Interannual Variability in the Interannual Variability in the ChEAS MesonetChEAS Mesonet
ChEAS XI, 12 August 2008UNDERC-East, Land O Lakes, WI
Ankur DesaiAtmospheric & 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
QuickTime™ and a decompressor
are needed to see this picture.
Coherence?
QuickTime™ and a decompressor
are needed to see this picture.
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
• 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.
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.
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
– parameter optimization with minimal 2 equation model
QuickTime™ and a decompressor
are needed to see this picture.
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)
QuickTime™ and a decompressor
are needed to see this picture.
Mean NEE
-100
-80
-60
-40
-20
0
20
40
1 2 3 4 5 6 7 8 9 10 11 12
Month
gC m-2 mo-1
Cumulative NEE
-200
-150
-100
-50
0
50
100
150
1 2 3 4 5 6 7 8 9 10 11 12
Month
gC m-2 mo-1
Many years of flux
-150
-130
-110
-90
-70
-50
-30
-10
10
30
50
1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
Year
NEE gC m-2 mo-1
Regional coherence?
• Desai et al., in prepRegional Flux
-120
-100
-80
-60
-40
-20
0
20
40
1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
NEE gC m-2 mo-1
Flux towers FIA Model ABL Budget
Regional coherence?
Annual flux (NEE)
-250
-200
-150
-100
-50
0
1997 1998 1999 2000 2001 2002 2003 2004
Year
gC m-2 yr-1
Flux towers
FIA model
ABL Budget
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
Top Related