1
Supporting Information for Optimal Estimates of Terrestrial Gross Primary
2
Production from Chlorophyll Fluorescence and Biosphere Models
3
Nicholas C. Parazoo1,2, Kevin Bowman1,2, Joshua B. Fisher1, Christian Frankenberg1,
4
Dylan B. A. Jones2,3, Alessandro Cescatti4, Óscar Pérez-Priego5, Georg Wohlfahrt6,
5
Leonardo Montagnani7,8
6
1
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109,
7
USA
8
2
Joint Institute for Regional Earth System Science and Engineering, University of
9
California, Los Angeles, Los Angeles, CA, 90095, USA
10
3
11
Department of Physics, University of Toronto, Toronto, Ontario, M5S 1A7, Canada
4
European Commission, Joint Research Center, Institute for Environment and
12
Sustainability, Ispra, Italy
13
14
15
16
17
18
19
20
5
6
7
Departamento de Física Aplicada, Universidad de Granada, 18071, Granada, Spain
Institut für Ökologie, Universität Innsbruck, Sternwartestr. 15, 6020 Innsbruck, Austria
Forest Services, Autonomous Province of Bolzano, Via Brennero 6, 39100 Bolzano, Italy
8
Faculty of Science and Technology, Free University of Bolzano, Piazza
Università 5, 39100 Bolzano, Italy
21
Supplementary material for this letter contains detailed information about
22
(1) illustration of optimal estimation framework, (2) sensitivity and error analysis
23
of SIF scaling strategy, (3) sampling coverage and biases associated with SIF
24
retrievals from GOSAT, (4) observation system simulation experiments, and (5) flux
25
tower data and comparison to grid scale estimates from model and optimal
26
approaches described in the main text.
27
28
29
Text S1. GPP Optimal Estimation Framework
30
Given the unique GPP estimation strategy employed in this study, we illustrate our
31
optimal estimation framework by providing an example of optimally constrained
32
GPP at an arbitrary location in North America [120W, 40N]. The first step is to
33
collect all midday GPP observations within a 2.5 x 2.0 grid box, in this case centered
34
at [120W, 40N]. These observations, denoted yj,k for location j and times k
35
(represented GOSAT overpass times), are inferred from measurements of SIF from
36
GOSAT. These are represented as black squares in Figure S1.
37
Next, we predict midday GPP at the time and location of observed GPP. For this, we use
38
a temporal downscaling function, denoted as f(βj , j, k), that relates midday GPP to a
39
monthly GPP. These values are represented as blue circles in Figure S1, and can be seen
40
as discrete points along a diurnally continuous time series generated through temporal
41
downscaling, denoted as f(βj , j) and represented as the solid blue line in Figure S1.
42
We then solve for a monthly grid-scale scaling factor, denoted as βj , which balances
43
differences between observed and predicted midday GPP, subject to uncertainties in each.
44
This is accomplished by minimizing a cost function, which leads to an optimal scaling
45
factor, denoted here as β′𝑗 . This is used to rescale temporally downscaled GPP,
46
represented as the dashed red line in Figure S1, as well as monthly GPP, which represents
47
the focus of monthly estimates in the results section in the main text.
48
49
50
Figure S1. Example of GPP optimal estimate for the period July 2009 at a grid box
51
centered at [120W, 40N]. Black squares represent midday GPP observations inferred
52
from GOSAT SIF measurements. Diurnal GPP, represented by the solid blue line, is
53
estimated by distributing monthly GPP estimates from TRENDY DGVM’s based on 3-
54
hour shortwave radiation estimates from MERRA. Solid blue circles represent model
55
samples at GPP observation times. We use a Bayesian optimal estimation framework
56
that minimizes a cost function to solve for a seasonally and spatially varying optimal
57
scaling factor, denoted as 𝛽𝑗 , which is used to scale temporally downscaled GPP (solid
58
red line) and solve for monthly GPP. Red diamonds are optimally scaled GPP sampled
59
at GPP observation times.
60
61
62
Text S2. Optimization Sensitivity Analysis
63
We provide an extended sensitivity analysis of SIF scaling to GPP and impact on
64
optimized GPP (G-opt). The optimal estimation framework presented in the main text
65
solves for grid-scale monthly GPP by minimizing a cost function to balance differences
66
between observed and predicted values of midday GPP subject to a priori uncertainty and
67
observation precision error. A-priori GPP is taken from an ensemble of dynamical global
68
vegetation models (DGVMs) from the TRENDY project (http://dgvm.ceh.ac.uk) [Sitch et
69
al., 2013]. The zonal mean distribution of the ensemble average of TRENDY, referred to
70
as G-pri, is shown in Figure S2.
71
The next step is to estimate midday GPP from satellite measurements of solar-induced
72
chlorophyll fluorescence (SIF) from GOSAT. SIF correlates strongly with GPP, but is
73
not a direct measure of GPP. In order to retain photosynthetic temporal variability
74
implied by fluorescence but estimate magnitude consistent with GPP observations, we
75
scale midday SIF against data-driven GPP models using least-squares linear regression to
76
solve for slope and y-intercept.
77
78
79
Figure S2. Zonally averaged GPP for G-pri (blue), G-mpi (green), and G-mod (brown).
80
Results are averaged over boreal summer (a) and annually (b). G-pri represents the
81
average from 2000-2009; G-mpi and G-mod are shown for 2010. Shading represents
82
zonally aggregated uncertainty for G-mpi based on Beer et al. [2010].
