A global Carbon Cycle Data Assimilation System (CCDAS) and its link to CAMELS

A global Carbon Cycle Data Assimilation System (CCDAS) and

its link to CAMELS

Marko Scholze1, Peter Rayner2, Wolfgang Knorr3, Thomas Kaminski4, Ralf Giering4 & Heinrich

Widmann3

1st CarboEurope Integration Workshop, Potsdam, 20042

FastOpt431

QUEST c

• QUEST is a newly, NERC funded directed programme (5 years).

• QUEST aims to achieve a better qualitative and quantitative understanding of large-scale processes and interactions in the Earth System, especially the interactions among biological, physical and chemical processes in the atmosphere, ocean and land and their implications for human activities.

• QUEST mainly focuses on: (1) the contemporary carbon cycle and its interactions with climate and atmospheric chemistry; (2) the natural regulation of atmospheric composition on glacial-interglacial and longer time scales; and (3) the implications of global environmental changes for the sustainable use of resources.

• QUEST consists of a core team, strategic activities, fellowships, and collaborative grants.

• QUEST website: http://quest.bris.ac.uk

CAMELS c

Carbon Assimilation and Modelling of the European Land Surface

an EU Framework V Project (Part of the CarboEurope Cluster)

CAMELS

CAMELS

CAMELS PARTICIPANTS (the “Jockeys”)

Hadley Centre, Met Office, UK – Coordinator: Peter Cox

LSCE, France MPI-BGC, Germany UNITUS, Italy ALTERRA, Netherlands European Forestry Institute, Finland CEH, UK IES/JRC, EC FastOpt, Germany

CAMELS

CAMELS AND INVERSE MODELLING

• CAMELS Goals and General Strategy: Combining

Inverse and Forward Model Strategies (material

by Peter Cox, Hadley Centre)

• Carbon Cycle Data Assimilation and Calculation of

Uncertainties (CCDAS consortium)

CAMELS

CAMELS

CAMELS Goals

• Best estimates and uncertainty bounds for the contemporary and historical land carbon sinks in Europe and elsewhere, isolating the effects of direct land-management.

• A prototype carbon cycle data assimilation system (CCDAS) exploiting existing data sources (e.g. flux measurements, carbon inventory data, satellite products) and the latest terrestrial ecosystem models (TEMs), in order to produce operational estimates of “Kyoto sinks“.

CAMELS

CAMELS Motivating Science Questions

• Where are the current carbon sources and sinks located on the land and how do European sinks compare with other large continental areas?

•  Why do these sources and sinks exist, i.e. what are the relative contributions of CO2 fertilisation, nitrogen deposition, climate variability, land management and land-use change?

• How could we make optimal use of existing data sources and the latest models to produce operational estimates of the European land carbon sink?

Inverse Modelling

Method : Use atmospheric transport model to infer CO2 sources and sinks most consistent with atmospheric CO2 measurements.

Pros : a) Large-scale; b) Data based (transparency).

Cons : a) Uncertain (network too sparse); b) not constrained by ecophysiological understanding; c) net CO2 flux only (cannot isolate land management).

CAMELS

CAMELS

Forward Modelling

Method : Build “bottom-up” process-based models of land and ocean carbon uptake.

Advantages : a) Include physical and ecophysiological constraints; b) Can isolate land-management effects; c) can be used predictively (not just monitoring).

Disadvantages : a) Uncertain (gaps in process understanding); b) Do not make optimal use of large-scale observational constraints.

CAMELS

The Case for Data-Model Fusion

• Mechanistic Models are needed to separate contributions to the land carbon sink (e.g. as required by KP).

• Large-scale data constraints (from CO2 and remote-sensing) are required to provide best estimates and error bars at regional and national scales.

• Data-Model Fusion = ecophysiological constraints from forward

modelling + large-scale CO2 constraints from

inversemodelling

CAMELS Flow Diagram

CAMELS

Combined ‘top-down’/’bottom-up’ 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)

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 dependent

• 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.

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 parameters1

2

2

ji,

p pJ

C = inverse Hessian

T

pX p)p(X

p)p(X

CC

• Covariance (uncertainties) of prognostic quantities

• Adjoint, Hessian, and Jacobian code generated automatically from model code by TAF

cost function J (p) p

Figure from Tarantola, 1987

Gradient Method

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

yields direction of steepest descent.

p

p

ppJ

)(

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

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

Low-resolution CCDAS

• A fully functional low resolution version of CCDAS, BETHY runs on the TM2 grid (appr. 10° x 7.8°)

• 506 vegetation points compared to 8776 (high-res.)• About a factor of 20 faster than high-res. Version -> ideal

for developing, testing and debugging• On a global scale results are comparable (can be used

for pre-optimising)

Conclusions

• CCDAS with 58 parameters can fit 20 years of CO2 concentration data; ~15 directions can be resolved

• 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 a prognostic ocean carbon cycle model.