1

CAMELS

Carbon Assimilation and Modelling of the European Land Surface

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

CAMELS

2

CAMELS

CAMELS PROJECT OVERVIEW

CAMELS Goals

Background: Kyoto Protocol

Background: Inverse Model vs Forward Model Estimates

Forward Model Constraints from Atmospheric Variability (“Mickey Mouse Data-Model Fusion” from Cox et al.)

Carbon Cycle Data Assimilation (“proper” example from Knorr et al.)

Peter Cox, Hadley Centre, Met Office

3

CAMELSCAMELS 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“.

4

CAMELS

Policy Motivation: Kyoto Sinks

Article 3.3 : “The net change in greenhouse gas emissions by sources

and removals by sinks resulting from direct human-induced land-use

change and forestry activities, …… measured as verifiable changes …

shall be used to meet the commitments.”

Article 3.4 : “……each Party …… shall provide …… data to establish

its level of carbon stocks in 1990 and to enable an estimate to be

made of its changes in carbon stocks in subsequent years……”

5

CAMELSCAMELS 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?

6

CAMELS

Inverse Modelling

Method : Use atmospheric transport model to infer CO2 sources and

sinks most consistent with atmospheric CO2 measurements.

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

Disadvantages : a) Uncertain (network too sparse); b) not

constrained by ecophysiological understanding; c) net CO2 flux only

(cannot isolate land management).

7

CAMELSInverse Model estimates of the carbon sink still have significant uncertainties, and are not strongly

constrained by ecophysiological understanding

within-modeluncertainty

between-modeluncertainty

(Gurney et al., Nature 2002)

8

CAMELS

Inverse Modelling - Uncertainties

Fan et al. (1998): 1.7 GtC/yr sink in North America.

Bousquet et al. (1999): 0.5 +/- 0.6 GtC/yr in North America,

1.3 GtC/yr in Siberia.

9

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.

10

CAMELS

Smoothed Mean and Standard Deviation of DGVM Predictions(Cramer et al., 2001)

Diagram from RoyalSoc. Sinks Report

Forward model estimates of the carbon sink still have significant uncertainties, and are not strongly

constrained by observations

11

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 inverse modelling

12

CAMELS

Observed Variability in CO2

Annual changes in atmospheric CO2 are dominated by ENSO

– after removing anthropogenic rise

– rise during El Nino

– fall during La Nina

– except during major volcanic eruptions

CO2 - black, Nino3 - red

PinatuboEl Chichon

13

CAMELSSoil Respiration Constraint from ENSO Sensitivity(Mickey Mouse Data-Model Fusion)

q10 is the factor by which soil respiration is

assumed to increase for each 10oC warming.

Model with q10=2 has realistic sensitivity to ENSO.

Reconstructions for range of q10.

Infer q10=2.1±0.7.

14

CAMELSInfluence of Pinatubo Eruption on Atmospheric CO2

Volcano causes surface cooling

model agrees with

– obs (red)

– “theory” (blue)

Cooling causes reduction in CO2

model agrees with reconstructed volcanic anomaly (blue)

phase of ENSO important ?

15

CAMELS

Constraint from Sensitivity to Volcanoes

Model with q10=2 has

realistic sensitivity to Pinatubo.

Reconstructions for range of q10.

Infer q10=1.9±0.4

16

CAMELS

Use of Data Constraints in CAMELS

OriginalTEM

OptimisedTEM for key

Sites

20th Century Simulation ofEuropean sink

Carbon CycleData AssimilationSystems

Fluxes of CO2 and H20,Inventory data

Weather data,Land management,

N deposition

Atmos CO2,Satellite data

LOCALCONSTRAINTS

HISTORICALCONSTRAINTS

SPATIALCONSTRAINTS

17

CAMELS

Flux Measurement in Amazonia

18

CAMELS

Interannual Variability in Atmospheric CO2

Annual CO2 increase fluctuates by up to 1 ppmv/yr even though emissions increase smoothly

IPCC TAR (2001)

19

CAMELS

Offline Carbon Cycle Data Assimilation(“proper” example after Wolfgang Knorr et al.)

OptimisationAlgorithm

Sensitivity toTEM parameters,State variables

TEM parameters,State variables

SurfaceCO2 fluxesOffline

TEMAtm Transport Model (ATM)

Adjoint offline TEM and ATM

SimulatedfAPAR

SatellitefAPAR

Simulated CO2

Concentrations

Measured CO2

Concentrations

Climate, soils,Land-use drivers

CostFunction

20

CAMELS

Slide from Wolfgang Knorr

21

CAMELS

Slide from Wolfgang Knorr

Slide from Wolfgang Knorr

22

CAMELSConclusions

CAMELS is an EU FP5 project motivated by the need to develop best estimates plus uncertainty bounds for the European (and global) land carbon sink.

CAMELS will make use of local flux measurements, the historical carbon balance, and large-scale constraints from remote-sensing and atmospheric CO2 measurements.

CAMELS ultimate aim is to develop a prototype Carbon Cycle Data Assimilation System.

23

CAMELSCAMELS Workpackages

WP1. Data Harmonisation and Consolidation

WP2. Model Validation and Uncertainty Analysis

WP3. Modelling of the 20th Century Land Carbon Balance

WP4. Development of a System for Carbon Data Assimilation

WP5. Dissemination of Information

24

CAMELS

CAMELS PARTICIPANTS (the “Jockeys”)

1. Met Office, UK

2. LSCE, France

3. MPI-BGC, Jena

4. UNITUS, Italy

5. ALTERRA, Netherlands

6. European Forestry Institute, Finland

7. CEH, UK

8. JRC, EC

25

CAMELS

CAMELS Flow Diagram

26

CAMELS

Influence of ENSO on CO2 Variability

Hadley Centre Model recreates observed sensitivity to ENSO

Ocean and terrestrial fluxes opposite variation with ENSO

– consistent with obs

land dominates overall response

NINO 3 index (K)

CO2

Growth RateAnomaly(ppmv/yr)

27

CAMELS

Forward Modelling - Ocean Uncertainties

Ocean UptakeFrom OCMIP II

ModelsSource: IPCC TAR