comparing simulated and observed gross primary productivity kevin schaefer, altaf arain, alan barr,...
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Comparing Simulated and Observed Gross Primary
Productivity
Kevin Schaefer, Altaf Arain, Alan Barr, Jing Chen, Ken Davis, Dimitre Dimitrov, Ni Golaz, Timothy Hilton, David Hollinger,
Elyn Humphreys, Benjamin Poulter, Brett Raczka, Andrew Richardson, Alok Sahoo, Christopher Schwalm, Peter Thornton,
Rodrigo Vargas, Hans Verbeeck, Chris Williams
NACP Synthesis Management Team
Ameriflux and Fluxnet Canada Investigators
Modeling Team Investigators
ObjectivesObjectives
• Quantify how well Quantify how well models simulate GPPmodels simulate GPP
• Identify sources of errorIdentify sources of error
32 Flux Tower Sites32 Flux Tower Sites
AGROIBIS LPJBEPS MODIS_algBIOMEBGC MODIS_C5CAN-IBIS MODIS_C5.1CNCLASS ORCHIDEEDLEM SIBDNDC SIBCASAECLUEEDCM SIBCROPECOSYS SSIB2ED2 TECOISAM TRIPLEXISOLSM Mean (all)LOTEC Mean (diurnal)
AGROIBIS LPJBEPS MODIS_algBIOMEBGC MODIS_C5CAN-IBIS MODIS_C5.1CNCLASS ORCHIDEEDLEM SIBDNDC SIBCASAECLUEEDCM SIBCROPECOSYS SSIB2ED2 TECOISAM TRIPLEXISOLSM Mean (all)LOTEC Mean (diurnal)
AGROIBIS LPJBEPS MODIS_algBIOMEBGC MODIS_C5CAN-IBIS MODIS_C5.1CNCLASS ORCHIDEEDLEM SIBDNDC SIBCASAECLUEEDCM SIBCROPECOSYS SSIB2ED2 TECOISAM TRIPLEXISOLSM Mean (all)LOTEC Mean (diurnal)
21 Models, 3 MODIS, 2 Model Mean21 Models, 3 MODIS, 2 Model Mean
Model RunsModel Runs
• Gap-filled observed weatherGap-filled observed weather
• Steady stateSteady state
• Observed NEE partitioned into GPP Observed NEE partitioned into GPP & respiration& respiration
• GPP UncertaintyGPP Uncertainty• RandomRandom• U* filteringU* filtering• Gap-fillingGap-filling• Partitioning Partitioning
Model-Data ComparisonModel-Data Comparison
• Daily average GPPDaily average GPP
• Performance MeasuresPerformance Measures• Chi-squared statisticChi-squared statistic• Root Mean Squared ErrorRoot Mean Squared Error• Normalized Mean Absolute ErrorNormalized Mean Absolute Error• BiasBias
Overall Model PerformanceOverall Model Performance
Model MeanMODIS
Optimized Model
C5C5.1
Daily GPP Taylor PlotSt
anda
rd D
evia
tion
Rat
ioG
PP
Rat
io
Typical GPPTypical GPP
Date (Year)
GP
P (m
ol m
-2 s
-1)
Monthly Average GPP for CA-Ca1
ObservedSimulated
Daily Average Shortwave Radiation (W m-2)
Light Use Efficiency CurvesLight Use Efficiency CurvesG
PP
(m
ol m
-2 s
-1)
Daily Average GPP for CA-Ca1
ObservedSimulated
Daily Average Temperature (°C)
Temperature Response CurvesTemperature Response CurvesG
PP
(m
ol m
-2 s
-1)
Daily Average GPP for CA-Ca1
ObservedSimulated
ConclusionsConclusions
• Models don’t simulate GPP wellModels don’t simulate GPP well
• Bias in seasonal amplitudeBias in seasonal amplitude
• Improve LUEImprove LUE
• Improve Temperature ResponseImprove Temperature Response
AcknowledgmentsAcknowledgments
• Funding provided by Funding provided by NASA, NOAA, and NSFNASA, NOAA, and NSF
StatisticsStatistics
2
2 1
GPP
estmod GPPGPP
nΧ
Chi-squared
21estmod GPPGPP
nRMSE
Root Mean Square Error
estmod GPPGPPn
B1
Bias
BB > 0 model greater than data > 0 model greater than data
RMSERMSE 0 perfect fit with data 0 perfect fit with data
22 ~ 1~ 1 model matches data within uncertainty
Normalized Mean Absolute Error
NMAENMAE 0 perfect fit with data 0 perfect fit with data estmod
est
GPPGPPnGPP
NMAE1
Overall Performance by ModelOverall Performance by Model
Ratio of Annual GPP AmplitudeRatio of Annual GPP Amplitude
Simulated:Observed Amplitude Ratio (-)
Per
cent
Yea
rs (
)
GPP Total Uncertainty for CA-Ca1GPP Total Uncertainty for CA-Ca1
Date (year)
GP
P U
ncer
tain
ty (m
ol m
-2 s
-1)
Daily Average GPP
GPP Uncertainty for CA-Ca1GPP Uncertainty for CA-Ca1
Date (year)
GP
P U
ncer
tain
ty (m
ol m
-2 s
-1)
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