Transcript Synthesis bottom-up scaling of regional carbon dioxide flux:

Multi-tower Synthesis Scaling of
Regional Carbon Dioxide Flux
Another fine mess of observed
data, remote sensing and
ecosystem model parameterization
Ankur Desai
Penn State University
Meteorology Dept.
ChEAS Meeting VII
June 2005
Goals
• Identify key processes of within-site and cross-site variability
of carbon dioxide flux in space and time with stand-scale
observations
• Develop simple multiple flux tower synthesis aggregation
methods to test the hypotheses that stand-scale towers can
sufficiently sample landscape for upscaling to regional flux
• Parameterize and optimize ecosystem models of varying
complexity to the region using biometric inventory, remote
sensing and component flux data and test effect of input
parameter resolution and type on model performance
• Constrain top-down regional CO2 flux using multi-tower
concentration measurements, and simple Eulerian and
Lagrangian/stochastic transport schema
ChEAS region and sites
• 13 stand-scale flux towers, 1 tall tower, new roving towers
Legend
MODIS IGBP 1km landcover
Interannual variability of NEE
• Interannual variability of NEE is coherent at many but not all
sites. This does not hold as well for GEP or ER
WLEF
Willow
Creek
Lost Creek
Sylvania
Intercomparisons and Upscaling
Flux tower spatial variability
• Stand age is a strong driver of variability within specific cover
types
ChEAS Summer 2003 Observed Fluxes
700
600
500
NEP (g C m-2)
400
300
200
100
0
-100
Hardwood
Red Pine
Young
Jack Pine
Intermediate
Wetland
Mature
Old
Pine Barren
CO2 flux variation drivers
• Canopy height serves as a
good proxy for stand age
• Canopy height is well correlated
to NEE and GEP, but not to ER,
as one might expect
• The relationship holds for
multiple vegetation types,
especially for GEP
• Thus, remotely sensed
measurements of forest height,
e.g., canopy lidar, could be
beneficial to regional scaling
Multi-tower aggregation method
ChEAS % Land Cover 40-km radius of tall tower
50%
45%
40%
35%
30%
% cover
• While mature
hardwood sites are
dominant in the 40-km
radius around WLEF
region according to FIA
and 30-m Wiscland
data, wetlands and
young and
intermediate aspen
sites cannot be ignored
25%
20%
15%
10%
5%
0%
Hardw ood
Red Pine
Young
Jack Pine
Wetland Pine Barren
Other
(Ag/Urban/Water)
Interm ediate
Mature
Old
• Simple method used to aggregate flux tower data using land
cover and FIA data and tower derived parameters:
Multi-tower aggregation results
• Multi-tower
synthesis
aggregation and
footprint weighted
decomposition
results for 40-km
radius are in very
close agreement
WLEF region bottom-up comparisons Jun-Aug 2003
800
700
600
gC m
-2
500
400
300
200
100
0
NEE * -1
Tall-tower
ER
Footprint weighted decomposition
GEP
Multi-tower aggregation
• Tall tower has
greater ER and
smaller NEE
compared to
bottom-up
methods
Multi-tower aggregation results
Regional flux comparisons
• Convergence in
regional estimates
of CO2 flux
• These estimates
are larger than
tall tower flux
• Reasons remain
elusive
Ecosystem modeling
• Competing effects of ecosystem model complexity and data
assimilation / parameterization in the upper Midwest
– Examine two models
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BIOME-BGC –stand-scale single-layer BGC model
ED – gap-scale model with explicit disturbance/mortality/size
Assimilate ChEAS area ecosystem information
Remotely sensed land cover, phenology
FIA stand age distribution, harvest rates, land use
Component flux optimized PFT rates and decomposition rates
– Compare model to tall tower and other regional estimates
• Compare to: multi-tower aggregation, footprint decomposition, ABL
budget based methods
• Assess impact of model complexity
• Assess role of data optimization, scale, density
• Predict future changes in regional CO2 flux
Biome-BGC
• Daily time step relatively
simple biome/stand-scale
ecosystem process model
• Stand age and disturbance can
be externally prescribed
• Initial work here will be used
with more elaborate scaling for
currently ongoing roving
tower/scaling project by F.A.
Heinsch, U. Montana
Ecosystem Demography model
Moorcroft, P. R, G. C. Hurtt, S. W. Pacala, A method for scaling vegetation
dynamics: the ecosystem demography model (ED), Ecological Monographs,
71, 557-585, 2001.
