SSL Man in the Middle Proxy Srinivas Inguva Dan Boneh Ian Baker Stanford University.
Ian Baker Colorado State University Department of Atmospheric Science/GDPE
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Transcript of Ian Baker Colorado State University Department of Atmospheric Science/GDPE
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Is There Utility in Simulating Ecosystem-Level Fluxes as a Collection of Fluxes
from Individual Species?
Ian BakerColorado State University
Department of Atmospheric Science/GDPE
With help from:Ken Davis; PSU
Vince Gutschick, Connie Maxwell, Mario Montes-Helu, Erika Mortenson, Alonzo
Soto, Felicia Najera, and Erik Jackson; NMSUJoe Berry, Bob Haxo, Carnegie Institute of Washington
A. Scott Denning, Neil Suits, Niall Hanan; CSU
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Motivation/Background
Comparison of model results (SiB) to observations
•Ewers et al (2002) - transpiration calculated from sap flux data
•Vince Gutshick – observations (LI-6200) at the leaf level (i.e. stomatal control, carboxylation capacity)
•Joe Berry – provided a suite of programs that can derive SiB-type vegetation parameters from leaf-level observations
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The Model: SiB2
Simple Biosphere Model, version 2 (SiB2) Sellers et al. 1996• Biophysical land surface model• Describes heat, water and carbon transfers in the soil, vegetation, atmosphere continuum• Developed for general circulation models• Useful at many scales, globe to point• Single canopy layer scheme• Highly non-linear• Large number of input parameters
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In SiB, WLEF site is ‘mixed forest’-biome 3. All biome 3 sites are parameterized identically wrt time-invariant properties
SiB Vegetation Params:
•20 time-invariant biome-specific variables
•13 soil properties (moisture, thermal and respiration)
•8 NDVI-derived time-varying phenological variables (heterogeneous in space)
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•Canopy height•Vegetation fractional cover•Leaf angle distribution•Transmittance/reflectance•Rubisco velocity of sun-leaf•Quantum efficiency (C3/C4)•BB slope/intercept•Coupling parameters for eqn A=min(ωc,ωe,ωs)•Low-and high-temperature inhibition functions•Canopy respiration factors •Respiration fraction of Vmax
These are the variables that we can adjust•Vmax0•BB slope•BB intercept•respcp•hhti•atheta
SiB Time-Invariant Parameters
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PROCEDURE
•Sort Vince Gutschick’s leaf-level obs by species•Obtain species-specific values of
•Vmax0 – maximum rubisco velocity•binter – BB intercept•gradm – BB slope•respcp – autotrophic respiration component•hhti – ½ point high-temp inhibition function•atheta – rubisco/light coupling factor
•Create a new SiB vegetation parameter file using these new values•Run SiB
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Data from Ewers et al, 2002
Red pineJack pine
Sugar maple
Trembling aspenBalsam fir
White cedarBalsam firSpeckled alder
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ASPEN/FIR
-1
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1
VAPOR PRESSURE DEFICIT (kPa)
TR
AN
SP
IRA
TIO
N (m
m/d
ay)
Ec
Poly. (Ec)
Poly. (MIXED_FOREST)
Obs data asymptotes slightly above 3.0
OBS: trembling aspen, balsam firMODEL: trembling aspen, balsam firComparison looks quite good
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CONIFER
-1
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1
VAPOR PRESSURE DEFICIT (kPa)
TR
AN
SP
IRA
TIO
N (
mm
/day
)
CONIFER_Ec
Poly. (CONIFER_Ec)
Poly. (MIXED_FOREST)
Obs data asymptotes near 2.0
OBS: red pine, Jack pineMODEL: Balsam fir, spruce
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WETLAND (Balsam fir, speckled alder)
-1
0
1
2
3
4
5
6
0 0.2 0.4 0.6 0.8 1
VAPOR PRESSURE DEFICIT (kPa)
TR
AN
SP
IRA
TIO
N (
mm
/da
y)
Ec
Poly. (Ec)
Poly. (MIXED_FOREST)
Obs data asymptotes near 2.0
OBS: dominated by white cedar, also balsam fir, speckled alder
MODEL: balsam fir, speckled alder
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MAPLE (NORTHERN HARDWOODS)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 0.2 0.4 0.6 0.8 1
VAPOR PRESSURE DEFICIT (kPa)
TR
AN
SP
IRA
TIO
N (m
m/d
ay)
maple_Ec
Poly. (maple_Ec)
Poly. (MIXED_FOREST)
Obs asymptotenear 1.0
OBS: sugar mapleMODEL: sugar maple
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SOME QUESTIONS
•Is it reasonable to create species-specific model vegetation for certain parameters, when a number of them, including
•aparc-canopy absorbed PAR•LAI•roughness length•leaf projectionare determined from canopy-level NDVI observations?
•Are the values I obtained internally consistent for each of the species I investigated?
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SOME MORE QUESTIONS
•Does this approach have the potential to lead to better simulation of fluxes of carbon, heat and moisture?
•Is there potential to utilize this approach in coupling SiB to mesoscale model(s) (RAMS)?•Will higher-resolution information (both on the
species and spatial/satellite scale) make species-specific land-atmosphere modeling more attractive?•Is the technique I’ve outlined adequate, or do the SiB parameters need to be made more fully self-consistent by including more parameters for each species?