Transcript Carbon_summer07

A parametric and processoriented view of the carbon
system
The challenge: explain the controls over
the system’s response
Qu i c k T i m e ™ a n d a
T I F F (L Z W ) d e c o m p re s s o r
a re n e e d e d to s e e th i s p i c t u re .
Carbon emissions and uptakes since 1800
(Gt C)
140
Land use
change
115
Oceans
110
265
Fossil
emissions
Terrestrial
180
Atmosphere
Expanding the model:
Qu i c k Ti m e ™ a n d a
TIF F (L ZW ) d e c o m p re s s o r
a re n e e d e d to s e e th i s p i c tu re .
A model for (Fba-Fab)
Fab = G(Di, pi, S i) = photosynthesis
Fba = G(Di, pi, S i) = respiration and fire
A Hierarchical view of the
carbon system
Causation goes in
this direction
Drivers (weather, nutrients, fires)
Fluxes
Concentrations
Inverse models do
something is 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 parameterized
NEE = 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
Photosynthesis
(Phenology,Soil Moisture,
Tair, VPD, PAR)
Precip.
Transpiration
Plant Respiration
(Plant C, Tair)
Plant Carbon
Litterfall
(Plant C, Phenology)
Soil Moisture
Soil Carbon
Drainage
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 prior
knowledge and observed carbon exchange
Control variables
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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 a
TIFF (Uncompressed) decompressor
are needed to see this picture.
My little flux tower….
More gadgets
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
More gadgets
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
CO2, H2O
T, u,v,w
w
Time-scale character of carbon modeling
Diurnal
Quic kTime™ and a
TIFF ( Unc ompres s ed) dec ompr ess or
are needed to s ee this pic ture.
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 accelerate
growth but also evaporation.
Despite the overall positive
response shown earlier, the
annual relationship of flux to
temperature is negative
Self-consistent parameter sets
6
NEE (g C m-2 day-1)
4
2
Fit to the diurnal cycle
(~12 hour time steps)
0
Modeled Daytime
Observed Daytime
Modeled Nighttime
Observed Nighttime
Modeled Total
Observed Total
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Fit to daily data: 24 hour
time steps
NEE (g C m-2 day-1)
4
2
0
Modeled Daytime
Observed Daytime
Modeled Nighttime
Observed Nighttime
Modeled Total
Observed Total
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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
QuickTime™ and a
TIFF (Uncom pressed) decompressor
are needed to see this picture.
Step 2…..
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Self-consistent parameter sets
Range from prior
knowledge
Second parameter dictated
CS,0 (g m-2)
Analysis of controls
The emergent
Relationship of
temperature and
carbon uptake.
Realized T response wet
Realized T response, dry
Note the multiple
Regimes. The
lower lines are
the water-limited
response
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
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
OCO uses reflected sunlight to make
measurements during the day
Day and Night
6
NEE (g C m-2 day-1)
4
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Modeled Daytime
Observed Daytime
Modeled Nighttime
Observed Nighttime
Modeled Total
Observed Total
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Remember, we’ve shown a
huge loss of process
information without
diurnal information
Future active CO2
experiments make day and
night observations
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
LIDAR
Process priors for global
models
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
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
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
ACME prepares for its first flight
Vertical profiles and CO2
“lakes”
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Carbon data assimilation and parametric estimation are fastmoving 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 multipleconstraint approach. I. Using remotely sensed data and
ecological observations of net primary production. Tellus (B)
55:270-289 (Synthesis inversion)