Improving understanding and forecasts of the terrestrial carbon cycle

Mathew WilliamsSchool of GeoSciences, University of Edinburgh

With input from: BE Law, A Fox, RF Fisher, J Grace, J Moncrieff, T Hill, P Meir, REFLEX team

Motivation

How is the Earth changing? What are the consequences of these

changes for life on Earth?

FossilFuels (7 per yr) &volcanoes

Atmosphere

Vegetation Ocean

SedimentsSoils

The Global Carbon Cycle – a simple model

Litterfall/sedimentation

Respiration

Photosynthesis

Combustion

The Carbon Cycle

Understanding, prediction and control of the Carbon cycle

Climate

Research Vision

To use EO data to test, constrain, modify and evolve models of the terrestrial biosphere

To focus on uncertainty throughout the process of linking observations to models

To guide experimental and observational science towards critical areas of uncertainty

To generate global bottom-up estimates of the terrestrial C cycle with quantified uncertainty

Outline

The problems Progress so far Challenges for the future

Friedlingstein et al 2006: C4MIP

Intercomparison of 11 coupled carbon climate models

Matrix of R2 for simulations of mean annual GPP for 36 major watersheds in Europe from different process- and data oriented models

Williams et al. 2009, BGD

Space (km)

time

s

hr

day

month

yr

dec

0.1 1.0 10 100 1000 10000

FlaskSite

Time and space scales in ecological processes

Physiology

Climate change

Succession

Growth and phenology

Adaptation

Disturbance

Photosynthesis and respiration

Clim

ate

varia

bilit

y

Nutrient cycling

GOSAT

Space (km)

time

s

hr

day

month

yr

dec

0.1 1.0 10 100 1000 10000

FluxTower

Aircraft

FlaskSite

FlaskSite

FieldStudies

MODIS

Time and space scales in ecological observations

Talltower

Williams et al. 2009, BGD

Progress so far in MDF

Model-data fusion with multiple constraints to improve analyses of C dynamics (Williams et al. 2005, GCB)

Assimilating EO data to improve C model state estimation (Quaife et al. 2008, RSE)

REFLEX: Intercomparison experiment on parameter estimation using synthetic and observed flux data (Fox et al, in press, AFM)

“Improving land surface models with FLUXNET data” (Williams et al 2009, BGD)

C cycling in Ponderosa Pine, OR

Flux tower (2000-2)Sap flowSoil/stem/leaf respirationLAI, stem, root biomassLitter fall measurements

Time (days since 1 Jan 2000)Williams et al GCB (2005)

ChambersSap-flowA/Ci

EC

Chambers

Time (days since 1 Jan 2000)

GPP Croot

Cwood

Cfoliage

Clitter

CSOM/CWD

Ra

Af

Ar

Aw

Lf

Lr

Lw

Rh

D

Photosynthesis &plant respiration

Phenology &allocation

Senescence & disturbance

Microbial &soil processes

Climate drivers

Non linear f(T)Simple linear functionsFeedback from Cf

The Kalman Filter

MODEL At Ft+1 F´t+1OPERATOR

At+1

Dt+1

Assimilation

Initial state Forecast ObservationsPredictions

Analysis

P

Drivers

Time (days since 1 Jan 2000) Williams et al GCB (2005)

= observation— = mean analysis| = SD of the analysis

Time (days since 1 Jan 2000) Williams et al GCB (2005)

= observation— = mean analysis| = SD of the analysis

0 365 730 1095-4

-3

-2

-1

0

1

2

0 365 730 1095-4

-2

0

2

Time (days, 1= 1 Jan 2000)

b) GPP data + model: -413±107 gC m-2

0 365 730 1095-4

-3

-2

-1

0

1

2

c) GPP & respiration data + model: -472 ±56 gC m-2NE

E (

g C

m-2 d

-1)

0 365 730 1095-4

-2

0

2

a) Model only: -251 ±197 g c m-2

d) All data: -419 ±29 g C m-2

Data bring confidence & test the model

Williams et al, GCB (2005)

= observation— = mean analysis| = SD of the analysis

REFLEX experiment

Objectives: To compare the strengths and weaknesses of various MDF techniques for estimating C model parameters and predicting C fluxes.

Evergreen and deciduous models and data Real and synthetic observations Multiple MDF techniques Links between stocks and fluxes are explicit

www.carbonfusion.org

Parameter constraint

Consistency among methodsConfidence intervals constrained by the dataConsistent with known “truth”

“truth”

Fox et al. in press

Atolab

GPP Cr

Cw

Cf

Clit

CSOM

Ra

Af

Ar

Aw

Lf

Lr

Lw

Rh1

D

Clab

Afromlab

Rh2

DALEC Model

Fox et al. in press

Fox et al. in press

Problems with SOM and wood

Fox et al. in press

Problems so far

Varied estimation of confidence intervals Equifinality Problems in defining priors Multiple time scales of response

Challenges for the future

Quantifying model skill across biomes

Williams et al. 2009, BGD

FLUXNET

Arctic Biosphere-Atmosphere Coupling across multiple Scales

ABACUS

WP1 PlantsWP2 Soils

WP3 Fluxes

WP4 Towers

WP MossWP York

WP5 Airborne

WP6 Earthobservation

Other data constraints?

Tree rings FPAR, NDVI, EVI time series Stem inventories chronosequences Phenology observations Soil moisture, LE, stream-flow Surface temperature Soil chambers

Manipulation Experiments

5

Drought : R2=0.75

Control : R2=0.81

SPA model output vs. data

Soil-Root Resistance(modelled)

Rp

lmin

v K v

LAI

Root

Met.

Fisher et al. 2007

Links to atmospheric CO2 observations…

Atmos.transport

Calibration/Validation

Satellite XCO2

vsModels

Flasks/aircraftGround XCO2

Satellite XCO2

Model intercomparison

AssimilationFlux analysis

Error/biascharacterisation

MODIS

Fire

Sciencequestions

Workflow for interpretation of GOSAT, flask, aircraft and tall tower data

Mod

el X

CO

2

Global C fluxes

Sciencequestions

Aircraft/ground XCO2

Landsurfacemodel

Thank you

Funding support:NERCNASADOE

Information content of data

(——) aircraft soundings + flux data(‑ ‑ ‑ ‑) flux data only; (— — —) aircraft soundings only

Hill et al. in prep.

Spadavecchia et al. in prep.

Quantifying driver uncertainty in carbon flux predictions

Parameter retrieval from a synthetic experiment using the DALEC model using EnKF

Williams et al. 2009, BGD