83
Two data-driven GPP estimates are shown in Figure S2; green is GPP from the MPI-
84
BGC product [Beer et al., 2010] (referred to as G-mpi) and brown from the MODIS
85
MOD17A2 product [Myneni et al., 2007] (referred to as G-mod). Both have similar
86
meridional distributions as G-pri, but the global total is 15% weaker in G-mod (106 Pg C
87
year-1 vs 121.2 Pg C year-1). Regressions of daily average SIF against G-mpi and G-mod
88
are shown in Figures S3 and S4, respectively, for specific biomes (a-g) and all grid points
89
(h). Biomes are defined according to a modified IGBP classification following
90
Frankenberg et al. [2011] and Guanter et al. [2012] consisting of needleleaf forests (NF),
91
evergreen broadleaf (EB), deciduous broadleaf (DB), shrubland (SH), savannah (SV),
92
grassland (GR), and cropland (CR), with a map of biome distribution is shown in Fig. 1
93
of the main text. Regression statistics for slope (m), y-intercept (b), sample size (n), and
94
correlation (r) are also shown. Correlation and slope are generally higher for G-mpi than
95
G-mod across all biomes and in the global average. This higher correlation leads to
96
reduced root-mean-squared error for slope and y-intercept for all regressions, reflecting
97
the reduced scatter in G-mpi compared to G-mod.
a. NF
b. EB
8 m: 14.87+/−2.4
8 m: 15.82+/−1.1
8 m: 14.69+/−2
4
4
4
2
2
2
b: 1+/−0.35
n: 13
6 r: 0.89
G−mpi (gC/m2/d)
0
−0.2
0
0.2
b: 1.7+/−0.36
n: 73
6 r: 0.86
0.4
0
−0.2
0
0.4
0
−0.2
0
0.2
e. SV
8 m: 6.437+/−0.82
8 m: 15.31+/−1.2
8 m: 13.46+/−0.91
4
4
4
2
2
2
b: 0.36+/−0.045
n: 103
6 r: 0.61
0
0.2
0.4
0
−0.2
0
0.2
8 m: 18.19+/−0.31
4
4
2
2
b: 0.044+/−0.16
n: 131
6 r: 0.88
0
0.2
b: 0.27+/−0.086
n: 80
6 r: 0.86
0.4
0
−0.2
0
0.2
0.4
h. All
8 m: 17.08+/−0.83
0
−0.2
0.4
f. GR
b: 0.62+/−0.24
n: 83
6 r: 0.82
g. CR
99
0.2
b: 0.54+/−0.35
n: 23
6 r: 0.85
d. SH
0
−0.2
98
c. DB
b: 0.11+/−0.056
n: 553
6 r: 0.93
0.4
0
−0.2
0
0.2
0.4
Daily Average SIF (W/m2/micron/sr)
Figure S3. Scatter plots of annual grid-scale G-mpi vs daily average SIF. Results are
100
shown for individual biomes (a-g) and all grid points combine (h). Solid blue line
101
represents the linear least squared regression line; dashed lines are the 95% confidence
102
bounds. Linear slope (m), y-intercept (b), sample size (n), and correlation also shown.
103
Slope and y-intercept include rmse, used to represent the SIF-GPP conversion error.
a. NF
b. EB
c. DB
8 m: 12.36+/−2.4
8 m: 10.99+/−1.1
8 m: 13.98+/−1.5
4
4
4
2
2
2
b: 1.2+/−0.36
n: 13
6 r: 0.84
G−mod (gC/m2/d)
0
−0.2
0
0.2
b: 2.6+/−0.35
n: 73
6 r: 0.76
0.4
0
−0.2
0
0.4
0
−0.2
0
0.2
d. SH
e. SV
8 m: 6.545+/−0.91
8 m: 11.18+/−1.2
8 m: 11.57+/−0.88
4
4
4
2
2
2
b: 0.52+/−0.049
n: 103
6 r: 0.58
0
−0.2
0
0.2
0.4
0
−0.2
0
0.2
8 m: 15.13+/−0.32
4
4
2
2
b: 0.44+/−0.16
n: 131
6 r: 0.79
0
0.2
b: 0.42+/−0.083
n: 80
6 r: 0.83
0.4
0
−0.2
0
0.2
0.4
h. All
8 m: 12.5+/−0.86
0
−0.2
0.4
f. GR
b: 1.1+/−0.25
n: 83
6 r: 0.71
g. CR
104
105
0.2
b: 0.15+/−0.28
n: 23
6 r: 0.89
b: 0.3+/−0.057
n: 553
6 r: 0.9
0.4
0
−0.2
0
0.2
0.4
Daily Average SIF (W/m2/micron/sr)
Figure S4. Same as Figure S3 except based on MODIS MOD17A2 GPP (G-mod).
106
107
A range of options exist for scaling SIF to GPP using this regression strategy. These
108
options, summarized in Table 1 of the main text, include sub-setting of global grid points
109
(e.g., biome specific), empirical GPP model (G-mpi vs G-mod), and estimates of
110
uncertainty due to (1) SIF measurement precision, (2) SIF conversion error, and (3) data-
111
based GPP product error. It is important to evaluate sensitivity of midday SIF, midday
112
SIF uncertainty, G-opt, and G-opt uncertainty to these options, which are designated as
113
SIF1-5.
114
Results for midday GPP and uncertainty are summarized in Figure S5. The meridional
115
distribution of midday G-sif and uncertainty during boreal summer is similar in all cases,
116
with highest GPP in the tropics and decreasing magnitude towards high latitudes.
