A model-data intercomparison of CO 2 exchange across North America: Results from the North American Carbon Program site synthesis
Journal of Geophysical Research (2010)
- ISSN: 01480227
- ISBN: 2010101029
- DOI: 10.1029/2009JG001229
Available from
Michael Dietze's profile on Mendeley.
or
Abstract
Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans similar to 220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was similar to 10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.
Available from
Michael Dietze's profile on Mendeley.
Page 1
A model-data intercomparison of CO 2 exchange across North America: Results from the North American Carbon Program site synthesis
A model‐data intercomparison of CO2 exchange across North
America: Results from the North American Carbon Program site
synthesis
Christopher R. Schwalm,1 Christopher A. Williams,1 Kevin Schaefer,2 Ryan Anderson,3
M. Altaf Arain,4 Ian Baker,5 Alan Barr,6 T. Andrew Black,7 Guangsheng Chen,8
Jing Ming Chen,9 Philippe Ciais,10 Kenneth J. Davis,11 Ankur Desai,12 Michael Dietze,13
Danilo Dragoni,14 Marc L. Fischer,15 Lawrence B. Flanagan,16 Robert Grant,17
Lianhong Gu,18 David Hollinger,19 R. César Izaurralde,20 Chris Kucharik,21
Peter Lafleur,22 Beverly E. Law,23 Longhui Li,10 Zhengpeng Li,24 Shuguang Liu,25
Erandathie Lokupitiya,5 Yiqi Luo,26 Siyan Ma,27 Hank Margolis,28 Roser Matamala,29
Harry McCaughey,30 Russell K. Monson,31 Walter C. Oechel,32 Changhui Peng,33
Benjamin Poulter,34 David T. Price,35 Dan M. Riciutto,18 William Riley,36
Alok Kumar Sahoo,37 Michael Sprintsin,9 Jianfeng Sun,33 Hanqin Tian,8
Christina Tonitto,38 Hans Verbeeck,39 and Shashi B. Verma40
Received 23 November 2009; revised 23 July 2010; accepted 29 July 2010; published 9 December 2010.
1Graduate School of Geography, Clark University, Worcester,
Massachusetts, USA.
2National Snow and Ice Data Center, University of Colorado at
Boulder, Boulder, Colorado, USA.
3Numerical Terradynamic Simulation Group, University of Montana,
Missoula, Montana, USA.
4School of Geography and Earth Sciences, McMaster University,
Hamilton, Ontario, Canada.
5Atmospheric Science Department, Colorado State University, Fort
Collins, Colorado, USA.
6Climate Research Division, Atmospheric Science and Technology
Directorate, Saskatoon, Saskatchewan, Canada.
7Faculty of Land and Food Systems, University of British Columbia,
Vancouver, B. C., Canada.
8School of Forestry and Wildlife Sciences, Auburn University, Auburn,
Alabama, USA.
9Department of Geography and Program in Planning, University of
Toronto, Toronto, Ontario, Canada.
10Laboratoire des Sciences du Climat et de l’Environnement, CE Orme
des Merisiers, Gif sur Yvette, France.
11Department of Meteorology, Pennsylvania State University,
University Park, Pennsylvania, USA.
12Center for Climatic Research, University of Wisconsin‐Madison,
Madison, Wisconsin, USA.
13Department of Plant Biology, University of Illinois‐Urbana
Champaign, Urbana, Illinois, USA.
14Department of Geography, Indiana University, Bloomington, Indiana,
USA.
15Atmospheric Science Department, Lawrence Berkeley National
Laboratory, Berkeley, California, USA.
16Department of Biological Sciences, University of Lethbridge,
Lethbridge, Alberta, Canada.
17Department of Renewable Resources, University of Alberta,
Edmonton, Alberta, Canada.
18Environmental Sciences Division, Oak Ridge National Laboratory,
Oak Ridge, Tennessee, USA.
19Northern Research Station, USDA Forest Service, Durham, New
Hampshire, USA.
20Joint Global Change Research Institute, Pacific Northwest National
Laboratory and University of Maryland, College Park, Maryland, USA.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JG001229
21Department of Agronomy and Nelson Institute Center for
Sustainability and the Global Environment, University of Wisconsin‐
Madison, Madison, Wisconsin, USA.
22Department of Geography, Trent University, Peterborough, Ontario,
Canada.
23College of Forestry, Oregon State University, Corvallis, Oregon,
USA.
24ASRC Research and Technology Solutions, Sioux Falls, South
Dakota, USA.
25Earth Resources Observation and Science, Sioux Falls, South Dakota,
USA.
26Department of Botany and Microbiology, University of Oklahoma,
Norman, Oklahoma, USA.
27Department of Environmental Science, Policy and Management and
Berkeley Atmospheric Science Center, University of California, Berkeley,
Berkeley, California, USA.
28Centre d’études de la forêt, Faculté de foresterie, de géographie et de
géomatique, Université Laval, Québec, Quebec, Canada.
29Argonne National Laboratory, Biosciences Division, Argonne,
Illinois, USA.
30Department of Geography, Queen’s University, Kingston, Ontario,
Canada.
31Department of Ecology and Evolutionary Biology, University of
Colorado at Boulder, Boulder, Colorado, USA.
32Department of Biology, San Diego State University, San Diego,
California, USA.
33Department of Biology Sciences, University of Quebec at Montreal,
Montreal, Quebec, Canada.
34Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.
35Northern Forestry Centre, Canadian Forest Service, Edmonton,
Alberta, Canada.
36Climate and Carbon Sciences, Earth Sciences Division, Lawrence
Berkeley National Laboratory, Berkeley, California, USA.
37Department of Civil and Environmental Engineering, Princeton
University, Princeton, New Jersey, USA.
38Department of Ecology and Evolutionary Biology, Cornell
University, Ithaca, New York, USA.
39Laboratory of Plant Ecology, Ghent University, Ghent, Belgium.
40School of Natural Resources, University of Nebraska‐Lincoln,
Lincoln, Nebraska, USA.
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, G00H05, doi:10.1029/2009JG001229, 2010
G00H05 1 of 22
America: Results from the North American Carbon Program site
synthesis
Christopher R. Schwalm,1 Christopher A. Williams,1 Kevin Schaefer,2 Ryan Anderson,3
M. Altaf Arain,4 Ian Baker,5 Alan Barr,6 T. Andrew Black,7 Guangsheng Chen,8
Jing Ming Chen,9 Philippe Ciais,10 Kenneth J. Davis,11 Ankur Desai,12 Michael Dietze,13
Danilo Dragoni,14 Marc L. Fischer,15 Lawrence B. Flanagan,16 Robert Grant,17
Lianhong Gu,18 David Hollinger,19 R. César Izaurralde,20 Chris Kucharik,21
Peter Lafleur,22 Beverly E. Law,23 Longhui Li,10 Zhengpeng Li,24 Shuguang Liu,25
Erandathie Lokupitiya,5 Yiqi Luo,26 Siyan Ma,27 Hank Margolis,28 Roser Matamala,29
Harry McCaughey,30 Russell K. Monson,31 Walter C. Oechel,32 Changhui Peng,33
Benjamin Poulter,34 David T. Price,35 Dan M. Riciutto,18 William Riley,36
Alok Kumar Sahoo,37 Michael Sprintsin,9 Jianfeng Sun,33 Hanqin Tian,8
Christina Tonitto,38 Hans Verbeeck,39 and Shashi B. Verma40
Received 23 November 2009; revised 23 July 2010; accepted 29 July 2010; published 9 December 2010.
1Graduate School of Geography, Clark University, Worcester,
Massachusetts, USA.
2National Snow and Ice Data Center, University of Colorado at
Boulder, Boulder, Colorado, USA.
3Numerical Terradynamic Simulation Group, University of Montana,
Missoula, Montana, USA.
4School of Geography and Earth Sciences, McMaster University,
Hamilton, Ontario, Canada.
5Atmospheric Science Department, Colorado State University, Fort
Collins, Colorado, USA.
6Climate Research Division, Atmospheric Science and Technology
Directorate, Saskatoon, Saskatchewan, Canada.
7Faculty of Land and Food Systems, University of British Columbia,
Vancouver, B. C., Canada.
8School of Forestry and Wildlife Sciences, Auburn University, Auburn,
Alabama, USA.
9Department of Geography and Program in Planning, University of
Toronto, Toronto, Ontario, Canada.
10Laboratoire des Sciences du Climat et de l’Environnement, CE Orme
des Merisiers, Gif sur Yvette, France.
11Department of Meteorology, Pennsylvania State University,
University Park, Pennsylvania, USA.
12Center for Climatic Research, University of Wisconsin‐Madison,
Madison, Wisconsin, USA.
13Department of Plant Biology, University of Illinois‐Urbana
Champaign, Urbana, Illinois, USA.
14Department of Geography, Indiana University, Bloomington, Indiana,
USA.
15Atmospheric Science Department, Lawrence Berkeley National
Laboratory, Berkeley, California, USA.
16Department of Biological Sciences, University of Lethbridge,
Lethbridge, Alberta, Canada.
17Department of Renewable Resources, University of Alberta,
Edmonton, Alberta, Canada.
18Environmental Sciences Division, Oak Ridge National Laboratory,
Oak Ridge, Tennessee, USA.
19Northern Research Station, USDA Forest Service, Durham, New
Hampshire, USA.
20Joint Global Change Research Institute, Pacific Northwest National
Laboratory and University of Maryland, College Park, Maryland, USA.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JG001229
21Department of Agronomy and Nelson Institute Center for
Sustainability and the Global Environment, University of Wisconsin‐
Madison, Madison, Wisconsin, USA.
22Department of Geography, Trent University, Peterborough, Ontario,
Canada.
23College of Forestry, Oregon State University, Corvallis, Oregon,
USA.
24ASRC Research and Technology Solutions, Sioux Falls, South
Dakota, USA.
25Earth Resources Observation and Science, Sioux Falls, South Dakota,
USA.
26Department of Botany and Microbiology, University of Oklahoma,
Norman, Oklahoma, USA.
27Department of Environmental Science, Policy and Management and
Berkeley Atmospheric Science Center, University of California, Berkeley,
Berkeley, California, USA.
28Centre d’études de la forêt, Faculté de foresterie, de géographie et de
géomatique, Université Laval, Québec, Quebec, Canada.
29Argonne National Laboratory, Biosciences Division, Argonne,
Illinois, USA.
30Department of Geography, Queen’s University, Kingston, Ontario,
Canada.
31Department of Ecology and Evolutionary Biology, University of
Colorado at Boulder, Boulder, Colorado, USA.
32Department of Biology, San Diego State University, San Diego,
California, USA.
33Department of Biology Sciences, University of Quebec at Montreal,
Montreal, Quebec, Canada.
34Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.
35Northern Forestry Centre, Canadian Forest Service, Edmonton,
Alberta, Canada.
36Climate and Carbon Sciences, Earth Sciences Division, Lawrence
Berkeley National Laboratory, Berkeley, California, USA.
37Department of Civil and Environmental Engineering, Princeton
University, Princeton, New Jersey, USA.
38Department of Ecology and Evolutionary Biology, Cornell
University, Ithaca, New York, USA.
39Laboratory of Plant Ecology, Ghent University, Ghent, Belgium.
40School of Natural Resources, University of Nebraska‐Lincoln,
Lincoln, Nebraska, USA.
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, G00H05, doi:10.1029/2009JG001229, 2010
G00H05 1 of 22
Page 2
[1] Our current understanding of terrestrial carbon processes is represented in various
models used to integrate and scale measurements of CO2 exchange from remote sensing
and other spatiotemporal data. Yet assessments are rarely conducted to determine how well
models simulate carbon processes across vegetation types and environmental conditions.
Using standardized data from the North American Carbon Program we compare observed
and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North
America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years,
10 biomes, and includes two large‐scale drought events, providing a natural experiment to
evaluate model skill as a function of drought and seasonality. We evaluate models’ ability
to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics
and analyze links between model characteristics, site history, and model skill. Overall
model performance was poor; the difference between observations and simulations was
∼10 times observational uncertainty, with forested ecosystems better predicted than
nonforested. Model‐data agreement was highest in summer and in temperate evergreen
forests. In contrast, model performance declined in spring and fall, especially in
ecosystems with large deciduous components, and in dry periods during the growing
season. Models used across multiple biomes and sites, the mean model ensemble, and a
model using assimilated parameter values showed high consistency with observations.
Models with the highest skill across all biomes all used prescribed canopy phenology,
calculated NEE as the difference between GPP and ecosystem respiration, and did not use
a daily time step.
Citation: Schwalm, C. R., et al. (2010), A model‐data intercomparison of CO2 exchange across North America: Results from
the North American Carbon Program site synthesis, J. Geophys. Res., 115, G00H05, doi:10.1029/2009JG001229.
1. Introduction
[2] There is a continued need for models to improve con-
sistency and agreement with observations [Friedlingstein
et al., 2006], both overall and under more frequent extreme
climatic events related to global environmental change such
as drought [Trenberth et al., 2007]. Past validation studies
of terrestrial biosphere models have focused only on few
models and sites, typically in close proximity and primarily
in forested biomes [e.g., Amthor et al., 2001; Delpierre et al.,
2009; Grant et al., 2005; Hanson et al., 2004; Granier et al.,
2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou
et al., 2008]. Furthermore, assessing model‐data agreement
relative to drought requires, in addition to high‐quality
observedCO2 exchange data, a reliable droughtmetric as well
as a natural experiment across sites and drought conditions.
[3] Drought is a reoccurring phenomenon in all climates
[Larcher, 1995] and is characterized by a partial loss in
plant function due to water limitation and heat stress. For
terrestrial CO2 exchange, drought typically reduces photo-
synthesis more than respiration [Baldocchi, 2008;Ciais et al.,
2005; Schwalm et al., 2010], resulting in decreased net
carbon uptake from the atmosphere. In the recent past
drought conditions have become more prevalent globally
[Dai et al., 2004] and in North America [Cook et al.,
2004b]. Both incidence and severity of drought [Seager
et al., 2007] as well as heatwaves [Meehl and Tebaldi,
2004] are expected to further increase in conjunction with
global warming [Houghton et al., 2001; Huntington, 2006;
Sheffield and Wood, 2008; Trenberth et al., 2007].
[4] In this study, we evaluate model performance using
terrestrial CO2 flux data and simulated fluxes collected from
1991 to 2007. This timeframe included two widespread
droughts in North America: (1) the turn‐of‐the‐century
drought from 1998 to 2004 that was centered in the western
interior of North America [Seager, 2007] and (2) a smaller‐
scale drought event in the southern continental Untied States
from winter of 2005/2006 through October 2007 [Seager
et al., 2009]. During these events Palmer Drought Severity
Index values [Cook et al., 2007; Dai et al., 2004] and pre-
cipitation anomalies [Seager, 2007; Seager et al., 2009]
were highly negative over broad geographic areas. Ongoing
eddy covariance measurements [Baldocchi et al., 2001],
active throughout the aforementioned drought periods,
provided flux data across gradients of time, space, season-
ality, and drought. We use these data to examine model skill
relative to site‐specific drought severity, climatic season,
and time. We also link model behavior to model architecture
and site‐specific attributes. Specifically, we address the
following questions: Are current state‐of‐the‐art terrestrial
biosphere models capable of simulating CO2 exchange
subject to gradients in dryness and seasonality? Are these
models able to reproduce the seasonal variation of observed
CO2 exchange across sites? Are certain characteristics of
model structure coincident with better model‐data agree-
ment? Which biomes are simulated poorly/well?
2. Methods
2.1. Observed and Simulated CO2 Exchange
[5] Modeled and observed net ecosystem exchange (NEE,
net carbon balance including soils where positive values
indicate outgassing of CO2 to the atmosphere) data were
analyzed from 21 terrestrial biosphere models (Table 1) and
44 eddy covariance (EC) sites spanning ∼220 site‐years and
10 biomes in North America (Table 2). All terrestrial bio-
sphere models analyzed simulated carbon cycling with
process based formulations of varying detail for component
carbon fluxes. Simulated NEE was based on model‐specific
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
2 of 22
models used to integrate and scale measurements of CO2 exchange from remote sensing
and other spatiotemporal data. Yet assessments are rarely conducted to determine how well
models simulate carbon processes across vegetation types and environmental conditions.
Using standardized data from the North American Carbon Program we compare observed
and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North
America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years,
10 biomes, and includes two large‐scale drought events, providing a natural experiment to
evaluate model skill as a function of drought and seasonality. We evaluate models’ ability
to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics
and analyze links between model characteristics, site history, and model skill. Overall
model performance was poor; the difference between observations and simulations was
∼10 times observational uncertainty, with forested ecosystems better predicted than
nonforested. Model‐data agreement was highest in summer and in temperate evergreen
forests. In contrast, model performance declined in spring and fall, especially in
ecosystems with large deciduous components, and in dry periods during the growing
season. Models used across multiple biomes and sites, the mean model ensemble, and a
model using assimilated parameter values showed high consistency with observations.
Models with the highest skill across all biomes all used prescribed canopy phenology,
calculated NEE as the difference between GPP and ecosystem respiration, and did not use
a daily time step.
Citation: Schwalm, C. R., et al. (2010), A model‐data intercomparison of CO2 exchange across North America: Results from
the North American Carbon Program site synthesis, J. Geophys. Res., 115, G00H05, doi:10.1029/2009JG001229.
1. Introduction
[2] There is a continued need for models to improve con-
sistency and agreement with observations [Friedlingstein
et al., 2006], both overall and under more frequent extreme
climatic events related to global environmental change such
as drought [Trenberth et al., 2007]. Past validation studies
of terrestrial biosphere models have focused only on few
models and sites, typically in close proximity and primarily
in forested biomes [e.g., Amthor et al., 2001; Delpierre et al.,
2009; Grant et al., 2005; Hanson et al., 2004; Granier et al.,
2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou
et al., 2008]. Furthermore, assessing model‐data agreement
relative to drought requires, in addition to high‐quality
observedCO2 exchange data, a reliable droughtmetric as well
as a natural experiment across sites and drought conditions.
[3] Drought is a reoccurring phenomenon in all climates
[Larcher, 1995] and is characterized by a partial loss in
plant function due to water limitation and heat stress. For
terrestrial CO2 exchange, drought typically reduces photo-
synthesis more than respiration [Baldocchi, 2008;Ciais et al.,
2005; Schwalm et al., 2010], resulting in decreased net
carbon uptake from the atmosphere. In the recent past
drought conditions have become more prevalent globally
[Dai et al., 2004] and in North America [Cook et al.,
2004b]. Both incidence and severity of drought [Seager
et al., 2007] as well as heatwaves [Meehl and Tebaldi,
2004] are expected to further increase in conjunction with
global warming [Houghton et al., 2001; Huntington, 2006;
Sheffield and Wood, 2008; Trenberth et al., 2007].
[4] In this study, we evaluate model performance using
terrestrial CO2 flux data and simulated fluxes collected from
1991 to 2007. This timeframe included two widespread
droughts in North America: (1) the turn‐of‐the‐century
drought from 1998 to 2004 that was centered in the western
interior of North America [Seager, 2007] and (2) a smaller‐
scale drought event in the southern continental Untied States
from winter of 2005/2006 through October 2007 [Seager
et al., 2009]. During these events Palmer Drought Severity
Index values [Cook et al., 2007; Dai et al., 2004] and pre-
cipitation anomalies [Seager, 2007; Seager et al., 2009]
were highly negative over broad geographic areas. Ongoing
eddy covariance measurements [Baldocchi et al., 2001],
active throughout the aforementioned drought periods,
provided flux data across gradients of time, space, season-
ality, and drought. We use these data to examine model skill
relative to site‐specific drought severity, climatic season,
and time. We also link model behavior to model architecture
and site‐specific attributes. Specifically, we address the
following questions: Are current state‐of‐the‐art terrestrial
biosphere models capable of simulating CO2 exchange
subject to gradients in dryness and seasonality? Are these
models able to reproduce the seasonal variation of observed
CO2 exchange across sites? Are certain characteristics of
model structure coincident with better model‐data agree-
ment? Which biomes are simulated poorly/well?
2. Methods
2.1. Observed and Simulated CO2 Exchange
[5] Modeled and observed net ecosystem exchange (NEE,
net carbon balance including soils where positive values
indicate outgassing of CO2 to the atmosphere) data were
analyzed from 21 terrestrial biosphere models (Table 1) and
44 eddy covariance (EC) sites spanning ∼220 site‐years and
10 biomes in North America (Table 2). All terrestrial bio-
sphere models analyzed simulated carbon cycling with
process based formulations of varying detail for component
carbon fluxes. Simulated NEE was based on model‐specific
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
2 of 22
Page 3
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il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
Su
rf
ac
e
In
ci
de
nt
L
on
gw
av
e
R
ad
ia
tio
n
So
il
C
ar
bo
n
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
L
ea
f
N
itr
og
en
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
N
itr
og
en
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
D
is
so
lv
ed
C
ar
bo
n
L
os
s
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
A
ut
ot
ro
ph
ic
R
es
pi
ra
tio
n
(A
R
)
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
Su
rf
ac
e
In
ci
de
nt
L
on
gw
av
e
R
ad
ia
tio
n
V
eg
et
at
io
n
C
ar
bo
n
A
ir
T
em
pe
ra
tu
re
G
PP
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
Su
rf
ac
e
In
ci
de
nt
L
on
gw
av
e
R
ad
ia
tio
n
V
eg
et
at
io
n
C
ar
bo
n
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
G
PP
So
il
T
em
pe
ra
tu
re
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
G
PP
Pr
op
or
tio
na
l
to
G
ro
w
th
E
co
sy
st
em
R
es
pi
ra
tio
n
(R
)
A
R
+
H
R
A
R
+
H
R
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
V
eg
et
at
io
n
C
ar
bo
n
L
A
I
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
N
et
Pr
im
ar
y
Pr
od
uc
tio
n
(N
PP
)
G
PP
‐
A
R
G
PP
‐
A
R
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
V
ap
or
Pr
es
su
re
D
ef
ic
it
C
O
2
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
L
A
I
G
PP
‐
A
R
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
G
PP
‐
A
R
A
ir
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Po
te
nt
ia
l
E
va
po
ra
tio
n
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
L
ea
f
N
itr
og
en
fP
A
R
G
PP
‐
A
R
G
PP
‐
A
R
A
ir
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
C
ar
bo
n
So
il
N
itr
og
en
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
L
A
I
N
et
E
co
sy
st
em
E
xc
ha
ng
e
(N
E
E
)
N
PP
‐
H
R
N
PP
‐
H
R
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
V
ap
or
Pr
es
su
re
D
ef
ic
it
N
PP
‐
H
R
G
PP
‐
R
N
PP
‐
H
R
N
PP
‐
H
R
G
PP
‐
R
N
PP
‐
H
R
N
PP
‐
H
R
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
3 of 22
ab
le
1.
