper level data from WLEF is replaced with 30m data (Yi et al, 2000) because the flow appears to be systematically 2-D. Thus from 3 NEE measurements, one “preferred” flux measurement is save for each hour. (But all flux levels and components are reported.) Some bias exists among flux measurement levels. Contribution to annual NEE is of the order of a few tens of gC m -2 yr -1 (Ricciuto et al, in review).

Nighttime drainage flows? Cook et al,2004; Davis et al, 2003 Loss of flux at low turbulence levels at the Willow Creek tower.

WLEF morning advection? Compute Del-NEE among levels. Find a persistent signature of advection during the morning transition. Loss of storage is not offset by turbulent flux. Hypothesis: Venting of the nocturnal pool occurs elsewhere, at a persistent location in the landscape? Yi et al, 2000, JGR. Due to storage term. Storage terms are small.

Flux measurement method Yi et al, 2000

Differences among levels at WLEF Grassy clearing is a significant part of daytime 30m footprint but not much of the 122m or 396m footprints. Difference between 30m and 122m implies that using 30m may cause daytime fluxes to be underestimated by 8-10% Daytime 396m fluxes 33% larger than 30m. Can’t explain meter difference. Davis et al, 2003; Wang et al, in press A and B; Ricciuto et al, in review

U * screening bias at the WLEF tower We use a u* cutoff value of 0.2 ms -1 This screens about 50% of nighttime growing season data. Data indicates modest flux loss even with 0.2 cutoff. Average annual NEE as a function of u * cutoff U * cutoff (ms -1 ) Annual NEE (gC m -2 yr -1 ) average Ricciuto et al, in review

Hourly fluxes at WLEF for 1997, observed and filled. Davis et al, 2003.

Net ecosystem-atmosphere exchange of CO 2 in northern Wisconsin

A net source of CO 2 to the atmosphere! … year after year!

I.Flux tower results Regional upscaling

WLEF tall tower wetland mature hardwood old growth Large differences in growing season fluxes among sites. Net annual source of CO 2 to the atmosphere observed at WLEF, caused by large respiratory fluxes. Desai et al, in press; Wang et al, in press A and B; Ricciuto et al, in review.

ChEAS observations Tall tower with F co2, [CO 2 ]Radar and ceilometer ABL profiling [CO 2 ] tower networkAirborne and satellite remote sensing Flux tower networkChamber and sap flux measurements Airborne [CO 2 ] profilesBiometric measurements FTIR column [CO 2 ]

NEE (gC m -2 ) Respiration (gC m -2 ) Photosynthesis (gC m -2 ) WLEF WLEF WLEF WLEF WLEF WLEF average Willow Creek Willow Creek Willow Creek Willow Creek average Lost creek Lost Creek Lost Creek average NEE and gross fluxes at ChEAS sites:

The difference appears to be large respiratory fluxes at WLEF (evident in nighttime flux data) Drying wetlands? Disturbance/logging?

Regional flux estimates Upscaling 1.Aggregate stand-level flux tower measurements. Desai et al, in press, AgFMet. 2.Flux footprint decomposition using the WLEF tall tower. Wang et al, in press, JTech, JGR. Atmospheric budget 1.Traditional ABL budget using WLEF tall tower [CO 2 ] data Wang et al, submitted and in preparation 2.ABL-free troposphere CO 2 mixing ratio difference Helliker et al, 2004; Bakwin et al, Ring of towers, mesoscale inversion Uliasz et al, under construction

WLEF 2003 May-Sept fluxes were “decomposed” using a flux footprint model, simple ecosystem model, and a six stand-type vegetation map. -Forested wetlands, mature deciduous and young aspen are implicated as strong respiratory sources. -Comparison of WLEF “mature deciduous” and Willow Creek fluxes suggest differences exist within this vegetation class. (Wang et al, in press JTECH, JGR)

Stand-level tower upscaling Twelve stand- level flux measurements are matched to vegetation categories. Stand age since disturbance is a primary control on the long-term net carbon flux in the region Desai et al, in press.