117
Midday estimates are about half as strong in mid-latitudes than in the tropics, in contrast
118
to daily and monthly averages (e.g., G-pri and G-mpi in Fig. S1), due to longer day
119
length at high latitudes. We also note that spatial gradients of G-sif are slightly different
120
from and more variable than G-pri and G-mpi due to regional variations in sampling
121
coverage (see Text S3).
122
The first two options (SIF1-2) are based on the choice of biome-specific or global
123
scaling, illustrated in Figures S3 and S4. Global regressions have higher linear slope and
124
reduced uncertainty, but it is clear from the range of regressions across biomes that SIF
125
scaling is unique to vegetation type and climate [Guanter et al., 2012]. Higher slope in
126
global scaling is reflected in estimates of midday GPP (denoted as G-sif) and uncertainty
127
in Figure S5 a and b, respectively, which are scaled using G-mpi. Uncertainty due to
128
scatter in regressions, captured in the rmse of slope and y-intercept, are added to the
129
observation error of option 3 (SIF3). This has no effect on G-sif but does increase
130
uncertainty. Uncertainty from errors in G-mpi (green shading in Figure S2) are included
131
in option 4 (SIF4), which leads to additional increases in uncertainty. Removing G-mpi
132
error and changing scaling from G-mpi to G-mod provides the basis for option 5 (SIF5),
133
which shows reductions in GPP and uncertainty resulting from low global GPP in G-mod
134
(Figure S2) and reduced linear slopes (Figure S4).
135
Sensitivity of G-opt and posterior uncertainty to different estimates of G-sif and
136
observation uncertainty are shown in Figure S6. GPP and uncertainty are weakly
137
sensitive to increased uncertainty in G-sif.(SIF2-4). G-sif and uncertainty increase
138
slightly in the case of global scaling (SIF1) and decrease with G-mod (SIF5).
139
140
Figure S5. Zonal mean estimates of midday GPP (a, G-sif) and uncertainty (b) inferred
141
from SIF, based on different scaling techniques and measures of uncertainty (SIF 1-5).
142
All estimates are scaled against G-mpi except SF5 (dashed brown), which is scaled
143
against G-mod. All estimates use biome-specific scale factors except SIF1 (dashed blue),
144
which is global. All estimates account for SIF measurement uncertainty. SIF 3-5
145
account for errors in the conversion from SIF to GPP related to scatter. SIF 4 includes
146
an estimate of uncertainty due to errors in G-mpi. SIF2 (light blue) and SIF3 (yellow)
147
lines are identical to SIF4 (red) in (a), and therefore are not visible.
148
149
150
Figure S6. Zonal mean estimates of G-opt (a), G-opt uncertainty (b), and optimized
151
scale factor uncertainty, based on different scaling techniques and measures of
152
uncertainty (SIF 1-5). All estimates are scaled against G-mpi except SF5 (dashed
153
brown), which is scaled against G-mod. All estimates use biome-specific scale factors
154
except SIF1 (dashed blue), which is global. All estimates account for SIF measurement
155
uncertainty. SIF 3-5 account for errors in the conversion from SIF to GPP related to
156
scatter. SIF 4 includes an estimate of uncertainty due to errors in G-mpi. SIF2 (light
157
blue) and SIF3 (yellow) lines are identical to SIF4 (red) in all panels, and therefore are
158
not visible.
159
160
161
Text S3. GOSAT Sampling Coverage and Biases
162
As an extension of Figure 2 of the main text, we calculate days sampled per month with
163
and without cloud filtering as well as temporal sampling biases of monthly GPP
164
associated with incomplete monthly sampling. Figure S7 shows the average number of
165
days per month from June – August 2010 in which 1 or more samples are collected
166
within 2.5°x2° pixels. Including retrievals flagged as cloudy (Figure S7A) gives
167
excellent coverage at all latitudes, with ~7-10 days per month in the tropics and 7-15 days
168
per month in the extra-tropics. Sampling coverage increases with latitude due to closer
169
proximity of orbits.
170
171
Figure S7. Days sampled per month from Jun-Aug 2010 in 2.5°x2° pixels without (A)
172
and with (B) clouds filtered.
173
174
Figure S8 shows the reduction of daily coverage after removing cloudy pixels. 1-2 days
175
of coverage are lost in the tropics, 1-4 days in middle latitudes (30-60°N), and 4-10 days
176
at high latitudes (> 60°N).
177
178
179
Figure S8. Sampling days lost after excluding pixels flagged as cloudy, taken as the
180
difference between panels A and B in Figure S7.
181
182
We estimate temporal sampling biases associated with fractional monthly coverage by
183
comparing monthly-averaged estimates of midday GPP from CASA-GFED3 (GT) to that
184
observed by GOSAT (GO). Biases that include cloudy retrievals we call the satellite
185
sampling bias, and those without cloudy retrievals we call the clear sky sampling bias.
𝐺𝑂 − 𝐺𝑇
⁄ ). Pixels with low monthly GPP
𝐺𝑇
186
Sampling bias is then defined as 100 ∗ (
187
(𝐺𝑇 < 1.0 gC m-2 day-1) are excluded so that biases are not skewed toward low
188
productivity regions. Results are shown at grid scale in Figure S9 and as zonal averages
189
in Figure S10. In general, sampling bias increases with latitude and is enhanced when
190
cloudy retrievals are excluded (sampling bias and cloudy samples are positively
191
correlated, r = 0.52), indicating potential systematic differences in GPP in cloudy
192
environments under diffuse radiation and changes in light use efficiency.
