ergy into latent heat and convect turbulent heat from the surface7–10 , in line with the standard measurement, reporting and verification
the local temperature response to LCMC can vary significantly in procedures adopted in climate policies for the accounting of biogeospace for equal magnitudes of radiative forcing at the top of the chemical mitigation potentials of land use activities19 .
atmosphere (TOA), making it difficult to characterize in a manner
Here, we combine predictions from a semi-mechanistic empirical
comparable with CO2 and other homogeneous global forcings11,12 .
model with satellite remote sensing and other global observations
10.1038/NCLIMATE3250
direct
surface temperature
As global radiative forcings from changes in surface albedo to provide global estimates of the localDOI:
become increasingly included alongside greenhouse gases (GHG) in response to nine common LCMC perturbations. Our data-driven
13,14
In
the format
provided
the
authorslocal
and non-radiative
unedited. mech- PUBLISHED
approachONLINE:
leverages
benefits
of both
the10.1038/NCLIMATE3250
high temporal resolution
impact
assessment
studiesby
, excluding
XXthe
MONTH
XXXX
| DOI:
anisms increases the risk of promoting land sector policies that and large temporal extent of a continuous in situ observational
may be counter to the aims of mitigation or adaptation4–6 . This is record of surface energy fluxes and meteorology, and the large spaparticularly important since local temperature change is directly tial extent of the various remote sensing and other Earth observation
felt and responded to by human societies and ecosystems6 . As an records used to define climatologies of important environmental
example, re-/afforestation projects may exert a positive radiative state variables. Combining in situ with satellite remote sensing and
forcing from an albedo decrease yet a cooling at the surface owing other global observations provides a means to efficiently map and
to enhanced evapotranspiration and turbulent mixing of air15 —two attribute local surface energy balance responses to multiple LCMC
key non-radiative drivers of the surface energy balance16 . Hence, types at the global scale20,21 , further providing a basis for additional
knowing where land-based projects will reduce surface temperature characterizations that inform about the relative importance of nonafter considering the combined effects of both radiative and non- radiative processes on land, and about the importance of local
radiative mechanisms can enhance mitigation benefits at both the LCMC relative to global drivers such as CO2 .
local and global scales.
Spatially explicit measures
that can account
for the combined ef- Surface
energy redistribution
on land
1
3,4
5
6
Ryan
Bright
, Edouard
Davin2,and
Thomas
O’Halloran
Juliaecosystem
Pongratz
, Kaiguang
Zhao
fects of M.
changes
to albedo,
evapotranspiration,
turbulence
at the For a ,given
or land
cover type,
in situ measurements
7
land–atmosphere
interface
can inform
decisions surrounding land of energy fluxes and other meteorological variables are necessary
and
Alessandro
Cescatti
SUPPLEMENTARY INFORMATION
ARTICLES
Local temperature response to land cover and
management change driven by
non-radiative processes
*
Following a land cover/land management change (LCMC), local surface temperature responds to both a change in available
1 The Norwegian Institute of Bioeconomy Research, 1431 Ås, Norway. 2 Institute for Atmospheric and Climate Science, ETH-Zürich, 8092 Zürich,
energy
and a change in the way energy is redistributed by various non-radiative mechanisms. However, the extent to
3 Department of Forestry and Environmental Conservation, Clemson University, Clemson, South Carolina 29634, USA. 4 Baruch Institute of
Switzerland.
which
non-radiative
mechanisms contribute to the local direct temperature response for different types of LCMC across
5 Max Planck Institute for Meteorology,
Coastal
Ecology
and Forest
Science, Clemson
University,
Georgetown,
South
Carolinaof29440,
USA.
the
world
remains
uncertain.
Here, we
combine
extensive
records
remote
sensing
and in situ observation to show that
6 School of Environment and Natural Resources, OARDC, The Ohio State University, Wooster, Ohio 44691,
Bundesstraße
53,
20146
Hamburg,
Germany.
non-radiative
mechanisms
dominate
the
local
response
in
most
regions
for
eight
of
nine
common LCMC perturbations. We
7 European Commission, Joint Research Centre, Directorate for Sustainable Resources, I-21027 Ispra (VA), Italy. *e-mail: [email protected]
USA. that
find
forest cover gains lead to an annual cooling in all regions south of the upper conterminous United States, northern
Europe, and Siberia—reinforcing the attractiveness of re-/afforestation as a local mitigation and adaptation measure in these
regions.
Our results
the
importance
accounting for non-radiative mechanisms when evaluating local land-based1
NATURE CLIMATE
CHANGE | affirm
ADVANCE
ONLINE
PUBLICATIONof| www.nature.com/natureclimatechange
mitigation/adaptation policies.
S
urface energy budgets are strongly influenced by the biogeophysical characteristics of the land surface controlling atmospheric exchanges of moisture, momentum and energy1–3 .
