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

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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

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Coherence?

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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

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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

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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

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Tall tower downscaling

• Wang et al., 2006

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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)

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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