Inferring terrestrial CO 2 fluxes from a global-scale Carbon Cycle Data Assimilation System (CCDAS) Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 1,

Inferring terrestrial CO2 fluxesfrom a global-scale

Carbon Cycle Data Assimilation System (CCDAS)

Marko Scholze1, Peter Rayner2, Wolfgang Knorr1, Thomas Kaminski3, Ralf Giering3 & Heinrich

Widmann1

Atmospheric Science Seminars, Harvard University, 16th January 2004FastOpt1 2 3

Overview

• Motivation• Top-down vs. bottom-up approach• CCDAS set-up• Calculation and propagation of

uncertainties• Data fit• Global results• Conclusions and outlook

Motivation

after Joos, 1996

Motivation

• Where are the sources/sinks?

• Which are the important processes?

• How do they evolve?

Sketch of the global carbon cycle

Fluxes in Gt C yr-1, pools in Gt C,after Prentice et al., 2001.

„top-down“ vs. „bottom-up“

net CO2

flux at thesurface

Process Model

climate and other driving data

atmospheric inversion

(Transport Model)

atm. CO2 dataAdvantages:• Fluxes consistent with

atm. data• Estimation of uncertainties

Disadvantages:• No process information• Coarse resolution

Advantages:• Process understanding

-> prognostic modeling• High resolution

Disadvantages:• Global validation difficult • Parameter validity

Combined MethodCCDAS – Carbon Cycle Data Assimilation

System

CO2 stationconcentration

Biosphere Model:BETHY

Atmospheric Transport Model: TM2

Misfit to observations

Model parameter

Fluxes

Misfit 1 Forward Modeling:

Parameters –> Misfit

Inverse Modeling:

Parameter optimization

CCDAS set-up

2-stage-assimilation:

1. AVHRR data(Knorr, 2000)

2. Atm. CO2 data

Background fluxes:1. Fossil emissions (Marland et al., 2001 und Andres et al., 1996)2. Ocean CO2 (Takahashi et al., 1999 und Le Quéré et al., 2000)3. Land-use (Houghton et al., 1990)

Transport Model TM2 (Heimann, 1995)

Station network

41 stations from Globalview (2001), no gap-filling, monthly values

1979-1999.

Annual uncertainty values from Globalview (2001).

Terminology

GPP Gross primary productivity (photosynthesis)NPP Net primary productivity (plant growth)NEP Net ecosystem productivity (undisturbed C storage)NBP Net biome productivity (C storage)

BETHY(Biosphere Energy-Transfer-Hydrology

Scheme)

• GPP:C3 photosynthesis – Farquhar et al. (1980)C4 photosynthesis – Collatz et al. (1992)stomata – Knorr (1997)

• Plant respiration:maintenance resp. = f(Nleaf, T) – Farquhar, Ryan (1991)

growth resp. ~ NPP – Ryan (1991) • Soil respiration:

fast/slow pool resp., temperature (Q10 formulation) and soil moisture dependant

• Carbon balance:average NPP = average soil resp. (at each grid point)

<1: source>1: sink

t=1h

t=1h

t=1day

lat, lon = 2 deg

Calibration Step

Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

Prognostic Step

Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

Methodology

Minimize cost function such as (Bayesian form):

DpMDpMpp pppJ D

T

pT

)()()( 2

1

2

1 10

10 0

-- C C

where- is a model mapping parameters to observable quantities- is a set of observations- error covariance matrixC

DM

p

need of (adjoint of the model)Jp

Calculation of uncertainties

• Error covariance of parameters

1

2

2

ji,

p pJ

C = inverse Hessian

T

pX p)p(X

p)p(X

CC

• Covariance (uncertainties) of prognostic quantities

Figure from Tarantola, 1987

Gradient Method

1st derivative (gradient) ofJ (p) to model parameters p:

yields direction of steepest descent.

p

p

ppJ

)(

cost function J (p) p

Model parameter space (p)p

2nd derivative (Hessian)of J (p):

yields curvature of J.Approximates covariance ofparameters.

p

22 ppJ

)(

Data fit

Seasonal cycle

Barrow Niwot Ridge

observed seasonal cycle

optimised modeled seasonal cycle

Global Growth Rate

Calculated as:

observed growth rate

optimised modeled growth rate

Atmospheric CO2 growth rate

MLOSPOGLOB CCC 75.025.0

Parameters I

• 3 PFT specific parameters (Jmax, Jmax/Vmax and )

• 18 global parameters• 57 parameters in all plus 1 initial value (offset)

Param InitialPredicted

Prior unc. (%) Unc. Reduction (%)

fautleafc-costQ10 (slow)

(fast)

0.41.251.51.5

0.241.271.351.62

2.50.57075

3917278

(TrEv)(TrDec) (TmpDec) (EvCn) (DecCn) (C4Gr) (Crop)

1.01.01.01.01.01.01.0

1.440.352.480.920.731.563.36

25252525252525

7895629591901

Parameters II

Relative Error Reduction

Some values of global fluxes

1980-2000 (prior)

1980-2000 1980-1990 1990-2000

GPPGrowth resp.Maint. resp.NPP

135.723.544.0468.18

134.822.3572.740.55

134.322.3172.1340.63

135.322.3973.2840.46

Fast soil resp.Slow soil resp.NEP

53.8314.46-0.11

27.410.692.453

27.610.712.318

27.2110.672.587

Value Gt C/yr

Carbon Balance

latitude N*from Valentini et al. (2000) and others

Euroflux (1-26) and othereddy covariance sites*

net carbon flux 1980-2000gC / (m2 year)

Uncertainty in net flux

Uncertainty in net carbon flux 1980-200gC / (m2 year)

Uncertainty in prior net flux

Uncertainty in net carbon flux from prior values 1980-2000gC / (m2 year)

NEP anomalies: global and tropical

global flux anomalies

tropical (20S to 20N) flux anomalies

IAV and processes

Major El Niño events

Major La Niña event

Post Pinatubo period

Interannual Variability I

Normalized CO2 flux and ENSO

Lag correlation(low-pass filtered)

ENSO and terr. biosph. CO2:Correlations seems strong with a maximum at ~4 months lag,for both El Niño and La Niña states.

Interannual Variabiliy II

Lagged correlation on grid-cell basis at 99% significance

correlation coefficient

Regional Net Carbon Balance and Uncertainties

Conclusions

• CCDAS with 58 parameters can fit 20 years of CO2 concentration data.

• Significant reduction of uncertainty for ~15 parameters.

• Terr. biosphere response to climate fluctuations dominated by El Nino.

• A tool to test model with uncertain parameters and to deliver a posterior uncertainties on parameters and prognostics.

Future

• Explore more parameter configurations.• Include missing processes (e.g. fire).• Upgrade transport model and extend data.• Include more data constraints (eddy

fluxes, isotopes, high frequency data, satellites) -> scaling issue.

• Projections of prognostics and uncertainties into future.

• Extend approach to ocean carbon cycle.