Through forestry and agricultural activities, humans perturb these
characteristics, with consequent direct impacts on the surface
energy balance, and in turn, on local and regional temperatures2–6 .
Owing to differences by which vegetated surfaces channel solar energy into latent heat and convect turbulent heat from the surface7–10 ,
the local temperature response to LCMC can vary significantly in
space for equal magnitudes of radiative forcing at the top of the
atmosphere (TOA), making it difficult to characterize in a manner
comparable with CO2 and other homogeneous global forcings11,12 .
As global radiative forcings from changes in surface albedo
become increasingly included alongside greenhouse gases (GHG) in
impact assessment studies13,14 , excluding local non-radiative mechanisms increases the risk of promoting land sector policies that
may be counter to the aims of mitigation or adaptation4–6 . This is
particularly important since local temperature change is directly
felt and responded to by human societies and ecosystems6 . As an
example, re-/afforestation projects may exert a positive radiative
forcing from an albedo decrease yet a cooling at the surface owing
to enhanced evapotranspiration and turbulent mixing of air15 —two
key non-radiative drivers of the surface energy balance16 . Hence,
knowing where land-based projects will reduce surface temperature
after considering the combined effects of both radiative and nonradiative mechanisms can enhance mitigation benefits at both the
local and global scales.
Spatially explicit measures that can account for the combined effects of changes to albedo, evapotranspiration, and turbulence at the
land–atmosphere interface can inform decisions surrounding land
management policy. Existing measures have limited utility in land
planning or management contexts because they: are not spatially
explicit17 ; are computed with respect to a non-vegetated land surface
baseline instead of comparing different vegetation covers17,18 ; or are
normalized to biogeochemical effects and integrated over a fixed
time horizon17 . Further, existing measures are derived from models
that are inherently uncertain. Observation-driven studies are more
in line with the standard measurement, reporting and verification
procedures adopted in climate policies for the accounting of biogeochemical mitigation potentials of land use activities19 .
Here, we combine predictions from a semi-mechanistic empirical
model with satellite remote sensing and other global observations
to provide global estimates of the local direct surface temperature
response to nine common LCMC perturbations. Our data-driven
approach leverages the benefits of both the high temporal resolution
and large temporal extent of a continuous in situ observational
record of surface energy fluxes and meteorology, and the large spatial extent of the various remote sensing and other Earth observation
records used to define climatologies of important environmental
state variables. Combining in situ with satellite remote sensing and
other global observations provides a means to efficiently map and
attribute local surface energy balance responses to multiple LCMC
types at the global scale20,21 , further providing a basis for additional
characterizations that inform about the relative importance of nonradiative processes on land, and about the importance of local
LCMC relative to global drivers such as CO2 .
Surface energy redistribution on land
For a given ecosystem or land cover type, in situ measurements
of energy fluxes and other meteorological variables are necessary
1 The
Norwegian Institute of Bioeconomy Research, 1431 Ås, Norway. 2 Institute for Atmospheric and Climate Science, ETH-Zürich, 8092 Zürich,
Switzerland. 3 Department of Forestry and Environmental Conservation, Clemson University, Clemson, South Carolina 29634, USA. 4 Baruch Institute of
Coastal Ecology and Forest Science, Clemson University, Georgetown, South Carolina 29440, USA. 5 Max Planck Institute for Meteorology,
Bundesstraße 53, 20146 Hamburg, Germany. 6 School of Environment and Natural Resources, OARDC, The Ohio State University, Wooster, Ohio 44691,
NATURE
CLIMATE CHANGE
| www.nature.com/natureclimatechange
USA. 7 European
Commission,
Joint Research Centre, Directorate
for Sustainable Resources, I-21027 Ispra (VA), Italy. *e-mail: [email protected]
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
FLUXNET data
The rationale for the six chosen land cover types is partly founded on analyses of Late-Holocene
land use reconstructions which peg the conversion of extra-tropical (ENF; DBF) and tropical
(EBF) forests to agricultural lands like pastures (GRA) and croplands (CRO) as being the most
common form of land cover change 1. As for land management change, common types include
the switch from deciduous broadleaf (DBF) to evergreen needleleaf (ENF) species 2 and the
addition of irrigation to agriculture 3. CRO was disaggregated into rainfed (CRO-Rain) and
irrigated (CRO-Irr.) sites to isolate the direct effects of irrigation on the surface energy balance 4,5.
We
employed
the
FLUXNET2015
v3
Tier
1
dataset
(http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/ ) augmented with the LaThuile Synthesis
“Opened” dataset (http://fluxnet.fluxdata.org/data/la-thuile-dataset/ ) for sites not contained in
FLUXNET2015 dataset. Sites used in our analysis were selected according to two main criteria:
i) they needed to be reported by site Principal Investigator(s) as being one of the six IGBP
vegetation cover types described above; and ii) they needed to have a record of all variables
required to compute f such as LW , LW , SW , SW , Ta , and G .
