Transcript Interannual Variability in the ChEAS Mesonet ChEAS XI, 12 August 2008 UNDERC-East, Land O Lakes, WI Ankur Desai Atmospheric & Oceanic Sciences, University of Wisconsin-Madison.

Interannual Variability in the
ChEAS Mesonet
ChEAS XI, 12 August 2008
UNDERC-East, Land O Lakes, WI
Ankur Desai
Atmospheric & Oceanic Sciences, University of Wisconsin-Madison
What’s the Deal?
• Interannual variation (IAV) in carbon fluxes
from land to atmosphere are significant at
most flux sites
• Key to understanding how climate affects
ecosystems comes from modeling IAV
• IAV (years-decade) is currently poorly
modeled, while hourly, seasonal, and even
successional (century) are better
Can we simulate this?
Sipnet
• A “simplified” model of
ecosystem carbon / water
and land-atmosphere
interaction
– Minimal number of
parameters
– Driven by
meteorological forcing
• Still has >60
parameters
• Braswell et al., 2005, GCB
• Sacks et al., 2006, GCB
added snow
• Zobitz et al., 2008
QuickTime™ and a
decompressor
are needed to see this picture.
Results
2 years = 7 years
1997
1998
1999
2000
2001
2002
2003
2004
2005
Ricciuto et al.
Ricciuto et al.
Our region
Any coherence?
Desai et al, 2008, Ag For Met
Cross-site IAV
• Hypothesis: IAV in flux towers in the same
region are coherent in time
• Hypothesis: Simple climate driven models
can explain this IAV
– Growing season length
– Climate thresholds
– Mean annual precip
A whole bunch of data
Quic kTime™ and a
dec ompr es sor
are needed to s ee this pic ture.
Coherence?
Quic kTime™ and a
dec ompr es sor
are needed to s ee this pic ture.
Growing season and IAV
• Does growing season start explain IAV?
• Can a very simple model be constructed to
explain IAV?
– Hypothesis: growing season length explains
IAV
• Can we make a cost function more
attuned to IAV?
– Hypothesis: MCMC overfits to hourly data
Hello again
The model
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•
•
•
•
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Driven by PAR, Air and Soil T, VPD, (Precip)
LUE based GPP model f(PAR,T,VPD)
Three respiration pools f(T, GPP)
Output: NEE, ER, GPP, LAI
Sigmoidal GDD function for leaf out
Sigmoidal Soil T function for leaf off
17 parameters, 3 are fixed
Desai et al., in prep (a)
The optimizer
• All flux towers with multiple years of data
• Estimate parameters with Markov Chain
Monte Carlo (smart random walk)
• Written in IDL
MCMC
•
MCMC is an optimizing method to minimize model-data mismatch
– Quasi-random walk through parameter space (Metropolis)
• Start at many random places (Chains) in prior parameter space
– Move “downhill” to minima in model-data RMS by randomly changing a
parameter from current value to a nearby value
– Avoid local minima by occasionally performing “uphill” moves in
proportion to maximum likelihood of accepted point
– Use simulated annealing to tune parameter space exploration
– Pick best chain and continue space exploration
– Requires 100,000-500,000 model iterations (chain exploration, spin-up,
sampling)
– End result – “best” parameter set and confidence intervals (from all the
iterations)
– Cost function compared to observed NEE
New cost function
• Original log likelihood computes sum of
squared difference at hourly timestep
• What if we also added monthly and annual
squared differences to this likelihood?
• Have to scale these less frequent values
• Have to deal with missing data
I like likelihood
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
Quic kTime™ and a
dec ompr es sor
are needed to s ee this pic ture.
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
Regional IAV
• How well do we know regional (scaled-up)
IAV?
• Do top-down and bottom-up regional flux
estimation techniques agree on IAV (if not
magnitude)?
• What controls regional IAV?
– Wetland IAV vs Upland IAV
• Step 1: Scale the towers
Heterogeneous footprint
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
Scaling with towers
• NEP (=-NEE) at
13 sites
• Stand age matters
• Ecosystem type
matters
• Is interannual
variability
coherent?
• Are we sampling
sufficient land
cover types”?
Desai et al., 2008, AFM
• Multi-tower synthesis aggregation
QuickTime™ and a
decompressor
are needed to see this picture.
– parameter optimization with minimal 2
equation model
Tall tower downscaling
• Wang et al., 2006
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
Scaling evaluation
• Desai et al., 2008
Next step
• Use our IAV model with all 17 (19) flux
towers - estimate parameters for each
• Use better landcover and better age
distribution from NASA project
• Upscale again - this time over long time
period
• This experiment for Northern Highlands
1989-2007 (Buffam et al., in prep)
Quic kTime™ and a
dec ompr es sor
are needed to s ee this pic ture.
Mean NEE
40
20
gC m-2 mo-1
0
-20
-40
-60
-80
-100
1
2
3
4
5
6
7
8
9
10
11
12
Month
Cum ulat iv e NEE
150
100
gC m-2 mo-1
50
0
-50
-100
-150
-200
1
2
3
4
5
6
7
Month
8
9
10
11
12
Many years of flux
50
30
NEE gC m-2 mo-1
10
-10
-30
-50
-70
-90
-110
-130
-150
1989
1991
1993
1995
1997
1999
Year
2001
2003
2005
2007
Regional coherence?
• Desai et al., in prep
Regional Flux
40
20
NEE gC m-2 mo-1
0
-20
-40
-60
-80
-100
-120
1997
1998
1999
2000
2001
2002
Year
Flux towers
FIA Model
ABL Budget
2003
2004
2005
Regional coherence?
Annual flux (NEE)
1997
1998
1999
2000
2001
2002
2003
2004
0
gC m-2 yr-1
-50
-100
Flux towers
FIA model
ABL Budget
-150
-200
-250
Year
Conclusions
• There is some coherence in IAV across
ChEAS
– Better statistical method to show this?
• A simple model with explicit phenology can
capture the IAV across sites only with a
better likelihood function
– Next step: Simple model with fixed phenology
• Limited convergence on IAV from regional
methods
Other things
• Sulman et al., in prep - the role of wetlands in regional
carbon balance
• Lake Superior carbon balance from ABL budgets (Atilla,
McKinley) - Urban et al, in prep
• Small lakes in the landscape (Buffam, Kratz)
• Successional trends and modeling (Dietze)
• Hyperspectral remote sensing (Townsend, Serbin, Cook)
• Top-down CO2 budgets in valeys and complex terrain
(Stephens, Schimel, Bowling, deWekker)
• CH4 (pending), advection (pending - Yi), urban micromet
and biogeochem (pending)
• NEON? (Schimel, UNDERC)
Thanks
• Desai lab: http://flux.aos.wisc.edu
– Ben Sulman, Jonathan Thom, Shelly Knuth
• DOE NICCR, NSF, UW, DOE, NASA,
USFS, Northern Research Station, Kemp
NRS
• All the tower people