Benefit of ASP, not generally being subjected to this:
What the flux?
Constraining ecosystem models
with flux tower mesonets
Ankur Desai
National Center for Atmospheric Research
ASP Research Review, 7 Mar 2007
Boulder, CO USA
Carbon Dioxide
• Carbon dioxide and climate are closely linked in our
atmospheric system
• Atmospheric mixing ratios of CO2 exceed anything seen
in last 650,000 yr
Carbon Dioxide
Carbon Dioxide
Carbon Dioxide
• Atmospheric CO2 growth rate
is not constant
– more variable than rate of
increase in fossil fuel use
• Land and ocean sources/sinks
– complex internal feedbacks
– also affected by external
episodic (e.g., volcano) and
oscillatory (e.g., ENSO) events
• Basic mechanisms understood
– specific processes in land and
ocean are not
– regional scale evaluation is
critically needed
Pools and Fluxes
The Terrestrial Ecosystem
• Responses between land and atmospheric CO2 are
highly variable and functions of:
–
–
–
–
geography (e.g., N.H. land sink)
land cover
management (e.g., tropical deforestation)
land-atmosphere feedbacks of carbon, water and energy
• Latest atmospheric data inversions and biogeochemical
models converge on terrestrial carbon cycle as primary
control on atmospheric CO2 growth rate variability
(Peylin et al, 2005, GBC)
• Measurements of atmospheric CO2 over land have, until
recently, been limited
Peylin et al, 2005, GBC
Terrestrial Ecosystem
• Regional biosphere flux variability is complex
• Source: NOAA/ESRL (Carbon Tracker), units Mg Ha-1 yr-1
Terrestrial Terminology
• The terrestrial CO2 cycle:
– Plants uptake CO2 by photosynthesis = Gross Primary
Production (GPP) = function of light, CO2, water,
temperature, humidity [Farquhar, Ball, Berry, Cook,
Collatz, Sharkey]
– Plants respire some of this CO2 during carbohydarate
conversion and utilization = Autotrophic Respiration (Ra)
= function of temperature and substrate availability
– Soil bacteria decompose organic carbon (dead plants) and
release CO2 back to the atmosphere = Heterotrophic
Respiration (Rh) = function of temperature, soil moisture,
substrate availability, bacterial community kinetics
– Total Ecosystem Respiration = Rh + Ra
– Lots of non-linear interactions
– Disturbance, land use, competitions are larger scale
effects
Terrestrial Terminology
• Most important term:
– NEE = Net Ecosystem Exchange = Net CO2 flux = ER – GPP
• Negative = sink from atmosphere to biosphere
• Positive = source from biosphere to atmosphere
• Modeling NEE, GPP, ER is hard because:
– Functions are empirical, typically enzyme kinetics
– Parameters are unknown, hard to measure
– Works well for a single leaf, simple soil but not always for
entire forests and realistic soils
• What are we trying to do
– Upscaling fluxes from leaf to forest stand, ecosystem, biome is
current heart of research enterprise called the “bottom-up”
approach
– Downscaling tracers/satellites from globe to continent to
region is heart of the “top-down” approach
– Convergence = we can measure/predict/test hypotheses with
regional fluxes
– At least 98 grad students agree and want to learn more
Measuring Stand Scale Flux
• We can measure ecosystem land-atmosphere flux (NEE)
at spatial length scales of 1-10 km with the Eddy
Covariance technique
– How? Use the ensemble-averaged turbulent scalar
conservation equation
Measuring Stand Scale Flux
• We have instruments to be able to do this
Measuring Stand Scale Flux
Measuring Stand Scale Flux
Respiration and Photosynthesis
Respiration
Measuring Stand Scale Flux
• Top: Daily NEE, Bottom: Cumulative NEE
Measuring Stand Scale Flux
Measuring Stand Scale Flux
• Lots of folks are now doing this (first in early 90s)
Pitfalls With Eddy Covariance
• Major assumptions for using time-averaged flux as
stand-in for ensemble average (Reynolds’ “frozen field”
hypothesis)
– flow is turbulent, above roughness sublayer, stationary
– signal spectral attenuation and instrument lags are
minimal and can be empirically corrected
– time period captures major scales of turbulence
Berger et al, 2001, JAOT
Pitfalls With Eddy Covariance
• Nocturnal stable boundary layer provides most
challenging conditions:
– nighttime NEE decline with u*
• suggests primary flow is not 1-D (e.g., advection)
• intermittent turbulence
– non-homogenous cover/terrain effects
Desai et al, 2005, Ag. For. Met
Cook et al, 2004, Ag. For. Met.