193
194
195
Figure S9. Biases in estimates of monthly GPP when sampling CASA-GFED3 (A) using
196
all samples (cloudy retrievals included) and (B) fair weather samples only (cloudy
𝐺𝑂 − 𝐺𝑇
⁄ ), where GT is the
𝐺𝑇
197
retrievals excluded). Sampling bias is defined as 100 ∗ (
198
true time average and GO is the satellite observed average based on a subset of midday
199
samples from GOSAT.
200
Percent Sampling Bias
30
25
Fair Weather Samples (Clouds Filtered)
20
15
10
5
−40
−20
0
−40
−20
0
20
40
60
80
40
60
80
100
Percent Sampling Bias
Percent Cloudy Samples
0
−60
201
All Samples (Clouds Not Filtered)
A
B
75
50
25
0
−60
20
Latitude
20
C
r = 0.519
15
10
5
0
0
10
20
30
40
50
Percent Cloudy Samples
60
70
202
Figure S10. Temporal sampling biases of monthly GPP in relation to percent cloud
203
coverage. (A) Zonally averaged sampling biases in estimates of monthly GPP when
204
sampling CASA-GFED3 with all possible samples (dashed, cloudy samples not filtered)
205
and fair weather samples (solid, cloudy retrievals filtered). Sampling bias is defined as
206
𝐺 − 𝐺𝑇
100 ∗ ( 𝑂
⁄ ), where GT is the true time average and GO is the satellite observed
𝐺𝑇
207
average based on a subset of midday samples from GOSAT. (B) Zonally averaged
208
percent coverage of cloudy samples, determined as (All Samples – Fair Weather
209
Samples) / All Samples. (C) Correlation of zonally averaged sampling bias against
210
zonally average cloudy sampling coverage.
211
212
213
Text S4. Observation System Simulation Experiments (OSSE’s)
214
OSSE’s are used in the main text to demonstrate proof of concept of our estimation
215
strategy, which aims to retrieve the true state of monthly GPP and uncertainty from a
216
priori estimates of GPP (from DGVMs) constrained by midday clear sky observations
217
from GOSAT and subject to temporal sampling biases and uncertainty in measurements
218
and models. We examine sensitivity of optimal GPP estimates to (a) a priori constraints
219
of diurnal GPP, (b) observational coverage, and (c) inclusion of cloudy retrievals. These
220
experiments are summarized in Table 2 of the main text.
221
Figures S11 and S12 show results in the tropics and northern mid-latitudes, respectively,
222
from annual experiments for Tests 1-4 to illustrate changes in uncertainty reduction and
223
mean absolute error associated with changes in a-priori and observational constraints. In
224
general, we find that increased sampling coverage from Test 1 (10% available sounding),
225
to Test 2 (100% of soundings), and Test 3 (inclusion of pixels with high aerosol optical
226
depth) leads to systematic reductions of uncertainty and mean absolute error. In the
227
tropics, we also find high sensitivity to estimates of diurnal GPP (Test 4) and therefore
228
dependence on the way monthly GPP is distributed at diurnal timescales. Tests 1-3 use a
229
land surface model (CASA-GFED3) and Test 4 shortwave radiation from MERRA.
230
231
232
Figure S11. Seasonal estimates of uncertainty reduction (a) and mean absolute error (b)
233
in the tropics (15°S – 15°N) for observation system simulation experiments (OSSE’s)
234
described in the main text. OSSE’s use a simulated world of true and observed GPP to
235
test our estimation methodology for optimal GPP (G-opt). Here, true GPP is prescribed
236
from CASA-GFED3 biosphere model simulations (G-sim), observed GPP from midday
237
samples of CASA-GFED3 collected from GOSAT footprints, and prior GPP from the
238
ensemble average of TRENDY models (G-pri). Test cases 1-4 are defined according to:
239
(a) a priori estimate of diurnal GPP, (b) percentage of synthetic GPP observations
240
retained, and (c) inclusion of cloudy retrievals (see Tab. 1). Mean absolute error in (b)
241
is estimated as the difference of G-pri (black) and G-opt (colors) from G-syn.
242
243
244
245
Figure S12. Same as Figure S11 but for northern mid-latitudes (30°N – 60°N).
246
Text S5. Evaluation of model and optimal GPP against flux tower data
247
A. Flux Tower Data
248
Estimates of GPP are taken from 49 eddy covariance flux tower sites in N. America and
249
Eurasia and 8 sites in S. America, shown in Figure 1 of the main text. Site location and
250
contact information are summarized in Table S1. Site-specific references are included
251
when possible, otherwise we refer the reader to Papale et al. [2006] and Reichstein et al.
252
[2005] for general information. In N. America and Europe, we select only sites that have
253
data in 2009 and 2010 and which have data in all 12 calendar months (but not necessarily
254
from both years). Flux tower data in tropical S. America are limited to the period 1999 to
255
2006. A more detailed discussion and analysis of sites in S. America can be found in
256
Restrepo-Coupe et al. [2013]. Since these data do not overlap in time with GOSAT,
257
comparisons in S. America are based on climatological seasonal cycles.