Su
m
m
ar
y
of
M
od
el
C
ha
ra
ct
er
is
tic
s
M
od
el
A
ttr
ib
ut
e
M
od
el
A
gr
oI
B
IS
B
E
PS
B
io
m
e‐
B
G
C
C
an
‐
IB
IS
C
N
‐
C
L
A
SS
D
L
E
M
D
N
D
C
E
co
sy
s
E
D
2
E
D
C
M
T
em
po
ra
l
R
es
ol
ut
io
n
H
al
f‐
ho
ur
ly
D
ai
ly
D
ai
ly
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
D
ai
ly
D
ai
ly
H
ou
rl
y
H
al
f‐
ho
ur
ly
M
on
th
ly
V
eg
et
at
io
n
Po
ol
s
4
4
7
3
4
6
3
9
9
8
So
il
Po
ol
s
7
9
4
7
3
3
9
9
4
5
So
il
L
ay
er
s
11
3
1
7
3
2
10
15
9
10
C
an
op
y
Ph
en
ol
og
y
Pr
og
no
st
ic
Se
m
ip
ro
gn
os
tic
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
og
no
st
ic
Se
m
ip
ro
gn
os
tic
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
og
no
st
ic
N
itr
og
en
C
yc
le
Y
es
Y
es
Y
es
Y
es
Y
es
Y
es
Y
es
Y
es
Y
es
Y
es
G
ro
ss
Pr
im
ar
y
Pr
od
uc
tiv
ity
(G
PP
)
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
L
ig
ht
U
se
E
ff
ic
ie
nc
y
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
L
ig
ht
U
se
E
ff
ic
ie
nc
y
M
od
el
H
et
er
ot
ro
ph
ic
R
es
pi
ra
tio
n
(H
R
)
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
E
va
po
ra
tio
n
So
il
C
ar
bo
n
So
il
N
itr
og
en
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
D
ec
ay
M
et
ha
ne
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
L
itt
er
an
d
So
il
C
ar
bo
n
So
il
N
itr
og
en
So
il
M
oi
st
ur
e
D
ec
ay
M
et
ha
ne
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
D
ec
ay
M
et
ha
ne
C
O
2
D
if
fu
si
on
D
is
so
lv
ed
C
ar
bo
n
L
os
s
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
Su
rf
ac
e
In
ci
de
nt
L
on
gw
av
e
R
ad
ia
tio
n
So
il
C
ar
bo
n
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
L
ea
f
N
itr
og
en
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
N
itr
og
en
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
D
is
so
lv
ed
C
ar
bo
n
L
os
s
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
A
ut
ot
ro
ph
ic
R
es
pi
ra
tio
n
(A
R
)
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
Su
rf
ac
e
In
ci
de
nt
L
on
gw
av
e
R
ad
ia
tio
n
V
eg
et
at
io
n
C
ar
bo
n
A
ir
T
em
pe
ra
tu
re
G
PP
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
Su
rf
ac
e
In
ci
de
nt
L
on
gw
av
e
R
ad
ia
tio
n
V
eg
et
at
io
n
C
ar
bo
n
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
G
PP
So
il
T
em
pe
ra
tu
re
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
G
PP
Pr
op
or
tio
na
l
to
G
ro
w
th
E
co
sy
st
em
R
es
pi
ra
tio
n
(R
)
A
R
+
H
R
A
R
+
H
R
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
V
eg
et
at
io
n
C
ar
bo
n
L
A
I
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
N
et
Pr
im
ar
y
Pr
od
uc
tio
n
(N
PP
)
G
PP
‐
A
R
G
PP
‐
A
R
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
V
ap
or
Pr
es
su
re
D
ef
ic
it
C
O
2
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
L
A
I
G
PP
‐
A
R
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
G
PP
‐
A
R
A
ir
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Po
te
nt
ia
l
E
va
po
ra
tio
n
V
eg
et
at
io
n
C
ar
bo
n
So
il
N
itr
og
en
L
ea
f
N
itr
og
en
fP
A
R
G
PP
‐
A
R
G
PP
‐
A
R
A
ir
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
C
ar
bo
n
So
il
N
itr
og
en
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
L
ea
f
N
itr
og
en
L
A
I
N
et
E
co
sy
st
em
E
xc
ha
ng
e
(N
E
E
)
N
PP
‐
H
R
N
PP
‐
H
R
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
V
ap
or
Pr
es
su
re
D
ef
ic
it
N
PP
‐
H
R
G
PP
‐
R
N
PP
‐
H
R
N
PP
‐
H
R
G
PP
‐
R
N
PP
‐
H
R
N
PP
‐
H
R
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
3 of 22
Page 4
T
ab
le
1.
(c
on
tin
ue
d)
M
od
el
A
ttr
ib
ut
e
M
od
el
A
gr
oI
B
IS
B
E
PS
B
io
m
e‐
B
G
C
C
an
‐
IB
IS
C
N
‐
C
L
A
SS
D
L
E
M
D
N
D
C
E
co
sy
s
E
D
2
E
D
C
M
B
io
m
es
Si
m
ul
at
ed
C
ro
pl
an
ds
6
8
10
9
10
C
ro
pl
an
ds
10
6
6
Si
te
s
Si
m
ul
at
ed
5
10
36
27
31
33
5
39
25
10
M
on
th
s
Si
m
ul
at
ed
19
2
94
5
20
01
19
78
20
82
22
46
19
2
24
50
16
84
65
8
So
ur
ce
K
uc
ha
rik
an
d
Tw
in
e
[2
00
7]
Li
u
et
al
.
[1
99
9]
Th
or
nt
on
et
al
.
[2
00
5]
W
ill
ia
m
so
n
et
al
.[
20
08
]
Ar
ai
n
et
al
.
[2
00
6]
Ti
an
et
al
.
[2
01
0]
Li
et
al
.
[2
01
0]
G
ra
nt
et
al
.
[2
00
5]
M
ed
vi
gy
et
al
.
[2
00
9]
Li
u
et
al
.
[2
00
3]
M
od
el
A
ttr
ib
ut
e
M
od
el
E
PI
C
IS
O
L
SM
L
oT
E
C
L
PJ
O
R
C
H
ID
E
E
Si
B
3
Si
B
C
A
SA
Si
B
cr
op
SS
iB
2
T
E
C
O
T
ri
pl
ex
‐
FL
U
X
T
em
po
ra
l
R
es
ol
ut
io
n
D
ai
ly
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
D
ai
ly
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
10
m
in
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
H
ou
rl
y
H
al
f‐
ho
ur
ly
V
eg
et
at
io
n
Po
ol
s
3
0
4
3
8
0
8
4
0
3
0
So
il
Po
ol
s
0
1
5
2
8
0
5
1
0
5
0
So
il
L
ay
er
s
15
0
14
2
0
10
15
10
3
10
0
C
an
op
y
Ph
en
ol
og
y
Pr
og
no
st
ic
Pr
es
cr
ib
ed
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
es
cr
ib
ed
Pr
es
cr
ib
ed
Pr
og
no
st
ic
Pr
es
cr
ib
ed
Pr
og
no
st
ic
Pr
es
cr
ib
ed
N
itr
og
en
C
yc
le
Y
es
N
o
N
o
N
o
N
o
Y
es
N
o
Y
es
N
o
N
o
N
o
G
ro
ss
Pr
im
ar
y
Pr
od
uc
tiv
ity
(G
PP
)
N
il
St
om
at
al
C
on
du
ct
an
ce
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
H
et
er
ot
ro
ph
ic
R
es
pi
ra
tio
n
(H
R
)
C
O
2
D
if
fu
si
on
D
is
so
lv
ed
C
ar
bo
n
L
os
s
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
Z
er
o‐
or
de
r
M
od
el
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
T
em
pe
ra
tu
re
So
il
C
ar
bo
n
Z
er
o‐
or
de
r
M
od
el
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
A
ut
ot
ro
ph
ic
R
es
pi
ra
tio
n
(A
R
)
N
il
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
R
el
at
iv
e
H
um
id
ity
L
A
I
fP
A
R
C
O
2
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
Fr
ac
tio
n
of
A
nn
ua
l
G
PP
E
co
sy
st
em
R
es
pi
ra
tio
n
(R
)
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
Fo
rc
ed
A
nn
ua
l
B
al
an
ce
A
R
+
H
R
Fo
rc
ed
A
nn
ua
l
B
al
an
ce
Fo
rc
ed
A
nn
ua
l
B
al
an
ce
A
R
+
H
R
A
R
+
H
R
N
et
Pr
im
ar
y
Pr
od
uc
tio
n
(N
PP
)
L
ig
ht
U
se
E
ff
ic
ie
nc
y
M
od
el
N
il
G
PP
‐
A
R
G
PP
‐
A
R
G
PP
‐
A
R
G
PP
‐
A
R
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
C
O
2
R
el
at
iv
e
H
um
id
ity
G
PP
‐
A
R
G
PP
‐
A
R
G
PP
‐
A
R
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
N
et
E
co
sy
st
em
E
xc
ha
ng
e
(N
E
E
)
N
PP
‐
H
R
G
PP
‐
R
N
PP
‐
H
R
N
PP
‐
H
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
B
io
m
es
Si
m
ul
at
ed
C
ro
pl
an
ds
5
6
9
10
10
10
C
ro
pl
an
ds
10
10
3
Si
te
s
Si
m
ul
at
ed
U
.S
.‐
N
e3
9
10
29
35
31
35
5
44
35
7
M
on
th
s
Si
m
ul
at
ed
48
90
9
82
5
21
26
23
32
22
58
24
02
19
2
28
00
24
14
29
1
So
ur
ce
C
au
sa
ra
no
et
al
.[
20
07
]
Ri
le
y
et
al
.
[2
00
2]
H
an
so
n
et
al
.
[2
00
4]
Si
tc
h
et
al
.
[2
00
3]
K
rin
ne
r
et
al
.
[2
00
5]
Ba
ke
r
et
al
.
[2
00
8]
Sc
ha
ef
er
et
al
.
[2
00
9]
Lo
ku
pi
tiy
a
et
al
.[
20
09
]
Zh
an
et
al
.
[2
00
3]
W
en
g
an
d
Lu
o
[2
00
8]
Zh
ou
et
al
.
[2
00
8]
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
4 of 22
ab
le
1.
(c
on
tin
ue
d)
M
od
el
A
ttr
ib
ut
e
M
od
el
A
gr
oI
B
IS
B
E
PS
B
io
m
e‐
B
G
C
C
an
‐
IB
IS
C
N
‐
C
L
A
SS
D
L
E
M
D
N
D
C
E
co
sy
s
E
D
2
E
D
C
M
B
io
m
es
Si
m
ul
at
ed
C
ro
pl
an
ds
6
8
10
9
10
C
ro
pl
an
ds
10
6
6
Si
te
s
Si
m
ul
at
ed
5
10
36
27
31
33
5
39
25
10
M
on
th
s
Si
m
ul
at
ed
19
2
94
5
20
01
19
78
20
82
22
46
19
2
24
50
16
84
65
8
So
ur
ce
K
uc
ha
rik
an
d
Tw
in
e
[2
00
7]
Li
u
et
al
.
[1
99
9]
Th
or
nt
on
et
al
.
[2
00
5]
W
ill
ia
m
so
n
et
al
.[
20
08
]
Ar
ai
n
et
al
.
[2
00
6]
Ti
an
et
al
.
[2
01
0]
Li
et
al
.
[2
01
0]
G
ra
nt
et
al
.
[2
00
5]
M
ed
vi
gy
et
al
.
[2
00
9]
Li
u
et
al
.
[2
00
3]
M
od
el
A
ttr
ib
ut
e
M
od
el
E
PI
C
IS
O
L
SM
L
oT
E
C
L
PJ
O
R
C
H
ID
E
E
Si
B
3
Si
B
C
A
SA
Si
B
cr
op
SS
iB
2
T
E
C
O
T
ri
pl
ex
‐
FL
U
X
T
em
po
ra
l
R
es
ol
ut
io
n
D
ai
ly
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
D
ai
ly
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
10
m
in
H
al
f‐
ho
ur
ly
H
al
f‐
ho
ur
ly
H
ou
rl
y
H
al
f‐
ho
ur
ly
V
eg
et
at
io
n
Po
ol
s
3
0
4
3
8
0
8
4
0
3
0
So
il
Po
ol
s
0
1
5
2
8
0
5
1
0
5
0
So
il
L
ay
er
s
15
0
14
2
0
10
15
10
3
10
0
C
an
op
y
Ph
en
ol
og
y
Pr
og
no
st
ic
Pr
es
cr
ib
ed
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
og
no
st
ic
Pr
es
cr
ib
ed
Pr
es
cr
ib
ed
Pr
og
no
st
ic
Pr
es
cr
ib
ed
Pr
og
no
st
ic
Pr
es
cr
ib
ed
N
itr
og
en
C
yc
le
Y
es
N
o
N
o
N
o
N
o
Y
es
N
o
Y
es
N
o
N
o
N
o
G
ro
ss
Pr
im
ar
y
Pr
od
uc
tiv
ity
(G
PP
)
N
il
St
om
at
al
C
on
du
ct
an
ce
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
E
nz
ym
e
K
in
et
ic
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
St
om
at
al
C
on
du
ct
an
ce
M
od
el
H
et
er
ot
ro
ph
ic
R
es
pi
ra
tio
n
(H
R
)
C
O
2
D
if
fu
si
on
D
is
so
lv
ed
C
ar
bo
n
L
os
s
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
Pr
ec
ip
ita
tio
n
So
il
M
oi
st
ur
e
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
Z
er
o‐
or
de
r
M
od
el
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
So
il
C
ar
bo
n
So
il
T
em
pe
ra
tu
re
So
il
C
ar
bo
n
Z
er
o‐
or
de
r
M
od
el
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
Fi
rs
t
or
G
re
at
er
O
rd
er
M
od
el
A
ut
ot
ro
ph
ic
R
es
pi
ra
tio
n
(A
R
)
N
il
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
V
eg
et
at
io
n
C
ar
bo
n
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
G
PP
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
Su
rf
ac
e
In
ci
de
nt
Sh
or
tw
av
e
R
ad
ia
tio
n
R
el
at
iv
e
H
um
id
ity
L
A
I
fP
A
R
C
O
2
A
ir
T
em
pe
ra
tu
re
V
eg
et
at
io
n
C
ar
bo
n
Fr
ac
tio
n
of
A
nn
ua
l
G
PP
E
co
sy
st
em
R
es
pi
ra
tio
n
(R
)
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
A
R
+
H
R
Fo
rc
ed
A
nn
ua
l
B
al
an
ce
A
R
+
H
R
Fo
rc
ed
A
nn
ua
l
B
al
an
ce
Fo
rc
ed
A
nn
ua
l
B
al
an
ce
A
R
+
H
R
A
R
+
H
R
N
et
Pr
im
ar
y
Pr
od
uc
tio
n
(N
PP
)
L
ig
ht
U
se
E
ff
ic
ie
nc
y
M
od
el
N
il
G
PP
‐
A
R
G
PP
‐
A
R
G
PP
‐
A
R
G
PP
‐
A
R
A
ir
T
em
pe
ra
tu
re
So
il
M
oi
st
ur
e
C
O
2
R
el
at
iv
e
H
um
id
ity
G
PP
‐
A
R
G
PP
‐
A
R
G
PP
‐
A
R
Fr
ac
tio
n
of
In
st
an
ta
ne
ou
s
G
PP
N
et
E
co
sy
st
em
E
xc
ha
ng
e
(N
E
E
)
N
PP
‐
H
R
G
PP
‐
R
N
PP
‐
H
R
N
PP
‐
H
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
G
PP
‐
R
B
io
m
es
Si
m
ul
at
ed
C
ro
pl
an
ds
5
6
9
10
10
10
C
ro
pl
an
ds
10
10
3
Si
te
s
Si
m
ul
at
ed
U
.S
.‐
N
e3
9
10
29
35
31
35
5
44
35
7
M
on
th
s
Si
m
ul
at
ed
48
90
9
82
5
21
26
23
32
22
58
24
02
19
2
28
00
24
14
29
1
So
ur
ce
C
au
sa
ra
no
et
al
.[
20
07
]
Ri
le
y
et
al
.
[2
00
2]
H
an
so
n
et
al
.
[2
00
4]
Si
tc
h
et
al
.
[2
00
3]
K
rin
ne
r
et
al
.
[2
00
5]
Ba
ke
r
et
al
.
[2
00
8]
Sc
ha
ef
er
et
al
.
[2
00
9]
Lo
ku
pi
tiy
a
et
al
.[
20
09
]
Zh
an
et
al
.
[2
00
3]
W
en
g
an
d
Lu
o
[2
00
8]
Zh
ou
et
al
.
[2
00
8]
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
4 of 22
Page 5
T
ab
le
2.
Su
m
m
ar
y
of
Si
te
C
ha
ra
ct
er
is
tic
sa
Si
te
ID
N
am
e
Pr
io
ri
ty
C
ou
nt
ry
L
at
itu
de
L
on
gi
tu
de
E
le
va
tio
n
(m
a.
s.
l.)
IG
B
P
C
la
ss
K
öp
pe
n‐
G
ei
ge
r
C
lim
at
e
C
la
ss
if
ic
at
io
n
C
A
‐
C
a1
B
ri
tis
h
C
ol
um
bi
a
‐
C
am
pb
el
l
R
iv
er
‐
M
at
ur
e
Fo
re
st
Si
te
1
C
an
ad
a
49
.8
7
−
12
5.
33
30
0
E
N
F
M
ar
iti
m
e
te
m
pe
ra
te
C
A
‐
C
a2
B
ri
tis
h
C
ol
um
bi
a
‐
C
am
pb
el
l
R
iv
er
‐
C
le
ar
cu
t
Si
te
2
C
an
ad
a
49
.8
7
−
12
5.
29
18
0
E
N
F
M
ar
iti
m
e
te
m
pe
ra
te
C
A
‐
C
a3
B
ri
tis
h
C
ol
um
bi
a
‐
C
am
pb
el
l
R
iv
er
‐
Y
ou
ng
Pl
an
ta
tio
n
Si
te
2
C
an
ad
a
49
.5
3
−
12
4.
90
16
5
E
N
F
M
ar
iti
m
e
te
m
pe
ra
te
C
A
‐
G
ro
O
nt
ar
io
‐
G
ro
un
dh
og
R
iv
er
‐
M
at
ur
e
B
or
ea
l
M
ix
ed
W
oo
d
1
C
an
ad
a
48
.2
2
−
82
.1
6
30
0
M
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
L
et
L
et
hb
ri
dg
e
1
C
an
ad
a
49
.7
1
−
11
2.
94
96
0
G
R
A
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
M
er
E
as
te
rn
Pe
at
la
nd
‐
M
er
B
le
ue
1
C
an
ad
a
45
.4
1
−
75
.5
2
70
W
E
T
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
O
as
Sa
sk
.
‐
SS
A
O
ld
A
sp
en
1
C
an
ad
a
53
.6
3
−
10
6.
20
53
0
D
B
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
O
bs
Sa
sk
.
‐
SS
A
O
ld
B
la
ck
Sp
ru
ce
1
C
an
ad
a
53
.9
9
−
10
5.
12
62
9
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
O
jp
Sa
sk
.
‐
SS
A
O
ld
Ja
ck
Pi
ne
1
C
an
ad
a
53
.9
2
−
10
4.
69
57
9
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
Q
fo
Q
ue
be
c
M
at
ur
e
B
or
ea
l
Fo
re
st
Si
te
1
C
an
ad
a
49
.6
9
−
74
.3
4
38
2
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
SJ
1
Sa
sk
.
‐
19
94
H
ar
ve
st
ed
Ja
ck
Pi
ne
2
C
an
ad
a
53
.9
1
−
10
4.
66
58
0
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
SJ
2
Sa
sk
.
‐
20
02
H
ar
ve
st
ed
Ja
ck
Pi
ne
2
C
an
ad
a
53
.9
4
−
10
4.
65
51
8
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
SJ
3
Sa
sk
.
‐
SS
A
19
75
H
ar
ve
st
ed
Y
ou
ng
Ja
ck
Pi
ne
2
C
an
ad
a
53
.8
8
−
10
4.
64
51
1
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
T
P3
O
nt
ar
io
‐
T
ur
ke
y
Po
in
t
M
id
dl
e‐
ag
ed
W
hi
te
Pi
ne
2
C
an
ad
a
42
.7
1
−
80
.3
5
21
9
E
N
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
T
P4
O
nt
ar
io
‐
T
ur
ke
y
Po
in
t
M
at
ur
e
W
hi
te
Pi
ne
1
C
an
ad
a
42
.7
1
−
80
.3
6
21
9
E
N
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
W
P1
W
es
te
rn
Pe
at
la
nd
‐
L
aB
ic
he
‐
B
la
ck
Sp
ru
ce
/L
ar
ch
Fe
n
1
C
an
ad
a
54
.9
5
−
11
2.
47
54
0
M
F
C
on
tin
en
ta
l
su
ba
rc
tic
U
.S
.‐
A
R
M
O
K
‐
A
R
M
So
ut
he
rn
G
re
at
Pl
ai
ns
Si
te
‐
L
am
on
t
1
U
SA
36
.6
1
−
97
.4
9
31
0
C
R
O
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
A
tq
A
K
‐
A
tq
as
uk
1
U
SA
70
.4
7
−
15
7.
41
16
W
E
T
T
un
dr
a
U
.S
.‐
B
rw
A
K
‐
B
ar
ro
w
1
U
SA
71
.3
2
−
15
6.
63
1
W
E
T
T
un
dr
a
U
.S
.‐
D
k2
N
C
‐
D
uk
e
Fo
re
st
‐
H
ar
dw
oo
ds
1
U
SA
35
.9
7
−
79
.1
0
16
0
D
B
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
D
k3
N
C
‐
D
uk
e
Fo
re
st
‐
L
ob
lo
lly
Pi
ne
1
U
SA
35
.9
8
−
79
.0
9
16
3
E
N
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
H
a1
M
A
‐
H
ar
va
rd
Fo
re
st
E
M
S
T
ow
er
(H
FR
1)
1
U
SA
42
.5
4
−
72
.1
7
30
3
D
B
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
H
o1
M
E
‐
H
ow
la
nd
Fo
re
st
(M
ai
n
T
ow
er
)
1
U
SA
45
.2
0
−
68
.7
4
60
E
N
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
IB
1
IL
‐
Fe
rm
i
N
at
io
na
l
A
cc
el
er
at
or
L
ab
or
at
or
y
‐
B
at
av
ia
(A
gr
ic
ul
tu
ra
l
Si
te
)
1
U
SA
41
.8
6
−
88
.2
2
22
7
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
IB
2
IL
‐
Fe
rm
i
N
at
io
na
l
A
cc
el
er
at
or
L
ab
or
at
or
y
‐
B
at
av
ia
(P
ra
ir
ie
Si
te
)
1
U
SA
41
.8
4
−
88
.2
4
22
7
G
R
A
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
L
os
W
I
‐
L
os
t
C
re
ek
1
U
SA
46
.0
8
−
89
.9
8
48
0
C
SH
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
M
M
S
IN
‐
M
or
ga
n
M
on
ro
e
St
at
e
Fo
re
st
1
U
SA
39
.3
2
−
86
.4
1
27
5
D
B
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
M
O
z
M
O
‐
M
is
so
ur
i
O
za
rk
Si
te
1
U
SA
38
.7
4
−
92
.2
0
21
9
D
B
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
M
e2
O
R
‐
M
et
ol
iu
s
‐
In
te
rm
ed
ia
te
A
ge
d
Po
nd
er
os
a
Pi
ne
1
U
SA
44
.4
5
−
12
1.
56
12
53
E
N
F
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
M
e3
2
U
SA
44
.3
2
−
12
1.
61
10
05
E
N
F
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
5 of 22
ab
le
2.
