Analysis of the terrestrial carbon cycle through data assimilation and remote sensing
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Analysis of the terrestrial carbon cycle through data assimilation and remote sensing Mathew Williams, University of Edinburgh Collaborators L Spadavecchia, M Van Wijk. B Law, J Irvine, P Schwarz, M Kurpius, T Quaife, P Lewis M Disney G Shaver, L Street
description
Analysis of the terrestrial carbon cycle through data assimilation and remote sensing. Mathew Williams, University of Edinburgh Collaborators L Spadavecchia, M Van Wijk. B Law, J Irvine, P Schwarz, M Kurpius, T Quaife, P Lewis M Disney G Shaver, L Street. - PowerPoint PPT Presentation
Transcript of Analysis of the terrestrial carbon cycle through data assimilation and remote sensing
Analysis of the terrestrial carbon cycle through data assimilation
and remote sensing
Mathew Williams, University of EdinburghCollaborators
L Spadavecchia, M Van Wijk. B Law, J Irvine, P Schwarz, M Kurpius,
T Quaife, P Lewis M DisneyG Shaver, L Street
Source: CD Keeling, NOAA/ESRL
Sampling at 3397 meters, well mixed free troposphere
Harvard ForestData since 1989
Harvard Forest [CO2]
340
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0 30 60 90 120 150 180 210 240 270 300 330 360
Time (day of year 1998)
[CO
2 ]Harvard Forest
Mauna Loa
Source: Wofsy et al, Harvard Forest LTER
Hourly data ~5 m above canopy
Talk outline
What are the uncertainties in temporal and spatial extrapolation of C cycle estimates?
1. Using multiple time series data to constrain C cycle analyses
2. Use multiscale spatial studies to determine up-scaling uncertainties
PART 1: Time
Improving estimates of C dynamics
MODELS OBSERVATIONS
FUSION
ANALYSIS
MODELS+ Capable of interpolation
& forecasts- Subjective & inaccurate?
OBSERVATIONS+Clear confidence limits
- Incomplete, patchy- Net fluxes
ANALYSIS+ Complete
+ Clear confidence limits+ Capable of forecasts
Time update“predict”
Measurement update
“correct”
A prediction-correction system
Initial conditions
The Kalman Filter
MODEL At Ft+1 F´t+1OPERATOR
At+1
Dt+1
Assimilation
Initial state Forecast ObservationsPredictions
Analysis
P
Ensemble Kalman Filter
Drivers
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
Temperature controlled
5 model pools10 model fluxes11 parameters10 data time series
Rtotal & Net Ecosystem Exchange of CO2
C = carbon poolsA = allocationL = litter fallR = respiration (auto- & heterotrophic)
Time (days since 1 Jan 2000)
= observation— = mean analysis| = SD of the analysis
(Williams et al 2005)
Time (days since 1 Jan 2000)
= observation— = mean analysis| = SD of the analysis
(Williams et al 2005)
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 brings confidence
= observation— = mean analysis| = SD of the analysis
(Williams et al 2005)
Assimilating EO reflectance data
DALECAt Ft+1
Reflectance
t+1
Radiativetransfer
At+1
MO
DIS
t+1
DA
GPP results
No assimilation
Assimilating MODIS
(bands 1 and 2)
Quaife et al, RSE (in press)
Summary: time
Multiple time series data generate powerful constraints on analyses
For improved predictions, better constraints on long time constant processes are required
Error characterisation is vital EO data can be assimilated with appropriate
observation operators
PART 2: Space
(Street et al 2007, Shaver et al 2007)
(Van Wijk & Williams 2005)
0.2 m 0.5 m
1.0 m
1.5 m
2.0 m
3.0 m
0.1 m0.75 m1.5 m2.35 m3.0 m4.5 m
Height of sensor and field of view
Distance (m) Distance (m)
(Williams et al. in press)
macroscale microscale
A multi-scale experimental design
0.4 0.5 0.6 0.7 0.80.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
y = 0.24 + 0.72 x
R2 = 0.90
y = 0.26 + 0.70 x
R2 = 0.86
0.4 0.5 0.6 0.7 0.80.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
9.0 m resolution
6.0 m resolution4.5 m resolution
3.0 m resolution1.5 m resolution
0.4 0.5 0.6 0.7 0.80.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
y = 0.25 + 0.70 x
R2 = 0.86
ND
VI (
diffu
ser)
0.4 0.5 0.6 0.7 0.80.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
y = 0.23 + 0.75 x
R2 = 0.81
0.4 0.5 0.6 0.7 0.80.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90NDVI (averaged)
y = 0.26 + 0.70 x
R2 = 0.91
NDVI (averaged)
Linear averaged Skye NDVIs (collected at 0.2 x 02 m resolution with diffuser off) versus measured NDVIs at coarser spatial scales with diffuser on
Microscale study:Scale invariance
0.5 0.6 0.7 0.8 0.90.0
0.4
0.8
1.2
1.6 y = 0.00067 exp(9.237x)
R2 = 0.80RMSE = 0.178
0.5 0.6 0.7 0.8 0.90.0
0.4
0.8
1.2
1.6 y = 0.00059 exp(9.502x)
R2 = 0.90RMSE = 0.119
9.0 m resolution
6.0 m resolution4.5 m resolution
3.0 m resolution1.5 m resolution
0.5 0.6 0.7 0.8 0.90.0
0.4
0.8
1.2
1.6
y = 0.00072 exp(9.294x)
R2 = 0.87RMSE = 0.120
Leaf
are
a in
dex
(LA
I)
0.5 0.6 0.7 0.8 0.90.0
0.4
0.8
1.2
1.6
y = 0.00156exp(8.212x)
R2 = 0.84RMSE = 0.131
0.5 0.6 0.7 0.8 0.90.0
0.4
0.8
1.2
1.6
y = 0.00044 exp(9.911x)
R2 = 0.94RMSE = 0.083
NDVI (averaged)
NDVI (averaged)
Relationships between estimated LAI (using both Skye NDVI and LI-COR LAI-2000 observations at 0.2 m resolution, linearly averaged for upscaling) versus Skye NDVI at different spatial scales.