258
Site
Biome
Coordinates
USMe2
NF
USVcm
Date
Reference
Contact
121.56W
/44.45N
Anthoni et al. [2002]
Bev.law@oregonst
ate.edu
NF
106.53W
/35.89N
Papale et al. [2006];
Reichstein et al. [2005]
mlitvak@unm.edu
CAQcu
NF
74.03W
/49.26N
Giasson et al. [2006]
Hank.margolis@sbf
.ulaval.ca
CAQc2
NF
74.57W
/49.76N
Papale et al. [2006];
Reichstein et al. [2005]
Hank.margolis@sbf
.ulaval.ca
ITLav
NF
11.28E
/45.95N
Marcolla et al. [2003]
damiano.gianelle@i
asma.it
01/09 –
12/10
ITRen
NF
RUFyo
ITSRo
FIHyy
stefano.minerbi@pr
ovincia.bz.it;
11.43E
/46.59N
Montagnani et al. [2009]
NF
32.92E
/56.46N
Kurbatova et al. [2008]
varlagin@sevin.ru
NF
10.28E
/43.72N
Chiesi et al. [2005]
alessandro.cescatti
@jrc.ec.europa.eu
NF
24.30E
/61.85N
leonar@inwind.it
Suni et al. [2003]
Timo.vesala@helsi
nki.fi
Ivan.mammarella@
helsinki.fi
Nina.buchmann@ip
w.agrl.ethz.ch
CHDav
NF
8.54E
/46.81N
Zweifel et al. [2010];
Etzold et al. [2011]
FRFbn
NF
5.68E
/43.24N
Papale et al. [2006];
Reichstein et al. [2005]
Roland.huc@avign
on.inra.fr
CZBk1
NF
18.54E
/49.49N
Papale et al. [2006];
Reichstein et al. [2005]
Pavelka.m@czechg
lobe.cz
USHa1
DB
72.17W
/42.54N
Barford et al. [2001]
jwmunger@seas.ha
rvard.edu
USMms
DB
86.41W
/39.32N
Schmid et al. [2000]
Werner.eugster@ag
rl.ethz.ch
farahman@indiana.
edu
knovick@indiana.e
du
davis@meteo.psu.e
du
USPFa
DB
90.27W
/45.95N
Berger et al. [2001];
Davis et al. [2003]
USUmb
DB
84.71W
/45.56N
Gough et al. [2008]
Bohrer.17@osu.edu
Curtis.7@osu.edu
cmgough@vcu.edu
USUmd
DB
84.70W
/45.56N
Papale et al. [2006];
Reichstein et al. [2005]
Bohrer.17@osu.edu
Curtis.7@osu.edu
cmgough@vcu.edu
desai@aos.wisc.ed
u
CAOmw
DB
82.15W
/48.22N
McCaughey et al. [2006]
mccaughe@queens
u.ca
CATP4
DB
80.36W
/42.71N
Peichl et al. [2010]
arainm@mcmaster.
ca
CHLae
DB
8.36E
/47.48N
Etzold et al. [2010;
2011]
nina.buchmann@ip
w.agrl.ethz.ch
FRHes
DB
7.06E
/48.67N
Granier et al. [2000]
agranier@nancy.inr
a.fr
bernard.longdoz@n
ancy.inra.fr
ITCol
DB
13.58E
/41.84N
Papale et al. [2006];
Reichstein et al. [2005]
Giorgio.matteucci
@isafom.cs.cnr.it
ITRo2
DB
11.92E
/42.39N
Tedeschi et al. [2006]
darpap@unitus.it
ITLMa
DB
7.58E
/39.48N
Papale et al. [2006];
Reichstein et al. [2005]
petrella@ipla.org
BEBra
DB
Reinhart.ceulemans
@ua.ac.be
4.52E
/41.31N
Carrara et al. [2003,
2004]
Pilegaard et al. [2003]
kipi@risoe.dtu.dk
Papale et al. [2006];
Reichstein et al. [2005]
brunsell@ku.edu
Ivan.janssens@ua.a
c.be
DKSor
USKfs
GR
(C3)
11.64E
/55.48N
95.19W
/39.06N
USKon
GR
(C4)
96.56W
/39.08N
Turner et al. [2003]
brunsell@ku.edu
USSeg
GR
(C4)
106.71W
/34.36N
Papale et al. [2006];
Reichstein et al. [2005]
mlitvak@unm.edu
USSnd
GR
(C3)
121.75W
/38.04N
Sonnentag et al. [2011]
baldocchi@berkele
y.edu
ATNeu
GR
(C3)
11.32E
/47.11N
Wohlfahrt et al. [2008]
Georg.wohlfahrt@u
ibk.ac.at
FRLq1
GR
(C3)
2.73E
/45.64N
Gilmanov et al. [2007]
kklump@clermont.i
nra.fr
DB
ITMbo
GR
(C3)
11.04E
/46.01N
Marcolla & Cescatti,
[2005]
Damiano.gianelle@
iasma.it
NLHor
GR
(C3)
5.06E
/52.02N
Jacobs et al. [2007]
Han.dolman@falw.
vu.nl
CZBk2
GR
(C3)
18.54E
/49.49N
Papale et al. [2006];
Reichstein et al. [2005]
Pavelka.m@czechg
lobe.cz
UKAmo
GR
(C3)
3.24W
/55.79N
Papale et al. [2006];
Reichstein et al. [2005]
ms@ceh.ac.uk
UKEbu
GR
(C3)
3.21W
/55.86N
Papale et al. [2006];
Reichstein et al. [2005]
ms@ceh.ac.uk
USNe1
CR
96.48W
/41.16N
Suyker et al [2004]
Sverma1@unl.edu
USTwt
CR
121.65W
/38.11N
Papale et al. [2006];
Reichstein et al. [2005]
baldocchi@berkely.
