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Model-Data Synthesis of CO2 Fluxes at Niwot
Ridge, Colorado
Bill Sacks, Dave SchimelNCAR Climate & Global Dynamics Division
Russ MonsonCU Boulder
Rob BraswellUniversity of New Hampshire
Motivation
• What processes do CO2 flux data contain information about?
• Can we separate NEE into its component fluxes?
• Scale up CO2 fluxes in space and time
• Improve parameterization of regional & global models, like CCSM
Derive general process-level information from eddy covariance data
Outline
• Methods overview
• Which parameters/processes are constrained by NEE data?
• Exploration of optimized model-data fit: What do we get right? What do we get wrong?
• Partitioning the net CO2 flux
• What do we gain by including an additional data type (H2O fluxes) in the optimization?
• Using model selection to explore controls over NEE
• Scaling up (briefly)
SIPNET Model
• Twice-daily time step (day & night)
• Goal: keep model as simple as possible
Photosynthesis: f (Leaf C, Tair, VPD, PAR, Soil Moisture)
Autotrophic Respiration:f (Plant C, Tair)
Heterotrophic Respiration:f (Soil C, Tsoil, Soil Moisture)
PLANT WOOD CARBON
PLANT LEAF CARBON
Photosynthesis Autotrophic Respiration
Leaf Creation
VEGETATION
SOIL CARBON
Wood Litter Leaf Litter
Heterotrophic Respiration
Precipitation
Tair > 0?
No: Snow
SNOW PACK
Sublimation
Yes: Rain
Interception & Evaporation
Throughfall
Fast flow (Drainage)
Infiltration
SOIL WATER: SURFACE LAYER
Snow melt
SOIL WATER: ROOT ZONE
Surface Layer Drainage
Root Zone Drainage
Evaporation
Transpiration
http://spot.colorado.edu/~monsonr/Ameriflux.html
Data• 5 years of half-hourly data from Niwot
Ridge, a 100 year-old subalpine forest just below the continental divide– Climate drivers (air & soil temp., precip.,
PAR, humidity, wind speed)
– Net CO2 flux (NEE) from eddy covariance
• Gaps in climate drivers and NEE filled using a variety of methods
• Half-hourly data aggregated up to day/night time step– Optimization only uses time steps with at least 50%
measured data
Parameter Optimization• 32 parameter values optimized to fit NEE data
– Initial conditions (e.g. initial C pools)– Rate constants (e.g. max. photosynthetic rate, respiration rates)
– Climate sensitivities (e.g. respiration Q10)
– Climate thresholds (e.g. minimum temp. for photosynthesis)
• Optimization performed using variation of Metropolis Algorithm: minimize sum of squares difference between model predicted NEE and observations
• Each parameter has fixed allowable range (uniform dist’n)
• Ran 500,000 iterations to generate posterior distributions
Parameter Histograms
Initial guess Initial guess
Initial guess Initial guess
PAR attenuation coefficient
Cou
nt
Min. temp. for photosynthesis
Cou
nt
Optimum temp. for photosynthesis
Cou
nt
Soil respiration Q10
Cou
nt
Parameter CorrelationsB
ase
soil
resp
iratio
n ra
te (
g C
g-1 C
day
-1)
C c
onte
nt o
f lea
ves
per
unit
area
(g C
m-2)
Initial soil C content(g C m-2)
PAR half-saturation point(mol m-2 day-1)
Some parameters can not be estimated well because of correlations with other parameters:
Parameter Behavior• 13 well-constrained parameters, 5 poorly-constrained
parameters, 14 edge-hitting parameters
• Initial conditions: mostly edge-hitting
• Parameters governing carbon dynamics: mostly well-constrained. Exceptions:– PAR attenuation coefficient– Parameters governing C allocation/turnover rate– Base soil respiration rate– Soil respiration Q10
• Parameters governing soil moisture dynamics: mostly poorly-constrained or edge-hitting
Optimized Model: Range of Predictions
ObservationsModel
Day
time
NE
E (
g C
m-2)
Day
time
NE
E r
esid
ual (
g C
m-2)
Day of Year Day of Year
Nig
httim
e N
EE
(g
C m
-2)
Nig
httim
e N
EE
res
idua
l (g
C m
-2)
Model vs. Data: Initial GuessN
EE
(g
C m
-2)
Cum
ulat
ive
NE
E (
g C
m-2)
Days after Nov. 1, 1998Observed nighttime NEE (g C m-2)
Observed daytime NEE (g C m-2)
Mod
eled
nig
httim
e N
EE
(g
C m
-2)
Mod
eled
da
ytim
e N
EE
(g
C m
-2)
ObservationsModel
Unoptimized vs. Optimized Model
Unoptimized nighttime NEE (g C m-2)
Unoptimized daytime NEE (g C m-2)
Opt
imiz
ed n
ight
time
NE
E (
g C
m-2)
Opt
imiz
ed d
aytim
e N
EE
(g
C m
-2)
Model vs. Data: Optimized ParametersN
EE
(g
C m
-2)
Cum
ulat
ive
NE
E (
g C
m-2)
Days after Nov. 1, 1998Observed nighttime NEE (g C m-2)
Observed daytime NEE (g C m-2)
Mod
eled
nig
httim
e N
EE
(g
C m
-2)
Mod
eled
da
ytim
e N
EE
(g
C m
-2)
ObservationsModel
Model vs. Data: Optimized Parameters
-5
-4
-3
-2
-1
0
1
2
3
4
5
J F M A M J J A S O N D
NEE (g C m
-2 day
-1)
Modeled DaytimeObserved DaytimeModeled NighttimeObserved Nighttime
Model vs. Data: Optimized Parameters
-60
-40
-20
0
20
40
60
1999 2000 2001 2002 2003
NEE Interannual Variability (g C m
-2 yr
-1)
Modeled DaytimeObserved DaytimeModeled NighttimeObserved Nighttime
Missing Variability in Nighttime Respiration
Air temperature (°C)
Nig
httim
e N
EE
(g
C m
-2 d
ay-1)
ObservationsModel
Days after Nov. 1, 1998
Fra
ctio
nal s
oil w
etne
ss
Pool DynamicsF
ract
ion
of in
itial
poo
l siz
e
Days after Nov. 1, 1998 Days after Nov. 1, 1998
Initial Guess Optimized
NEW!