Monthly FLUXNET2015 data were filtered such that only months containing less than 10% gapfilled data were included, which significantly reduced the sample size yet ensured those data
which were included in our final dataset were of exceptionally high quality.
See
6
http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/ and ref. for additional
detail surrounding the gap-filling methodology, and ref.7 for additional detail surrounding the
QA/QC protocol. As for the LaThuile “Opened” dataset, we computed monthly means of daily
means after initial filtering to exclude daily values comprising less than 90% of the observed or
high-confidence gap-filled half-hourly record denoted by the quality flag “fqcOK”. Only full
months were included, which again reduced the total number of data points including in our
sample.
After controlling for valid sites and acceptable data quality, our in-situ dataset was left with 324.5
site-years of monthly mean observations distributed over 96 sites and nine Köppen-Geiger
climate zones 8. See Table S6 at the end of this document for additional site detail and
acknowledgements.
Figure S1 | Site locations. Geographic locations of the sites comprising our final in-situ dataset
after applying variable-of-interest and quality assurance filtering.
We chose a monthly time step for several reasons. Firstly, the highest quality gridded global data
products driving the temperature response model (Eq. (1) of the main article) are commonly
available at monthly resolution. Secondly, seasonality is preserved while a sufficient number of
data points for model training are retained. Thirdly, greater computational efficiency is achieved
compared to running at a daily time step. Lastly, aggregation reduces some of the noise in the insitu dataset.
Previous research has shown that the ecological representativeness of FLUXNET sites is
superior to the geographical representativeness 9 in terms of the climate and ecological variables
of interest. The in-situ record underlying our analysis represented the climate-vegetation space
quite well – illustrated in Figure S2. These indications instill confidence that our predictions of
the monthly energy redistribution factor f are minimally compromised by the unequal spatial
sampling of the global terrestrial surface afforded by FLUXNET siting.
Figure S2 | Climate-vegetation space. Global terrestrial monthly mean near surface air
temperature and precipitation totals10 (blue dots, background, 2001-2011 means) relative to
monthly means seen at our FLUXNET sites (legend).
Modeling of the energy redistribution factor
Two important considerations went into the design of the f model. Firstly, given the reduced
sample size of the in-situ dataset following the data filtering algorithm, it was important to aim for
a more mechanistic model that preserved as much of the knowledge of the primary system as
possible in terms of structural connectivity and functional mechanisms. Secondly, site-level
explanatory variables needed to be available at the global gridded scale. In other words, some
otherwise pertinent data must be ignored because the respective global datasets either did not
exist or were insufficient. The requirement that all predictor variables be available for all sites
and grid cells is a major obstacle to empirical upscaling 11; nevertheless, we were able to develop
models with reasonably good predictive performance, driven with predictors for which good
quality globally-gridded datasets existed:
1 (4 kTa3 )
f  k1 
( Rn*  G )
k2 exp( )  k3 P
(S1)
where Eq. (S1) is Eq. (3) of the main article (Methods), reproduced here for the reader’s
convenience. The model retains the original structure of Eq. (2) of the main article (Methods)
but employs Ta rather than Ts in the longwave feedback expression of the numerator, and surface
albedo  and precipitation P in the denominator as proxies for both the environmental and
biological controls governing the Ts  Ta gradient.
Prior to regression analysis, we removed outliers of f defined as all values outside the 5th and
95th percentiles. The performance of the final models can be visualized in Figure S3, whereas
parameter values and a statistical summary are reported in Table S1.
Figure S3 | Model performance – monthly f . Predictions vs. observations of monthly
energy redistribution f for the six grouped land cover types. Model predictions presented here
are based on Eq. (S1) (or Eq. (3) of the main article Methods).
GRA occupies the broadest range of climate space (Fig. S3) making f more challenging to
predict in the absence of additional explanatory variables in the model function, which becomes
apparent when looking at the distribution of bias presented in Figure S4, which, fortunately, is
rather heteroscedastic. Predictions for croplands are complicated by the extra variability
introduced by anthropogenic factors influencing the specific crop type, growing season timing (of
sowing and harvest events) and phenology, productivity, surface and soil properties, etc. Despite
this extra variability, both crop model fits are good, and all parameters are statistically significant
with the exception of the y-intercept term for irrigated crops ( k1 ; Table S1).
Table S1 | f model parameters and performance summary. Performance statistics and
parameter values for the models used to predict the monthly mean energy redistribution factor
f for the six major IGBP land cover types. All parameters except those shown in boldface font
are statistically significant at p<0.05, with standard errors (SE) shown in parentheses.