edu
CHOe2
CR
7.73E
/47.28N
Dietiker et al. [2010]
Nina.buchmann@ip
w.agrl.ethz.ch
FRAur
CR
1.11E
/43.55N
Papale et al. [2006];
Reichstein et al. [2005]
Eric.ceschia@cesbi
o.cnes.fr
FRLam
CR
1.23E
/43.49N
Papale et al. [2006];
Reichstein et al. [2005]
Eric.ceschia@cesbi
o.cnes.fr
ITCas
CR
8.72E
/45.07N
Papale et al. [2006];
Reichstein et al. [2005]
Alessandro.cescatti
@jrc.ec.europa.eu
DEGeb
CR
10.91E
/51.10N
Anthoni et al. [2004]
Werner.kutsch@vti
.bund.de
DESeh
CR
6.44E
/50.87N
Papale et al. [2006];
Reichstein et al. [2005]
Olaf.kolle@bgcKarl.schneider@uni
jena.mpg.de
-koeln.de
USMpj
SH
106.23W
/34.43N
Papale et al. [2006];
Reichstein et al. [2005]
mlitvak@unm.edu
USSes
SH
106.75W
/34.33N
Papale et al. [2006];
Reichstein et al. [2005]
mlitvak@unm.edu
ESAgu
(BB)
SH
2.03W
/36.94N
Rey et al. [2012];
Domingo et al. [2011]
poveda@eeza.csic.
es
ESLJu
SH
2.75W
/36.92N
BRK34
EB
60.21W
/2.61S
BRK83
EB
BRK67
Kowalski et al. [2008]
Penelope@ugr.es
Serrano-Ortiz et al.
[2009]
andyk@ugr.es
6/9909/06
Araujo et al. [2002]
Bart.kruijt@wur.nl
54.93W
/3.05S
07/0012/04
da Rocha et al. [2004];
Goulden et al. [2004]
mgoulden@uci.edu
EB
54.97W
/2.85S
01/02 –
01/06
Saleska et al. [2003]
wofsy@fas.harvard
.edu
BRCax
EB
51.46W
/1.72S
06/99 –
07/03
BRRja
EB
62.36W
/10.08S
03/99 –
10/02
BRK77
SV
62.36W
/3.02S
01/00 –
12/05
Sakai et al. [2004]
fitz@asrc.cestm.alb
any.edu
BRFns
SV
52.36W
/10.76S
03/99 –
10/02
Von Randow et al.
[2004]
watm@geo.vu.nl
Carswell et al. [2002];
Souza Filho et al.
[2005]
Kruijt et al. [2004]; von
Randow et al. [2004]
humberto@model.i
ag.usp.br
watm@geo.vu.nl
259
260
Table S1: Flux tower information for sites in N. America, Europe, and S. America.
261
Column 1 (Site) refers to the Fluxnet Site Code (Country-Site); Column 2 (Biome) is the
262
IGBP biome type (NF = needleleaf forest, DB = deciduous broadleaf forest, GR =
263
grasslands, CR = croplands, SH = shrublands, EB = evergreen broadleaf forest, and SV
264
= savannah); third column is the coordinate (also plotted in Figure 2); Column 3
265
(Coordinate) is the longitude/latitude coordinate; Column 4 (Date) is the data range used
266
in this study, given in MM/YY; Column 5 (Reference) is the site specific reference;
267
Column 6 (Contact) is the primary email contact.
268
269
Data for the continental United States are provided by the Ameriflux network at half
270
hourly or hourly resolution. Data for Canada are provided by the Canadian Carbon
271
Program (CCP) at half hourly or hourly resolution. Data for Europe are provided by the
272
Infrastructure for Measurements of the European Carbon Cycle (IMECC) at monthly
273
resolution. Half hourly and hourly data is averaged to monthly means. Gap filled or PI
274
preferred gap filled data is used when available (this is better defined below).
275
GPP is inferred from observations of net ecosystem exchange (NEE) and modeled
276
ecosystem respiration (Reco) as GPP = Reco – NEE using partitioning techniques based on
277
models of temperature sensitivity [Reichstein et al., 2005], artificial neural networks
278
[Papale et al., 2006], and/or light response curves [Lasslop et al., 2010] to interpret NEE
279
and GPP as biological responses. In some cases, negative GPP may be found due to the
280
effect of abiotic processes that uncouple atmosphere exchanges due to NEE from
281
concurrent biological processes, for example due to subterranean carbon ventilation in
282
carbonate soils [Rey et al., 2012]. A new partitioning methodology that characterizes
283
biological fluxes in terms of evapotranspiration through their link to carbon assimilation
284
by stomatal conductance [Pérez-Priego et al., 2013] has therefore been applied to two
285
dryland sites (ES-Agu(BB) and ES-LJu; see Table 1) to improve decomposition of the
286
net flux into GPP values.
287
Most sites provide gap-filled GPP estimates for both Reichstein et al. [2005] and Papale
288
et al. [2003] partitioning methodologies, although we find little sensitivity to these
289
approaches for sites used in this study (not shown as seasonal plots are indistinguishable).
290
ES-Agu and ES-LJu also provide an estimate based on Lasslop et al. [2010], which
291
shows slightly enhanced seasonal amplitude relative to Reichstein et al. [2005] but little
292
effect on variability (Figure S13). However, for consistency we use estimates from
293
Reichstein et al. [2005] for the remainder of the supplementary material and in the main
294
text.