Su
m
m
ar
y
of
Si
te
C
ha
ra
ct
er
is
tic
sa
Si
te
ID
N
am
e
Pr
io
ri
ty
C
ou
nt
ry
L
at
itu
de
L
on
gi
tu
de
E
le
va
tio
n
(m
a.
s.
l.)
IG
B
P
C
la
ss
K
öp
pe
n‐
G
ei
ge
r
C
lim
at
e
C
la
ss
if
ic
at
io
n
C
A
‐
C
a1
B
ri
tis
h
C
ol
um
bi
a
‐
C
am
pb
el
l
R
iv
er
‐
M
at
ur
e
Fo
re
st
Si
te
1
C
an
ad
a
49
.8
7
−
12
5.
33
30
0
E
N
F
M
ar
iti
m
e
te
m
pe
ra
te
C
A
‐
C
a2
B
ri
tis
h
C
ol
um
bi
a
‐
C
am
pb
el
l
R
iv
er
‐
C
le
ar
cu
t
Si
te
2
C
an
ad
a
49
.8
7
−
12
5.
29
18
0
E
N
F
M
ar
iti
m
e
te
m
pe
ra
te
C
A
‐
C
a3
B
ri
tis
h
C
ol
um
bi
a
‐
C
am
pb
el
l
R
iv
er
‐
Y
ou
ng
Pl
an
ta
tio
n
Si
te
2
C
an
ad
a
49
.5
3
−
12
4.
90
16
5
E
N
F
M
ar
iti
m
e
te
m
pe
ra
te
C
A
‐
G
ro
O
nt
ar
io
‐
G
ro
un
dh
og
R
iv
er
‐
M
at
ur
e
B
or
ea
l
M
ix
ed
W
oo
d
1
C
an
ad
a
48
.2
2
−
82
.1
6
30
0
M
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
L
et
L
et
hb
ri
dg
e
1
C
an
ad
a
49
.7
1
−
11
2.
94
96
0
G
R
A
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
M
er
E
as
te
rn
Pe
at
la
nd
‐
M
er
B
le
ue
1
C
an
ad
a
45
.4
1
−
75
.5
2
70
W
E
T
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
O
as
Sa
sk
.
‐
SS
A
O
ld
A
sp
en
1
C
an
ad
a
53
.6
3
−
10
6.
20
53
0
D
B
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
O
bs
Sa
sk
.
‐
SS
A
O
ld
B
la
ck
Sp
ru
ce
1
C
an
ad
a
53
.9
9
−
10
5.
12
62
9
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
O
jp
Sa
sk
.
‐
SS
A
O
ld
Ja
ck
Pi
ne
1
C
an
ad
a
53
.9
2
−
10
4.
69
57
9
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
Q
fo
Q
ue
be
c
M
at
ur
e
B
or
ea
l
Fo
re
st
Si
te
1
C
an
ad
a
49
.6
9
−
74
.3
4
38
2
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
SJ
1
Sa
sk
.
‐
19
94
H
ar
ve
st
ed
Ja
ck
Pi
ne
2
C
an
ad
a
53
.9
1
−
10
4.
66
58
0
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
SJ
2
Sa
sk
.
‐
20
02
H
ar
ve
st
ed
Ja
ck
Pi
ne
2
C
an
ad
a
53
.9
4
−
10
4.
65
51
8
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
SJ
3
Sa
sk
.
‐
SS
A
19
75
H
ar
ve
st
ed
Y
ou
ng
Ja
ck
Pi
ne
2
C
an
ad
a
53
.8
8
−
10
4.
64
51
1
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
C
A
‐
T
P3
O
nt
ar
io
‐
T
ur
ke
y
Po
in
t
M
id
dl
e‐
ag
ed
W
hi
te
Pi
ne
2
C
an
ad
a
42
.7
1
−
80
.3
5
21
9
E
N
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
T
P4
O
nt
ar
io
‐
T
ur
ke
y
Po
in
t
M
at
ur
e
W
hi
te
Pi
ne
1
C
an
ad
a
42
.7
1
−
80
.3
6
21
9
E
N
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
C
A
‐
W
P1
W
es
te
rn
Pe
at
la
nd
‐
L
aB
ic
he
‐
B
la
ck
Sp
ru
ce
/L
ar
ch
Fe
n
1
C
an
ad
a
54
.9
5
−
11
2.
47
54
0
M
F
C
on
tin
en
ta
l
su
ba
rc
tic
U
.S
.‐
A
R
M
O
K
‐
A
R
M
So
ut
he
rn
G
re
at
Pl
ai
ns
Si
te
‐
L
am
on
t
1
U
SA
36
.6
1
−
97
.4
9
31
0
C
R
O
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
A
tq
A
K
‐
A
tq
as
uk
1
U
SA
70
.4
7
−
15
7.
41
16
W
E
T
T
un
dr
a
U
.S
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B
rw
A
K
‐
B
ar
ro
w
1
U
SA
71
.3
2
−
15
6.
63
1
W
E
T
T
un
dr
a
U
.S
.‐
D
k2
N
C
‐
D
uk
e
Fo
re
st
‐
H
ar
dw
oo
ds
1
U
SA
35
.9
7
−
79
.1
0
16
0
D
B
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
D
k3
N
C
‐
D
uk
e
Fo
re
st
‐
L
ob
lo
lly
Pi
ne
1
U
SA
35
.9
8
−
79
.0
9
16
3
E
N
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
H
a1
M
A
‐
H
ar
va
rd
Fo
re
st
E
M
S
T
ow
er
(H
FR
1)
1
U
SA
42
.5
4
−
72
.1
7
30
3
D
B
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
H
o1
M
E
‐
H
ow
la
nd
Fo
re
st
(M
ai
n
T
ow
er
)
1
U
SA
45
.2
0
−
68
.7
4
60
E
N
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
IB
1
IL
‐
Fe
rm
i
N
at
io
na
l
A
cc
el
er
at
or
L
ab
or
at
or
y
‐
B
at
av
ia
(A
gr
ic
ul
tu
ra
l
Si
te
)
1
U
SA
41
.8
6
−
88
.2
2
22
7
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
IB
2
IL
‐
Fe
rm
i
N
at
io
na
l
A
cc
el
er
at
or
L
ab
or
at
or
y
‐
B
at
av
ia
(P
ra
ir
ie
Si
te
)
1
U
SA
41
.8
4
−
88
.2
4
22
7
G
R
A
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
L
os
W
I
‐
L
os
t
C
re
ek
1
U
SA
46
.0
8
−
89
.9
8
48
0
C
SH
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
M
M
S
IN
‐
M
or
ga
n
M
on
ro
e
St
at
e
Fo
re
st
1
U
SA
39
.3
2
−
86
.4
1
27
5
D
B
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
M
O
z
M
O
‐
M
is
so
ur
i
O
za
rk
Si
te
1
U
SA
38
.7
4
−
92
.2
0
21
9
D
B
F
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
M
e2
O
R
‐
M
et
ol
iu
s
‐
In
te
rm
ed
ia
te
A
ge
d
Po
nd
er
os
a
Pi
ne
1
U
SA
44
.4
5
−
12
1.
56
12
53
E
N
F
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
M
e3
2
U
SA
44
.3
2
−
12
1.
61
10
05
E
N
F
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
5 of 22
Page 6
T
ab
le
2.
(c
on
tin
ue
d)
Si
te
ID
N
am
e
Pr
io
ri
ty
C
ou
nt
ry
L
at
itu
de
L
on
gi
tu
de
E
le
va
tio
n
(m
a.
s.
l.)
IG
B
P
C
la
ss
K
öp
pe
n‐
G
ei
ge
r
C
lim
at
e
C
la
ss
if
ic
at
io
n
O
R
‐
M
et
ol
iu
s
‐
Se
co
nd
Y
ou
ng
A
ge
d
Pi
ne
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
M
e4
O
R
‐
M
et
ol
iu
s
‐
O
ld
A
ge
d
Po
nd
er
os
a
Pi
ne
2
U
SA
44
.5
0
−
12
1.
62
91
5
E
N
F
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
M
e5
O
R
‐
M
et
ol
iu
s
‐
Fi
rs
t
Y
ou
ng
A
ge
d
Pi
ne
2
U
SA
44
.4
4
−
12
1.
57
11
83
E
N
F
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
N
R
1
C
O
‐
N
iw
ot
R
id
ge
Fo
re
st
(L
T
E
R
N
W
T
1)
1
U
SA
40
.0
3
−
10
5.
55
30
50
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
U
.S
.‐
N
e1
N
E
‐
M
ea
d
‐
Ir
ri
ga
te
d
C
on
tin
uo
us
M
ai
ze
Si
te
1
U
SA
41
.1
7
−
96
.4
8
36
1
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
N
e2
N
E
‐
M
ea
d
‐
Ir
ri
ga
te
d
M
ai
ze
‐
So
yb
ea
n
R
ot
at
io
n
Si
te
1
U
SA
41
.1
6
−
96
.4
7
36
1
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
N
e3
N
E
‐
M
ea
d
‐
R
ai
nf
ed
M
ai
ze
‐
So
yb
ea
n
R
ot
at
io
n
Si
te
1
U
SA
41
.1
8
−
96
.4
4
36
1
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
PF
a
W
I
‐
Pa
rk
Fa
lls
/W
L
E
F
1
U
SA
45
.9
5
−
90
.2
7
48
5
M
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
SO
2
C
A
‐
Sk
y
O
ak
s
‐
O
ld
St
an
d
1
U
SA
33
.3
7
−
11
6.
62
13
92
C
SH
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
Sh
d
O
K
‐
Sh
id
le
r‐
O
kl
ah
om
a
1
U
SA
36
.9
3
−
96
.6
8
35
0
G
R
A
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
Sy
v
M
I
‐
Sy
lv
an
ia
W
ild
er
ne
ss
A
re
a
1
U
SA
46
.2
4
−
89
.3
5
54
0
M
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
T
on
C
A
‐
T
on
zi
R
an
ch
1
U
SA
38
.4
3
−
12
0.
97
17
7
W
SA
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
U
M
B
M
I
‐
U
ni
ve
rs
ity
of
M
ic
hi
ga
n
B
io
lo
gi
ca
l
St
at
io
n
1
U
SA
45
.5
6
−
84
.7
1
23
4
D
B
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
V
ar
C
A
‐
V
ai
ra
R
an
ch
‐
Io
ne
1
U
SA
38
.4
1
−
12
0.
95
12
9
G
R
A
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
W
C
r
W
I
‐
W
ill
ow
C
re
ek
1
U
SA
45
.8
1
−
90
.0
8
52
0
D
B
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
Si
te
ID
A
nn
ua
l
N
E
E
(g
C
m
2
−
)
A
nn
ua
l
N
E
E
E
rr
or
(g
C
m
2
−
)
D
ay
tim
e
D
at
a
C
ov
er
ag
e
(%
)
N
ig
ht
tim
e
D
at
a
C
ov
er
ag
e
(%
)
L
A
I
A
nn
ua
l
Pr
ec
ip
ita
tio
n
(m
m
)
M
ea
n
A
nn
ua
l
A
ir
T
em
pe
ra
tu
re
(°
C
)
M
ea
su
re
m
en
t
Pe
ri
od
B
io
m
e
So
ur
ce
C
A
‐
C
a1
−
24
4.
3
61
.1
99
26
6.
1
12
56
8.
7
19
98
–
20
06
E
N
FT
Sc
hw
al
m
et
al
.[
20
07
]
C
A
‐
C
a2
57
1.
7
31
.5
96
23
4.
4
12
22
8.
8
20
01
–
20
06
E
N
FT
Sc
hw
al
m
et
al
.[
20
07
]
C
A
‐
C
a3
91
.2
37
.9
91
27
2.
2
15
54
9.
5
20
02
–
20
06
E
N
FT
Sc
hw
al
m
et
al
.[
20
07
]
C
A
‐
G
ro
−
36
.5
33
.5
93
34
4.
1
42
7
3.
3
20
04
–
20
06
M
F
M
cC
au
gh
ey
et
al
.[
20
06
]
C
A
‐
L
et
−
13
2.
9
14
.3
96
46
0.
7
33
5
6.
5
19
97
–
20
06
G
R
A
Fl
an
ag
an
et
al
.[
20
02
]
C
A
‐
M
er
−
68
.5
21
.6
79
56
1.
3
93
5
6.
2
19
99
–
20
06
W
E
T
La
fle
ur
et
al
.[
20
03
]
C
A
‐
O
as
−
15
8.
0
28
.5
94
56
3.
8
46
0
2.
3
19
97
–
20
06
D
B
F
Ba
rr
et
al
.[
20
04
]
C
A
‐
O
bs
−
56
.3
16
.1
89
45
5.
6
47
0
1.
6
20
00
–
20
06
E
N
FB
G
rif
fis
et
al
.[
20
03
]
C
A
‐
O
jp
−
29
.9
16
.6
91
50
3.
4
46
1
1.
5
20
00
–
20
06
E
N
FB
G
rif
fis
et
al
.[
20
03
]
C
A
‐
Q
fo
−
13
.7
21
.0
93
40
4
81
9
2.
7
20
04
–
20
06
E
N
FB
Be
rg
er
on
et
al
.[
20
07
]
C
A
‐
SJ
1
28
.0
15
.3
87
31
0.
8
34
4
0.
6
20
02
–
20
05
E
N
FB
Zh
a
et
al
.[
20
09
]
C
A
‐
SJ
2
11
7.
0
6.
1
89
47
1.
3
53
7
0.
1
20
03
–
20
06
E
N
FB
Zh
a
et
al
.[
20
09
]
C
A
‐
SJ
3
−
82
.0
17
.7
92
34
4.
3
69
4
0.
8
20
04
–
20
05
E
N
FB
Zh
a
et
al
.[
20
09
]
C
A
‐
T
P4
−
13
3.
2
29
.5
95
43
3.
5
95
9
8.
6
20
02
–
20
07
E
N
FT
Pe
ic
hl
an
d
Ar
ai
n
[2
00
7]
C
A
‐
W
P1
−
19
5.
8
16
.4
96
50
2.
7
48
1
1.
7
20
03
–
20
07
W
E
T
Sy
ed
et
al
.[
20
06
]
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
6 of 22
ab
le
2.
(c
on
tin
ue
d)
Si
te
ID
N
am
e
Pr
io
ri
ty
C
ou
nt
ry
L
at
itu
de
L
on
gi
tu
de
E
le
va
tio
n
(m
a.
s.
l.)
IG
B
P
C
la
ss
K
öp
pe
n‐
G
ei
ge
r
C
lim
at
e
C
la
ss
if
ic
at
io
n
O
R
‐
M
et
ol
iu
s
‐
Se
co
nd
Y
ou
ng
A
ge
d
Pi
ne
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
M
e4
O
R
‐
M
et
ol
iu
s
‐
O
ld
A
ge
d
Po
nd
er
os
a
Pi
ne
2
U
SA
44
.5
0
−
12
1.
62
91
5
E
N
F
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
M
e5
O
R
‐
M
et
ol
iu
s
‐
Fi
rs
t
Y
ou
ng
A
ge
d
Pi
ne
2
U
SA
44
.4
4
−
12
1.
57
11
83
E
N
F
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
N
R
1
C
O
‐
N
iw
ot
R
id
ge
Fo
re
st
(L
T
E
R
N
W
T
1)
1
U
SA
40
.0
3
−
10
5.
55
30
50
E
N
F
C
on
tin
en
ta
l
su
ba
rc
tic
U
.S
.‐
N
e1
N
E
‐
M
ea
d
‐
Ir
ri
ga
te
d
C
on
tin
uo
us
M
ai
ze
Si
te
1
U
SA
41
.1
7
−
96
.4
8
36
1
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
N
e2
N
E
‐
M
ea
d
‐
Ir
ri
ga
te
d
M
ai
ze
‐
So
yb
ea
n
R
ot
at
io
n
Si
te
1
U
SA
41
.1
6
−
96
.4
7
36
1
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
N
e3
N
E
‐
M
ea
d
‐
R
ai
nf
ed
M
ai
ze
‐
So
yb
ea
n
R
ot
at
io
n
Si
te
1
U
SA
41
.1
8
−
96
.4
4
36
1
C
R
O
H
ot
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
PF
a
W
I
‐
Pa
rk
Fa
lls
/W
L
E
F
1
U
SA
45
.9
5
−
90
.2
7
48
5
M
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
SO
2
C
A
‐
Sk
y
O
ak
s
‐
O
ld
St
an
d
1
U
SA
33
.3
7
−
11
6.
62
13
92
C
SH
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
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Sh
d
O
K
‐
Sh
id
le
r‐
O
kl
ah
om
a
1
U
SA
36
.9
3
−
96
.6
8
35
0
G
R
A
H
um
id
su
bt
ro
pi
ca
l
U
.S
.‐
Sy
v
M
I
‐
Sy
lv
an
ia
W
ild
er
ne
ss
A
re
a
1
U
SA
46
.2
4
−
89
.3
5
54
0
M
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
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T
on
C
A
‐
T
on
zi
R
an
ch
1
U
SA
38
.4
3
−
12
0.
97
17
7
W
SA
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
U
M
B
M
I
‐
U
ni
ve
rs
ity
of
M
ic
hi
ga
n
B
io
lo
gi
ca
l
St
at
io
n
1
U
SA
45
.5
6
−
84
.7
1
23
4
D
B
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
U
.S
.‐
V
ar
C
A
‐
V
ai
ra
R
an
ch
‐
Io
ne
1
U
SA
38
.4
1
−
12
0.
95
12
9
G
R
A
D
ry
‐
su
m
m
er
su
bt
ro
pi
ca
l
U
.S
.‐
W
C
r
W
I
‐
W
ill
ow
C
re
ek
1
U
SA
45
.8
1
−
90
.0
8
52
0
D
B
F
W
ar
m
su
m
m
er
co
nt
in
en
ta
l
Si
te
ID
A
nn
ua
l
N
E
E
(g
C
m
2
−
)
A
nn
ua
l
N
E
E
E
rr
or
(g
C
m
2
−
)
D
ay
tim
e
D
at
a
C
ov
er
ag
e
(%
)
N
ig
ht
tim
e
D
at
a
C
ov
er
ag
e
(%
)
L
A
I
A
nn
ua
l
Pr
ec
ip
ita
tio
n
(m
m
)
M
ea
n
A
nn
ua
l
A
ir
T
em
pe
ra
tu
re
(°
C
)
M
ea
su
re
m
en
t
Pe
ri
od
B
io
m
e
So
ur
ce
C
A
‐
C
a1
−
24
4.
3
61
.1
99
26
6.
1
12
56
8.
7
19
98
–
20
06
E
N
FT
Sc
hw
al
m
et
al
.[
20
07
]
C
A
‐
C
a2
57
1.
7
31
.5
96
23
4.
4
12
22
8.
8
20
01
–
20
06
E
N
FT
Sc
hw
al
m
et
al
.[
20
07
]
C
A
‐
C
a3
91
.2
37
.9
91
27
2.
2
15
54
9.
5
20
02
–
20
06
E
N
FT
Sc
hw
al
m
et
al
.[
20
07
]
C
A
‐
G
ro
−
36
.5
33
.5
93
34
4.
1
42
7
3.
3
20
04
–
20
06
M
F
M
cC
au
gh
ey
et
al
.[
20
06
]
C
A
‐
L
et
−
13
2.
9
14
.3
96
46
0.
7
33
5
6.
5
19
97
–
20
06
G
R
A
Fl
an
ag
an
et
al
.[
20
02
]
C
A
‐
M
er
−
68
.5
21
.6
79
56
1.
3
93
5
6.
2
19
99
–
20
06
W
E
T
La
fle
ur
et
al
.[
20
03
]
C
A
‐
O
as
−
15
8.
0
28
.5
94
56
3.
8
46
0
2.
3
19
97
–
20
06
D
B
F
Ba
rr
et
al
.[
20
04
]
C
A
‐
O
bs
−
56
.3
16
.1
89
45
5.
6
47
0
1.
6
20
00
–
20
06
E
N
FB
G
rif
fis
et
al
.[
20
03
]
C
A
‐
O
jp
−
29
.9
16
.6
91
50
3.
4
46
1
1.
5
20
00
–
20
06
E
N
FB
G
rif
fis
et
al
.[
20
03
]
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A
‐
Q
fo
−
13
.7
21
.0
93
40
4
81
9
2.
7
20
04
–
20
06
E
N
FB
Be
rg
er
on
et
al
.[
20
07
]
C
A
‐
SJ
1
28
.0
15
.3
87
31
0.
8
34
4
0.
6
20
02
–
20
05
E
N
FB
Zh
a
et
al
.[
20
09
]
C
A
‐
SJ
2
11
7.
0
6.
1
89
47
1.
3
53
7
0.
1
20
03
–
20
06
E
N
FB
Zh
a
et
al
.[
20
09
]
C
A
‐
SJ
3
−
82
.0
17
.7
92
34
4.
3
69
4
0.
8
20
04
–
20
05
E
N
FB
Zh
a
et
al
.[
20
09
]
C
A
‐
T
P4
−
13
3.
2
29
.5
95
43
3.
5
95
9
8.
6
20
02
–
20
07
E
N
FT
Pe
ic
hl
an
d
Ar
ai
n
[2
00
7]
C
A
‐
W
P1
−
19
5.
8
16
.4
96
50
2.
7
48
1
1.
7
20
03
–
20
07
W
E
T
Sy
ed
et
al
.[
20
06
]
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
6 of 22
Page 7
T
ab
le
2.
(c
on
tin
ue
d)
Si
te
ID
A
nn
ua
l
N
E
E
(g
C
m
2
−
)
A
nn
ua
l
N
E
E
E
rr
or
(g
C
m
2
−
)
D
ay
tim
e
D
at
a
C
ov
er
ag
e
(%
)
N
ig
ht
tim
e
D
at
a
C
ov
er
ag
e
(%
)
L
A
I
A
nn
ua
l
Pr
ec
ip
ita
tio
n
(m
m
)
M
ea
n
A
nn
ua
l
A
ir
T
em
pe
ra
tu
re
(°
C
)
M
ea
su
re
m
en
t
Pe
ri
od
B
io
m
e
So
ur
ce
U
.S
.‐
A
R
M
−
12
8.
4
74
.4
89
36
3.
1
62
9
15
.6
20
00
–
20
06
C
R
O
Fi
sc
he
r
et
al
.[
20
07
]
C
A
‐
T
P4
−
13
3.
2
29
.5
95
43
3.
5
95
9
8.
6
20
02
–
20
07
E
N
FT
Pe
ic
hl
an
d
Ar
ai
n
[2
00
7]
U
.S
.‐
A
tq
−
12
.8
‐
50
22
1.
1
11
8
−
10
.6
19
99
–
20
06
T
U
N
O
be
rb
au
er
et
al
.[
20
07
]
U
.S
.‐
B
rw
−
72
.0
‐
49
29
1.
5
10
8
−
10
.9
19
99
–
20
02
T
U
N
H
ar
az
on
o
et
al
.[
20
03
]
U
.S
.‐
D
k2
−
71
8.
1
‐
48
1
7
10
91
15
.1
20
03
–
20
05
D
B
F
Si
qu
ei
ra
et
al
.[
20
06
]
U
.S
.‐
D
k3
−
35
0.
0
13
9.
0
75
37
5.
6
11
26
14
.7
19
98
–
20
05
E
N
FT
Si
qu
ei
ra
et
al
.[
20
06
]
U
.S
.‐
H
a1
−
21
7.
4
65
.9
78
34
3.