Microscale study:Scale invariance
0 1 2 3 40
500
1000
15000.2 m
0 1 2 3 40
50
100
150
2001.5 m
0 1 2 3 40
20
40
60
Fre
quen
cy
3.0 m
0 1 2 3 40
5
10
15
20
254.5 m
0 1 2 3 40
5
10
15
LAI
6.0 m
0 1 2 3 40
1
2
3
LAI
9.0 m
Frequency histograms for LAI estimates in the microscale site at a range of resolutions.
(Williams et al. in press)
0.00
0.05
0.10
0.15
0.20
0.25
0 1 2 3 4 5 6 7 8
Separation (m)
Sem
ivar
ianc
eSemi-variogram for LAI in the microscale study
0.60 0.64 0.68 0.72 0.76 0.80 0.840
10
20
30
40
50
Fre
qu
en
cy
NDVI (ground)
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.605
1015202530
LAI
0.20 0.25 0.30 0.35 0.40 0.45 0.500
10
20
30
40
50
NDVI (Landsat)
Measured in a ground survey, 2004
Satellite overpass, ETM+, August 2001
Inferred from ground NDVI
Macroscale study: Frequency histograms
A significant but poor correlation with LandSat data
Macroscale study: Semivariograms
Measured in a ground survey, 2004
Satellite overpass, ETM+, August 2001
Inferred from ground NDVI
Technique Ground LAI
Landsat NDVI
RMSE MAE
Inverse distance weighting (IDW)
yes no 0.27 0.21
Linear correlation model (LCM)
yes yes 0.28 0.21
Ordinary Kriging (OK) yes no 0.28 0.21
External drift Kriging (EDK)
yes yes 0.29 0.22
Extrapolation models
Kriging Error
(Williams et al. in press)
IDWLandsat
Kriging
Summary: space
Scale invariance in LAI-NDVI relationships at scales > vegetation patches
However spatial variability is high so Kriging has limited usefulness
Over scales >50 m interpolation error was of similar magnitude to the uncertainty in the Landsat NDVI calibration to LAI
Characterisation of spatial LAI errors provides key data for spatial data assimilation
Key challenges and opportunities
Coping with variable data richness Identifying and removing model bias Estimating representation and data errors Making use of remote sensing (optical
and XCO2) Links to atmospheric CO2 using CTMs. Designing experimental network Boundaries in natural systems
Thank you
Funding support:NERCNASADOE
REFLEX: GOALS
To identify and compare the strengths and weaknesses of various MDF techniques
To quantify errors and biases introduced when extrapolating fluxes made at flux tower sites using EO data
Closing date for contributions: 31 October
www.carbonfusion.org
Regional Flux Estimation Experiment, stage 1
Flux dataMODIS LAI
MDF
Full analysisModel parameters
Forecasts
DALECmodel
Training Runs- FluxNet data- synthetic data
Deciduous forest sites
Coniferous forest sites
Assimilation
Output
www.carbonfusion.org
observations (with noise) truth predictions uncertainty
Synthetic evergreen forest2 years obs., 1 year prediction
Figure by Andrew Fox
REFLEX, stage 2
Flux dataMODIS LAI
MDF
Model parameters
DALECmodel
Testing predictionsWith only limited EO data
MDF
MODIS LAI
Analysis
Flux data
testing
Assimilation
FluxNet – Integrating worldwide CO2 flux measurements
How to upscale from site locations to regions and the globe?
Mauna Loa CO2 record
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[CO
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