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Using Virtual Tall Tower [CO 2 ] Data in Global Inversions

Using Virtual Tall Tower [CO 2 ] Data in Global Inversions. Joanne Skidmore 1 , Scott Denning 1 , Kevin Gurney 1 , Ken Davis 2 , Peter Rayner 3 , John Kleist 1 1 Department of Atmospheric Science, Colorado State University, USA

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Using Virtual Tall Tower [CO 2 ] Data in Global Inversions

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  1. Using Virtual Tall Tower [CO2] Data in Global Inversions Joanne Skidmore1, Scott Denning1, Kevin Gurney1, Ken Davis2, Peter Rayner3, John Kleist1 1 Department of Atmospheric Science, Colorado State University, USA 2 Department of Meteorology, Pennsylvania State University, USA 3 CSIRO Atmospheric Research, Aspendale, Victoria, Australia

  2. Introduction • Previous network optimization studies considered any global grid cell as fair game (Patra 2002, Gloor 2000, Rayner 1996), but it’s hard to measure mean [CO2] in a GCM grid cell! • Ken Davis and colleagues have proposed a methodology for estimating the mid-day CBL [CO2] from calibrated [CO2] measurements at flux towers • Could feasibly produce daily CBL [CO2] at many continental sites, right now, at minimal cost! • This “Virtual Tall Tower” method uses existing infrastructure: implementation involves only an additional LI-COR sensor and calibration gases! (Davis, 2003)

  3. Observing and Modeling the Continental PBL • Continental PBL characterized by vertical gradients and diurnal cycles • Large-scale models can’t resolve these • Successful use in inversions would require harmonization between obs and models

  4. “Virtual Tall Towers” • Use surface layer flux and mixing ratio data to infermid-CBL CO2 mixing ratios over the continents, by estimating surface layer gradient. • Methodology has been tested and refined at WLEF (400 m tower, six years of concentration data) • Estimate turbulent mixing and vertical gradient from sensible heat flux and momentum stress • Predict to mid-CBL [CO2] from measurements at 30 m • Compare prediction to observed [CO2] at 396 m • Works best in well-developed CBL … • rms error comparable to analytical error under good conditions

  5. Mixed-Layer Similarity Theory Correction for surface layer to mid-CBL bias C = scalar mixing ratio [CO2] F0C, FziC = surface and entrainment fluxes zi = depth of convective boundary layer w* = convective velocity scale ( a function of surface buoyancy flux and zi) z = altitude above ground or displacement height gb, gt = dimensionless gradient functions, depend on normalized altitude within convective layer (Wyngaard and Brost, 1984)

  6. WLEF: Sept 1997 • Synoptic variability ~ 35 ppm over month, well captured by mid-day obs at 30 m • VTT method does well in estimating mid-day 396 m [CO2] from 30 m • Monthly mean bias = 0.2 ppm < 0.2 ppm • Mean of 30 days probably has less representationerror than weekly flasks [CO2] temperature SL gradient Figure by Dan Ricciuto, Ken Davis

  7. TransCom Pseudo-Inversionof VTTs • Assume calibrated CO2 is measured at subsets of FluxNet towers • Assume VTT method can be applied once per day, in midafternoon • Subsample model response functions to sample CBL only at this time of day • Compute monthly mean mid-day CBL [CO2] at each tower • Assume various levels of representation error in this monthly mean mid-day quantity, and propagate through inversion

  8. A network of “process observatories” using eddy covariance to estimate H, LE, and NEE • Each site also measures [CO2] continuously, but not calibrated • If these data could be used in inverse models, network would more than double, dense over some continental areas

  9. Potential Impact of Calibrated CO2 Measurements at Ameriflux Sites

  10. Potential Impact of Calibrated CO2 Measurements at Ameriflux Sites

  11. Potential Impact of Calibrated CO2 Measurements at Ameriflux Sites WLEF

  12. Overview of This Study Using tower [CO2] data in global inversions • Estimate ML [CO2] at mid-day from SL measurement • Optimize fluxes to fit monthly mean of mid-day values (by sub-sampling global fields by time of day) • Assume 2 ppm VTT data uncertainty in monthly means (very conservativebased on WLEF results!) • Assume 4 DOFs in seasonal cycle due to temporal autocorrelation • Compare/prioritize particular flux tower sites

  13. Optimization with Genetic Algorithms * is not a random search for a solution to a problem, (solution = highly fit individual) uses stochastic processes, but result is distinctly non-random Generation 0: process operates on a population of randomly generated individuals Generation 1 … : operations use fitness measure to improve population Cross-over: genes paired at random, are left alone or recombined element-by-element; children murder parents and replace them Mutation: every element in every list is subject to random variation according to mutation rate Culling: given population is scored, then ranked; each genome is assigned a survival probability based on its ranking; a random number comparison decides its fate Re-filling: survivors replace culled members

  14. GA Parameters Population size (100) – # of station lists (genomes) competing against each other Genome length (10) – # of genes in network Mutation rate (0.01) – probability that a given station will be changed, … probability of list changing increases with list length Cross-over probability (0.3) – probability that two genes will be combined Iterations/Generations(100) – usually converges earlier!

  15. Global Tower Network Which 10 towers should be implemented first ? • Existing Tower [CO2] measurements • Possible Tower [CO2] measurements(high-freq records saved in T3)

  16. Optimal Global Network

  17. Regional Constraint [1.72 Gt ] VTT data uncertainty = 2 ppm [1.45 Gt ] [0.87 Gt]

  18. Atmospheric CO2 and 222Rn observations Rn-222 continuous CO2 continuous bi weekly aircraft soundings weekly flasks tall towers CO2 * Map of European atmospheric network in 2001 (7 european labs , CMDL, CSIRO)

  19. Optimal Global Network … assuming calibrated [CO2] measurements at all EuroFlux towers!

  20. Existing Flux Towers,Temperate North America Choose 5 new VTT sites from existing Ameriflux towers

  21. The “Best” and “Worst” Scenarios Best #2 #1 Worst

  22. We Can Do Much Better • Daily or even hourly data have much more information content than monthly means! • Global inversions of frequent (hourly or daily) measurements (see Law et al, GBC, 2002) • Take advantage of much bigger signals at synoptic time scales (35 ppm synoptic variations at WLEF Sept 1997) • Requires accurate transport on regional, synoptic scales • Results show dramatic improvement in uncertainty over inversions of monthly mean concentrations

  23. Conclusions • Routine continuous calibrated measurements of [CO2] and other tracers could dramatically improve the uncertainty of regional flux estimates • Combining tall towers and calibrated measurements at flux towers could provide such a network • Optimal VTT networks emphasize placement in and just downwind of strong fluxes, not “bracketing” or “gradient” approaches • Future work 1:generate pseudodata with diurnal fluxes, then invert using T3 basis functions • Future work 2:invert daily data instead of monthly means

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