ENF
DBF
EBF
GRA
CRO
(Irr.)
CRO
(Rain)
R2
Mean
Median
RMSE
-8.7e-3 (7.2e-4)
-8.5e-3 (6.3e-4)
-6.6e-3 (1.7e-3)
1,192
448
115
1,126
213
0.82
0.85
0.87
0.69
0.73
3.38
3.79
5.67
1.73
3.29
3.22
3.29
6.00
1.87
3.30
1.35
1.06
1.31
1.13
1.40
-6.8e-3 (1.6e-3)
536
0.68
1.71
1.65
1.29
k1
k2
k3
#Obs.
-0.49 (0.07)
-0.72 (0.92)
1.04 (0.43)
-0.52 (0.06)
-3.2e-3 (5.7 e-4)
-0.08 (0.18)
3.89 (0.07)
3.29 (0.08)
4.22 (0.31)
5.80 (0.13)
4.36 (0.27)
-0.52 (0.09)
5.12 (0.19)
-9.4e-5 (5.9e-4)
(months)
The coefficient k3 represents the sensitivity of f to water available for evaporation and
transpiration, which is represented by the monthly mean precipitation (P ; in mm) in our models.
The k3 term tends to be greater in magnitude for EBF, CRO and GRA – or those vegetation
types typically having shallower rooting depths like tropical forests, croplands, and grasslands 12.
Trees outside the tropics typically have deeper rooting depth, thus transpiration is less sensitive to
frequent precipitation events, and k3 is found to be smaller for the ENF and DBF forest cases.
Note that for DBF the k3 term is not statistically significant.
For all vegetation cover types except EBF the y-intercept term is negative. Negative values are
confined to months with low external energy inputs such as in high latitude winter in which the
eco-physiological mechanisms are dormant. Since negative values of f have no meaningful
physical interpretation they were excluded from our analysis.
Potential temperature vs. air temperature
We computed f using the air temperature above reference height ( Ta ) in the denominator of Eq.
(2) in the main article rather than the potential temperature  a since reference heights were rarely
over 40 m in our analysis – a distance under which differences in lapse rates are deemed to have
negligible impacts on f 13. Using the DBF model and assuming a dry adiabatic lapse rate of
0.0098 ◦C m-1, for instance, f only decreases from 7.66 to 7.63 when Ta is 15 ◦C and when
Rn*  G is 200 Wm-2.
Modeling of the ground heat flux
Properties of the soil along with vegetation cover and structure are strong controls of the share
of the net radiation flux diffused into the subsurface medium – or G. Because soil heat influxes
and effluxes are approximately balanced over longer time periods (i.e., annual and interannual
means around zero), some modelers assume a zero heat flux, although there is some evidence to
support that this may not be true 14, particularly under a changing climate 15,16. We chose to
include G in our analysis in order to better represent the seasonality of the surface energy budget
in the different land cover types. For example, forests are typically less sensitive than herbaceous
land cover types to strong diurnal and seasonal fluctuations in G given their large leaf areas that
shade the ground surface for most of the year 17.
In the absence of soil property and vegetation structure information, we are forced to rely on
environmental explanatory variables when modeling the monthly mean ground heat flux.
Because surface albedo is a strong function of canopy cover and structure, we find that it acts as
a reasonable explanatory variable of the monthly G, finding a good linear relationship between
the monthly mean G and the monthly mean incoming solar radiation absorbed by the surface –
or SW (1   ) -- illustrated in Figure S4.
Figure S4 | G vs SWnet . Relationship between the monthly mean soil heat flux G and the
monthly mean net shortwave radiation SWnet -- or SW (1   ) . Parameter values of the linear
fits are shown in legends and in Table S2.
Table S2 | G model parameters and performance summary. Performance statistics and
parameter values for the models used to predict the monthly mean ground heat flux G for each
vegetation cover type. Model form is shown in legends of Fig. S4. Note that the same cropland
model is used for both rainfed and irrigated crops. All parameters are statistically significant at p
< 0.05, with standard errors (SE) shown in parentheses.
ENF
DBF
EBF
GRA
CRO (All)
a
b
# Obs.
R2
RMSE
-3.98 (0.09)
-5.29 (0.18)
-5.90 (0.21)
-7.19 (0.23)
-6.85 (0.35)
0.030 (5.9 e-4)
0.044 (1.3 e-3)
0.032 (1.1 e-3)
0.054 (1.4 e-3)
0.058 (2.6 e-3)
1,642
557
178
1,354
671
0.62
0.67
0.82
0.52
0.42
1.7
2.1
0.9
3.9
4.5
As expected, under high solar radiation loading the highest G values were found for those land
cover types having low vegetation cover fractions and leaf area indices, such as CRO (Rain) and
GRA. Monthly G at these sites is more sensitive to solar radiation loading than the forest cover
types as indicated by their higher slopes ((“b” in Table S2). Also as expected, average values
hovered around zero for all land cover types with the exception of CRO (Irr.). Here, average
values were negative implying that CRO (Irr.) behaves as a net source of heat to the atmosphere
which is clearly not the case. We offer two possible explanations of the negative trend here:
either i) frequent irrigation was convoluting the flux data at the sites, or ii) a fraction of G was
transported by the percolation of water along the soil profile and thus not detected by the heat
flux plates.
