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

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5

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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)

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0 2 4 6 8 10"data uncertainty" (ppm)

Flu

x u

nc

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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

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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