a parametric and process- oriented view of the carbon system

A parametric and process-oriented view of the carbon

system

The challenge: explain the controls over the system’s response

Carbon emissions and uptakes since 1800 (Gt C)

180

110

115

265

140Land use change

Fossil emissions

Atmosphere

Oceans

Terrestrial

Expanding the model:

A model for (Fba-Fab)

Fab = G(Di, pi, S i) = photosynthesisFba = G(Di, pi, S i) = respiration and fire

A Hierarchical view of the carbon system

Drivers (weather, nutrients, fires)

Fluxes

ConcentrationsInverse models do something is this

direction

Causation goes in this direction

A-R: A key feature of the system

What we measure: Net Ecosystem Exchange(the flux of CO2 across an imaginary plane above the canopy)

But: NEE cannot be directly parameterizedNEE = Photosynthesis - Respiration

The model (or observation equation) must “transform” the observation (NEE) into physically modeling components.

This is neglecting complex but different processes such as fire and forest harvest.

Ecosystem Model Structure

Plant Carbon

Soil CarbonSoil Moisture

Drainage

Precip. Transpiration

Photosynthesis (Phenology,Soil Moisture,

Tair, VPD, PAR)

Plant Respiration(Plant C, Tair)

Litterfall(Plant C, Phenology)

Soil Respiration(Soil C, Soil Moisture,

Tsoil)

Some key model equations

NEE = Ra +Rh - GPP

GPPmax = AamaxAd+Rleaf

GPPpot = GPPmaxDtempDvpdDlight

Rh = CsKhQ10sTsoil/10(W/Wc)

GPP = canopy photosynthesis, R denotes respiration, Amax = max

leaf-level carbon assimilation, Ds are scalars for environmental factors, Ad, a scaling factor over time, Cs = substrate, K, rate

constant, Q10 the temperature scalar and W, water scalars.

Estimation

(zj - H(Fapj,Fpaj))tR-j1 (zj - H(Fapj,Fpaj))/2 +

(pj - Pj)tR-j1 (pj - Pj) /2

The rubber bands are the prior estimates of parameters

Assimilation of fluxes provides consistency between priorknowledge and observed carbon exchange

Control variables

• Temperature• Soil moisture• Nutrient availability• Fire regime• Light interception• Land management

• Atmospheric CO2

• etc

Concentrations have less information about processes and parameters

than do fluxes

Why?

They are “one step more removed” (by transport)

That step includes “invertible” (advective) processes and irreversible (diffusive) processes

There is information loss along the chain of causation

Get closer to the answer: measure fluxes

Tower-based measurements

FLUXNET

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

More gadgets



My little flux tower….

More gadgets

CO2, H2O T, u,v,w



w

Time-scale character of carbon modeling



Diurnal

Seasonal

1. Variability is at a maximum on the strongly forced time scales

2. They have an annual sum of ~0

3. Modeling the carbon storage time scales (years) is the goal

Observed variability of fluxes

Analyzed variability of processes

Analysis of controls

Warm springs accelerategrowth but also evaporation.Despite the overall positive response shown earlier, the annual relationship of flux to temperature is negative

Self-consistent parameter sets

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J F M A M J J A S O N D

NEE (g C m

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Modeled DaytimeObserved DaytimeModeled NighttimeObserved NighttimeModeled TotalObserved Total

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NEE (g C m

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Fit to the diurnal cycle (~12 hour time steps)

Fit to daily data: 24 hour time steps

Assimilating water and carbon

Just water

Carbon only or carbon plus water

Adding water doesn’t help carbon, but it helps water

Carbon only

Carbon and water

Evaluation against an independent water flux measurement

Normal Model Parameterization Method



Step 2…..



Self-consistent parameter sets

CS,0 (g m-2)

KH (g g-1 y-1)

Range from priorknowledge

First p

ara

me

ter

Validate-tune

Second parameter dictated

Analysis of controls

The emergentRelationship of temperature and carbon uptake.

Note the multipleRegimes. The lower lines are the water-limited response

Realized T response, dry

Realized T response wet

What does this type of local study contribute to global

modeling?

We can use this to understand the information in different types of

observation

Carbon from space



OCO uses reflected sunlight to make measurements during the day

Day and Night

Remember, we’ve shown a huge loss of process information without diurnal information

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NEE (g C m

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Future active CO2 experiments make day and

night observations



LIDAR

Process priors for global models



Tower-based estimates of parameters can be used as priors to invert global concentration data to estimate parameters controlling fluxes instead of fluxes (Knorr, Wofsy, Rayner)

The global scale is very distant from processes

Distributed local measurements and innovative measurement

approaches can bridge the gap

ACME prepares for its first flight

Vertical profiles and CO2 “lakes”



Carbon data assimilation

Carbon data assimilation and parametric estimation are fast-moving fields

A few references

• Vukicevic, T., B.H. Braswell and D.S. Schimel. 2001. A diagnostic study of temperature controls on global terrestrial carbon exchange. Tellus (B) 53:150-170. (variational)

• Braswell, B.H., W.J. Sacks, E. Linder and D.S. Schimel. 2004. Estimating ecosystem process parameters by assimilation of eddy flux observations of NEE. Global Change Biol. 11:335-355 (MCMC)

• Williams, M. Schwarz, B.E. Law, J. Irvine, and M.R. Kurpius. 2005. An improved analysis of forest carbon dynamics using data assimilation. Glov=bal Change Biol. 11:85-105 (EKF)

• Wang, Y-P. and D Barrett. 2003. stimating regional terrestrial carbon fluxes for the Australian continent using a multiple-constraint approach. I. Using remotely sensed data and ecological observations of net primary production. Tellus (B) 55:270-289 (Synthesis inversion)

a parametric and process- oriented view of the carbon system

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