Because of this uncertainty we elected to exclude the CRO (Irr.) model when calculating the
monthly f (Eq. (S1) & Eq. (3) Methods) and instead use the model for CRO (Rain). Thus G
= 0 in our CRO (Rain)CRO (Irr.) case and the Ts presented in Figure 2i of the main article is
driven solely by f .
Quantifying local Ts at the global scale
Quantifying Ts at the global scale first involved predicting f in the valid vegetation space for
which the six vegetation cover types were allowed to occur. This was done by first overlaying
MODIS-derived IGBP land cover maps (for 2005)18 with maps of Köppen-Geiger climate zones
(1901-2000 climate)8, allowing the spatial extent of each climate zone for which the vegetation
cover of interest was found to occur to comprise part of the valid vegetation space for that
vegetation cover. For the nine LCMC cases, Ts was predicted only for the overlapping portions
of the two vegetation cover spaces defining the LCMC case in question.
Table S3. | Global statistical summary for the annual mean energy re-distribution factor
f . Annual eco-regiona means, medians, and inner-90th percentile range of f .
Eco-regiona
Eco-regiona Annual 95th
5th
Annual Mean
Median
ENF
3.4
2.7
5.8
2.3
DBF
3.2
2.4
5.4
2.1
EBF
7.5
7.3
9.9
6.2
GRA
2.1
1.5
3.9
1.0
CRO
2.5
1.9
4.6
1.1
CRO (Irr.) 3.3
2.7
6.2
1.5
a
Refers to the valid vegetation cover space defined above and the sub-panel area presented as
Figure 1 of the main article.
Six independent variables were required to scale f globally: downwelling shortwave radiation at
surface level ( SW ), downwelling longwave radiation at surface level ( LW ), precipitation (P), air
temperature ( Ta ), surface albedo (  ), and surface upwelling longwave radiation ( LW ) – with
the two latter variables being vegetation cover-dependent. For surface albedo, we employed
look-up maps of vegetation-dependent gridded monthly means of bihemispherical reflectance in
the entire shortwave broadband based on a 2001-2011 MODIS 43A Albedo/BRDF retrieval
time series produced in ref19. For the upwelling longwave radiation flux – also vegetation cover
dependent – we first identified all pixels of the CERES EBAF-Surface product20 containing
greater than 95% of one of our five vegetation covers using a MODIS-derived land cover map 18.
We then regressed pure-pixel LW with Ta and LAI to obtain a vegetation-cover dependent
LW model whose performance summary is provided in Table S4. These were required to
compute the land-cover specific 0 (a weak function of LW ) needed to estimate Ts (Eq. (1),
Methods). LW and  for both cropland cases were considered identical in our analysis.
Table S4 | LW model performance and parameter values. Regression parameters and
goodness-of-fit statistics for the regression models used to predict a 2001-2011 monthly mean
land-cover dependent LW flux for each 1◦ x 1◦ grid cell. All parameters are statistically
significant at p<0.05, with standard errors (SE) shown in parentheses. The regression equation
is: LW  a  b(Ta )  c( LAI ) .
ENF
DBF
EBF
GRA
CRO (All)
a
b
c
# Obs.
R2
RMSE
315 (0.34)
327 (0.88)
324 (0.57)
323 (0.08)
312 (0.19)
5.2 (0.01)
5.4 (0.03)
5.3 (9.4 e-3)
5.3 (3.1 e-3)
5.8 (6.1 e-3)
-0.53 (0.11)
-3.4 (0.12)
-2.1 (0.08)
-0.02 (0.06)
0.26 (0.07)
4,356
3,828
73,920
100,056
39,600
0.97
0.94
0.96
0.97
0.97
8.4
10.3
9.5
14.3
12.4
Monthly mean 2001-2011 climatologies for SW and LW were also based on CERES EBAFSurface product20; monthly climatologies of Ta and P for the same time period were sourced
from the University of Delaware empirically-derived gridded monthly terrestrial precipitation and
temperature product 10.
Annual Ts – contribution analysis
The contributions to the annual mean Ts (Fig. 2 in main article) by the three individual
mechanisms -- G , Rn* , and f -- are presented in Figures S5-S7.
Figure S5 | Ts from G . Contribution to annual mean Ts from G .