38
11
22
7.
9
19
91
–
20
06
D
B
F
U
rb
an
sk
ie
ta
l.
[2
00
7]
U
.S
.‐
H
o1
−
22
3.
0
33
.4
70
47
5.
2
81
8
6.
6
19
96
–
20
04
E
N
FT
Ri
ch
ar
ds
on
et
al
.[
20
09
]
U
.S
.‐
IB
1
−
26
9.
0
31
.3
92
46
1.
29
71
8
10
.1
20
05
–
20
07
C
R
O
Po
st
et
al
.[
20
04
]
U
.S
.‐
IB
2
−
86
.0
42
.0
80
49
5.
38
81
8
10
.4
20
04
–
20
07
G
R
A
Po
st
et
al
.[
20
04
]
U
.S
.‐
L
os
−
78
.0
19
.2
82
54
4.
24
66
6
3.
8
20
00
–
20
06
W
E
T
Su
lm
an
et
al
.[
20
09
]
U
.S
.‐
M
M
S
−
34
6.
1
66
.3
97
46
4.
9
11
09
12
.4
19
99
–
20
06
D
B
F
Sc
hm
id
et
al
.[
20
00
]
U
.S
.‐
M
O
z
−
30
5.
7
48
.9
94
33
3.
91
73
0
13
.3
20
04
–
20
07
D
B
F
G
u
et
al
.[
20
06
]
U
.S
.‐
M
e2
−
53
6.
0
65
.8
63
46
3.
62
43
4
7.
6
20
02
–
20
07
E
N
FT
Th
om
as
et
al
.[
20
09
]
U
.S
.‐
M
e3
−
19
8.
0
32
.7
83
28
0.
52
42
3
8.
5
20
04
–
20
05
E
N
FT
Vi
ck
er
s
et
al
.[
20
09
]
U
.S
.‐
M
e4
−
61
2.
3
‐
55
41
2.
1
64
1
8.
3
19
96
–
20
00
E
N
FT
Ir
vi
ne
et
al
.[
20
04
]
U
.S
.‐
M
e5
−
20
6.
0
10
.6
97
48
1.
1
35
0
7.
6
19
99
–
20
02
E
N
FT
Ir
vi
ne
et
al
.[
20
04
]
U
.S
.‐
N
R
1
−
37
.2
27
.0
89
44
4.
2
66
3
2.
5
19
98
–
20
07
E
N
FT
Br
ad
fo
rd
et
al
.[
20
08
]
U
.S
.‐
N
e1
−
42
4.
0
41
.8
93
42
6.
5
83
2
11
.1
20
01
–
20
06
C
R
O
Ve
rm
a
et
al
.[
20
05
]
U
.S
.‐
N
e2
−
38
2.
0
41
.8
96
51
6.
5
82
3
10
.8
20
01
–
20
06
C
R
O
Ve
rm
a
et
al
.[
20
05
]
U
.S
.‐
N
e3
−
25
8.
0
43
.3
94
55
6.
2
62
7
10
.9
20
01
–
20
06
C
R
O
Ve
rm
a
et
al
.[
20
05
]
U
.S
.‐
PF
a
45
.0
41
.1
85
30
4.
05
73
6
5.
1
19
97
–
20
05
M
F
D
av
is
et
al
.[
20
03
]
U
.S
.‐
SO
2
22
.4
25
.6
87
30
3
69
5
13
.8
19
98
–
20
06
SH
R
Lu
o
et
al
.[
20
07
]
U
.S
.‐
Sh
d
−
75
.5
22
.0
96
49
5.
9
11
79
14
.8
19
97
–
20
01
G
R
A
Su
yk
er
et
al
.[
20
03
]
U
.S
.‐
Sy
v
48
.5
34
.7
53
51
4.
1
70
0
4.
4
20
01
–
20
06
M
F
D
es
ai
et
al
.[
20
05
]
U
.S
.‐
T
on
−
67
.8
52
.0
77
25
0.
6
54
9
16
.4
20
01
–
20
07
W
SA
M
a
et
al
.[
20
07
]
U
.S
.‐
U
M
B
−
13
2.
0
42
.4
86
39
4.
23
62
9
7.
4
19
98
–
20
06
D
B
F
Sc
hm
id
et
al
.[
20
03
]
U
.S
.‐
V
ar
7.
3
11
0.
6
80
22
2.
4
56
3
15
.9
20
01
–
20
07
G
R
A
M
a
et
al
.[
20
07
]
U
.S
.‐
W
C
r
−
22
2.
6
54
.1
48
55
5.
36
71
2
5.
3
19
98
–
20
06
D
B
F
C
oo
k
et
al
.[
20
04
a]
a S
ou
rc
es
:I
G
B
P
cl
as
si
fi
ca
tio
n,
Lo
ve
la
nd
et
al
.[
20
01
];
K
öp
pe
n‐
G
ei
ge
r,
Pe
el
et
al
.[
20
07
];
L
A
I
fo
r
U
SA
si
te
s,
ht
tp
://
pu
bl
ic
.o
rn
l.g
ov
/a
m
er
if
lu
x/
;L
A
I
fo
r
C
an
ad
ia
n
si
te
s,
C
he
n
et
al
.[
20
06
]
an
d
Sc
hw
al
m
et
al
.[
20
06
].
A
nn
ua
lp
re
ci
pi
ta
tio
n
an
d
m
ea
n
an
nu
al
ai
r
te
m
pe
ra
tu
re
ar
e
m
ea
su
re
m
en
tp
er
io
d
av
er
ag
es
of
m
et
eo
ro
lo
gi
ca
li
np
ut
s
us
ed
to
dr
iv
e
m
od
el
si
m
ul
at
io
ns
.N
E
E
va
lu
es
sh
ow
ye
ar
ly
in
te
gr
al
s
an
d
as
so
ci
at
ed
er
ro
r:
on
e
st
an
da
rd
de
vi
at
io
n
ba
se
d
on
un
ce
rt
ai
nt
y
du
e
to
ra
nd
om
no
is
e
an
d
th
e
fr
ic
tio
n
ve
lo
ci
ty
th
re
sh
ol
d
ag
gr
eg
at
ed
to
ye
ar
ly
va
lu
es
an
d
su
m
m
ed
in
qu
ad
ra
tu
re
[B
ar
re
ta
l.,
20
09
].
D
at
a
co
ve
ra
ge
s
ar
e
pe
rc
en
ta
ge
s
of
ha
lf
‐
ho
ur
ly
N
E
E
m
ea
su
re
m
en
ts
th
at
sa
tis
fi
ed
qu
al
ity
co
nt
ro
ls
ta
nd
ar
ds
(f
ri
ct
io
n
ve
lo
ci
ty
th
re
sh
ol
d)
fo
r
da
y‐
an
d
ni
gh
tti
m
e
se
pa
ra
te
ly
.P
ri
or
ity
:(
1)
Pr
im
ar
y
si
te
s
w
ith
co
m
pl
et
e
(i
nc
lu
de
s
an
ci
lla
ry
an
d
bi
ol
og
ic
al
da
ta
te
m
pl
at
es
)
re
co
rd
s;
(2
)
Se
co
nd
ar
y
ch
ro
no
se
qu
en
ce
si
te
s.
St
an
da
rd
iz
ed
Pr
ec
ip
ita
tio
n
In
de
x
av
ai
la
bl
e
on
ly
fo
r
Pr
io
ri
ty
1
si
te
s
ex
cl
ud
in
g
U
S
‐
A
tq
,
U
S‐
B
rw
,
U
S
‐
D
k2
,
U
S‐
IB
1,
an
d
U
S
‐
Sh
d.
C
A
‐
T
P3
,
U
S
‐
A
tq
,
U
S
‐
B
rw
,
U
S
‐
D
k2
,
an
d
U
S
‐
M
e4
si
te
s
us
ed
po
st
pr
oc
es
si
ng
pr
ot
oc
ol
fr
om
th
e
L
a
T
hu
ile
an
d
A
si
lo
m
ar
FL
U
X
N
E
T
Sy
nt
he
si
s
da
ta
se
t(
ht
tp
://
w
w
w
.f
lu
xd
at
a.
or
g/
)
[M
of
fa
te
ta
l.,
20
07
;P
ap
al
e
et
al
.,
20
06
]
an
d
la
ck
N
E
E
un
ce
rt
ai
nt
ie
s.
B
io
m
e
is
co
m
bi
na
tio
n
of
IG
B
P
cl
as
s
an
d
K
öp
pe
n‐
G
ei
ge
r
cl
im
at
e.
U
S
‐
A
tq
an
d
U
S
‐
B
rw
‐
A
rc
tic
w
et
la
nd
s
cl
as
si
fi
ed
as
tu
nd
ra
bi
om
e
C
A
‐
W
P1
,
tr
ee
d
fe
n
(I
G
B
P
m
ix
ed
fo
re
st
)
gr
ou
pe
d
w
ith
w
et
la
nd
s
bi
om
e
U
S‐
L
os
,
sh
ru
b
w
et
la
nd
si
te
(I
G
B
P
cl
os
ed
sh
ru
bl
an
ds
)
gr
ou
pe
d
w
ith
w
et
la
nd
s
bi
om
e
U
S
‐
SO
2,
cl
os
ed
sh
ru
bl
an
ds
gr
ou
pe
d
w
ith
sh
ru
bl
an
ds
(o
pe
n
or
cl
os
ed
)
bi
om
e.
IG
B
P
cl
as
s
an
d
bi
om
e
co
de
s:
C
R
O
,
cr
op
la
nd
s;
C
SH
,
cl
os
ed
sh
ru
bl
an
ds
;
G
R
A
,
gr
as
sl
an
ds
,
E
N
F,
ev
er
gr
ee
n
ne
ed
le
le
af
fo
re
st
;
E
N
FB
,
ev
er
gr
ee
n
ne
ed
le
le
af
fo
re
st
‐
bo
re
al
zo
ne
;
E
N
FT
,
ev
er
gr
ee
n
ne
ed
le
le
af
fo
re
st
‐
te
m
pe
ra
te
zo
ne
;
D
B
F,
de
ci
du
ou
s
br
oa
dl
ea
f
fo
re
st
;
M
F,
m
ix
ed
(d
ec
id
uo
us
/e
ve
rg
re
en
)
fo
re
st
;
W
SA
,
w
oo
dy
sa
va
nn
a;
SH
R
,
sh
ru
bl
an
ds
;
T
U
N
,
tu
nd
ra
;
W
E
T
,
w
et
la
nd
s.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
7 of 22
ab
le
2.
(c
on
tin
ue
d)
Si
te
ID
A
nn
ua
l
N
E
E
(g
C
m
2
−
)
A
nn
ua
l
N
E
E
E
rr
or
(g
C
m
2
−
)
D
ay
tim
e
D
at
a
C
ov
er
ag
e
(%
)
N
ig
ht
tim
e
D
at
a
C
ov
er
ag
e
(%
)
L
A
I
A
nn
ua
l
Pr
ec
ip
ita
tio
n
(m
m
)
M
ea
n
A
nn
ua
l
A
ir
T
em
pe
ra
tu
re
(°
C
)
M
ea
su
re
m
en
t
Pe
ri
od
B
io
m
e
So
ur
ce
U
.S
.‐
A
R
M
−
12
8.
4
74
.4
89
36
3.
1
62
9
15
.6
20
00
–
20
06
C
R
O
Fi
sc
he
r
et
al
.[
20
07
]
C
A
‐
T
P4
−
13
3.
2
29
.5
95
43
3.
5
95
9
8.
6
20
02
–
20
07
E
N
FT
Pe
ic
hl
an
d
Ar
ai
n
[2
00
7]
U
.S
.‐
A
tq
−
12
.8
‐
50
22
1.
1
11
8
−
10
.6
19
99
–
20
06
T
U
N
O
be
rb
au
er
et
al
.[
20
07
]
U
.S
.‐
B
rw
−
72
.0
‐
49
29
1.
5
10
8
−
10
.9
19
99
–
20
02
T
U
N
H
ar
az
on
o
et
al
.[
20
03
]
U
.S
.‐
D
k2
−
71
8.
1
‐
48
1
7
10
91
15
.1
20
03
–
20
05
D
B
F
Si
qu
ei
ra
et
al
.[
20
06
]
U
.S
.‐
D
k3
−
35
0.
0
13
9.
0
75
37
5.
6
11
26
14
.7
19
98
–
20
05
E
N
FT
Si
qu
ei
ra
et
al
.[
20
06
]
U
.S
.‐
H
a1
−
21
7.
4
65
.9
78
34
3.
38
11
22
7.
9
19
91
–
20
06
D
B
F
U
rb
an
sk
ie
ta
l.
[2
00
7]
U
.S
.‐
H
o1
−
22
3.
0
33
.4
70
47
5.
2
81
8
6.
6
19
96
–
20
04
E
N
FT
Ri
ch
ar
ds
on
et
al
.[
20
09
]
U
.S
.‐
IB
1
−
26
9.
0
31
.3
92
46
1.
29
71
8
10
.1
20
05
–
20
07
C
R
O
Po
st
et
al
.[
20
04
]
U
.S
.‐
IB
2
−
86
.0
42
.0
80
49
5.
38
81
8
10
.4
20
04
–
20
07
G
R
A
Po
st
et
al
.[
20
04
]
U
.S
.‐
L
os
−
78
.0
19
.2
82
54
4.
24
66
6
3.
8
20
00
–
20
06
W
E
T
Su
lm
an
et
al
.[
20
09
]
U
.S
.‐
M
M
S
−
34
6.
1
66
.3
97
46
4.
9
11
09
12
.4
19
99
–
20
06
D
B
F
Sc
hm
id
et
al
.[
20
00
]
U
.S
.‐
M
O
z
−
30
5.
7
48
.9
94
33
3.
91
73
0
13
.3
20
04
–
20
07
D
B
F
G
u
et
al
.[
20
06
]
U
.S
.‐
M
e2
−
53
6.
0
65
.8
63
46
3.
62
43
4
7.
6
20
02
–
20
07
E
N
FT
Th
om
as
et
al
.[
20
09
]
U
.S
.‐
M
e3
−
19
8.
0
32
.7
83
28
0.
52
42
3
8.
5
20
04
–
20
05
E
N
FT
Vi
ck
er
s
et
al
.[
20
09
]
U
.S
.‐
M
e4
−
61
2.
3
‐
55
41
2.
1
64
1
8.
3
19
96
–
20
00
E
N
FT
Ir
vi
ne
et
al
.[
20
04
]
U
.S
.‐
M
e5
−
20
6.
0
10
.6
97
48
1.
1
35
0
7.
6
19
99
–
20
02
E
N
FT
Ir
vi
ne
et
al
.[
20
04
]
U
.S
.‐
N
R
1
−
37
.2
27
.0
89
44
4.
2
66
3
2.
5
19
98
–
20
07
E
N
FT
Br
ad
fo
rd
et
al
.[
20
08
]
U
.S
.‐
N
e1
−
42
4.
0
41
.8
93
42
6.
5
83
2
11
.1
20
01
–
20
06
C
R
O
Ve
rm
a
et
al
.[
20
05
]
U
.S
.‐
N
e2
−
38
2.
0
41
.8
96
51
6.
5
82
3
10
.8
20
01
–
20
06
C
R
O
Ve
rm
a
et
al
.[
20
05
]
U
.S
.‐
N
e3
−
25
8.
0
43
.3
94
55
6.
2
62
7
10
.9
20
01
–
20
06
C
R
O
Ve
rm
a
et
al
.[
20
05
]
U
.S
.‐
PF
a
45
.0
41
.1
85
30
4.
05
73
6
5.
1
19
97
–
20
05
M
F
D
av
is
et
al
.[
20
03
]
U
.S
.‐
SO
2
22
.4
25
.6
87
30
3
69
5
13
.8
19
98
–
20
06
SH
R
Lu
o
et
al
.[
20
07
]
U
.S
.‐
Sh
d
−
75
.5
22
.0
96
49
5.
9
11
79
14
.8
19
97
–
20
01
G
R
A
Su
yk
er
et
al
.[
20
03
]
U
.S
.‐
Sy
v
48
.5
34
.7
53
51
4.
1
70
0
4.
4
20
01
–
20
06
M
F
D
es
ai
et
al
.[
20
05
]
U
.S
.‐
T
on
−
67
.8
52
.0
77
25
0.
6
54
9
16
.4
20
01
–
20
07
W
SA
M
a
et
al
.[
20
07
]
U
.S
.‐
U
M
B
−
13
2.
0
42
.4
86
39
4.
23
62
9
7.
4
19
98
–
20
06
D
B
F
Sc
hm
id
et
al
.[
20
03
]
U
.S
.‐
V
ar
7.
3
11
0.
6
80
22
2.
4
56
3
15
.9
20
01
–
20
07
G
R
A
M
a
et
al
.[
20
07
]
U
.S
.‐
W
C
r
−
22
2.
6
54
.1
48
55
5.
36
71
2
5.
3
19
98
–
20
06
D
B
F
C
oo
k
et
al
.[
20
04
a]
a S
ou
rc
es
:I
G
B
P
cl
as
si
fi
ca
tio
n,
Lo
ve
la
nd
et
al
.[
20
01
];
K
öp
pe
n‐
G
ei
ge
r,
Pe
el
et
al
.[
20
07
];
L
A
I
fo
r
U
SA
si
te
s,
ht
tp
://
pu
bl
ic
.o
rn
l.g
ov
/a
m
er
if
lu
x/
;L
A
I
fo
r
C
an
ad
ia
n
si
te
s,
C
he
n
et
al
.[
20
06
]
an
d
Sc
hw
al
m
et
al
.[
20
06
].
A
nn
ua
lp
re
ci
pi
ta
tio
n
an
d
m
ea
n
an
nu
al
ai
r
te
m
pe
ra
tu
re
ar
e
m
ea
su
re
m
en
tp
er
io
d
av
er
ag
es
of
m
et
eo
ro
lo
gi
ca
li
np
ut
s
us
ed
to
dr
iv
e
m
od
el
si
m
ul
at
io
ns
.N
E
E
va
lu
es
sh
ow
ye
ar
ly
in
te
gr
al
s
an
d
as
so
ci
at
ed
er
ro
r:
on
e
st
an
da
rd
de
vi
at
io
n
ba
se
d
on
un
ce
rt
ai
nt
y
du
e
to
ra
nd
om
no
is
e
an
d
th
e
fr
ic
tio
n
ve
lo
ci
ty
th
re
sh
ol
d
ag
gr
eg
at
ed
to
ye
ar
ly
va
lu
es
an
d
su
m
m
ed
in
qu
ad
ra
tu
re
[B
ar
re
ta
l.,
20
09
].
D
at
a
co
ve
ra
ge
s
ar
e
pe
rc
en
ta
ge
s
of
ha
lf
‐
ho
ur
ly
N
E
E
m
ea
su
re
m
en
ts
th
at
sa
tis
fi
ed
qu
al
ity
co
nt
ro
ls
ta
nd
ar
ds
(f
ri
ct
io
n
ve
lo
ci
ty
th
re
sh
ol
d)
fo
r
da
y‐
an
d
ni
gh
tti
m
e
se
pa
ra
te
ly
.P
ri
or
ity
:(
1)
Pr
im
ar
y
si
te
s
w
ith
co
m
pl
et
e
(i
nc
lu
de
s
an
ci
lla
ry
an
d
bi
ol
og
ic
al
da
ta
te
m
pl
at
es
)
re
co
rd
s;
(2
)
Se
co
nd
ar
y
ch
ro
no
se
qu
en
ce
si
te
s.
St
an
da
rd
iz
ed
Pr
ec
ip
ita
tio
n
In
de
x
av
ai
la
bl
e
on
ly
fo
r
Pr
io
ri
ty
1
si
te
s
ex
cl
ud
in
g
U
S
‐
A
tq
,
U
S‐
B
rw
,
U
S
‐
D
k2
,
U
S‐
IB
1,
an
d
U
S
‐
Sh
d.
C
A
‐
T
P3
,
U
S
‐
A
tq
,
U
S
‐
B
rw
,
U
S
‐
D
k2
,
an
d
U
S
‐
M
e4
si
te
s
us
ed
po
st
pr
oc
es
si
ng
pr
ot
oc
ol
fr
om
th
e
L
a
T
hu
ile
an
d
A
si
lo
m
ar
FL
U
X
N
E
T
Sy
nt
he
si
s
da
ta
se
t(
ht
tp
://
w
w
w
.f
lu
xd
at
a.
or
g/
)
[M
of
fa
te
ta
l.,
20
07
;P
ap
al
e
et
al
.,
20
06
]
an
d
la
ck
N
E
E
un
ce
rt
ai
nt
ie
s.
B
io
m
e
is
co
m
bi
na
tio
n
of
IG
B
P
cl
as
s
an
d
K
öp
pe
n‐
G
ei
ge
r
cl
im
at
e.
U
S
‐
A
tq
an
d
U
S
‐
B
rw
‐
A
rc
tic
w
et
la
nd
s
cl
as
si
fi
ed
as
tu
nd
ra
bi
om
e
C
A
‐
W
P1
,
tr
ee
d
fe
n
(I
G
B
P
m
ix
ed
fo
re
st
)
gr
ou
pe
d
w
ith
w
et
la
nd
s
bi
om
e
U
S‐
L
os
,
sh
ru
b
w
et
la
nd
si
te
(I
G
B
P
cl
os
ed
sh
ru
bl
an
ds
)
gr
ou
pe
d
w
ith
w
et
la
nd
s
bi
om
e
U
S
‐
SO
2,
cl
os
ed
sh
ru
bl
an
ds
gr
ou
pe
d
w
ith
sh
ru
bl
an
ds
(o
pe
n
or
cl
os
ed
)
bi
om
e.
IG
B
P
cl
as
s
an
d
bi
om
e
co
de
s:
C
R
O
,
cr
op
la
nd
s;
C
SH
,
cl
os
ed
sh
ru
bl
an
ds
;
G
R
A
,
gr
as
sl
an
ds
,
E
N
F,
ev
er
gr
ee
n
ne
ed
le
le
af
fo
re
st
;
E
N
FB
,
ev
er
gr
ee
n
ne
ed
le
le
af
fo
re
st
‐
bo
re
al
zo
ne
;
E
N
FT
,
ev
er
gr
ee
n
ne
ed
le
le
af
fo
re
st
‐
te
m
pe
ra
te
zo
ne
;
D
B
F,
de
ci
du
ou
s
br
oa
dl
ea
f
fo
re
st
;
M
F,
m
ix
ed
(d
ec
id
uo
us
/e
ve
rg
re
en
)
fo
re
st
;
W
SA
,
w
oo
dy
sa
va
nn
a;
SH
R
,
sh
ru
bl
an
ds
;
T
U
N
,
tu
nd
ra
;
W
E
T
,
w
et
la
nd
s.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
7 of 22
Page 8
runs using gap‐filled observed weather at each site and
locally observed values of soil texture according to a stan-
dard protocol [Ricciuto et al., 2009] (http://www.nacarbon.
org/nacp/), including a target NEE of zero integrated over
the last 5 years of the simulation period. In addition, a mean
model ensemble (hereafter: MEAN) was also analyzed.
MEAN was calculated as the mean monthly value across all
simulations. Furthermore, in contrast to other models, the
parameter values used in the model LoTEC were optimized
using data assimilation [Ricciuto et al., 2008]. LoTEC
simulations were however retained when calculating MEAN
as their effect on model skill was negligible due to the rel-
atively small number of site‐months simulated.
