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