For the forest gain cases (Figure S5 a-f) G is positive during northern hemisphere winters and
negative during northern hemisphere summers. At northern latitudes the signal from the positive
G in winter months (henceforth DJF; December-January-February) dominates the annual
mean signal resulting in a net cooling that is more prominent at high latitudes where the Planck
sensitivity 0 is largest at this time of year. The opposite seasonal pattern is true for
DBFENF and GRACRO (Rain) where a positive G signal in winter dominates the annual
mean, increasing in magnitude with latitude.
Forest cover gains at high latitudes (Fig. S6 a, b, d, e) exerts a strong radiative warming signal in
winter (henceforth DJF; December-January-February) and spring (henceforth MAM; MarchApril-May) that dominates the annual mean. The annual response is slightly larger in these
regions when GRA becomes forest (Fig. S6 c&d) relative to CRO cases (Fig. S6 a&b).
Figure S6 | Ts from Rn* . Contribution to annual mean Ts from Rn* (  ).
The conversion of DBF to ENF forest results in annual radiative warming that tends to increase
with latitude as one moves further inland (Fig. S6g) in northern N. America and Asia. The
latitudinal gradient becomes obscured in southern N. America and China.
Conversion of GRA to CRO results in annual radiative warming in most regions to varying
magnitudes, with the largest radiative warming effect found in northern N. America and eastern
Siberia (Fig. S6h). EBF gains in the tropics results in a negligible radiative signal (Fig. S6 c&f).
The same albedo dataset19 is used for both CRO (Rain) and CRO (Irr.) thus there is zero radiative
signal in the irrigated crop conversion case (Fig. S6i).
Forest cover gains leads to strong annual cooling signals from f in all regions of the planet
which is slightly larger for the GRA cases than for CRO (Fig. S7 a-f). Conversion of forest
cover from DBF to ENF results in moderate cooling from f that is strongest in northern N.
America and eastern Asia (Fig. S7g).
Figure S7 | Ts from f . Contribution to annual mean Ts from f (  and z0 ).
The conversion of GRA to CRO (Rain) often leads to a cooling signal from f although there is
no discernible spatial pattern (Fig. S7h). Cooling is seen everywhere for the irrigated cropland
conversion case (Fig. S7i) and tends to scale negatively with latitude and positively with net
radiation.
Seasonal patterns in local energy redistribution ( f )
Seasonal means of monthly f for the six land cover types are presented in Figures S8-10.
Starting with DJF, little differences in f are seen at mid- and high latitudes between tall (Fig. S8
a&c) and short-statured vegetation cover types which, for many regions, is zero for the latter (Fig.
S8 b, d, f). Differences between tall- and short-statured land covers emerge at the tropics/subtropics. For short-statured vegetation covers, GRA tends to be lower than CRO (Rain) which in
turn is lower than CRO (Irr.) (Fig. S8 b, d, f).
Figure S8 | DJF mean f . Mean of monthly mean f for December, January, and February.
Above 50◦N, ENF can be slightly higher than DBF which might be attributable to their larger
aerodynamic roughness heights relative to DBF at these latitudes.
Figure S9 | MAM mean f . Mean of monthly mean f for March, April, and May.
During MAM, differences in f between DBF and ENF are seen most clearly over 60◦N, below
20◦S, and in northwest China (Fig. S9 a&c), where f is higher for ENF than for DBF. Apart
from their larger roughnesses, Bowen ratios may be lower in ENF relative to DBF during spring
months in these regions21. In spring, spatial patterns and magnitudes in CRO (Rain) and GRA
are less similar than in winter at lower latitudes (Fig. S9, b & f).
Figure S10 | JJA mean f . Mean of monthly mean f for June, July, and August.
During JJA (June-July-August), spatial patterns of f in GRA and CRO (Rain) are similar
although values for GRA are typically lower in magnitude. Values for ENF mostly exceed those
for DBF in the northern hemisphere with the exception of 30-50◦N, where DBF values can be
slightly higher. Differences in JJA f between ENF and DBF are most noticeable in China, where
values for ENF can be 0.5-1 lower than for DBF (Fig. S10 a&c). For temperate mid-latitudes,
during JJA values for CRO (Irr.) can be of similar magnitude as for ENF and DBF forests,
although the spatial variability within latitude bands is much larger than for forests (Fig. S10d).
Figure S11 | SON mean f .
November.
Mean of monthly mean f for September, October, and
At mid- and high latitudes during SON (September-October-November), spatial patterns for
GRA and CRO (Rain) (Fig. S11 b&f) and ENF and DBF (Fig. S11 a&c) become similar again,
although values for CRO (Rain) are typically larger than GRA in the tropics and southern
hemisphere.
Seasonal patterns in local Ts
Figures S12-S14 present seasonal responses by Ts to the nine LCMC cases.