295
296
297
Figure S13. Seasonal cycle of gross primary production (GPP) averaged from 2009-
298
2010 across two shrubland sites in Spain (ES-Agu and ES-LJu) using flux partitioning
299
techniques based on Reichstein et al. [2005] (solid) and Lasslop et al. [2010] (dashed).
300
301
In order to provide as many flux-tower level comparisons as possible without
302
compromising robustness of analysis, we group flux tower data by land use type, or
303
biome, defined according to modified IGBP classification and require a minimum of four
304
sites per biome. IGBP biomes are grouped into a broader subset of biomes following
305
Frankenberg et al. [2011] and Guanter et al. [2012] consisting of needleleaf forests (NF),
306
evergreen broadleaf (EB), deciduous broadleaf (DB), shrubland (SH), savannah (SV),
307
grassland (GR), and cropland (CR). Site-specific biomes are shown in Table S1.
308
Filtering for these criteria yields 12 NF sites, 14 DB sites, 11 GR sites, 8 CR sites, and 4
309
SH sites spread throughout N. America and Europe from 2009-2010. For S. America, we
310
group all available towers together, and compare to the average from 5 EB and 2 SV sites
311
from 1999-2006.
312
B. Flux Tower Variability
313
N. America and Europe
314
Seasonal cycles across sites in N. America and Europe are shown in Figure S14. For
315
each biome there is significant site-to-site variability with respect to seasonal amplitude,
316
phase, and multiple peaks. Reasons for these differences include management practice
317
(e.g., harvesting frequency, irrigation, etc.), elevation (e.g., mountain vs lowland), and
318
variability among biomes with respect to vegetation type (C3 vs C4 grassland vegetation)
319
and climate (desert vs prairie). For example, a grassland site in the Alps, AT-Neu, is
320
harvested three times per year, which is visible as multiple peaks in May and July, while
321
others are intensively managed (e.g., NL-Hor) or even close-to-natural (e.g., UK-AMo).
322
In some cases, regional differences in management lead to earlier peak productivity in
323
cropland sites in Europe (e.g., FR-Aur and CH-Oe2) vs N. America (e.g., US-Ne1 and
324
US-Twt). Climate differences cause variations in productivity, as seen for example when
325
comparing desert grassland sites (US-Seg) to tall prairie sites (US-Kon).
326
327
328
Figure S14. Seasonal cycle of gross primary production (GPP) averaged from 2009-
329
2010 for all 49 sites listed in Table S1 and grouped according to biome (sub-panels) and
330
by region (dashed = N. America, solid = Europe). Across-site averages are shown for
331
each biome as black diamonds. Biome type and number of sites per biome are shown on
332
the top left of sub-panels.
333
334
Amazon Basin
335
Seasonal cycles across sites in S. America are shown in Figure S15. For each biome
336
there is significant site-to-site variability with respect to seasonal amplitude and phase.
337
In general, GPP magnitude is larger in EB biomes compared to SV biomes. Seasonal
338
minimums in GPP typically occur between June and September except BR-CAX, which
339
is minimum from April-May and then increases through the dry season.
340
341
Figure S15. Seasonal cycle of gross primary production (GPP) averaged across 7 sites
342
in S. America and grouped by biome (solid = Evergreen Broadleaf, dashed = Savannah).
343
Black diamonds represent the average across all sites.
344
345
C. Comparison to Grid-Scale Estimates
346
N. America and Europe
347
Comparison of ensemble average GPP from eight TRENDY models (G-pri) and
348
optimal GPP (G-opt) using assimilation of solar induced chlorophyll fluorescence
349
(SIF) from GOSAT to across-site flux tower averages are shown for each biome in
350
Figure S16. In this case, seasonal GPP for G-pri and G-opt is calculated using all
351
northern hemisphere pixels (> 20N) containing that biome and with each pixel
352
weighted by its fractional biome coverage, which ranges from a fraction of 1% to
353
nearly 100%. The use of fractional biome coverage rather than grid scale averages
354
provides a way to account for sub-grid heterogeneity, which can be quite significant
355
especially in more developed regions such as Europe.
356
Seasonal variability is in good agreement with site level data in NF, DB, and CR
357
biomes, suggesting that flux tower data in these biomes, particularly DB, is fairly
358
representative of the large scale and well represented by models and SIF. This
359
comparison falls apart, however, in SH and GR biomes. Estimates of GPP are too
360
strong and the timing occurs between peaks of flux tower data in the SH biome,
361
while estimates in the GR biomes are too weak by nearly a factor of 4. These sites
362
are clearly not representative of the large scale due to strong variability within
363
these biomes, enhanced variability within FLUXNET biomes due to higher incidence
364
of managed vs natural land use, and the tendency for eddy covariance sites to be
365
biased towards more productive areas of their biome [Cescatti et al., 2012].
366
367
Figure S16. Seasonal cycle of gross primary production (GPP) grouped by biome for
368
across-site flux tower average (black diamonds), TRENDY model average (G-pri, blue),
369
and optimal GPP (G-opt, red). Flux tower data is averaged from 2009-2010. G-pri &
370
G-opt are averaged from 2009-2012 and use all pixels in the Northern Hemisphere
371
(north of 20N) than contain that biome, with each pixel weighted by its fractional biome
372
coverage. Biome type, number of sites per biome (first number in paranethesis), and
373
number of pixels per biome (second number) are shown on the top left of sub-panels.