Landsat-based land cover map (WISCLAND) used for upscaling – 40x40 km 2 4 meter 30 meter1 kilometer

Comparison of two independent upscaling approaches is promising. Uncertainty in each aggregate flux, however, is fairly large and difficult to quantify. (Desai et al, in press, AFM; Wang et al, in press JTech, JGR)

Regional upscaling appears to work? Independent methods and data! Forested wetlands and young aspen implicated as strong respiratory sources Uncertainties in region upscaling: Flux footprint accuracy Land cover classification Representativeness of stand-level flux measurements Systematic errors in eddy-covariance flux measurements

WLEF tall tower wetland mature hardwood old growth Interannual variability in WLEF fluxes are statistically significant, and strongly correlated with climate (and, for 2001, insects). Multi-year record begins to suggest degree of coherence in interannual variability among sites. Desai et al, 2005, in press; Ricciuto et al, in review.

Hypotheses: Temporal variability in NEE of CO 2 is governed primarily by climate and weather. Site-to-site variability in NEE of CO 2 is governed primarily by ecosystem properties. Temporal variability in NEE is easier to upscale than, say, the summer regional value of NEE? Plans: Apply parameter estimation to multiple towers over multiple years. Test hypotheses. Assess applications to inverse modeling, carbon cycle prediction.

II. Atmospheric boundary layer continuous CO 2 measurements Atmospheric profiling VTTs seasonal phase lag synoptic cycles Atmospheric budgets

Diurnal cycle of CO 2 in the ABL Bakwin et al, 1998

Daily Mixing Ratio Profile from a Tall Tower

Midday difference in CO 2 between the mid-CBL and the surface layer

If You Prefer Numbers… Month CO2 (ppm) at 30m, midday CO2 (ppm) at 396m, midday  CO2 (ppm) 30m-396m, midday σ (396m-30m) CO2 (ppm) at 396m, entire day  CO2 (ppm) 396m(pm) 396m(entire) Annual Mean Monthly Summary for 1998

Synoptic variability in CO 2

What Is This Correction? Following the mixed layer similarity theory of Wyngaard & Brost [1984] and Moeng & Wyngaard [1989], the vertical gradient of a scalar in the boundary layer: where g b and g t are bottom-up and top-down gradient functions scaled by boundary layer depth z i w * is the convective velocity scale wc 0 and wc zi are the surface and entrainment fluxes of the scalar C

The Gradient Functions The LES gradient functions are from a study by Patton et al. [2003]. The observed gradient functions will be in Wang et al., [in prep].

Hourly 396 – 30 m CO 2 difference Spring/Summer

What a surface layer observation is missing: Unlike a tall tower: –Nocturnal boundary layer profile is missing (see Wang et al, in prep, budget estimates) –Midday observations only (night very hard to interpret, though some are trying) –Limited number of species observed (add flasks to VTTs?) –Surface layer measurement adds bias, variance (but not a great deal!)

Net ecosystem-atmosphere exchange of CO 2 in northern Wisconsin

Flux-CBL-FT phase lag CBL mixing ratio leads local NEE. (Davis et al, 2003; Yi et al, 2004) Evidence of large-scale transport. (Hurwitz et al, 2004) ABL-FT CO 2 difference used to compute regional fluxes (Helliker et al, 2004). Applied successfully to 4 flux tower sites by Bakwin et al (2004).

Regional-Scale Inventories

Simple observational approaches show signs of convergence Uncertainties in methods are large Plan: Enhance the upscaling approach. Deploy additional CO 2 sensors. Reduce uncertainty in both approaches and intercompare again.