Figure S12 | DJF Ts . Mean Ts for December, January, and February.
High latitude warming from forest cover gains in DJF (Fig. S12 a, b, d, e) is mostly driven by the
negative  . The switch from DBF to ENF mostly results in moderate warming in the boreal
belt and a slight cooling in the southern hemisphere (Fig. S12g). For GRACRO (Rain), the
strong DJF warming seen in northern N. America and eastern Siberia is dominated by a negative
 signal (Fig. S6h); DJF also dominates the annual mean in these regions (Fig. 2h of main
article). Moderate DJF cooling is detected for CRO (Rain)CRO (Irr.) in the southern
hemisphere and low-latitudes of the northern hemisphere (Fig. S12i).
Figure S13 | MAM Ts . Mean Ts for March, April, and May.
In MAM, forest cover gains on CRO (Rain) result in moderate warming at high northern
latitudes (above 60◦N) owed to a large negative  in this region; here  is smaller between
GRA and forests during MAM which – combined with a larger positive f -- results in
negligible warming in these regions. Below 60◦N forest cover gains mostly result in a net MAM
cooling (Fig. S13 a-f) which is dominated by a large cooling signal from f during May that
offsets a warming signal from  during March and April.
Differences are negligible in MAM for the DBFENF case (Fig. S13g), with the weak warming
signal seen above 45◦N mostly dominated by a difference in surface albedo. For GRACRO
(Rain), the cooling seen above 60◦N is attributed to a large positive  .
Figure S14 | JJA Ts . Mean Ts for June, July, and August.
The JJA cooling seen for forest cover gain cases (Fig. S14 a-f) is attributed to a dominant signal
from a large positive f that is greatest for the two GRA cases (Fig. S14 d&e). For the
DBFENF case, a positive f signal in China, temperate S. America, and the southeast U.S.
outweighs the negative  resulting in a slight net JJA cooling.
Cooling signals from the conversion of grassland to cropland (Fig. S14h) are strongest in the
northern hemisphere, whereas rainfed to irrigated cropland results in mostly uniform cooling
across all study regions during JJA (Fig. S14i).
Figure S15 | SON Ts . Mean Ts for September, October, and November.
Forest gains on croplands (Fig. S15 a&b) result in a slight SON warming in central Asia and
northern Canada with no change for the rest of the boreal belt, whereas for gains on grasslands
(Fig. S15 d&e) a stronger and more pronounced SON warming is found in these regions owed to
a stronger negative  signal. For all forest gain cases (Fig. S15 a-f) a net cooling in SON is
found for temperate and tropical latitudes.
Moderate SON warming for the DBFENF (Fig. S15h) and GRACRO (Rain) cases (Fig.
S15h) is confined to the northern latitudes.
Uncertainty
Parameter uncertainty, local Ts
Parameter uncertainty connected to the models employed to predict monthly f is evaluated with
a measure of normalized error (NE) that expresses deviations in the annual Ts as a percentage
of the original Ts (Fig. 2 of main article) after changing model parameters:
NE ( f )  100 T ( f  f SE )  T ( f ) T ( f )1
(S2)
where T ( f  f SE ) is the annual mean temperature response computed with plus/minus a
standard error in f ’s model parameters.
Increases in f model parameters increases f for all land cover types and vice versa. The effect of
increases expressed in terms of NE (Eq. (S2)) is illustrated in Fig. S16. In general, positive NEs
equate to less cooling if the sign of the original Ts is negative and additional warming if the
original sign is positive. Negative NEs equate additional cooling if the sign of the original Ts is
negative and less warming if the original sign is positive.
Figure S16 | Normalized Error, T ( f  f SE ) . Normalized error, or the change in annual
Ts expressed as a percentage of the original Ts due to a one stand error (one sigma) increase
in f model parameter values. Latitude band means of normalized error are shown below each
panel (in %) with the absolute normalized error show in parentheses (also in %).
For all forest gain cases (Fig. S16 a-f), increases to f model parameters have little effect on Ts .
For the DBFENF case, increases in f result in additional warming in parts of China, the
midwestern U.S., central Europe, Fennoscandia, and S. America (Fig. S16g). Increases in f ’s
parameters would result in suppressed warming in the southeastern U.S., central Asia, and the
Mediterranean region. Most of the uncertainty for the GRACRO (Rain) case (Fig. S16h) is
found in the tropics where f parameter increases result in both weakened (red areas) and
enhanced (blue areas) cooling although the general NE trend here is negative (i.e., enhanced
cooling). Largest uncertainty is found for the CRO (Rain)CRO (Irr.) case (Fig. S16i) at high
latitudes where the NE is 31% for the 45-75N zone which equates to suppressed cooling.