374
375
We therefore also compare flux tower data to G-pri and G-opt using only pixels
376
containing the flux tower site (Figure S17) based on fractional biome weighting
377
(solid) and unweighted (dashed) grid-scale averages (fractional biome weighting,
378
represented by solid lines, provides the basis for G-pri and G-opt in Figure 10 of the
379
main text). Both cases show significant improvements in the seasonal phase and
380
magnitude of GPP in SH and GR biomes as well as improved GPP magnitude in NF
381
biomes, with R2 exceeding 0.90 in all cases except SH biomes, which dips to 0.60.
382
For the most part, G-pri and G-opt are in better agreement with each other than with
383
flux tower data.
384
Accounting for fractional biome coverage produces the best agreement of seasonal
385
phase and amplitude in all biomes except GR, which is underestimated by a factor of
386
two and just outside the range of site-to-site variability. The grid cell average
387
produces better agreement of magnitude but only because of aliasing of high
388
productivity from other vegetation, particularly crops.
389
The different range of agreement of phase, and especially amplitude, across biomes
390
is partly an issue of representation error, due to comparison of 1 km flux tower
391
scale to 2.5°x2° scale of G-opt and G-pri. In terms of the average fraction of biome
392
coverage with model grid scales, we find that poor agreement of amplitude in GR
393
sites is likely a result of low fractional coverage of 7.4% and higher agreement at
394
other sites is associated with higher fraction coverage, ranging from a factor of 3-5
395
higher coverage at SH, NF and DB biomes (25.7% at SH, 25.9% at NF, and 35.5%)
396
and greater than 50% coverage at CR biomes (63.4%), which explains the higher
397
agreement with flux tower data.
398
399
400
401
Figure S17. Similar to Figure S16 except seasonal averages from G-pri and G-opt use
402
only pixels containing the flux tower. Solid lines use the fractional biome weighting as in
403
Figure S16 while dashed lines use unweighted grid scale averages. We note that most of
404
the sites are contained within land pixels except ES-Agu and ES-LJu, which are located
405
very close to the Mediterranean Sea. For these sites we choose the next pixel to the west
406
over Spain.
407
408
We test for representation error by comparing flux tower data directly to biome-
409
specific SIF retrievals from GOSAT. SIF data is retrieved at high resolution (~10 km
410
diameter) and provides an improved representation of sub-grid heterogeneity
411
within flux tower grid cells. We sample biome type using IGBP land cover based on
412
the center of each retrieval, and convert midday GPP to monthly averages (see
413
Section 2.3 of the main text) by first scaling from midday to daily average using the
414
cosine of the solar zenith angle and length of day, scaling to GPP using global and
415
biome-specific linear slopes of fit against MPI-BGC GPP and taking the average of the
416
two, and then converting to monthly average using all available data in one month
417
within +/- 1.0 latitude and 1.25 longitude of the flux tower.
418
Results are shown in Figure S18. SIF data provides an improved comparison against
419
flux tower data in DB and CR biomes relative to models in Figure S17, but
420
divergence from model and flux tower data in NF, SH, and GR biomes. The
421
improvement in DB and SR biomes is correlated with sampling density, which
422
averages to about 91.4 samples per year per site in DB and 278 samples per year
423
per site in CR and, combined with high signal-to-noise ratio (SNR; smaller error bars
424
in Figure S18), leads to strong uncertainty reduction (Figure 10 of main text).
425
Degradation in NF, GR, and CR biomes is a result of relatively low sampling density
426
(56.9, 86.5, and 81.6 samples per year per site, respectively), much lower SNR
427
(larger error bars in Figure S18), and less uncertainty reduction (Figure 10 of main
428
text).
429
430
Figure S18. Seasonally averaged GPP from flux towers and SIF in N. America and
431
Europe. Flux tower data (black) is the same as in Figures S16 and S17. SIF data (red)
432
is averaged from 2009-2012 using biome-specific retrievals sampled within a 1.25°x1°
433
box surrounding flux towers, and scaled to monthly GPP by taking the average of scaling
434
with global and biome-specific linear slopes of fit against the MPI-BGC upscaled flux
435
tower product (Section 2.3 of the main text). Error bars on SIF data represent the
436
standard deviation across all available retrievals.
437
438
Amazon Basin
439
Figure S19 compares GPP estimates from G-pri and G-opt to climatological flux
440
tower sites in S. America from Figure S15. In both cases, GPP is weighted by
441
fractional biome coverage. Sampling G-pri and G-opt at flux tower pixels (Figure
442
S19A) indicates significant improvement in G-opt in seasonal magnitude, shape, and
443
phase. Both estimates are within the statistical uncertainty of flux towers, but
444
seasonal phase is delayed too strongly in G-pri, which has minimum values in
445
October and November compared to flux tower minimum in July and August.
446
Since these are non-overlapping periods, we also compare flux tower data to the
447
average across all Amazon pixels to better gauge seasonal climatology. In this case,
448
the seasonal phase of G-opt has better overlap with flux towers. G-pri phase is
449
improved but still delayed by 1-2 months.
450
451
452
Figure S19. Seasonally averaged GPP in Amazon Basin from flux towers (1999-2006),
453
TRENDY model average (G-pri, 2000-2010), and optimal GPP (G-opt, 2009-2012).
454
Seasonal averages in (A) use only pixels containing the flux tower and in (B) all
455
available pixels in Amazon Basin. In all cases grid scale GPP is based on weighting by
456
fractional biome coverage.
457
458
459
460
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