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3. Conduct outlier assessment of NPP and model driver data
6. Compare model results with field measurements
Outlier Analyses
What is an outlier? data point unrepresentative of its general location or otherwise
“difficult” to represent by a generalized NPP model.
Initial problems identified and resolved by EMDI participants:
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•
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Outlier analysis tests used to assign individual flags:
 outside of reasonable fixed limits for variables (e.g., NPP > 2000 gC/m2)
 unrepresentative site for model comparisons (e.g., crop, pasture, plantation or
wetland sites)
 extreme values within a biome, (e.g., outside of .95 percentiles)
 inconsistencies at a site, such as precipitation reported for the site being different
than the precipitation derived from global climate data
 comparison of measured versus modeled ensemble NPP (average NPP from all
models): Bias, Normalized Error, and Mean Absolute Error
 outliers based on regressions with driver variables: NPP vs evapotranspiration
(AET), NPP vs precipitation, and NPP vs temperature
 unrepresentative based on visual inspection and knowledge of sites
inconsistent assignment of biome/vegetation type to Class A and B sites
inconsistent NPP units of measure in Class C sites
error in extrapolation process for site-specific monthly climate
lack of consistent set of sub-monthly climate data
Preliminary results:
• general agreement between model predictions and field measurements
• obvious differences that indicated areas for potential data and model
improvement
Overall site flag:
 overall flag calculated as weighted sum of individual flags
 minor outlier comparisons: weight = 1
 major outlier comparisons: weight = 10
 managed sites: weight = 100
 sites with flag > 5 were excluded
Temp_ann(sites)
TAVEbetween
X TEMP_ANN
Comparison
Temperature
reported
y = 0.7668x + 3.1052
for sites and
estimated
from
gridded
40
2
climate R = 0.6822
Class ASites
EMDI Initial Results: 11 Models
and Field NPP Data at 87 sites
30
20
Measure
d
NPP
10
0
-20
-10
-10
0
10
20
30
40
-20
Tave(PIK)
Outlier Analysis Results
Number of records
Steps in Outlier Analysis
Class A
B
C
Initial NPP measurements, sites with multiple measurements 162 2363 2768
NPP measurements passing outlier analysis
151 1689 2624
Percent measurements that were excluded
7% 29% 5%
Mean NPP measurements for unique sites excluding outliers
81
933 1637
Class B Sites
4. Provide model driver data for sites to modelers
5. Run global/regional models for sites without fine tuning models
EMDI Modeling Groups
Model
Modelers
Institution
AVIM
Jinjun Ji
Institute of Atmospheric Physics, China
BGC
Peter Thornton
University of Montana, USA
CARAIB
Bernard Nemry
University of Liege, Belgium
CENTURY Bill Parton
Colorado State University, USA
GLO-PEM Steve Prince,
University of Maryland, USA
Daolan Zheng
GTEC
Mac Post
Oak Ridge National Lab., USA
IBIS
Chris Kucharik
University of Wisconsin, USA
LPJ
Victor Brovkin.
Potsdam Institute for Climate Impact
Galina Churkina
Research, Germany
PNet
John Aber
University of New Hampshire, USA
SIB2
Jörg Kaduk
Carnegie Institution, Stanford, USA
STOMATE Nicolas Viovy
CNRS, France
VECODE Victor Brovkin
PIK, Germany
Class C Cells
Summary
What have we learned?
 problems in NPP and driver data were identified and resolved
 there was general agreement between model predictions of NPP and NPP
field measurements
 there were obvious differences that indicated areas for potential data and
model improvement
 model data comparison is a complex task that we are only beginning
 unique database of terrestrial productivity information will be available for
modelers and ecologists
What’s next?
 identify and explain outliers in both data and model predictions
 re-run model simulations with new land cover and climate datasets, with
outliers excluded
 assemble multiyear NPP measurements from CO2 flux towers, long-term
research plots (e.g., LTER projects, the ORNL Throughfall Displacement
Experiment), forest inventory remeasurement data, and agricultural production
data
 EMDI-2 workshop to evaluate interannual variation, spring 2001