[6] Gaps in the meteorological data record occurred at
EC sites due to data quality control or instrument failure.
Missing values of air temperature, humidity, shortwave
radiation, and precipitation data, i.e., key model inputs, were
filled using DAYMET [Thornton et al., 1997] before 2003
or the nearest available climate station in the National
Climatic Data Center’s Global Surface Summary of the Day
(GSOD) database. Daily GSOD and DAYMET data were
temporally downscaled to hourly or half‐hourly values using
the phasing from observed mean diurnal cycles calculated
from a 15 day moving window. The phasing used a sine wave
assuming peak values at 1500 local standard time (LST) and
lowest values at 0300 LST. In the absence of station data
a 10 day running mean diurnal cycle was used [Ricciuto et al.,
2009] (http://nacp.ornl.gov/docs/Site_Synthesis_Protocol_v7.
pdf).
[7] EC data were produced by AmeriFlux and Fluxnet‐
Canada investigators and processed as a synthesis product of
the North American Carbon Program (NACP) Site Level
Interim Synthesis (http://www.nacarbon.org/nacp/). The
observed NEE were corrected for storage, despiked (i.e.,
outlying values removed), filtered to remove conditions of
low turbulence (friction velocity filtered), and gap‐filled to
create a continuous time series [Barr et al., 2004]. The time
series included estimates of random uncertainty and uncer-
tainty due to friction velocity filtering [Barr et al., 2004,
2009]. In this analysis, NEE was aggregated to monthly
values using only non‐gap‐filled data, i.e., observed values
deemed spurious and subsequently infilled were not con-
sidered. Coincident modeled NEE values were similarly
excluded. This removed the influence of gap‐filling algo-
rithms in the comparison of observed and modeled NEE.
[8] Drought level was quantified using the 3 month
Standard Precipitation Index (SPI) [McKee et al., 1993].
Monthly SPI values were taken from the U.S. Drought
Monitor (http://drought.unl.edu/DM/) whereby each tower
was matched to nearby meteorological station(s) indicative
of local drought conditions given proximity, topography,
and human impact. This study used three drought levels: dry
required SPI < −0.8, wet corresponded to SPI > +0.8, other-
wise normal conditions existed. Climatic season was defined
by four seasons of 3 months each with winter given by
December, January, and February.
2.2. Model Skill
[9] Model‐data mismatch was evaluated using normalized
mean absolute error (NMAE) [Medlyn et al., 2005], the
reduced c2 statistic (c2) [Taylor, 1996] as well as Taylor
diagrams and skill (S) [Taylor, 2001]. The first metric
quantifies bias, the “average distance” between observations
and simulations in units of observed mean NEE:
NMAE ¼
X
ijkl
NEE
obs
NEE
sim
nNEE
obs
; ð1Þ
where the overbar indicates averaging across all values, n is
sample size, the subscript obs is for observations and sim is
for modeled estimates. The summation is for any arbitrary
data group (denoted by subscripts on the summation oper-
ator only) where subscript i is for site, j is for model, k is for
climatic season, l is for drought level.
[10] The second metric used to evaluate model perfor-
mance was the reduced c2 statistic. This is the squared
difference between paired model and data points over
observational error normalized by degrees of freedom:
2
¼
1
n
X
ijkl
NEE
obs
NEE
sim
2
NEE
2
; ð2Þ
where d NEE is uncertainty of monthly NEE (see section 2.3),
2 normalizes the uncertainty in observed NEE to correspond
to a 95% confidence interval, the summation is across any
arbitrary data group (denoted by subscripts on the summa-
tion operator). c2 values are linked to model‐data mismatch
where a value of unity indicates that model and data are in
agreement relative to data uncertainty.
[11] A final characterization of model performance used
Taylor diagrams [Taylor, 2001]; visual displays based on
pattern matching, i.e., the degree to which simulations
matched the temporal evolution of monthly NEE. Taylor
plots are polar coordinate displays of the linear correlation
coefficient (r), centered root mean squared error (RMSE;
pattern error without considering bias), and the standard
deviation of NEE (s). Taylor diagrams were constructed for
the mean model ensemble (MEAN) and across‐site mean
model performance using the full data record for each com-
bination of site and model (ranging from 7 to 178 months).
More generally, each polar coordinate point for any arbitrary
data group can be scored:
S ¼
2 1 þ ð Þ
norm
þ 1=
norm
ð Þ
2
; ð3Þ
where S is the model skill metric bound by zero and unity
where unity indicates perfect agreement, and snorm is the
ratio of simulated to observed standard deviation [Taylor,
2001].
[12] To scale model skill metrics across gradients of site,
biome, model, seasonality, and dryness level we aggregated
across data groups weighting each by sample size. For
example, c2 for model I, denoted by subscript j = I, is given
by
2
j¼I
¼
X
ikl
n
ikl
2
ikl
n
j¼I
ð4Þ
where the summation is over all sites, seasons, and levels of
dryness where model I was used as denoted by subscripts i,
k, and l, respectively; nj=I is the total site‐months simulated
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
8 of 22
locally observed values of soil texture according to a stan-
dard protocol [Ricciuto et al., 2009] (http://www.nacarbon.
org/nacp/), including a target NEE of zero integrated over
the last 5 years of the simulation period. In addition, a mean
model ensemble (hereafter: MEAN) was also analyzed.
MEAN was calculated as the mean monthly value across all
simulations. Furthermore, in contrast to other models, the
parameter values used in the model LoTEC were optimized
using data assimilation [Ricciuto et al., 2008]. LoTEC
simulations were however retained when calculating MEAN
as their effect on model skill was negligible due to the rel-
atively small number of site‐months simulated.
[6] Gaps in the meteorological data record occurred at
EC sites due to data quality control or instrument failure.
Missing values of air temperature, humidity, shortwave
radiation, and precipitation data, i.e., key model inputs, were
filled using DAYMET [Thornton et al., 1997] before 2003
or the nearest available climate station in the National
Climatic Data Center’s Global Surface Summary of the Day
(GSOD) database. Daily GSOD and DAYMET data were
temporally downscaled to hourly or half‐hourly values using
the phasing from observed mean diurnal cycles calculated
from a 15 day moving window. The phasing used a sine wave
assuming peak values at 1500 local standard time (LST) and
lowest values at 0300 LST. In the absence of station data
a 10 day running mean diurnal cycle was used [Ricciuto et al.,
2009] (http://nacp.ornl.gov/docs/Site_Synthesis_Protocol_v7.
pdf).
[7] EC data were produced by AmeriFlux and Fluxnet‐
Canada investigators and processed as a synthesis product of
the North American Carbon Program (NACP) Site Level
Interim Synthesis (http://www.nacarbon.org/nacp/). The
observed NEE were corrected for storage, despiked (i.e.,
outlying values removed), filtered to remove conditions of
low turbulence (friction velocity filtered), and gap‐filled to
create a continuous time series [Barr et al., 2004]. The time
series included estimates of random uncertainty and uncer-
tainty due to friction velocity filtering [Barr et al., 2004,
2009]. In this analysis, NEE was aggregated to monthly
values using only non‐gap‐filled data, i.e., observed values
deemed spurious and subsequently infilled were not con-
sidered. Coincident modeled NEE values were similarly
excluded. This removed the influence of gap‐filling algo-
rithms in the comparison of observed and modeled NEE.
[8] Drought level was quantified using the 3 month
Standard Precipitation Index (SPI) [McKee et al., 1993].
Monthly SPI values were taken from the U.S. Drought
Monitor (http://drought.unl.edu/DM/) whereby each tower
was matched to nearby meteorological station(s) indicative
of local drought conditions given proximity, topography,
and human impact. This study used three drought levels: dry
required SPI < −0.8, wet corresponded to SPI > +0.8, other-
wise normal conditions existed. Climatic season was defined
by four seasons of 3 months each with winter given by
December, January, and February.
2.2. Model Skill
[9] Model‐data mismatch was evaluated using normalized
mean absolute error (NMAE) [Medlyn et al., 2005], the
reduced c2 statistic (c2) [Taylor, 1996] as well as Taylor
diagrams and skill (S) [Taylor, 2001]. The first metric
quantifies bias, the “average distance” between observations
and simulations in units of observed mean NEE:
NMAE ¼
X
ijkl
NEE
obs
NEE
sim
nNEE
obs
; ð1Þ
where the overbar indicates averaging across all values, n is
sample size, the subscript obs is for observations and sim is
for modeled estimates. The summation is for any arbitrary
data group (denoted by subscripts on the summation oper-
ator only) where subscript i is for site, j is for model, k is for
climatic season, l is for drought level.
[10] The second metric used to evaluate model perfor-
mance was the reduced c2 statistic. This is the squared
difference between paired model and data points over
observational error normalized by degrees of freedom:
2
¼
1
n
X
ijkl
NEE
obs
NEE
sim
2
NEE
2
; ð2Þ
where d NEE is uncertainty of monthly NEE (see section 2.3),
2 normalizes the uncertainty in observed NEE to correspond
to a 95% confidence interval, the summation is across any
arbitrary data group (denoted by subscripts on the summa-
tion operator). c2 values are linked to model‐data mismatch
where a value of unity indicates that model and data are in
agreement relative to data uncertainty.
[11] A final characterization of model performance used
Taylor diagrams [Taylor, 2001]; visual displays based on
pattern matching, i.e., the degree to which simulations
matched the temporal evolution of monthly NEE. Taylor
plots are polar coordinate displays of the linear correlation
coefficient (r), centered root mean squared error (RMSE;
pattern error without considering bias), and the standard
deviation of NEE (s). Taylor diagrams were constructed for
the mean model ensemble (MEAN) and across‐site mean
model performance using the full data record for each com-
bination of site and model (ranging from 7 to 178 months).
More generally, each polar coordinate point for any arbitrary
data group can be scored:
S ¼
2 1 þ ð Þ
norm
þ 1=
norm
ð Þ
2
; ð3Þ
where S is the model skill metric bound by zero and unity
where unity indicates perfect agreement, and snorm is the
ratio of simulated to observed standard deviation [Taylor,
2001].
[12] To scale model skill metrics across gradients of site,
biome, model, seasonality, and dryness level we aggregated
across data groups weighting each by sample size. For
example, c2 for model I, denoted by subscript j = I, is given
by
2
j¼I
¼
X
ikl
n
ikl
2
ikl
n
j¼I
ð4Þ
where the summation is over all sites, seasons, and levels of
dryness where model I was used as denoted by subscripts i,
k, and l, respectively; nj=I is the total site‐months simulated
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
8 of 22
Page 9
with model I; and c2j=I is aggregated c2 for model I. We did
not evaluate model performance for any data group with n <
3. In sum, Taylor displays and skill examined models’
ability to mimic the monthly trajectory of observed NEE, the
calculation of NMAE quantified bias in units of mean
observed NEE, and c2 values quantified how well model‐
data mismatch scales with flux uncertainty.
2.3. Observational Flux Uncertainty
[13] We calculated the standard error of monthly NEE
(dNEE) [Barr et al., 2009] by combining random uncertainty
and uncertainty associated with the friction velocity
threshold (u*Th), a value used to identify and reject spurious
nighttime NEE measurements. Random uncertainty was
estimated following Richardson and Hollinger [2007]:
(1) generate synthetic NEE data using the gap‐filling model
[Barr et al., 2004, 2009] for a given site‐year, (2) introduce
gaps as in the observed data with u*Th filtering, (3) add noise,
(4) infill gaps using the gap‐filling model, and (5) repeat the
process 1000 times for each site. The random uncertainty
component of dNEE was then the standard deviation across
all 1000 realizations aggregated to months.
[14] The u*Th uncertainty component of dNEE was also
estimated using Monte Carlo methods. Here 1000 realiza-
tions of NEE were generated using 1000 draws from a
distribution of u*Th. This distribution was based on binning
the raw flux data with respect to climatic season, temperature,
and site‐year and estimating u*Th in each bin [Papale et al.,
2006]. The standard deviation across all realizations gave
the u*Th uncertainty component of dNEE. Both components
were combined in quadrature to one standard error of
monthly NEE (= dNEE) [Barr et al., 2009].
2.4. Relating Model Skill to Model Structure and Site
History
[15] The models evaluated here range widely in their
emphasis and structure (Table 1). Some focus on biophy-
sical calculations (SiB3, BEPS), where others emphasize
biogeochemistry (DLEM), or ecosystem dynamics (ED2).
However, as terrestrial biosphere models simulate carbon
cycling with hydrological variables, most models contain
both biophysics and biogeochemistry. This motivated
characterizing model structure with definite attributes, e.g.,
prognostic versus prescribed canopy phenology, number of
soil pools, and type of NEE algorithm (Table 3). To resolve
how such characteristics and site history impacted model
skill we calculated S for all observed combinations of site,
model, seasonality, and drought level and cross‐referenced
Table 3. Model Structural and Site History Predictors Used to Classify Taylor Skill With Regression Tree
Analysisa
Predictor Value
Model temporal resolution Daily, half‐hourly or less, hourly, monthly
Canopy Prognostic, semiprognostic, prescribed. Prescribed canopy
from remote sensing, semiprognostic has some
prescribed input into canopy leaf biomass but
calculates phenology with other prognostic variables.
Number of vegetation pools Number of pools, both dynamic and static
Number of soil pools Number of pools, both dynamic and static
Number of soil layers Number of layers
Nitrogen True if the model has a nitrogen cycle; otherwise false.
Steady state True if the simulated long‐term NEE integral approaches zero;
otherwise false.
Autotrophic respiration (AR) Fraction of annual GPP, fraction of instantaneous GPP,
explicitly calculated, nil, proportional to growth
Ecosystem respiration (R) AR + HR, explicitly calculated, forced annual balance
Gross primary productivity (GPP) Enzyme kinetic model, light use efficiency model, nil, stomatal
conductance model
Heterotrophic respiration (HR) Explicitly calculated, first or greater order model,
zero‐order model
Net ecosystem exchange (NEE) Explicitly calculated, GPP ‐ R, NPP ‐ HR
Net primary productivity (NPP) Explicitly calculated, fraction of instantaneous GPP, GPP ‐ AR,
light use efficiency model
Overall model complexity Low, average, high
Values correspond to terciles of the total amount of first‐order
functional arguments for the following model‐generated
variables/outputs: AR, canopy leaf biomass, R,
evapotranspiration, GPP, HR, NEE, NPP, soil moisture.
Site history True if the below listed management activity or disturbance or
event occurred on site; otherwise false.
Grazed, fertilized, fire, harvest, herbicide, insects and pathogens,
irrigation, natural regeneration, pesticide, planted, residue
management, thinning
Stand age class Young, intermediate, nil, mature, multicohort.
Values based on stand age in forested sites; stands without a clear
dominant stratum are treated as multicohort; nonforest types
have nil.
aTaylor skill (S; equation (3)) was divided into three classes using terciles. Model structural predictants are from the Metadata for
Forward (Ecosystem) Model Intercomparison survey collated by the NACP Site Synthesis (http://daac.ornl.gov/SURVEY8/
survey_results.shtml). Site history data are from http://public.ornl.gov/ameriflux/, http://www.fluxnet.org, and Schwalm et al.
[2006].
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
9 of 22
not evaluate model performance for any data group with n <
3. In sum, Taylor displays and skill examined models’
ability to mimic the monthly trajectory of observed NEE, the
calculation of NMAE quantified bias in units of mean
observed NEE, and c2 values quantified how well model‐
data mismatch scales with flux uncertainty.
2.3. Observational Flux Uncertainty
[13] We calculated the standard error of monthly NEE
(dNEE) [Barr et al., 2009] by combining random uncertainty
and uncertainty associated with the friction velocity
threshold (u*Th), a value used to identify and reject spurious
nighttime NEE measurements. Random uncertainty was
estimated following Richardson and Hollinger [2007]:
(1) generate synthetic NEE data using the gap‐filling model
[Barr et al., 2004, 2009] for a given site‐year, (2) introduce
gaps as in the observed data with u*Th filtering, (3) add noise,
(4) infill gaps using the gap‐filling model, and (5) repeat the
process 1000 times for each site. The random uncertainty
component of dNEE was then the standard deviation across
all 1000 realizations aggregated to months.
[14] The u*Th uncertainty component of dNEE was also
estimated using Monte Carlo methods. Here 1000 realiza-
tions of NEE were generated using 1000 draws from a
distribution of u*Th. This distribution was based on binning
the raw flux data with respect to climatic season, temperature,
and site‐year and estimating u*Th in each bin [Papale et al.,
2006]. The standard deviation across all realizations gave
the u*Th uncertainty component of dNEE. Both components
were combined in quadrature to one standard error of
monthly NEE (= dNEE) [Barr et al., 2009].
2.4. Relating Model Skill to Model Structure and Site
History
[15] The models evaluated here range widely in their
emphasis and structure (Table 1). Some focus on biophy-
sical calculations (SiB3, BEPS), where others emphasize
biogeochemistry (DLEM), or ecosystem dynamics (ED2).
However, as terrestrial biosphere models simulate carbon
cycling with hydrological variables, most models contain
both biophysics and biogeochemistry. This motivated
characterizing model structure with definite attributes, e.g.,
prognostic versus prescribed canopy phenology, number of
soil pools, and type of NEE algorithm (Table 3). To resolve
how such characteristics and site history impacted model
skill we calculated S for all observed combinations of site,
model, seasonality, and drought level and cross‐referenced
Table 3. Model Structural and Site History Predictors Used to Classify Taylor Skill With Regression Tree
Analysisa
Predictor Value
Model temporal resolution Daily, half‐hourly or less, hourly, monthly
Canopy Prognostic, semiprognostic, prescribed. Prescribed canopy
from remote sensing, semiprognostic has some
prescribed input into canopy leaf biomass but
calculates phenology with other prognostic variables.
Number of vegetation pools Number of pools, both dynamic and static
Number of soil pools Number of pools, both dynamic and static
Number of soil layers Number of layers
Nitrogen True if the model has a nitrogen cycle; otherwise false.
Steady state True if the simulated long‐term NEE integral approaches zero;
otherwise false.
Autotrophic respiration (AR) Fraction of annual GPP, fraction of instantaneous GPP,
explicitly calculated, nil, proportional to growth
Ecosystem respiration (R) AR + HR, explicitly calculated, forced annual balance
Gross primary productivity (GPP) Enzyme kinetic model, light use efficiency model, nil, stomatal
conductance model
Heterotrophic respiration (HR) Explicitly calculated, first or greater order model,
zero‐order model
Net ecosystem exchange (NEE) Explicitly calculated, GPP ‐ R, NPP ‐ HR
Net primary productivity (NPP) Explicitly calculated, fraction of instantaneous GPP, GPP ‐ AR,
light use efficiency model
Overall model complexity Low, average, high
Values correspond to terciles of the total amount of first‐order
functional arguments for the following model‐generated
variables/outputs: AR, canopy leaf biomass, R,
evapotranspiration, GPP, HR, NEE, NPP, soil moisture.
Site history True if the below listed management activity or disturbance or
event occurred on site; otherwise false.
Grazed, fertilized, fire, harvest, herbicide, insects and pathogens,
irrigation, natural regeneration, pesticide, planted, residue
management, thinning
Stand age class Young, intermediate, nil, mature, multicohort.
Values based on stand age in forested sites; stands without a clear
dominant stratum are treated as multicohort; nonforest types
have nil.
aTaylor skill (S; equation (3)) was divided into three classes using terciles. Model structural predictants are from the Metadata for
Forward (Ecosystem) Model Intercomparison survey collated by the NACP Site Synthesis (http://daac.ornl.gov/SURVEY8/
survey_results.shtml). Site history data are from http://public.ornl.gov/ameriflux/, http://www.fluxnet.org, and Schwalm et al.
[2006].
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
9 of 22
Page 10
these with 13 site history variables and 14 model attributes
(Table 3). Only 20 models were available for this exercise;
MEAN and the optimized LoTEC were excluded. We used
S as it is bound by zero (no agreement) and unity (perfect
agreement) in contrast to NMAE and c2 which are unbound.
The Taylor skill metric (S) was first discretized into three
classes based on terciles. These classes, representing three
tiers of model‐data agreement, were then related to biome,
climatic season, drought level, site history, and model
structure using regression tree analysis (RTA) as a super-
vised classification algorithm. RTA is a form of binary
recursive partitioning [Breiman et al., 1984] that succes-
sively splits the data (Taylor skill classes as the response; all
other attributes as predictors) into subsets (nodes) by mini-
mizing within‐subset variation. The result is a pruned tree‐
like topology whereby predicted values (Taylor skill metric
class) are derived by a top‐to‐bottom traversal following the
rules (branches) that govern subset membership until a
predicted value is reached (terminal node). The splitting
rules at each node as well as its position allow for a cal-
culation of relative variable importance [Breiman et al.,
1984] with the most important variable given a score of
100. Variables of high importance were further analyzed
using conditional means, i.e., comparing mean values for
each predictor value, with statistical differences determined
using Bonferroni corrections for multiple comparisons
[Hochberg and Tamhane, 1987].
3. Results
3.1. Model‐Data Agreement Relative to Climatic
Season, Dryness, and Biome
[16] Overall agreement across n = 31025 months was
better in forested than nonforested biomes; both NMAE
(Table 4) and c2 values (Table 5) were closer to zero and
unity, respectively. At the biome level, model skill was
loosely ranked in five tiers: evergreen needleleaf forests in
the temperate zone, mixed forests > deciduous broadleaf
forests, evergreen needleleaf forests in the boreal zone >
grasslands, woody savannahs > croplands, shrublands,
wetlands > tundra. These rankings were robust across
models used in the majority of biomes, although some
divergence was apparent for croplands and shrublands
(Figure 1). Relative to seasonality and drought level models
were most consistent with observations during periods of
peak biological activity (climatic summer) and under dry
conditions (Figure 2). However, across the three levels of
dryness, changes in model‐data agreement were negligible
Table 4. Normalized Mean Absolute Error by Climatic Season, Drought Level, and Biomea
Biomeb
Climatic Season Drought Level
OverallWinter Spring Summer Fall Dry Normal Wet
CRO 1.90 4.64 −0.79 12.73 −1.43 −1.54 −1.59 −1.55
DBF 0.81 93.7 −0.52 −2.14 −1.01 −1.00 −0.95 −1.00
ENFB 1.52 −1.12 −0.69 −1.92 −0.87 −1.15 −3.43 −1.12
ENFT −6.34 −0.66 −0.50 −0.76 −0.63 −0.72 −0.63 −0.68
GRA −25.46 −0.84 −1.11 5.19 −1.52 −1.32 −3.07 −1.51
MF 1.10 −7.48 −0.47 57.70 −1.42 −1.04 −1.15 −1.12
SHR −87.37 −1.37 −3.03 −140.17 −1.82 −2.18 −41.13 −2.88
TUN −1.43 −11.07 −20.63 6.38 19.22 −24.06 −1.81 −20.15
WET 1.80 −5.07 −0.59 −4.72 −1.21 −1.20 −2.38 −1.27
WSA −2.73 −0.75 −1.47 10.56 −1.39 −1.32 −1.51 −1.37
Overall 2.42 −1.35 −0.61 −1.94 −0.97 −1.01 −1.00 −1.00
aDrought level was based on monthly values of 3 month Standard Precipitation Index (SPI): dry value were < −0.8; wet >
+0.8. Otherwise normal conditions existed.
bBiome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen
needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody
savanna; SHR, shrubland; TUN, tundra; WET, wetland.