• Explicit consideration of stochastic disturbance events, effect
of stand age and mortality
Remote-sensing
• IKONOS 4-m
10x10 km around
tall tower
(courtesy B. Cook)
• Legend:
Spatial resolution and land cover
• Land cover in region is highly sensitive to resolution due to
large number of small area cover types, especially wetlands
• Land cover change is also important due to logging and
disturbance
Cover Comparison 10x10 WLEF
Built-up
Water
Other wetland
Cover type
Forested wetland
Shrub wetland
Ag./Grassland
Shrubland/Woody savanna
Mixed Forest
Evergreen
Aspen/Birch
N. Hardwood
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
Percent Cover
IKONOS (4m)
WISCLAND (30m)
MODIS IGBP (1km)
60.00%
70.00%
80.00%
90.00%
Incorporation of FIA data
• FIA statistics on age, biomass, mortality and CWD can be
used to prescribe model parameters
Multi-tower ABL budget
• Simple Eulerian models with 1-D ABL depth model and NOAA
aircraft CO2 profile data can be used to test ring of tower
validity and provide confidence for inversion
• More sophisticated stochastic
Lagrangian model, similar to
COBRA, to be developed to
test methods to assimilate
multi-tower synthesis data
Conclusions
• Coherent variations in time for NEE across most sites but not
as much for ER and GEP
• Stand age, canopy height, cover type can explain large
proportion of cross-site variation
• Convergence is seen in bottom-up and top-down regional flux
estimates – but they generally differ from tall-tower flux,
except when “reweighted” for footprint contribution
• Ecosystem models to be run this summer
• Resolution of remotely sensed data can have large impact on
scaling results in heterogeneous region
• Simple budget methods with “ring of towers” suggests that
more complex inversions will work
• Multi-tower work here complements single-tower footprint and
budget work of W. Wang and tall-tower modeling of D.
Ricciuto
Some publications
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Cook, B.D., Davis, K.J., Wang, W., Desai, A.R., Berger, B.W., Teclaw, R.M.,
Martin, J.M., Bolstad, P., Bakwin, P., Yi, C. and Heilman, W., 2004. Carbon
exchange and venting anomalies in an upland deciduous forest in northern
Wisconsin, USA. Agricultural and Forest Meteorology, 126(3-4): 271-295
(doi:10.1016/j.agrformet.2004.06.008).
Desai, A.R., Bolstad, P., Cook, B.D., Davis, K.J. and Carey, E.V., 2005.
Comparing net ecosystem exchange of carbon dioxide between an old-growth
and mature forest in the upper Midwest, USA. Agricultural and Forest
Meteorology, 128(1-2): 33-55 (doi: 10.1016/j.agrformet.2004.09.005).
Desai, A.R., Noormets, A., Bolstad, P.V., Chen, J., Cook, B.D., Davis, K.J.,
Euskirchen, E.S., Gough, C.M., Martin, J.M., Ricciuto, D.M., Schmid, H.P., Tang,
J. and Wang, W., submitted. Influence of vegetation and climate on carbon
dioxide fluxes across the Upper Midwest, USA: Implications for regional scaling,
Agricultural and Forest Meteorology.
Heinsch, F.A., Zhao, M., Running, S.W., Kimball, J.S., Nemani, R.R., Davis,
K.J., Bolstad, P.V., Cook, B.D., Desai, A.R., et al., in press. Evaluation of remote
sensing based terrestrial producitivity from MODIS using regional tower eddy
flux network observations, IEEE Transactions on Geosciences and Remote
Sensing.
Ph.D. plans
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May: ChEAS meeting, fieldwork
Jun-Aug: Ecosystem model parameterization and runs, potential return visits to
Montana/Harvard for model work
July-Aug: Top-down Lagrangian ABL budget
Jun-Oct: ChEAS special issue paper reviews
Sep: pre-dissertation defense committee meeting
Sep-Dec: dissertation writing, redo footprint model, add 2004 tower data to 1st
chapter, finalize multi-tower aggregation chapter, apply to jobs
Sep: present at International CO2 conference, Boulder, CO
Oct: ChEAS fall fieldwork
Oct-Nov: present at Ameriflux, Boulder, CO
Dec: present at AGU, San Francisco, CA
Dec-Feb: finish dissertation, send to committee and to format review
Jan: present ABL research at AMS, Atlanta, GA?
Mar: defend dissertation!
Mar-Sep: submit final model results for publication, party, travel
Fall 2006: post-doc?
Thank You