“ring” of towers inversion Tall tower with F co2, [CO 2 ]Radar and ceilometer ABL profiling [CO 2 ] tower networkAirborne and satellite remote sensing Flux tower networkChamber and sap flux measurements Airborne [CO 2 ] profilesBiometric measurements FTIR column [CO 2 ]

1200 UTC April 29, 2004 CO2 from 5 sites, April 29, 2004

The Richardson-Miles Package For more information, see

Performance Testing Difference between the PSU system and WLEF 76m CO 2 measurements in a test from April-August [Miles/Richardson/Uliasz, in prep.] Difference of daily averages

See for calibrated CO 2 at AmeriFlux siteshttp:// Ring of towers, Summer 2004

Ideas for research next fall Consider how (when?) to merge flux and mixing ratio measurements in a single inversion. Study the spatial and temporal coherence of flux (and mixing ratio?) observations, and consider the implications for inverse flux estimates and observational network design. Evaluate the ability of forwards models of CO 2 transport to resolve, e.g., seasonal and synoptic events. –Region? –Continent?

Acknowledgements Department of Energy Terrestrial Carbon Processes Program National Institutes for Global Environmental Change Midwestern Regional Center, DoE National Oceanic and Atmospheric Administration Office of Global Programs National Science Foundation Division of Environmental Biology National Aeronautics and Space Administration Terrestrial Ecology Program

Instruments at WLEF Berger et al, 2001

Instruments at WLEF Two “profiling” LI-CORs in the trailer, one sampling 396m, one cycling among all 6 levels. “Slow” time response. High-precision and accuracy calibration (Bakwin et al, 1998). C-bar. Vaisala humidity and temperature sensors at 3 levels (30, 122 and 396m). “Slow” Q-bar, T-bar. Three sonic anemometers (30, 122 and 396m). w’, T’ Three LI-CORs in the trailer, one for each sonic level. “Fast” time response. Long tubes, big pumps. Measure CO 2 and H 2 O. c’, q’ Two LI-CORs on the tower (122 and 396m). “Fast” time response. Short tubes, smaller pumps.

Calibration of “fast” CO 2 and H 2 O sensors at ChEAS towers Calibration occurs using the fluctuations in the ambient atmospheric CO 2 and H 2 O mixing ratios. “Slow” sensors provide absolute values of these mixing ratios used to calibrate the “fast” LI- CORs. Ideal gas law corrections to LI-COR cell temperature, pressure and humidity are applied. Calibration slope and intercept are derived every 2 days. These values are smoothed (monthly running mean) to derive the long-term calibration factors used for the “fast” LI-CORs.

Calibration of “fast” CO 2 and H 2 O sensors Berger et al, 2001

What’s up? (Sonic rotations) Sonic anemometers are oriented perfectly in the vertical, (and the wind’s “streamlines” aren’t always perpendicular to gravity). Data is collected over a long time (about a year) and we define “up” by forcing the mean vertical wind speed to be zero.

Sonic rotations Berger et al, 2001

Lag time calculation We must correct for the delay between the CO 2 and H 2 O measurements and the vertical velocity measurements. Lag time is determined by finding the maximum in the lagged covariance between vertical velocity and CO 2 /H 2 O for every hour. Level (m)IRGA position Tube length (m) Lag time (s) Tube inner diameter (m) Flow rate (L min -1 ) Reynolds number 396Trailer Trailer Trailer Tower Tower Berger et al, 2001

Lag time calculation Berger et al, 2001

Spectral corrections Flow through tubes smears out some of the atmospheric fluctuations, especially the small (high frequency) eddies. –Obvious for H 2 O. Much worse than theory predicts. –Not directly observed for CO 2. Small effect. The sonic anemometer (virtual) temperature measurement is not smeared out, so we use similarity between the virtual temperature spectrum and the water vapor spectrum to correct for the loss of high frequency eddies in H 2 O. We use past studies of flow in tubes to correct for the loss of high frequency eddies in CO 2.

Spectral corrections Berger et al, 2001 CO 2 H2OH2O TvTv

Spectral corrections Level (m) IRGA position CO 2 (day) CO 2 (night) H2OH2O 396Trailer Trailer Trailer Tower< Tower< Table shows the typical % of flux lost due to smearing of small eddies. Berger et al, 2001