Table 5. Reduced c2 Statistic by Climatic Season, Drought Level, and Biomea
Biomeb
Climatic Season Drought Level
OverallWinter Spring Summer Fall Dry Normal Wet
CRO 3.22 10.66 39.75 49.71 14.43 23.54 32.75 25.8
DBF 5.29 10.74 8.77 4.55 5.58 7.86 8.67 7.34
ENFB 21.25 17.75 4.98 6.61 11.64 12.02 18.51 12.61
ENFT 4.39 7.90 3.27 2.26 4.71 4.29 4.60 4.45
GRA 10.89 11.38 25.01 17.22 13.97 10.99 26.01 16.07
MF 3.74 4.67 2.05 2.02 2.92 3.24 2.98 3.08
SHR 13.34 27.98 12.52 11.2 9.26 21.31 10.31 16.26
WET 23.65 27.27 11.74 7.54 21.51 17.36 12.91 17.47
WSA 0.61 5.81 11.88 3.39 6.73 4.64 6.35 5.37
Overall 8.18 11.95 11.27 9.45 8.10 9.98 12.72 10.26
aDrought level was based on monthly values of 3 month Standard Precipitation Index (SPI): dry value were < −0.8; wet >
+0.8. Otherwise normal conditions existed.
bBiome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen
needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody
savanna; SHR, shrubland; WET, wetland.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
10 of 22
(Table 3). Only 20 models were available for this exercise;
MEAN and the optimized LoTEC were excluded. We used
S as it is bound by zero (no agreement) and unity (perfect
agreement) in contrast to NMAE and c2 which are unbound.
The Taylor skill metric (S) was first discretized into three
classes based on terciles. These classes, representing three
tiers of model‐data agreement, were then related to biome,
climatic season, drought level, site history, and model
structure using regression tree analysis (RTA) as a super-
vised classification algorithm. RTA is a form of binary
recursive partitioning [Breiman et al., 1984] that succes-
sively splits the data (Taylor skill classes as the response; all
other attributes as predictors) into subsets (nodes) by mini-
mizing within‐subset variation. The result is a pruned tree‐
like topology whereby predicted values (Taylor skill metric
class) are derived by a top‐to‐bottom traversal following the
rules (branches) that govern subset membership until a
predicted value is reached (terminal node). The splitting
rules at each node as well as its position allow for a cal-
culation of relative variable importance [Breiman et al.,
1984] with the most important variable given a score of
100. Variables of high importance were further analyzed
using conditional means, i.e., comparing mean values for
each predictor value, with statistical differences determined
using Bonferroni corrections for multiple comparisons
[Hochberg and Tamhane, 1987].
3. Results
3.1. Model‐Data Agreement Relative to Climatic
Season, Dryness, and Biome
[16] Overall agreement across n = 31025 months was
better in forested than nonforested biomes; both NMAE
(Table 4) and c2 values (Table 5) were closer to zero and
unity, respectively. At the biome level, model skill was
loosely ranked in five tiers: evergreen needleleaf forests in
the temperate zone, mixed forests > deciduous broadleaf
forests, evergreen needleleaf forests in the boreal zone >
grasslands, woody savannahs > croplands, shrublands,
wetlands > tundra. These rankings were robust across
models used in the majority of biomes, although some
divergence was apparent for croplands and shrublands
(Figure 1). Relative to seasonality and drought level models
were most consistent with observations during periods of
peak biological activity (climatic summer) and under dry
conditions (Figure 2). However, across the three levels of
dryness, changes in model‐data agreement were negligible
Table 4. Normalized Mean Absolute Error by Climatic Season, Drought Level, and Biomea
Biomeb
Climatic Season Drought Level
OverallWinter Spring Summer Fall Dry Normal Wet
CRO 1.90 4.64 −0.79 12.73 −1.43 −1.54 −1.59 −1.55
DBF 0.81 93.7 −0.52 −2.14 −1.01 −1.00 −0.95 −1.00
ENFB 1.52 −1.12 −0.69 −1.92 −0.87 −1.15 −3.43 −1.12
ENFT −6.34 −0.66 −0.50 −0.76 −0.63 −0.72 −0.63 −0.68
GRA −25.46 −0.84 −1.11 5.19 −1.52 −1.32 −3.07 −1.51
MF 1.10 −7.48 −0.47 57.70 −1.42 −1.04 −1.15 −1.12
SHR −87.37 −1.37 −3.03 −140.17 −1.82 −2.18 −41.13 −2.88
TUN −1.43 −11.07 −20.63 6.38 19.22 −24.06 −1.81 −20.15
WET 1.80 −5.07 −0.59 −4.72 −1.21 −1.20 −2.38 −1.27
WSA −2.73 −0.75 −1.47 10.56 −1.39 −1.32 −1.51 −1.37
Overall 2.42 −1.35 −0.61 −1.94 −0.97 −1.01 −1.00 −1.00
aDrought level was based on monthly values of 3 month Standard Precipitation Index (SPI): dry value were < −0.8; wet >
+0.8. Otherwise normal conditions existed.
bBiome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen
needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody
savanna; SHR, shrubland; TUN, tundra; WET, wetland.
Table 5. Reduced c2 Statistic by Climatic Season, Drought Level, and Biomea
Biomeb
Climatic Season Drought Level
OverallWinter Spring Summer Fall Dry Normal Wet
CRO 3.22 10.66 39.75 49.71 14.43 23.54 32.75 25.8
DBF 5.29 10.74 8.77 4.55 5.58 7.86 8.67 7.34
ENFB 21.25 17.75 4.98 6.61 11.64 12.02 18.51 12.61
ENFT 4.39 7.90 3.27 2.26 4.71 4.29 4.60 4.45
GRA 10.89 11.38 25.01 17.22 13.97 10.99 26.01 16.07
MF 3.74 4.67 2.05 2.02 2.92 3.24 2.98 3.08
SHR 13.34 27.98 12.52 11.2 9.26 21.31 10.31 16.26
WET 23.65 27.27 11.74 7.54 21.51 17.36 12.91 17.47
WSA 0.61 5.81 11.88 3.39 6.73 4.64 6.35 5.37
Overall 8.18 11.95 11.27 9.45 8.10 9.98 12.72 10.26
aDrought level was based on monthly values of 3 month Standard Precipitation Index (SPI): dry value were < −0.8; wet >
+0.8. Otherwise normal conditions existed.
bBiome codes: CRO, cropland; GRA, grassland; ENFB, evergreen needleleaf forest‐boreal zone; ENFT, evergreen
needleleaf forest‐temperate zone; DBF, deciduous broadleaf forest; MF, mixed (deciduous/evergreen) forest; WSA, woody
savanna; SHR, shrubland; WET, wetland.
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for NMAE (∼4% change, Table 4) but more pronounced for
c2 (from 8.10 to 12.72, Table 5). Averaged over just the
warm season (excluding climatic winter) dry conditions
were coincident with worse model‐data agreement, e.g.,
NMAE was −0.99, −0.91, and −0.84 for dry, normal, and
wet, respectively. In biomes with a clear seasonal cycle in
leaf area index (LAI) a loss of model skill occurred during
climatic spring and fall (Tables 4 and 5), especially for
NMAE.
3.2. Skill Metrics by Model
[17] Regardless of metric, model skill was highly variable.
Of the three model skill metrics, NMAE was related to both
Taylor skill and c2 (r = −0.65; p < 0.0001). Jointly, high
Taylor skill co‐occurred with NMAE and c2 values closer
to zero and unity, respectively (Figure 3). Across models
NMAE ranged from −0.42 of the overall mean observed
flux to −2.18 for LoTEC and DNDC, respectively. Values
of c2 varied from 2.17 to 29.87 for LoTEC and CN‐CLASS,
respectively. Alternatively, the degree of model‐data mis-
match (the distance between observations and simulations)
was at least 2.17 times the observational flux uncertainty.
Similarly, Taylor skill showed a high degree of scatter
(Figure 4), although two crop only models (SiBcrop and
AgroIBIS), LoTEC, and ISOLSM were more conservative
and showed a general high degree of consistency with
observations.
[18] Among crop models, SiBCrop and AgroIBIS per-
formed well, especially in climatic spring and during wet
conditions. In contrast, the crop only DNDC model exhibited
poor model‐data agreement with c2 > 15 in climatic spring
and summer as well as across all drought levels. Although
four crop only simulators were analyzed, the best agreement
in croplands (NMAE and c2 closer to zero and unity,
respectively) was achieved by SiB3 and Ecosys, models
used in multiple biomes. Based on all three skill metrics the
LoTEC model (NMAE = −0.42, c2 = 2.17, S = 0.95) was
most consistent with observations across all sites, dryness
levels, and climatic seasons. This platform was optimized
using a data assimilation technique, unique among model
runs evaluated here, and was applied at 10 sites. In addi-
tion, the mean model ensemble (MEAN) performed well
(NMAE = −0.74, c2 = 3.35, S = 0.80). For individual models
(n = 12) used at a wider range of sites (at least 24 sites),
model consistency with observations was highest for Ecosys
(NMAE = −0.69, c2 = 7.71, S = 0.94) and lowest for
CN‐CLASS (NMAE = −1.50, c2 = 29.87, S = 0.48).
[19] Site‐level model‐data agreement also showed a high
degree of variability (Figure 4). At three croplands sites
(US‐Ne1, US‐Ne2, and US‐Ne3) Taylor skill ranged
from zero to unity. Both NMAE and c2 exhibited similar
Figure 1. Normalized mean absolute error (NMAE) by biome for each model. Biomes in ascending
order based on model‐specific NMAE; biomes on the left show better average agreement with observa-
tions. NMAE is normalized by mean observed flux. Across all sites, seasons, and drought levels within a
given biome this value is negative (NEE < 0), indicating a sink. NMAE values closer to zero coincide
with a higher degree of model‐data agreement. Woody savannahs and shrublands not shown: only one
site each. Tundra (n = 2 sites) has NMAE < −10 for all models. CN‐CLASS croplands value is off‐scale
(= −8.98). Black cross, no observations; white circle, undersampled (n < 100 months).
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c2 (from 8.10 to 12.72, Table 5). Averaged over just the
warm season (excluding climatic winter) dry conditions
were coincident with worse model‐data agreement, e.g.,
NMAE was −0.99, −0.91, and −0.84 for dry, normal, and
wet, respectively. In biomes with a clear seasonal cycle in
leaf area index (LAI) a loss of model skill occurred during
climatic spring and fall (Tables 4 and 5), especially for
NMAE.
3.2. Skill Metrics by Model
[17] Regardless of metric, model skill was highly variable.
Of the three model skill metrics, NMAE was related to both
Taylor skill and c2 (r = −0.65; p < 0.0001). Jointly, high
Taylor skill co‐occurred with NMAE and c2 values closer
to zero and unity, respectively (Figure 3). Across models
NMAE ranged from −0.42 of the overall mean observed
flux to −2.18 for LoTEC and DNDC, respectively. Values
of c2 varied from 2.17 to 29.87 for LoTEC and CN‐CLASS,
respectively. Alternatively, the degree of model‐data mis-
match (the distance between observations and simulations)
was at least 2.17 times the observational flux uncertainty.
Similarly, Taylor skill showed a high degree of scatter
(Figure 4), although two crop only models (SiBcrop and
AgroIBIS), LoTEC, and ISOLSM were more conservative
and showed a general high degree of consistency with
observations.
[18] Among crop models, SiBCrop and AgroIBIS per-
formed well, especially in climatic spring and during wet
conditions. In contrast, the crop only DNDC model exhibited
poor model‐data agreement with c2 > 15 in climatic spring
and summer as well as across all drought levels. Although
four crop only simulators were analyzed, the best agreement
in croplands (NMAE and c2 closer to zero and unity,
respectively) was achieved by SiB3 and Ecosys, models
used in multiple biomes. Based on all three skill metrics the
LoTEC model (NMAE = −0.42, c2 = 2.17, S = 0.95) was
most consistent with observations across all sites, dryness
levels, and climatic seasons. This platform was optimized
using a data assimilation technique, unique among model
runs evaluated here, and was applied at 10 sites. In addi-
tion, the mean model ensemble (MEAN) performed well
(NMAE = −0.74, c2 = 3.35, S = 0.80). For individual models
(n = 12) used at a wider range of sites (at least 24 sites),
model consistency with observations was highest for Ecosys
(NMAE = −0.69, c2 = 7.71, S = 0.94) and lowest for
CN‐CLASS (NMAE = −1.50, c2 = 29.87, S = 0.48).
[19] Site‐level model‐data agreement also showed a high
degree of variability (Figure 4). At three croplands sites
(US‐Ne1, US‐Ne2, and US‐Ne3) Taylor skill ranged
from zero to unity. Both NMAE and c2 exhibited similar
Figure 1. Normalized mean absolute error (NMAE) by biome for each model. Biomes in ascending
order based on model‐specific NMAE; biomes on the left show better average agreement with observa-
tions. NMAE is normalized by mean observed flux. Across all sites, seasons, and drought levels within a
given biome this value is negative (NEE < 0), indicating a sink. NMAE values closer to zero coincide
with a higher degree of model‐data agreement. Woody savannahs and shrublands not shown: only one
site each. Tundra (n = 2 sites) has NMAE < −10 for all models. CN‐CLASS croplands value is off‐scale
(= −8.98). Black cross, no observations; white circle, undersampled (n < 100 months).
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Page 12
scatter by site (not shown). Even for the best predicted site
(US‐Syv), S ranged from 0.19 to 0.95. Only two forested
sites (CA‐Qfo and CA‐TP4) were predicted well (S > 0.5)
by all models; whereas only one tundra site (US‐Atq) was
consistently poorly predicted (S < 0.5). Despite the wide
range in model performance, model skill (NMAE, c2, and S)
was not correlated with the number of sites (p > 0.5) or
biomes (p > 0.3) simulated, i.e., using a more general rather
than a specialized model did not result in a loss in model
performance. Also, model‐data agreement was not better at
sites with longer data records (p > 0.1).
[20] The steady state protocol had negligible effect on
model skill. Long‐term simulated NEE by site and model
varied from −2904 to 2227 g C m−2 yr−1 with 90% of all
values between −600 and 100 g C m−2 yr−1. The extreme
values were primarily croplands simulated outside of crop
only models. Overall, only 5 models achieved steady state
(simulated NEE→ 0) over the full simulation: Biome‐BGC,
LPJ, SiBCASA, SiB3, and TECO. Similar to simulated
values, observed annual integrals at the 44 sites examined
did not show steady state (Table 1) and varied from −718 to
571 g C m−2 yr−1. Nonetheless, model skill was not related
to how close model spinup and initial conditions approxi-
mated steady state or how close a given site was to an
observed NEE of zero. All three skill metrics were uncor-
related with long‐term observed or simulated average
annual NEE (p > 0.05). However, two models did show
significant relationships: For Ecosys, c2 increased (decrease
in model skill) and S decreased as observed or simulated
NEE approached zero; a system closer to steady state was
coincident with less model‐data agreement. BEPS was
similar, showing lower S and more negative NMAE
(decrease in model skill) for sites closer to steady state.
3.3. Model and Site‐Specific Consistency With
Observations Using Taylor Diagrams
[21] Average model performance (both across‐site and
across‐model) was evaluated using Taylor diagrams based
on all simulated and observed monthly NEE data. Better
model performance was indicated by proximity to the
benchmark, representing the observed state. The benchmark
was normalized by observed standard deviation such that the
distance of s and RMSE from the benchmark was in
observed s units. Similar to model skill metrics, forested
sites were better predicted than nonforested ones. The
MEAN model showed r ≥ 0.2, apart from CA‐SJ2 and
US‐Atq, but generally (33 of 44 sites) underpredicted
the variability associated with monthly NEE at forested
(Figure 5) and nonforested (Figure 6) sites. Similarly, 40 of
44 sites were predicted with RMSE < s. Also 8 (6 forested
and two croplands sites: CA‐Obs, CA‐Qfo, CA‐TP4,
US‐Ho1, US‐IB1, US‐MMS, US‐Ne3, US‐UMB) of the
Figure 2. Normalized mean absolute error (NMAE) by climatic season and drought level. NMAE is nor-
malized by mean observed flux such that most values are negative (NEE < 0), indicating a sink.
Positive values indicate a source (NEE > 0). These occur in winter for all models as well as spring
and fall for all crop only models: AgroIBIS, DNDC, EPIC, SiBcrop. Such values are displayed on the
same color bar but with opposite sign. Off‐scale values: AgroIBIS and SiBcrop in fall are −7.1 and
−11.1, respectively. DNDC in fall and spring is −11.4 and −8.7, respectively. Black cross, no observations;
white circle, undersampled (n < 100 months).
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(US‐Syv), S ranged from 0.19 to 0.95. Only two forested
sites (CA‐Qfo and CA‐TP4) were predicted well (S > 0.5)
by all models; whereas only one tundra site (US‐Atq) was
consistently poorly predicted (S < 0.5). Despite the wide
range in model performance, model skill (NMAE, c2, and S)
was not correlated with the number of sites (p > 0.5) or
biomes (p > 0.3) simulated, i.e., using a more general rather
than a specialized model did not result in a loss in model
performance. Also, model‐data agreement was not better at
sites with longer data records (p > 0.1).
[20] The steady state protocol had negligible effect on
model skill. Long‐term simulated NEE by site and model
varied from −2904 to 2227 g C m−2 yr−1 with 90% of all
values between −600 and 100 g C m−2 yr−1. The extreme
values were primarily croplands simulated outside of crop
only models. Overall, only 5 models achieved steady state
(simulated NEE→ 0) over the full simulation: Biome‐BGC,
LPJ, SiBCASA, SiB3, and TECO. Similar to simulated
values, observed annual integrals at the 44 sites examined
did not show steady state (Table 1) and varied from −718 to
571 g C m−2 yr−1. Nonetheless, model skill was not related
to how close model spinup and initial conditions approxi-
mated steady state or how close a given site was to an
observed NEE of zero. All three skill metrics were uncor-
related with long‐term observed or simulated average
annual NEE (p > 0.05). However, two models did show
significant relationships: For Ecosys, c2 increased (decrease
in model skill) and S decreased as observed or simulated
NEE approached zero; a system closer to steady state was
coincident with less model‐data agreement. BEPS was
similar, showing lower S and more negative NMAE
(decrease in model skill) for sites closer to steady state.
3.3. Model and Site‐Specific Consistency With
Observations Using Taylor Diagrams
[21] Average model performance (both across‐site and
across‐model) was evaluated using Taylor diagrams based
on all simulated and observed monthly NEE data. Better
model performance was indicated by proximity to the
benchmark, representing the observed state. The benchmark
was normalized by observed standard deviation such that the
distance of s and RMSE from the benchmark was in
observed s units. Similar to model skill metrics, forested
sites were better predicted than nonforested ones. The
MEAN model showed r ≥ 0.2, apart from CA‐SJ2 and
US‐Atq, but generally (33 of 44 sites) underpredicted
the variability associated with monthly NEE at forested
(Figure 5) and nonforested (Figure 6) sites. Similarly, 40 of
44 sites were predicted with RMSE < s. Also 8 (6 forested
and two croplands sites: CA‐Obs, CA‐Qfo, CA‐TP4,
US‐Ho1, US‐IB1, US‐MMS, US‐Ne3, US‐UMB) of the
Figure 2. Normalized mean absolute error (NMAE) by climatic season and drought level. NMAE is nor-
malized by mean observed flux such that most values are negative (NEE < 0), indicating a sink.
Positive values indicate a source (NEE > 0). These occur in winter for all models as well as spring
and fall for all crop only models: AgroIBIS, DNDC, EPIC, SiBcrop. Such values are displayed on the
same color bar but with opposite sign. Off‐scale values: AgroIBIS and SiBcrop in fall are −7.1 and
−11.1, respectively. DNDC in fall and spring is −11.4 and −8.7, respectively. Black cross, no observations;
white circle, undersampled (n < 100 months).
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Page 13
44 sites were predicted with r ≥ 0.95 and RMSE < 1. The
worst predicted site was CA‐SJ2 with r = −0.67, s = 4.3,
and RMSE = 5.1.
[22] Overall model performance, aggregated across sites,
was similar (Figure 7). Most models underpredicted vari-
ability and showed RMSE < s. Of all 22 models only
DNDC exhibited r < 0.2. Based on proximity to the
benchmark, i.e., a high S value (Figure 3), the best models
were: EPIC (crop only model used on one site), ISOLSM
(used on 9 sites), LoTEC (data assimilation model), SiBcrop
and AgroIBIS (crop only models), EDCM (used on
10 sites), Ecosys and SiBCASA (models used on most sites,
39 and 35, respectively), and MEAN (mean model ensemble
for all 44 sites). All of these “best” models had r > 0.75,
RMSE < 0.75 and slightly underpredicted variability; except
the crop only models and Ecosys where variability was
overpredicted. Models whose average behavior was furthest
away from the benchmark were DNDC followed by BEPS.
3.4. Links Between Model Skill, Model Structure,
and Site History
[23] Biome classification was the most important factor in
the distribution of model skill (Figure 8) sampled across all
combinations of site, model, climatic season, and drought
(n = 3132 groups). Climatic season and stand age, the
highest scored site‐specific attribute, followed biome as lead
determinants of model skill. Of the 12 evaluated site dis-
turbances (Table 3) only grazing, which occurred on crop-
lands, grasslands, and woody savannahs, achieved an
importance score of at least 25. Apart from drought and
grazing activity, the remaining determinants were model‐
specific: the number of soil layers, vegetation pools, canopy
phenology, and soil pools. Two carbon flux calculations
also had a variable score > 25, with NEE being the highest.
[24] Comparing mean S for these relatively important
model attributes (Figure 9) revealed three instances where
model structure showed a statistically significant relation-
ship with model skill: prescribed canopy phenology, a daily
time step, and calculating NEE as the difference between
GPP and ecosystem respiration. Models using canopy
characteristics and phenology prescribed from remotely
sensed products achieved higher skill (S = 0.54) than either
prognostic or semiprognostic models (S = 0.43; p < 0.05).
Using a daily time step showed lower model skill (S = 0.40)
relative to nondaily time steps (S = 0.50; p < 0.05). Finally,
calculating NEE as the difference between GPP and total
ecosystem respiration showed greater skill (S = 0.50) than
other calculation methods (S = 0.42; p < 0.05). None of the
other model attributes we studied showed statistically sig-
nificant relationships between model structure and skill.
[25] While not statistically significant, both vegetation
pools and soil layers exhibited a weak pattern whereby the
simplest and most complex models showed higher skill than
models of intermediate complexity (Figure 9). Models with
no soil model (zero soil layers) or no vegetation pools
showed greater skill than models with the simplest soil
model or smallest number of vegetation pools. As the
number of soil layers or pools increased, so did model skill,
indicating that a more comprehensive treatment of biologi-
cal and physical processes can improve model skill. For
vegetation pools, there was a limit where increased com-
Figure 3. Model skill metrics for all 22 models. Skill metrics are Taylor skill (S; equation (3)),
normalized mean absolute error (NMAE), and reduced c2 statistic (c2). Better model‐data agreement
corresponds to the upper left corner. Benchmark represents perfect model‐data agreement: S = 1,NMAE = 0,
and c2 = 1. Gray interpolated surface added and model names jittered to improve readability.
