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Improving understanding and forecasts of the terrestrial carbon cycle. Mathew Williams School 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? - PowerPoint PPT Presentation
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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