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, USA2 Department of Meteorology, Pennsylvania State University, USA3 CSIRO Atmospheric Research, Aspendale, Victoria, Australia
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)
Observing and Modeling the Continental PBL
Apples and Oranges
0
5
10
15
20
25
280 300 320 340 360 380 400 420
CO2 concentration
Hei
gh
t
GCM 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
“Virtual Tall Towers”Use surface layer flux and mixing ratio data to infer mid-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
Correction for surface layer to mid-CBL bias
0
* *
i
CCz
b ti i i i
FFC z zg g
z z w z z w z
C = scalar mixing ratio [CO2]
F0C, Fzi
C = surface and entrainment fluxes
zi = depth of convective boundary layerw* = 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
Mixed-Layer Similarity Theory
(Wyngaard and Brost, 1984)
WLEF: Sept 1997
Figure by Dan Ricciuto, Ken Davis
[CO2]
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 [CO2] 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 gradient2
TransCom Pseudo-Inversion of 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
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
Potential Impact of Calibrated CO2 Measurements at Ameriflux Sites
Uncertainty in Retrieved Annual Mean Flux: Temperate North America (mean of 12 models)
0
0.1
0.2
0.3
0.4
0.5
0 2 4 6 8 10"data uncertainty" (ppm)
Flu
x u
nc
ert
ain
ty (
GtC
/yr)
All Ameriflux towers
4 exising towers
10 Towers
76 Flasks
Potential Impact of Calibrated CO2 Measurements at Ameriflux Sites
Uncertainty in Retrieved Annual Mean Flux: Temperate North America (mean of 12 models)
0
0.1
0.2
0.3
0.4
0.5
0 2 4 6 8 10"data uncertainty" (ppm)
Flu
x u
nc
ert
ain
ty (
GtC
/yr)
All Ameriflux towers
4 exising towers
10 Towers
76 Flasks
Potential Impact of Calibrated CO2 Measurements at Ameriflux Sites
Uncertainty in Retrieved Annual Mean Flux: Temperate North America (mean of 12 models)
0
0.1
0.2
0.3
0.4
0.5
0 2 4 6 8 10"data uncertainty" (ppm)
Flu
x u
nc
ert
ain
ty (
GtC
/yr)
All Ameriflux towers
4 exising towers
10 Towers
76 Flasks
WLEF
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 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
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
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!
Global Tower Network
• Existing Tower [CO2] measurements• Possible Tower [CO2] measurements (high-freq records saved in T3)
Which 10 towers should be implemented first ?
Optimal Global Network
Flux uncertainty = 3.3106 Gt/yr
Regional Constraint
0
0.5
1
1.5
2
2.5
TransCom Regions
Flu
x U
nce
rtai
nty
(G
tC/y
r) 76 flasks
76 Flasks + 4 Existing Towers
76 + 4 + 10 Global Towers
VTT data uncertainty = 2 ppm[1.72 Gt ]
[1.45 Gt ]
[0.87 Gt]
Atmospheric COAtmospheric CO22 and and 222222Rn observationsRn observations
* Map of European atmospheric network in 2001 (7 european labs , CMDL, CSIRO)
Rn-222 continuous
CO2 continuous
bi weekly aircraft soundings
weekly flasks
tall towers CO2
assuming calibrated [CO2] measurements at all EuroFlux towers!
Optimal Global Network …
Existing Flux Towers, Temperate North America
Choose 5 new VTT sites from existing Ameriflux towers
The “Best” and “Worst” Scenarios
#1 #2
Best
Worst
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
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