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worst predicted site was CA‐SJ2 with r = −0.67, s = 4.3,
and RMSE = 5.1.
[22] Overall model performance, aggregated across sites,
was similar (Figure 7). Most models underpredicted vari-
ability and showed RMSE < s. Of all 22 models only
DNDC exhibited r < 0.2. Based on proximity to the
benchmark, i.e., a high S value (Figure 3), the best models
were: EPIC (crop only model used on one site), ISOLSM
(used on 9 sites), LoTEC (data assimilation model), SiBcrop
and AgroIBIS (crop only models), EDCM (used on
10 sites), Ecosys and SiBCASA (models used on most sites,
39 and 35, respectively), and MEAN (mean model ensemble
for all 44 sites). All of these “best” models had r > 0.75,
RMSE < 0.75 and slightly underpredicted variability; except
the crop only models and Ecosys where variability was
overpredicted. Models whose average behavior was furthest
away from the benchmark were DNDC followed by BEPS.
3.4. Links Between Model Skill, Model Structure,
and Site History
[23] Biome classification was the most important factor in
the distribution of model skill (Figure 8) sampled across all
combinations of site, model, climatic season, and drought
(n = 3132 groups). Climatic season and stand age, the
highest scored site‐specific attribute, followed biome as lead
determinants of model skill. Of the 12 evaluated site dis-
turbances (Table 3) only grazing, which occurred on crop-
lands, grasslands, and woody savannahs, achieved an
importance score of at least 25. Apart from drought and
grazing activity, the remaining determinants were model‐
specific: the number of soil layers, vegetation pools, canopy
phenology, and soil pools. Two carbon flux calculations
also had a variable score > 25, with NEE being the highest.
[24] Comparing mean S for these relatively important
model attributes (Figure 9) revealed three instances where
model structure showed a statistically significant relation-
ship with model skill: prescribed canopy phenology, a daily
time step, and calculating NEE as the difference between
GPP and ecosystem respiration. Models using canopy
characteristics and phenology prescribed from remotely
sensed products achieved higher skill (S = 0.54) than either
prognostic or semiprognostic models (S = 0.43; p < 0.05).
Using a daily time step showed lower model skill (S = 0.40)
relative to nondaily time steps (S = 0.50; p < 0.05). Finally,
calculating NEE as the difference between GPP and total
ecosystem respiration showed greater skill (S = 0.50) than
other calculation methods (S = 0.42; p < 0.05). None of the
other model attributes we studied showed statistically sig-
nificant relationships between model structure and skill.
[25] While not statistically significant, both vegetation
pools and soil layers exhibited a weak pattern whereby the
simplest and most complex models showed higher skill than
models of intermediate complexity (Figure 9). Models with
no soil model (zero soil layers) or no vegetation pools
showed greater skill than models with the simplest soil
model or smallest number of vegetation pools. As the
number of soil layers or pools increased, so did model skill,
indicating that a more comprehensive treatment of biologi-
cal and physical processes can improve model skill. For
vegetation pools, there was a limit where increased com-
Figure 3. Model skill metrics for all 22 models. Skill metrics are Taylor skill (S; equation (3)),
normalized mean absolute error (NMAE), and reduced c2 statistic (c2). Better model‐data agreement
corresponds to the upper left corner. Benchmark represents perfect model‐data agreement: S = 1,NMAE = 0,
and c2 = 1. Gray interpolated surface added and model names jittered to improve readability.
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Page 14
plexity beyond eight pools did not improve model‐data
agreement.
[26] Despite these effects, model attributes were of sec-
ondary importance. The change in S relative to biome varied
from 0.28 to 0.55; a much larger range than seen for model
attributes. Similarly, the high variable importance scores for
biome and climatic season, as well as the lower score for
drought level, corroborated the relationships between these
factors and model skill as seen with NMAE and c2. While
the regression tree algorithm achieved an accuracy of 68.5%
for predicting Taylor skill class, the site history and model
characteristics considered here did not explain the underly-
ing cause of biome and seasonal differences in model skill.
4. Discussion
4.1. Effect of Parameter Sets on Model Performance
[27] Model parameter sets are a large source of variability
in terms of model performance [Jung et al., 2007b]. They
influence output and accuracy [Grant et al., 2005] and are
more important for accurately simulating CO2 exchange
than capturing effects of interannual climatic variability
[Amthor et al., 2001]. For at least some of the models
studied here this can be related to the use of biome‐specific
parameters relative to within‐biome variability [Purves and
Pacala, 2008]. A corollary occurs in the context of EC
observations as tower footprints can exhibit heterogeneity,
particularly in soils, that is not reproduced in site‐specific
parameters [Amthor et al., 2001].
[28] The importance of model parameter sets was visible
in this intercomparison in two ways. First, biome had the
highest variable importance score. Insomuch as models rely
on biome‐specific parameter values, this finding indicates
that model parameter sets are a key factor in the distribution
of model skill. This extends to plant functional types due to
the high degree of overlap between both. Furthermore, the
variability (Figure 4) in model skill across parameter sets, i.e.,
across models, underscores that biomes may be too het-
erogeneous in time [Stoy et al., 2005, 2009] and space to be
well‐represented by constant parameters relative to, e.g.,
within‐biome climate variability [Hargrove et al., 2003].
Second, the general high degree of site‐specific variation in
model skill (Figure 4) suggested that model parameter sets
may need to be refined to capture local, site‐specific realities.
4.2. Effect of Model Structure on Model Performance
[29] In general, models with the highest model‐data
agreement all used prescribed canopy phenology, calculated
NEE as the difference between GPP and ecosystem respira-
tion, and did not use a daily time step. Models that exhibited
all of these structural characteristics (SiBCASA, SiB3, and
ISOLSM) showed high degrees of model‐data agreement
across all three skill metrics. Similarly, Ecosys, which used
a prognostic canopy but otherwise had similar structural
characteristics as SiBCASA, also performed well. Relative
to model complexity, consistency with observations was
highest in those models with either the simplest structure
(e.g., one soil carbon pool in ISOLSM) or the most complex
(e.g., SiBCASA with 13 carbon pools). Models with a
prognostic canopy seem to perform better with more carbon
pools and soil layers (e.g., Ecosys). No model with a
prognostic canopy and a low number of carbon pools and
soil layers placed in the top tercile of model skill for any
skill metric, except SiBcrop and AgroIBIS for Taylor skill in
croplands. Using multimodel ensembles (MEAN) or data
assimilation to optimize model parameter sets (LoTEC) can
compensate for differences in model structure to improve
model skill.
[30] The relationships between model structure and model
skill were consistent across all biomes. As a whole, the
models performed better at forested sites than nonforested
sites, but the same models showed the highest consistency
Figure 4. Boxplots of Taylor skill by model and site. Taylor
skill (S; equation (3)) is a single value summary of a Taylor
diagram where unity indicates perfect agreement with
observations. Panels show interquartile range (blue box),
median (solid red line), range (whiskers), and outliers (red
cross; values more than 1.5 × interquartile range from the
median). (top) Only models (n = 21) used on at least two
sites shown. (bottom) Only sites (n = 32) simulated with at
least 10 unique models, excluding the mean model ensemble
(MEAN) and the assimilated LoTEC, shown. Models and
sites sorted by median Taylor skill.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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agreement.
[26] Despite these effects, model attributes were of sec-
ondary importance. The change in S relative to biome varied
from 0.28 to 0.55; a much larger range than seen for model
attributes. Similarly, the high variable importance scores for
biome and climatic season, as well as the lower score for
drought level, corroborated the relationships between these
factors and model skill as seen with NMAE and c2. While
the regression tree algorithm achieved an accuracy of 68.5%
for predicting Taylor skill class, the site history and model
characteristics considered here did not explain the underly-
ing cause of biome and seasonal differences in model skill.
4. Discussion
4.1. Effect of Parameter Sets on Model Performance
[27] Model parameter sets are a large source of variability
in terms of model performance [Jung et al., 2007b]. They
influence output and accuracy [Grant et al., 2005] and are
more important for accurately simulating CO2 exchange
than capturing effects of interannual climatic variability
[Amthor et al., 2001]. For at least some of the models
studied here this can be related to the use of biome‐specific
parameters relative to within‐biome variability [Purves and
Pacala, 2008]. A corollary occurs in the context of EC
observations as tower footprints can exhibit heterogeneity,
particularly in soils, that is not reproduced in site‐specific
parameters [Amthor et al., 2001].
[28] The importance of model parameter sets was visible
in this intercomparison in two ways. First, biome had the
highest variable importance score. Insomuch as models rely
on biome‐specific parameter values, this finding indicates
that model parameter sets are a key factor in the distribution
of model skill. This extends to plant functional types due to
the high degree of overlap between both. Furthermore, the
variability (Figure 4) in model skill across parameter sets, i.e.,
across models, underscores that biomes may be too het-
erogeneous in time [Stoy et al., 2005, 2009] and space to be
well‐represented by constant parameters relative to, e.g.,
within‐biome climate variability [Hargrove et al., 2003].
Second, the general high degree of site‐specific variation in
model skill (Figure 4) suggested that model parameter sets
may need to be refined to capture local, site‐specific realities.
4.2. Effect of Model Structure on Model Performance
[29] In general, models with the highest model‐data
agreement all used prescribed canopy phenology, calculated
NEE as the difference between GPP and ecosystem respira-
tion, and did not use a daily time step. Models that exhibited
all of these structural characteristics (SiBCASA, SiB3, and
ISOLSM) showed high degrees of model‐data agreement
across all three skill metrics. Similarly, Ecosys, which used
a prognostic canopy but otherwise had similar structural
characteristics as SiBCASA, also performed well. Relative
to model complexity, consistency with observations was
highest in those models with either the simplest structure
(e.g., one soil carbon pool in ISOLSM) or the most complex
(e.g., SiBCASA with 13 carbon pools). Models with a
prognostic canopy seem to perform better with more carbon
pools and soil layers (e.g., Ecosys). No model with a
prognostic canopy and a low number of carbon pools and
soil layers placed in the top tercile of model skill for any
skill metric, except SiBcrop and AgroIBIS for Taylor skill in
croplands. Using multimodel ensembles (MEAN) or data
assimilation to optimize model parameter sets (LoTEC) can
compensate for differences in model structure to improve
model skill.
[30] The relationships between model structure and model
skill were consistent across all biomes. As a whole, the
models performed better at forested sites than nonforested
sites, but the same models showed the highest consistency
Figure 4. Boxplots of Taylor skill by model and site. Taylor
skill (S; equation (3)) is a single value summary of a Taylor
diagram where unity indicates perfect agreement with
observations. Panels show interquartile range (blue box),
median (solid red line), range (whiskers), and outliers (red
cross; values more than 1.5 × interquartile range from the
median). (top) Only models (n = 21) used on at least two
sites shown. (bottom) Only sites (n = 32) simulated with at
least 10 unique models, excluding the mean model ensemble
(MEAN) and the assimilated LoTEC, shown. Models and
sites sorted by median Taylor skill.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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Page 15
with observations in each biome (Ecosys and SiB3). This is
true even for agriculture sites, where Ecosys and SiB3
scored as high as crop only models. This suggests that any
model with requisite structural attributes can successfully
simulate carbon flux in all types of ecosystems.
4.3. Links Between Model Performance and
Environmental Factors
[31] Model skill was only weakly linked to drought,
showing high variability across dryness level by biome and
model. Only during the warm season (all climatic seasons
excluding winter) did aggregate model skill decline under
drought conditions. While this points to process uncertainty
[Sitch et al., 2008], ecosystem response to longer‐term
drought can exhibit lags and positive feedbacks [Arnone
et al., 2008; Granier et al., 2007; Thomas et al., 2009;
Williams et al., 2009] that were not explicitly included in the
drought metric used here but did influence simulation
behavior through model structure, e.g., soil moisture model
and soil resolution.
[32] In spring and fall, especially for biomes with a sig-
nificant deciduous component, models showed a decline in
model skill (Table 4) relative to periods of peak biological
activity (climatic summer) [see also Morales et al., 2005].
While this was more pronounced for NMAE (Table 4) than
c2 (Table 5), phenological cues are known to influence the
annual carbon balance at multiple scales [Barr et al., 2007;
Delpierre et al., 2009; Keeling et al., 1996]. The loss of
model skill seen in this study during spring and fall was
likely linked to poor treatment of leaf initiation and senes-
cence as well as season‐specific effects of soil moisture and
soil temperature on canopy photosynthesis [Hanson et al.,
2004]. In this study seasonality was second only to biome
in driving model skill (Figure 8). This and the lack of link
between model skill and site history strongly implicate
phenology as a needed refinement of terrestrial biosphere
simulators.
[33] The evergreen needleleaf forest biome diverged in
performance based on whether the sites were located in the
temperate or boreal zones. A similar divergence was reported
using Biome‐BGC, LPJ, and ORCHIDEE to simulate gross
CO2 uptake across a temperature gradient in Europe [Jung
et al., 2007a]; average relative RMSE was higher for
evergreen needleleaf forests in the boreal zone. This was
linked to an overestimation of LAI at the boreal sites and
relationships between resource availability and leaf area
[Friedlingstein et al., 2006; Jung et al., 2007a; Sitch et al.,
2008]. Additionally, recent observations in the circumboreal
region, where all boreal evergreen needleleaf forested sites
are located, suggest that transient effects of climate change,
e.g., increased severity and intensity of natural disturbances
(fire, pest outbreaks) and divergence from climate normals
in temperature, have already occurred [Soja et al., 2007] and
influence resource availability. We speculate the loss of
Figure 5. Taylor diagram of normalized mean model performance for forested sites. Each circle (n =
26 sites) is the site‐specific mean model ensemble (MEAN). Benchmark (red square) corresponds to
observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding
of site letter and circles indicates biome: evergreen needleleaf forest‐ temperate zone (red), deciduous
broadleaf forest (brown), mixed (deciduous/evergreen) forest (blue), evergreen needleleaf forest‐boreal
zone (black). Outlying sites (evergreen needleleaf forest‐boreal zone) not shown: CA‐SJ1 (r = 0.81,
s = 3.9, RMSE = 3.1) and CA‐SJ2 (r = −0.67, s = 4.3, RMSE = 5.1).
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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true even for agriculture sites, where Ecosys and SiB3
scored as high as crop only models. This suggests that any
model with requisite structural attributes can successfully
simulate carbon flux in all types of ecosystems.
4.3. Links Between Model Performance and
Environmental Factors
[31] Model skill was only weakly linked to drought,
showing high variability across dryness level by biome and
model. Only during the warm season (all climatic seasons
excluding winter) did aggregate model skill decline under
drought conditions. While this points to process uncertainty
[Sitch et al., 2008], ecosystem response to longer‐term
drought can exhibit lags and positive feedbacks [Arnone
et al., 2008; Granier et al., 2007; Thomas et al., 2009;
Williams et al., 2009] that were not explicitly included in the
drought metric used here but did influence simulation
behavior through model structure, e.g., soil moisture model
and soil resolution.
[32] In spring and fall, especially for biomes with a sig-
nificant deciduous component, models showed a decline in
model skill (Table 4) relative to periods of peak biological
activity (climatic summer) [see also Morales et al., 2005].
While this was more pronounced for NMAE (Table 4) than
c2 (Table 5), phenological cues are known to influence the
annual carbon balance at multiple scales [Barr et al., 2007;
Delpierre et al., 2009; Keeling et al., 1996]. The loss of
model skill seen in this study during spring and fall was
likely linked to poor treatment of leaf initiation and senes-
cence as well as season‐specific effects of soil moisture and
soil temperature on canopy photosynthesis [Hanson et al.,
2004]. In this study seasonality was second only to biome
in driving model skill (Figure 8). This and the lack of link
between model skill and site history strongly implicate
phenology as a needed refinement of terrestrial biosphere
simulators.
[33] The evergreen needleleaf forest biome diverged in
performance based on whether the sites were located in the
temperate or boreal zones. A similar divergence was reported
using Biome‐BGC, LPJ, and ORCHIDEE to simulate gross
CO2 uptake across a temperature gradient in Europe [Jung
et al., 2007a]; average relative RMSE was higher for
evergreen needleleaf forests in the boreal zone. This was
linked to an overestimation of LAI at the boreal sites and
relationships between resource availability and leaf area
[Friedlingstein et al., 2006; Jung et al., 2007a; Sitch et al.,
2008]. Additionally, recent observations in the circumboreal
region, where all boreal evergreen needleleaf forested sites
are located, suggest that transient effects of climate change,
e.g., increased severity and intensity of natural disturbances
(fire, pest outbreaks) and divergence from climate normals
in temperature, have already occurred [Soja et al., 2007] and
influence resource availability. We speculate the loss of
Figure 5. Taylor diagram of normalized mean model performance for forested sites. Each circle (n =
26 sites) is the site‐specific mean model ensemble (MEAN). Benchmark (red square) corresponds to
observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding
of site letter and circles indicates biome: evergreen needleleaf forest‐ temperate zone (red), deciduous
broadleaf forest (brown), mixed (deciduous/evergreen) forest (blue), evergreen needleleaf forest‐boreal
zone (black). Outlying sites (evergreen needleleaf forest‐boreal zone) not shown: CA‐SJ1 (r = 0.81,
s = 3.9, RMSE = 3.1) and CA‐SJ2 (r = −0.67, s = 4.3, RMSE = 5.1).
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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Page 16
model skill in boreal relative to temperate evergreen
needleleaf forests was linked to insufficient characterization
of cold temperature sensitivity of metabolic processes and
water flow in plants as well as freeze‐thaw dynamics
[Schaefer et al., 2007, 2009] and that this was exacerbated
by the effects of transient climate change.
4.4. Effects of Site History and Protocol on Model
Evaluation
[34] Disturbance regime and how a model treats distur-
bance are known to impact model performance [Ito, 2008].
In this study, stand age impacted model skill whereas site
history was of marginal importance (Figure 8). However,
CA‐SJ2, the worst predicted site (Figure 5), was harvested
in 2000 and scarified in 2002, and US‐SO2, a second
poorly predicted shrubland site (Figure 6), suffered cata-
strophic wildfire during the analysis period. The poor model
performance for recently disturbed sites followed from
assumed steady state as used in some simulations and the
absence of modeling logic to accommodate disturbance.
However, the distribution of site history metrics was
skewed; only few sites were burned, harvested, or in the
early stages of recovery from disturbance when NEE is
more nonlinear relative to established stands. Furthermore,
age class was biased toward older stands; of the 17 forested
sites only one was classified as a young stand. Other site
characteristics were also unbalanced; all nonforested biomes
occurred on five or less sites; with only one site each for
shrublands and woody savannahs. While regression trees are
inherently robust, additional observed and simulated fluxes
in rapidly growing young forested stands, recently burned or
harvested sites, and undersampled biomes are desirable to
better characterize model performance.
[35] Aspects of the NACP site synthesis protocol and
analysis framework also influenced the interpretation of our
results. First, this analysis focused solely on non‐gap‐filled
data to allow the model‐data intercomparison to inform
model development. However, the low turbulence (friction
velocity) filtering removed more data at night than during
the day. Average data coverage across all sites was 82% for
daytime and 39% at night, respectively (Table 2), so our
analysis is skewed toward daytime conditions. Second, each
model that used remotely sensed inputs (such as LAI)
repeated an average seasonal cycle calculated from site‐
specific time series based on all pixels within 1 km of the
tower site. This likely deflated relevant variable importance
scores (Figure 8) and precluded a full comparison of pre-
scribed versus prognostic LAI. While only few models used
such inputs (Table 1), removing the inherent bias of an
invariant seasonal cycle over multiple years may improve
model performance. Incorporating disturbance information
to recreate historical land use and disturbance, especially for
recent site entries, could also improve model performance.
Last, despite the model simulation protocol’s emphasis on
steady state, this condition was not achieved for most sites
(Table 2), even when discounting observational uncertainty,
Figure 6. Taylor diagram of normalized mean model performance for nonforested sites. Each circle (n =
16 sites) is the site‐specific mean model ensemble (MEAN). Benchmark (red square) corresponds to
observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding
of site letter and circles indicates biome: croplands (red), grasslands (brown), wetlands (blue), all other
biomes (black).
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16 of 22
needleleaf forests was linked to insufficient characterization
of cold temperature sensitivity of metabolic processes and
water flow in plants as well as freeze‐thaw dynamics
[Schaefer et al., 2007, 2009] and that this was exacerbated
by the effects of transient climate change.
4.4. Effects of Site History and Protocol on Model
Evaluation
[34] Disturbance regime and how a model treats distur-
bance are known to impact model performance [Ito, 2008].
In this study, stand age impacted model skill whereas site
history was of marginal importance (Figure 8). However,
CA‐SJ2, the worst predicted site (Figure 5), was harvested
in 2000 and scarified in 2002, and US‐SO2, a second
poorly predicted shrubland site (Figure 6), suffered cata-
strophic wildfire during the analysis period. The poor model
performance for recently disturbed sites followed from
assumed steady state as used in some simulations and the
absence of modeling logic to accommodate disturbance.
However, the distribution of site history metrics was
skewed; only few sites were burned, harvested, or in the
early stages of recovery from disturbance when NEE is
more nonlinear relative to established stands. Furthermore,
age class was biased toward older stands; of the 17 forested
sites only one was classified as a young stand. Other site
characteristics were also unbalanced; all nonforested biomes
occurred on five or less sites; with only one site each for
shrublands and woody savannahs. While regression trees are
inherently robust, additional observed and simulated fluxes
in rapidly growing young forested stands, recently burned or
harvested sites, and undersampled biomes are desirable to
better characterize model performance.
[35] Aspects of the NACP site synthesis protocol and
analysis framework also influenced the interpretation of our
results. First, this analysis focused solely on non‐gap‐filled
data to allow the model‐data intercomparison to inform
model development. However, the low turbulence (friction
velocity) filtering removed more data at night than during
the day. Average data coverage across all sites was 82% for
daytime and 39% at night, respectively (Table 2), so our
analysis is skewed toward daytime conditions. Second, each
model that used remotely sensed inputs (such as LAI)
repeated an average seasonal cycle calculated from site‐
specific time series based on all pixels within 1 km of the
tower site. This likely deflated relevant variable importance
scores (Figure 8) and precluded a full comparison of pre-
scribed versus prognostic LAI. While only few models used
such inputs (Table 1), removing the inherent bias of an
invariant seasonal cycle over multiple years may improve
model performance. Incorporating disturbance information
to recreate historical land use and disturbance, especially for
recent site entries, could also improve model performance.
Last, despite the model simulation protocol’s emphasis on
steady state, this condition was not achieved for most sites
(Table 2), even when discounting observational uncertainty,
Figure 6. Taylor diagram of normalized mean model performance for nonforested sites. Each circle (n =
16 sites) is the site‐specific mean model ensemble (MEAN). Benchmark (red square) corresponds to
observed normalized monthly NEE; units of s and RMSE are multiples of observed s. Color coding
of site letter and circles indicates biome: croplands (red), grasslands (brown), wetlands (blue), all other
biomes (black).
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
16 of 22
Page 17
Figure 8. Variable importance scores for model‐specific (blue) and site‐specific (green) predictors.
Scores were generated from a regression tree with the Taylor skill classes based on terciles (n = 3132)
as the response. Only the 12 of 28 predictants with score > 25 shown; see Table 3 for complete listing
of evaluated model structural and site attributes.
Figure 7. Taylor diagram of normalized across‐site average model performance. Model s and RMSE
were normalized by observed s. Each circle (n = 22 models) corresponds to the mean across all sites.
