1. 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 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 [CO 2 ] in a GCM grid cell! Ken Davis and colleagues have proposed a methodology for estimating the mid-day CBL [CO 2 ] from calibrated [CO 2 ] measurements at flux towers Could feasibly produce daily CBL [CO 2 ] 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 infer mid- CBL CO 2 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 [CO 2 ] from measurements at 30 m Compare prediction to observed [CO 2 ] at 396 m Works best in well-developed CBL … rms error comparable to analytical error under good conditions

5. Correction for surface layer to mid-CBL bias C = scalar mixing ratio [CO 2 ] F 0 C, F zi C = surface and entrainment fluxes z i = depth of convective boundary layer w* = convective velocity scale ( a function of surface buoyancy flux and z i ) z = altitude above ground or displacement height g b, g t = dimensionless gradient functions, depend on normalized altitude within convective layer Mixed-Layer Similarity Theory (Wyngaard and Brost, 1984)

6. WLEF: Sept 1997 Figure by Dan Ricciuto, Ken Davis [CO 2 ] temperature 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 [CO 2 ] from 30 m Monthly mean bias = 0.2 ppm < 0.2 ppm Mean of 30 days probably has less representation error than weekly flasks SL gradient

7. TransCom Pseudo-Inversion of VTTs Assume calibrated CO 2 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 [CO 2 ] 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 [CO 2 ] 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 CO 2 Measurements at Ameriflux Sites

11. WLEF

12. Using tower [CO 2 ] data in global inversions Estimate ML [CO 2 ] 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 conservative based on WLEF results!) Assume 4 DOFs in seasonal cycle due to temporal autocorrelation Compare/prioritize particular flux tower sites Overview of This Study

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 Existing Tower [CO 2 ] measurements Possible Tower [CO 2 ] measurements  (high-freq records saved in T3) Which 10 towers should be implemented first ?

16. Optimal Global Network

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

18. Atmospheric CO 2 and 222 Rn observations * Map of European atmospheric network in 2001 (7 european labs, CMDL, CSIRO) Rn-222 continuous CO 2 continuous bi weekly aircraft soundings weekly flasks tall towers CO 2

19. assuming calibrated [CO 2 ] measurements at all EuroFlux towers! Optimal Global Network …

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

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