IMPROVED!Parameter Optimization
• Used a single soil water pool
• Held about 1/2 of parameters fixed at best guess values; estimated 17 parametersFixed parameters for which:– Value was relatively well known, and/or– NEE data contained little information; and– Fixing the parameter did NOT cause significantly worse model-data fit
This included:– Most initial conditions– Many soil moisture parameters– A few parameters that were highly correlated with another parameter– Turnover rate of wood
Incorporating knowledge of which parameters/processes are not well constrained by the data
Days after Nov. 1, 1998
Fra
ctio
nal s
oil w
etne
ss
New Parameter Optimization
Fra
ctio
n of
initi
al p
ool s
ize
Days after Nov. 1, 1998
Almost all parametersare now well-constrained
Partitioning the Net Flux
0
100
200
300
400
500
600
Initial Opt. New Opt.Mean Annual Flux (g C m
-2 yr
-1)
RA
RH
NPP
Partitioning the Net FluxFlux partitioning using the optimization with fewer free parameters
-2
-1
0
1
2
3
4
5
J F M A M J J A S O N D
Mean Monthly Flux (g C m
-2 day
-1)
GPPRtotNEE (modeled)
Partitioning the Net FluxFlux partitioning using the optimization with fewer free parameters
-200
-100
0
100
200
300
400
500
600
700
800
1999 2000 2001 2002 2003 Mean
Annual Flux (g C m
-2 yr
-1)
GPPRtotNEE (modeled)
Optimization on H2O Fluxes
• Using H2O fluxes in the optimization would allow better separation of NEE into GPP and R, since GPP is highly correlated with transpiration fluxes
• Using multiple data types would allow better estimates of previously highly-correlated parameters
Optimized simultaneously on H2O fluxes and CO2 fluxesH2O fluxes also measured using eddy covariance
Hypotheses:
Optimization on H2O Fluxes
Optimized H2O fluxes:
Optimized CO2 fluxes: similar to optimization on CO2 only, although slightly worse fit to observations when optimize on both fluxes
H2O
flu
x (c
m p
reci
p. e
quiv
.)
H2O
flu
x (c
m p
reci
p. e
quiv
.)
Days after Nov. 1, 1998 Days after Nov. 1, 1998
ObservationsModel
Opt. on CO2 only: Opt. on CO2 & H2O:
Days after Nov. 1, 1998Fra
ctio
nal s
oil
wet
ness
Optimization on H2O Fluxes
-200
-100
0
100
200
300
400
500
600
700
800
CO2 only CO2 &H2O
Mean Annual Flux (g C m
-2 yr
-1)
GPP
Rtot
NEE (modeled)
Flux breakdown:
111
25
94 4 0 3 1 1
80
2515
81 2 1 3 2
0
20
40
60
80
100
120
.1 -.2
.2 -.3
.3 -.4
.4 -.5
.5 -.6
.6 -.7
.7 -.8
.8 -.9
.9 - 1
|r|
Count
CO2 only
CO2 and H2O
Parameter correlations:
Model Structural Changes
• Tested whether hypothesis-driven changes to model structure improve model-data fit in the face of an optimized parameter set
• Goal: learn more about controls over NEE
• Evaluated improvement using Bayesian Information Criterion (BIC):
BIC = -2 * LL + K * ln (n)(LL = Log Likelihood; K = # of free parameters; n = # of data points)
Model Structural Changes
• No longer shut down photosynthesis & foliar respiration with frozen soils
• Separated summer and winter soil respiration parameters
• Split soil carbon pool into two pools
• Made soil respiration independent of soil moisture
Four changes:
Model Structural Changes: Results
• No shut down of photosynthesis & foliar respiration with frozen soil: significantly worse fit
• Separate summer/winter soil respiration parameters: slightly better fit
• Two soil carbon pools: slightly worse fit
• Soil respiration independent of soil moisture: little change
Scaling Up
SIPNET Flux Model
Niwot Ridge Flux Data
SIPNET Optimized for Niwot Ridge
Satellite Data (e.g. MODIS LAI)
Spatially-explicit Estimate of GPP/NEE Across Colorado Coniferous Forest Biome
Comparisons with Top-down Flux Estimates (e.g. Flux Estimates from Airborne Carbon in the
Mountains Experiment (ACME), MODIS GPP)
Conclusions
• Eddy covariance CO2 flux data can be used to constrain most model parameters that directly affect CO2 flux
Optimization yields better fit of CO2 flux data, but can force other model behavior (e.g. pool dynamics) to become unrealistic
• Parameter optimization can be used to probe model structure and learn about controls over NEEIn this ecosystem, it appears that photosynthesis, and possibly foliar respiration, are down-regulated when the soil is frozen
• NEE partitioning: GPP = 600 - 700 g C m-2 yr-1
Rtot = 550 - 600 g C m-2 yr-1
• Including H2O fluxes in optimization does NOT help us learn more about controls over CO2 flux