Benchmark (red square) corresponds to observed normalized monthly NEE; units of s and RMSE are
multiples of observed s. Color coding of model letter and circles indicates generality of model perfor-
mance: specialist models used only in croplands (n ≤ 5 sites; black), generalist models used across a range
of biomes and sites (n ≥ 30 sites, blue), all other models (red). The correlation for DNDC (r = −0.13) is
displayed as zero for readability.
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17 of 22
Scores were generated from a regression tree with the Taylor skill classes based on terciles (n = 3132)
as the response. Only the 12 of 28 predictants with score > 25 shown; see Table 3 for complete listing
of evaluated model structural and site attributes.
Figure 7. Taylor diagram of normalized across‐site average model performance. Model s and RMSE
were normalized by observed s. Each circle (n = 22 models) corresponds to the mean across all sites.
Benchmark (red square) corresponds to observed normalized monthly NEE; units of s and RMSE are
multiples of observed s. Color coding of model letter and circles indicates generality of model perfor-
mance: specialist models used only in croplands (n ≤ 5 sites; black), generalist models used across a range
of biomes and sites (n ≥ 30 sites, blue), all other models (red). The correlation for DNDC (r = −0.13) is
displayed as zero for readability.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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Page 18
or most models. None of the four crop only models achieved
steady state. This followed from site history of croplands in
general where active management precluded any system
steady state, e.g., DNDC allowed for prescribed initial soil
carbon pools. For those models (5 of the 21 evaluated) that
achieved steady state in initialization this resulted in an
inherent bias between simulated and observed NEE for all
sites regardless of site history. However, as biome and
seasonality largely governed the distribution of model skill,
this bias was too small to manifest itself in this study.
Relaxing the steady state assumption [Carvalhais et al.,
2008] or initializing using observed wood biomass and the
quasi‐steady state assumption [Schaefer et al., 2008] could
improve these models’ performance.
5. Conclusion
[36] We used observed CO2 exchange from 44 eddy
covariance towers in North America with simulations from
21 terrestrial biosphere models and a mean model ensemble
to examine model skill across gradients in dryness, sea-
sonality, biome, site history, and model structure. Models’
ability to match observed monthly net ecosystem exchange
was generally poor; the mean squared distance between
observations and simulations was ∼10 times observational
error. Overall, forested sites were better predicted than
nonforested sites. Weaknesses in model performance
concerned model parameter sets and phenology, especially
for biomes with a clear seasonal cycle in leaf area index.
Drought was weakly linked to model skill with abnormally
dry conditions during the growing season showing mar-
ginally worse model‐data agreement compared to nondry
conditions. Sites with disturbances during the analysis
period and undersampled biomes (grasslands, shrublands,
wetlands, woody savannah, and tundra) also showed a large
divergence between observations and simulations. The
highest degree of model‐data agreement occurred in tem-
perate evergreen forests in all climatic seasons and during
summer across all biomes. Overall skill was higher for
models that estimated net ecosystem exchange as the differ-
ence between gross primary productivity and ecosystem
respiration, used prescribed canopy phenology, and did not
use a daily time step. The model ensemble (mean simulated
value across all models) and an optimized model (para-
meters tuned using data assimilation) also performed well.
Models with preferred structural attributes included gener-
alist models (models used at multiple sites and biomes, e.g.,
SiB3, Ecosys) that exhibited high degrees of model‐data
agreement across all biomes, indicating that a single model
can successfully simulate carbon flux in all types of eco-
systems. That is, different model architectures were not
needed for different types of ecosystems and model choice
is recast as a function of ease of parameterization and
initialization.
[37] Acknowledgments. C.R.S., C.A.W., and K.S. were supported
by the U.S. National Science Foundation grant ATM‐0910766. We would
like to thank the North American Carbon Program Site‐Level Interim
Synthesis team, the Modeling and Synthesis Thematic Data Center, and
the Oak Ridge National Laboratory Distributed Active Archive Center
for collecting, organizing, and distributing the model output and flux obser-
vations required for this analysis. This study was in part supported by the
U.S. National Aeronautics and Space Administration (NASA) grant
NNX06AE65G, the U.S. National Oceanic and Atmospheric Administra-
tion (NOAA) grant NA07OAR4310115, and the U.S. National Science
Foundation (NSF) grant OPP‐0352957 to the University of Colorado at
Boulder.
Figure 9. Bar graphs of mean Taylor skill by model attribute. Whiskers represent one standard error of
the mean. Only model‐specific attributes with variable important scores >25 shown. Note y axis on right
panels starts at 0.4.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
18 of 22
steady state. This followed from site history of croplands in
general where active management precluded any system
steady state, e.g., DNDC allowed for prescribed initial soil
carbon pools. For those models (5 of the 21 evaluated) that
achieved steady state in initialization this resulted in an
inherent bias between simulated and observed NEE for all
sites regardless of site history. However, as biome and
seasonality largely governed the distribution of model skill,
this bias was too small to manifest itself in this study.
Relaxing the steady state assumption [Carvalhais et al.,
2008] or initializing using observed wood biomass and the
quasi‐steady state assumption [Schaefer et al., 2008] could
improve these models’ performance.
5. Conclusion
[36] We used observed CO2 exchange from 44 eddy
covariance towers in North America with simulations from
21 terrestrial biosphere models and a mean model ensemble
to examine model skill across gradients in dryness, sea-
sonality, biome, site history, and model structure. Models’
ability to match observed monthly net ecosystem exchange
was generally poor; the mean squared distance between
observations and simulations was ∼10 times observational
error. Overall, forested sites were better predicted than
nonforested sites. Weaknesses in model performance
concerned model parameter sets and phenology, especially
for biomes with a clear seasonal cycle in leaf area index.
Drought was weakly linked to model skill with abnormally
dry conditions during the growing season showing mar-
ginally worse model‐data agreement compared to nondry
conditions. Sites with disturbances during the analysis
period and undersampled biomes (grasslands, shrublands,
wetlands, woody savannah, and tundra) also showed a large
divergence between observations and simulations. The
highest degree of model‐data agreement occurred in tem-
perate evergreen forests in all climatic seasons and during
summer across all biomes. Overall skill was higher for
models that estimated net ecosystem exchange as the differ-
ence between gross primary productivity and ecosystem
respiration, used prescribed canopy phenology, and did not
use a daily time step. The model ensemble (mean simulated
value across all models) and an optimized model (para-
meters tuned using data assimilation) also performed well.
Models with preferred structural attributes included gener-
alist models (models used at multiple sites and biomes, e.g.,
SiB3, Ecosys) that exhibited high degrees of model‐data
agreement across all biomes, indicating that a single model
can successfully simulate carbon flux in all types of eco-
systems. That is, different model architectures were not
needed for different types of ecosystems and model choice
is recast as a function of ease of parameterization and
initialization.
[37] Acknowledgments. C.R.S., C.A.W., and K.S. were supported
by the U.S. National Science Foundation grant ATM‐0910766. We would
like to thank the North American Carbon Program Site‐Level Interim
Synthesis team, the Modeling and Synthesis Thematic Data Center, and
the Oak Ridge National Laboratory Distributed Active Archive Center
for collecting, organizing, and distributing the model output and flux obser-
vations required for this analysis. This study was in part supported by the
U.S. National Aeronautics and Space Administration (NASA) grant
NNX06AE65G, the U.S. National Oceanic and Atmospheric Administra-
tion (NOAA) grant NA07OAR4310115, and the U.S. National Science
Foundation (NSF) grant OPP‐0352957 to the University of Colorado at
Boulder.
Figure 9. Bar graphs of mean Taylor skill by model attribute. Whiskers represent one standard error of
the mean. Only model‐specific attributes with variable important scores >25 shown. Note y axis on right
panels starts at 0.4.
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
18 of 22
Page 19
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bg-4-647-2007.
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and spatial variability of ecosystem‐scale carbon dioxide, water vapor,
and energy flux densities, Bull. Am. Meteorol. Soc., 82, 2415–2434,
doi:10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.
Barr, A. G., et al. (2004), Inter‐annual variability in the leaf area index of a
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balances of a boreal aspen forest, 1994–2003, Global Change Biol.,
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boreal black spruce forests in Canada, Global Change Biol., 13, 89–107,
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age, disturbance history, and carbon stocks and fluxes in subalpine
Rocky Mountain forests, Global Change Biol., 14, 2882–2897,
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fication and Regression Trees, 358 pp., Wadsworth, Belmont, Calif.
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assumption for biogeochemical modeling performance and inverse
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Causarano, H. J., J. N. Shaw, A. J. Franzluebbers, D. W. Reeves, R. L.
Raper, K. S. Balkcom, M. L. Norfleet, and R. C. Izaurralde (2007),
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Chen, J. M., A. Govind, O. Sonnentag, Z. Zhang, A. Barr, and B. Amiro
(2006), Leaf area index measurements at Fluxnet‐Canada forest sites,
Agric. For. Meteorol., 140, 257–268, doi:10.1016/j.agrformet.2006.
08.005.
Ciais, P., et al. (2005), Europe‐wide reduction in primary productivity
caused by the heat and drought in 2003, Nature, 437, 529–533,
doi:10.1038/nature03972.
Cook, B. D., et al. (2004a), Carbon exchange and venting anomalies in an
upland deciduous forest in northern Wisconsin, USA, Agric. For.
Meteorol., 126, 271–295, doi:10.1016/j.agrformet.2004.06.008.
Cook, E. R., C. A. Woodhouse, C. M. Eakin, D. M. Meko, and D. W.
Stahle (2004b), Long‐term aridity changes in the western United States,
Science, 306, 1015–1018, doi:10.1126/science.1102586.
Cook, E. R., R. Seager, M. A. Cane, and D. W. Stahle (2007), North
American droughts: Reconstructions, causes and consequences, Earth
Sci. Rev., 81, 93–134, doi:10.1016/j.earscirev.2006.12.002.
Dai, A., K. E. Trenberth, and T. Qian (2004), A global data set of Palmer
Drought Severity Index for 1870–2002: Relationship with soil moisture
and effects of surface warming, J. Hydrometeorol., 5, 1117–1130,
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and J. G. Isebrands (2003), The annual cycles of CO2 and H2O exchange
over a northern mixed forest as observed from a very tall tower, Global
Change Biol., 9, 1278–1293, doi:10.1046/j.1365-2486.2003.00672.x.
Delpierre, N., et al. (2009), Exceptional carbon uptake in European forests
during the warm spring of 2007: A data‐model analysis, Global Change
Biol., 15, 1455–1474, doi:10.1111/j.1365-2486.2008.01835.x.
Desai, A. R., P. V. Bolstad, B. D. Cook, K. J. Davis, and E. V. Carey
(2005), Comparing net ecosystem exchange of carbon dioxide between
an old‐growth and mature forest in the upper midwest, USA, Agric.
For. Meteorol., 128, 33–55, doi:10.1016/j.agrformet.2004.09.005.
Fischer, M. L., D. P. Billesbach, W. J. Riley, J. A. Berry, and M. S. Torn
(2007), Spatiotemporal variations in growing season exchanges of CO2,
H2O, and sensible heat in agricultural fields of the southern Great Plains,
Earth Interact., 11, 1–21.
Flanagan, L. B., L. A. Wever, and P. J. Carlson (2002), Seasonal and inter-
annual variation in carbon dioxide exchange and carbon balance in a
northern temperate grassland, Global Change Biol., 8, 599–615,
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Friedlingstein, P., et al. (2006), Climate‐carbon cycle feedback analysis,
results from the C4MIP model intercomparison, J. Clim., 19, 3337–
3353, doi:10.1175/JCLI3800.1.
Granier, A., et al. (2007), Evidence for soil water control on carbon and
water dynamics in European forests during the extremely dry year:
2003, Agric. For. Meteorol., 143, 123–145, doi:10.1016/j.agrformet.
2006.12.004.
Grant, R. F., et al. (2005), Intercomparison of techniques to model high
temperature effects on CO2 and energy exchange in temperate and boreal
coniferous forests, Ecol. Modell., 188, 217–252, doi:10.1016/j.ecolmodel.
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Griffis, T. J., T. A. Black, K. Morgenstern, A. G. Barr, Z. Nesic, G. B.
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indirect effects of atmospheric conditions and soil moisture on surface
energy partitioning revealed by a prolonged drought at a temperate forest
site, J. Geophys. Res., 111, D16102, doi:10.1029/2006JD007161.
Hanson, P. J., et al. (2004), Oak forest carbon and water simulations: Model
intercomparisons and evaluations against independent data, Ecol.
Monogr., 74, 443–489, doi:10.1890/03-4049.
Harazono, Y., M. Mano, A. Miyata, R. C. Zulueta, and W. C. Oechel
(2003), Inter‐annual carbon dioxide uptake of a wet sedge tundra ecosys-
tem in the Arctic, Tellus, 55B(2), 215–231.
Hargrove, W. W., F. M. Hoffman, and B. E. Law (2003), New analysis
reveals representativeness of the AmeriFlux network, EOS Trans.
AGU, 84(48), 529.
Hochberg, Y., andA. C. Tamhane (1987),Multiple Comparison Procedures,
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Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden,
D. Xia, K. Maskell, and C. A. Johnson (Eds.) (2001), Climate Change
2001: The Scientific Basis: Contributions of Working Group I to the Third
Assessment Report of the Intergovernmental Panel on Climate Change,
881 pp., Cambridge Univ. Press, New York.
Huntington, T. G. (2006), Evidence for intensification of the global water
cycle: Review and synthesis, J. Hydrol., 319, 83–95, doi:10.1016/j.jhydrol.
2005.07.003.
Ichii, K., et al. (2009), Multi‐model analysis of terrestrial carbon cycles in
Japan: Reducing uncertainties in model outputs among different terres-
trial biosphere models using flux observations, Biogeosci. Discuss., 6,
8455–8502, doi:10.5194/bgd-6-8455-2009.
Irvine, J., B. E. Law, M. Kurpius, P. M. Anthoni, D. Moore, and P. Schwarz
(2004), Age related changes in ecosystem structure and function and the
effects on carbon and water exchange in ponderosa pine, Tree Physiol.,
24, 753–763.
Ito, A. (2008), The regional carbon budget of East Asia simulated with a
terrestrial ecosystem model and validated using AsiaFlux data, Agric.
For. Meteorol., 148, 738–747, doi:10.1016/j.agrformet.2007.12.007.
Jung, M., et al. (2007a), Assessing the ability of three land ecosystem
models to simulate gross carbon uptake of forests from boreal to Medi-
terranean climate in Europe, Biogeosciences, 4, 647–656, doi:10.5194/
bg-4-647-2007.
Jung, M., et al. (2007b), Uncertainties of modeling gross primary produc-
tivity over Europe: A systematic study on the effects of using different
drivers and terrestrial biosphere models, Global Biogeochem. Cycles,
21, GB4021, doi:10.1029/2006GB002915.
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P. Friedlingstein, P. Ciais, S. Sitch, and I. C. Prentice (2005), A dynamic
global vegetation model for studies of the coupled atmosphere‐biosphere
system, Global Biogeochem. Cycles, 19, GB1015, doi:10.1029/
2003GB002199.
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system model predictions of carbon and water fluxes in major European
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j.1365-2486.2005.01036.x.
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tem exchange measured with eddy covariance technique: Algorithms and
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571-2006.
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soils, BioScience, 54(10), 895–908, doi:10.1641/0006-3568(2004)054
[0895:EOCSIU]2.0.CO;2.
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terrestrial carbon cycle model parameters by assimilation of FLUXNET
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Ricciuto, D. M., P. E. Thornton, K. Schaefer, R. B. Cook, and K. J. Davis
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j.1365-2486.2005.01036.x.
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mental warming across latitudinal and moisture gradients, Ecol. Monogr.,
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tem exchange measured with eddy covariance technique: Algorithms and
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571-2006.
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[0895:EOCSIU]2.0.CO;2.
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Ricciuto, D. M., A. W. King, L. Gu, and W. M. Post (2008), Estimates of
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18O and C18OO fluxes between ecosystems and the atmo-
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Schaefer, K., T. Zhang, A. G. Slater, L. Lu, A. Etringer, and I. Baker
(2009), Improving simulated soil temperatures and soil freeze/thaw at
high‐latitude regions in the Simple Biosphere/Carnegie‐Ames‐Stanford
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Schmid, H. P., C. S. B. Grimmond, F. Cropley, B. Offerle, and H. B. Su
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Schwalm, C. R., T. A. Black, K. Morgenstern, and E. R. Humphreys
(2007), A method for deriving net primary productivity and component
respiratory fluxes from tower‐based eddy covariance data: A case study
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Change Biol., 13, 370–385, doi:10.1111/j.1365-2486.2006.01298.x.
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2039, doi:10.1111/j.1365-2486.2008.01626.x.
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University, Hamilton, ON L8S 4K1, Canada. (arainm@mcmaster.ca)
I. Baker and E. Lokupitiya, Atmospheric Science Department, Colorado
State University, Fort Collins, CO 80523, USA. (baker@atmos.colostate.
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ec.gc.ca)
T. A. Black, Faculty of Land and Food Systems, University of British
Columbia, Vancouver, BC V6T 1Z4, Canada. (andrew.black@ubc.ca)
G. Chen and H. Tian, School of Forestry and Wildlife Sciences, Auburn
University, Auburn, AL 36849, USA. (chengu1@auburn.edu; tianhan@
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J. M. Chen and M. Sprintsin, Department of Geography and Program in
Planning, University of Toronto, Toronto, ON M5S 3G3, Canada. (chenj@
geog.utoronto.ca; misprin@gmail.com)
P. Ciais and L. Li, Laboratoire des Sciences du Climat et de
l’Environnement, CE Orme des Merisiers, Gif sur Yvette, 91191 France.
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K. J. Davis, Department of Meteorology, Pennsylvania State University,
University Park, PA 16802, USA. (davis@meteo.psu.edu)
A. Desai, Center for Climatic Research, University of Wisconsin‐
Madison, Madison, WI 53706, USA. (desai@aos.wisc.edu)
M. Dietze, Department of Plant Biology, University of Illinois‐Urbana
Champaign, Urbana, IL 61801, USA. (mdietze@life.uiuc.edu)
D. Dragoni , Department of Geography, Indiana Universi ty,
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M. L. Fischer, Atmospheric Science Department, Lawrence Berkeley
National Laboratory, Berkeley, CA 94720, USA. (mlfischer@lbl.gov)
L. B. Flanagan, Department of Biological Sciences, University of
Lethbridge, Lethbridge, AB T1K 3M4, Canada. (larry.flanagan@uleth.ca)
R. Grant, Department of Renewable Resources, University of Alberta,
Edmonton, AB T6G 2E3, Canada. (robert.grant@afhe.ualberta.ca)
L. Gu and D. M. Riciutto, Environmental Sciences Division, Oak Ridge
National Laboratory, Oak Ridge, TN 37831, USA. (lianhong‐gu@ornl.
gov; ricciutodm@ornl.gov)
D. Hollinger, Northern Research Station, USDA Forest Service, Durham,
NH 03824, USA. (davidh@hypatia.unh.edu)
R. C. Izaurralde, Joint Global Change Research Institute, Pacific
Northwest National Laboratory and University of Maryland, College
Park, MD 20740, USA. (cesar.izaurralde@pnl.gov)
C. Kucharik, Department of Agronomy and Nelson Institute Center for
Sustainability and the Global Environment, University of Wisconsin‐
Madison, Madison, WI 53706, USA. (kucharik@wisc.edu)
P. Lafleur, Department of Geography, Trent University, Peterborough,
ON K9J 7B8, Canada. (plafleur@trentu.ca)
B. E. Law, College of Forestry, Oregon State University, Corvallis, OR
97331, USA. (bev.law@oregonstate.edu)
Z. Li, ASRC Research and Technology Solutions, Sioux Falls, SD
57198, USA. (zli@usgs.gov)
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USA. (sliu@usgs.gov)
Y. Luo, Department of Botany and Microbiology, University of
Oklahoma, Norman, OK 73019, USA. (yluo@ou.edu)
S. Ma, Department of Environmental Science, Policy and Management
and Berkeley Atmospheric Science Center, University of California,
Berkeley, Berkeley, CA 94720, USA. (sma@berkeley.edu)
H. Margolis, Centre d’études de la forêt, Faculté de foresterie, de
géographie et de géomatique, Université Laval, Québec, QC G1V 0A6,
Canada. (hank.margolis@sbf.ulaval.ca)
R. Matamala, Argonne National Laboratory, Biosciences Division,
Argonne, IL 60439, USA. (matamala@anl.gov)
H. McCaughey, Department of Geography, Queen’s University,
Kingston, ON K7L 3N6, Canada. (mccaughe@post.queensu.ca)
R. K. Monson, Department of Ecology and Evolutionary Biology,
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SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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Page 22
W. C. Oechel, Department of Biology, San Diego State University, San
Diego, CA 92182, USA. (oechel@sunstroke.sdsu.edu)
C. Peng and J. Sun, Department of Biology Sciences, University of
Quebec at Montreal, Montreal, QC H3C 3P8, Canada. (peng.changhui@
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B. Poulter, Swiss Federal Research Institute WSL, Birmensdorf,
CH‐8903, Switzerland. (benjamin.poulter@wsl.ch)
D. T. Price, Northern Forestry Centre, Canadian Forest Service,
Edmonton, AB T6H 3S5, Canada. (dprice@nrcan.gc.ca)
W. Riley, Climate and Carbon Sciences, Earth Sciences Division,
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
(wjriley@lbl.gov)
A. K. Sahoo, Department of Civil and Environmental Engineering,
Princeton University, Princeton, NJ 08544, USA. (sahoo@princeton.edu)
C. Tonitto, Department of Ecology and Evolutionary Biology, Cornell
University, Ithaca, NY 14853, USA. (ctonitto@cornell.edu)
H. Verbeeck, Laboratory of Plant Ecology, Ghent University, 9000
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S. B. Verma, School of Natural Resources, University of Nebraska‐
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SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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Diego, CA 92182, USA. (oechel@sunstroke.sdsu.edu)
C. Peng and J. Sun, Department of Biology Sciences, University of
Quebec at Montreal, Montreal, QC H3C 3P8, Canada. (peng.changhui@
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B. Poulter, Swiss Federal Research Institute WSL, Birmensdorf,
CH‐8903, Switzerland. (benjamin.poulter@wsl.ch)
D. T. Price, Northern Forestry Centre, Canadian Forest Service,
Edmonton, AB T6H 3S5, Canada. (dprice@nrcan.gc.ca)
W. Riley, Climate and Carbon Sciences, Earth Sciences Division,
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
(wjriley@lbl.gov)
A. K. Sahoo, Department of Civil and Environmental Engineering,
Princeton University, Princeton, NJ 08544, USA. (sahoo@princeton.edu)
C. Tonitto, Department of Ecology and Evolutionary Biology, Cornell
University, Ithaca, NY 14853, USA. (ctonitto@cornell.edu)
H. Verbeeck, Laboratory of Plant Ecology, Ghent University, 9000
Ghent, Belgium. (hans.verbeeck@ugent.be)
S. B. Verma, School of Natural Resources, University of Nebraska‐
Lincoln, Lincoln, NE 68583, USA. (sverma1@unl.edu)
SCHWALM ET AL.: MODELED VERSUS OBSERVED CO2 EXCHANGE G00H05G00H05
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