Upscaling Goals
• Upscaling fluxes from sites (e.g., measured with eddy covarinace)
to regions is a pressing research issue
– Helps understand land-atmosphere interaction at scales
relevant to global models, decisions support
– Emergent properties of land-atmosphere interaction may
appear
– But: upscaling is hard when landcover or terrain is complex
• Hypotheses:
– Inversion of NEE from multiple tower sites can lead to regional
scale ecosystem parameters that reproduce regional flux
– Parameters are significantly different across major ecosystem
type boundaries
– Wetlands are more sensitive to precipitation variability than
uplands
• Several regions have dense flux tower networks that could be used
to constrain a regional ecosystem model
• Northern Wisconsin is one of these regions
– Plus we can evaluate this flux with the 447-m tall flux tower,
tall tower ABL budgets, forest inventory, and a regional
mesoscale CO2 inversion
Upscale This!
Already upscaled
Dense Mesonet
Tall Tower Cumulative NEE
• Net annual source since 1997
Complex Landcover
Regional Flux?
Stand Scale Flux Variability
Method
• We can use models constrained with data to get
regional flux
• Ecosystem models do generally well at simulating daily
and seasonal cycle
– Poor at interannual variability, long term trends
– Also, parameters are unknown
• Parameter estimation using well established method –
Markov Chain Monte Carlo (MCMC)
• Ecosystem Model to be used is SipNET
• SipNET parameter estimation was designed from the
get-go to be “spatial”
– Multiple sites can be assimilated at once
– Some parameters vary spatially, others are fixed
– Cost function reflects this by summing RMS model-data
error across sites and modifying parameter walk
Method
• MCMC is an optimizing method to minimize model-data mismatch
– Quasi-random walk through parameter space (Metropolis)
• Prior parameters distribution needed
• Start at many places (random) in prior parameter space
–
–
–
–
Move “downhill” to minima in model-data RMS
Avoid local minima by occasionally performing “uphill” moves
Requires ~100,000 model iterations
End result – “best” parameter set and confidence intervals (from all
the iterations)
– NEE, Latent Heat Flux (LE) and Sensible Heat Flux (H) can all be used
• Nighttime NEE good measure of respiration, maybe H?
• Daytime NEE, LE good measures of photosynthesis
• SipNET is fast (~100 ms year-1), so good for MCMC (hours)
– Based on PNET ecosystem model
– Tested at several sites
– Driven by climate, parameters and initial carbon pools
– Trivially parallelizable (needs to be done, though)
Simple Test of SipNET & MCMC
The Next Test
• Region is 70% upland, 30% wetland
• Combine the 3 hardwood sites together to estimate
upland NEE
• Combine the 3 wetland sites to estimate wetland NEE
• Use remote sensing to add hardwood+wetland
• Compare to using only 1 hardwood tower, 1 wetland
tower, 1 hardwood+wetland tower
• Compare to the independent regional flux estimates
(tall tower, FIA driven model, ABL budgets, regional
inverse methods)
• See if parameters can predict interannual variability
over next several years at tall tower
Progress
• Not much, ACME07 and RBGC07 take all my time. Need
a catchy acronym to get more work done!
• Test assimilation with tall tower done
• SipNET probably not a good wetland model, proposal
funded to fix that
• Number of parameters one can constrain with flux data
is relatively small (4-10), other data (transpiration,
vegetation indices, …) could help
– Meteorologists are better at this kind of data assimilation
but goal is different (forecast, equations are known,
model is slower, [3,4]DVAR or EKF better suited)
• Could regional tracer mesonets also be used here?
• Another oversampled test case this summer is the North
American Carbon Program (NACP) Mid-Continent
Intensive (MCI) over Iowa
Conclusions
• Atmospheric CO2 growth rates are mediated by land
fluxes
– Problem is nonlinear - land fluxes are also functions of
CO2 and temperature
• There’s lots to learn about land-atmosphere trace gas
exchange and interaction
– Regional scales are key in terms of understanding whole
ecosystems, emergent responses, regional impacts,
decision support and global model evaluation
• We can measure fluxes with the eddy covariance
technique
• Scaling up and down is hard
• Ecosystem models can be constrained with eddy
covariance flux data
• Ecologists, meteorologists, foresters, and hydrologists
will one day live in perfect harmony
Thanks
• Collaborators: Dave Schimel (CGD), Dave Moore
(CIRES), Steve Aulenbach (CGD), Ken Davis (PSU), Bill
Sacks (UWI)
• Funding: NSF, DOE, NASA, USDA
• Thanks: Land owners, technicians, students
Lots of Fluxes
WLEF
tall tower
Willow Creek
hardwood
Lost Creek
wetland
Sylvania
old-growth
Fluxes and Age
ABL Budget Equation