Transcript Spatial Processes and Land-atmosphere Flux Constraining ecosystem models with regional flux tower data assimilation Flux Measurements and Advanced Modeling, 22 July 2008 CU Mountain Research Station,

Spatial Processes and
Land-atmosphere Flux
Constraining ecosystem models
with regional flux tower data
assimilation
Flux Measurements and Advanced Modeling, 22 July 2008
CU Mountain Research Station, “Ned”, Colorado
Ankur Desai
Atmospheric & Oceanic Sciences, University of Wisconsin-Madison
Let’s get spacey…
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And regional
Why regional?
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Spatial interpolation/extrapolation
Evaluation across scales
Landscape level controls on biogeochem.
Understand cause of spatial variability
Emergent properties of landscapes
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Why regional?
Courtesy: Nic Saliendra
Why regional?
• NEP (=-NEE) at
13 sites
• Stand age matters
• Ecosystem type
matters
• Is interannual
variability
coherent?
• Are we sampling
sufficient land
cover types”?
Why data assimilation?
• Meteorological, ecosystem, and parameter
variability hard to observe/model
• Data assimilation can help isolate model
mechanisms responsible for spatial
variability
• Optimization across multiple types of data
• Optimization across space
Why data assimilation?
• Old way:
– Make a model
– Guess some parameters
– Compare to data
– Publish the best comparisons
– Attribute discrepancies to error
– Be happy
Why data assimilation?
• New way:
– Constrain model(s) with observations
– Find where model or parameters cannot
explain observations
– Learn something about fundamental
interactions
– Publish the discrepancies and knowledge
gained
– Work harder, be slightly less happy, but
generate more knowledge
Back to those stats…
[A|B] = [AB] / [B]
[P|D] = ( [D|P ] [P ] ) / [D]
(parameters given data) =
[ (data given parameters)× (parameters) ] / (data)
Posterior =
(Likelihood x Prior) / Normalizing Constraint
For the visually minded
• D Nychka, NCAR
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Some case studies
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Prediction
Up and down scaling
Regional evaluation
Interannual variability
Forest disturbance and succession
Regional Prediction
Our tower is bigger…
Is there a prediction signal?
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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Parameter estimation
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MCMC is an optimizing method to minimize model-data mismatch
– Quasi-random walk through parameter space (Metropolis)
• Prior parameters distribution needed
• 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 ~500,000 model iterations (chain exploration, spin-up, sampling)
– End result – “best” parameter set and confidence intervals (from all the iterations)
– NEE, Latent Heat Flux (LE), Sensible Heat Flux (H), soil moisture can all be used
• Nighttime NEE good measure of respiration, maybe H?
• Daytime NEE, LE good measures of photosynthesis
SipNET is fast (<10 ms year-1), so good for MCMC (4 hours for 7yr WLEF)
– Based on PNET ecosystem model
– Driven by climate, parameters and initial carbon pools
– Trivially parallelizable (needs to be done, though)
Goldilocks effect…
2 years = 7 years
1997
1998
1999
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2001
2002
2003
2004
2005
Regional futures
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Regional futures
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Upscaling and Downscaling
So many towers…
…so much variability
Simple comparisons…
Desai et al, 2008, Ag For Met
…don’t work
We need to do better
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Lots of flux towers (how many?)
Lots of cover types
A very simple model
Have to think about the tall tower flux, too
– What does it sample?
• Multi-tower synthesis aggregation with large
number of towers (12) in same climate space
– towers mapped to cover/age types
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– parameter optimization with minimal 2 equation model
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Heterogeneous footprint
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Tall tower downscaling
• Wang et al., 2006
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Scaling evaluation
• Desai et al., 2008
Scaling sensitivity
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Now we can wildly extrapolate
• Take 17 towers
• Fill the met data
• Use a simple model to estimate parameters for
each tower using MCMC
• Apply parameters to other region meteorology
data
• Scale to region by cover/age class
Another simple(r) model
• No carbon pools
• GPP model driven by LAI, PAR, Air temp,
VPD, Precip
• LAI model driven by GDD (leaf on) and
soil temp (leaf off)
• 3 pool ER, driven by Soil temp and GPP
• 19 parameters, fix 3
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20 yr regional NEE
• Cover types
+
• Age structure
+
• Parameters
• Forcing for a
lake organic
carbon input
model
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Regional scale evaluation
Top down and bottom up
IAV not modeled well
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Region Interannual variability
Ricciuto et al.
Ricciuto et al.
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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
New cost function
• Original log likelihood computes sum of
squared difference at hourly
• What if we also added monthly and annual
squared differences to this likelihood?
• Have to scale these less frequent values
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QuickTime™ and a
decompressor
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Regional Succession
History of land use
Ecosystem Demography
• Moorcroft et al., 2001; Albani et al., 2006; Desai
et al., 2007
• Height and age structured statistical gap model
• Well suited to data assimilation of regional
inventory data (e.g., USFS FIA)
– Use multiple FIA observation periods - estimate
carbon pools by allometry, segregate by type, age,
height classes
– Tune growth parameters until forest growth matches
FIA growth
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Enough?
What did we learn?
• Spatial prediction, scaling, parameterizing all
benefit from data assimilation
• Interannual variability has interesting spatial
attributes that are hard to model!
• Wetlands and land use history matter
• You can’t build infinite towers, or even a
sufficient number
– Use data assim to discover optimal design?
• Spatial covariate information needs a formal way
to be used in data assimilation
Where is your research
headed?
• What questions do you have?
– Mechanisms, forcings, inference, evaluation,
prediction, estimating error or uncertainty
• What kinds of data do you have, can get, can
steal?
– “Method-hopping”
• A model can mean many things…
• Data assimilation can be another tool in your
toolbox to answer questions, discover new ones
Data assimilation uses
• Not just limited to ecosystem carbon flux
models
• E.g. estimating surface or boundary layer
values (e.g., z0), advection, transpiration,
data gaps, tracer transport
• Many kinds, for estimating state or
parameters
TOMORROW
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Lab - 6 hour tour
Sipnet at Niwot Ridge
Parameter estimation with MCMC
Sipnet group projects
– Several ideas: parameter sensitivity across
sites, gap filling, prediction, regional
extrapolation
Enough!
• Time for a beer?