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Using AmeriFlux Observations in the NACP
Site-level Interim Synthesis
Kevin Schaefer
NACP Site Synthesis Team
Flux Tower PIs
Modeling Teams
Do models match observations? If not, why?
30 Models47 Flux Tower Sites
36 AmeriFlux11 Fluxnet Canada
24 submitted output10 runs per site
Num Model Num Model
1 Agro-IBIS 16 GTEC2 BEPS 17 ISAM3 Biome-BGC 18 ISOLSM4 Can-IBIS 19 LoTEC5 CLM-CASA' 20 LoTEC-DA6 CLM-CN 21 LPJ_wsl7 CN-CLASS 22 ORCHIDEE8 DAYCENT 23 ORCHIDEE-STICS9 DLEM 24 SiB310 DNDC 25 SiBCASA11 ecosys 26 SiBCrop12 ED2 27 SIPNET13 EDCM 28 SSiB214 EPIC 29 TECO15 GFDL LM3V 30 TRIPLEX-Flux
Analysis Projects
Published
Submitted
Num Title Lead author1 Agriculture Sites Erandi Lokupitiya2 Disturbance Peter Thornton3 Flux uncertainty Alan Barr4 Forest sites Bill Munger5 GPP Kevin Schaefer6 IAV Guerric Lemaire7 Interannual Variability Trevor Keenan8 Isotopes Chun-Ta Lai9 Latent and Sensible Heat Flux Alok Sahoo10 Model Parameters Hans Verbeeck11 Model uncertainty Ensheng Weng12 NEE Seasonal Cycle Christopher Schwalm13 NEE Spectral Error Michael Dietze14 NEE Wavelet Coherence Paul Stoy15 Phenology Andrew Richardson16 Regional vs. Sites Brett Raczka17 Weather Uncertainty Dan Ricciuto18 Wetland Sites Ankur Desai
Products Derived from Flux Data
• Gap-filled observed weather (Ricciuto et al.)
• BADM files (everyone)
• Gap-filled fluxes & Uncertainty (Barr et al.)• Random• U* threshold• Gap-filling Algorithm• Partitioning Algorithm
Random Uncertainty (Barr et al.)Needleleaf
ForestBroadleaf
ForestMixedwood
ForestJuvenileForest
WetlandGrasslandShrublandCropland
▲ USA ● Canada
Ann
ual
NE
P (
g C
m-2 y
-1)
Annual Re (g C m-2 y-1)
• Random NEP ~4% Re
• U*th NEP ~1.3% Re
BADM Files
• Extremely useful to modelers• Soil texture• Site history• Initial pools sizes• Leaf Area Index
• We strongly encourage more submissions
Weather Uncertainty (Ricciuto et al.)
Swdown (W m-2) Delta (%)Observed 65.38LaThu 65.74 0.55ECMWF 74.76 14.35NARR 83.98 28.45NCEP 90.7 38.73NCEP2 84.24 28.85
Princeton 69.18 5.81NLDAS 79.6 21.75
CRU-NCEP 62.45 -4.48
• Bias in radiation produces bias in GPP
Agriculture Sites (Lokupitiya et al.)
• Need crop specific parameterizations
Soybean CornCorn Soybean
US-Ne3
Wetland Sites (Desai et al.)
• Residuals correlate to water table depth
• Models should include water table dynamics
GPP residuals
Res
idua
ls
mol
m-2 s
-1
Reco residuals
Res
idua
ls
mol
m-2 s
-1
Water Table Depth (cm)
Spectral NEE Error (Dietze et al.)
Diurnal
Annual
Synoptic
Month
• Error peak at diurnal & annual time scales• Errors at synoptic & monthly time scales
Not Significant
NEE Wavelet Coherence (Stoy et al.)
Tim
e S
cale
(ho
urs)
Hour
Diurnal
Month
Synoptic
Annual
SiB at US-UMB
Sig
nifi
cant
• Models match observations only some of the time
NEE Seasonal Cycle (Schwalm et al.)
0.4
0.5
0.6
0 11 151 2 3 7 9 10Number Soil Layers
Tay
lor
Ski
ll
0.4
0.5
0.6
Semi-Prog PrescribedPrognostic
PhenologyImprove prognostic phenology
Add soil layers
0.4
0.5
0.6
8 90 3 4 6 7Number Veg Pools
Add vegetation
pools Tay
lor
Ski
llT
aylo
r S
kill
Phenology (Richardson et al.)
• Early/late uptake means positive GPP bias
• Models need better phenology
Regional vs. Site (Raczka et al.)
• Enzyme kinetic models biased high• LUE models biased low
Flux Towers
Light Use EfficiencyEnzyme Kinetic
GPP Annual Bias (Schaefer et al.)
• Slope of LUE Curve drives Annual bias
• Models need better Vmax, leaf-to-canopy scaling, …
Daily Average Shortwave Radiation (W m-2)
Dai
ly A
vera
ge G
PP (m
ol m
-2 s
-1)
ObservedSimulated
US-Me2 Light Use Efficiency Curve
Areas For Model Development
• Better Phenology
• More soil layers
• More vegetation pools
• Slopes to LUE curve
• Water table dynamics
• Crop parameterizations
Extra Slides
Annual GPP Bias due to PhenologyEvergreen sitesDeciduous sites
160±145
75±130
40±80
-5±65
Multi-Model wavelet CoherenceSc
ale
(hou
rs)
NEE Seasonal Cycle (Schwalm et al.)T
aylo
r S
kill
Normalized Mean Absolute Error Chi-squared
Our 1st published paper!
Perfect Model
Needleleaf Forest
Broadleaf Forest
Mixedwood Forest
JuvenileForest
WetlandGrasslandShrublandCropland
▲ USA ● Canada
U*t
h A
nnua
l N
EP
(g C
m-2 y
-1)
Random Annual NEP (g C m-2 y-1)
U*th vs. Random Uncertainty (Barr et al.)