The effect of decreases in f ’s parameters measured in terms of NE is illustrated in Fig. S17. As
for increases, parameter decreases result in negligible NE for the forest gain cases (Fig. S17 a-f).
For DBFENF (Fig. S17g), NE spatial patterns and magnitudes are similar to the increasing
parameter case (Fig. S16g), where the largest NE is found in the midwestern U.S., temperate S.
America, China, and Europe.
Figure S17 | Normalized error, T ( f  f SE ) . Normalized error, or the change in annual Ts
expressed as a percentage of the original Ts due to a one stand error (one sigma) decrease in f
model parameter values. Latitude band means of normalized error are shown below each panel
(in %) with the absolute normalized error show in parentheses (also in %).
For GRACRO (Rain), parameter decreases result in a mostly positive NE trend in the tropics
– or weakened cooling. As for parameter increases, f parameter decreases also results in large
positive net NE at northern latitudes (45-75◦N) for the CRO (Rain)CRO (Irr.) case (Fig. S17i)
– or a weakened cooling. Here, however, a non-negligible NE is found for the tropics where the
decreases to f ’s parameter values equates to additional cooling.
Parameter uncertainty, local G
Similar to f , we compute the NE linked to the one-sigma confidence bounds (66%) connected
to the models used to estimate monthly G. Error connected to G propagates to Ts indirectly
through error associated with predictions of f (i.e., G is part of a predictor variable) and directly
through its role as a system variable in the surface energy balance model.
Figure S18 | Normalized error, T (G  GSE ) Normalized error, or the change in annual Ts
expressed as a percentage of the original Ts due to a one stand error (one sigma) increase in the
parameters of the models employed to predict G . Latitude band means of normalized error are
shown below each panel (in %) with the absolute normalized error show in parentheses (also in
%).
However, relative to the uncertainty connected to f , the uncertainty connected G tends to play a
more negligible role in influencing Ts judging by the NEs of Figures S18 & S19 relative to S16
& S17.
Figure S19 | Normalized error, T (G  GSE ) . Normalized error, or the change in annual
Ts expressed as a percentage of the original Ts due to a one stand error (one sigma) decrease
in the parameters of the models employed to predict G . Latitude band means of normalized
error are shown below each panel (in %) with the absolute normalized error show in parentheses
(also in %).
Linearization of surface outwelling longwave radiation
The formulation of f involves a linearization of the surface upwelling longwave radiation term
 sTs4 which can lead to biased predictions22,23. To gauge the magnitude of this bias we evaluate a
fictitious LCMC case, finding that bias is slightly negative when f is less than 1, switching to
positive at around 1.25 and thereafter remaining positive and increasing in magnitude as
illustrated in Figure S20.
Figure S20 | Ts model bias. Illustration of model bias connected to the linearization of the
upwelling surface longwave radiation flux.
At f = 2 the normalized bias is 18%, becoming 36% at f = 3. For the nine LCMC cases
considered in our analysis, the largest bias connected to the linearization of  sTs4 likely occurred
in the “GRAENF” case which had the largest f . For this case, the global annual mean f
was 1.8, implying a mean annual normalized prediction bias of ~15%.
Local Ts from historical LCMC
Actual Ts from net gains or losses in forest cover on (or at the expense of) croplands and
grasslands during the 1951-2010 period (Fig. 4 a&d) were computed using maps of historical land
cover change reconstructions24 based on the ISAM-HYDE dataset24 (accessed at:
https://gis.ncdc.noaa.gov/geoportal/catalog/search/resource/details.page?id=gov.noaa.ncdc:C0
0814 ). Seven primary and seven secondary forest cover types were aggregated into a single
Forests cover type. Croplands comprised the aggregate of “C3 Cropland” and “C4 Cropland”
covers, while Grasslands comprised the aggregate of both “C3/C4 Grassland/Steppe” and
“C3/C4 Pastureland” covers. The net 1951-2010 change is represented as the percentage of
forest cover gain within each 1◦ x 1◦ pixel on either Croplands or Grasslands, as shown in Figure
S21. The fraction gained/lost in each grid cell is then multiplied by the potential local Ts for
that grid cell and conversion type (gain vs. loss).
Figure S21 | Reconstructed forest cover gain and CO2 Ts over the 1951-2010 period.
Spatial patterns in the response by surface radiometric temperature Ts to historical late-20th
century CO2 radiative forcing (top panel), and reconstructed forest cover gains on grasslands
(“GRA”; middle) and croplands (“CRO”; bottom).
The Ts attributable to CO2 is based on a combination of output from HadGEM-ES – a state of
the art earth system model25 – and historical radiative forcing reconstructions for CO2 and
WMGHGs26. The HadGEM-ES simulation followed the CMIP5 design protocol27 for WMGHG
over the 20th century (simulation “historicalGHG_r1i1p1,” accessed via the Earth System Grid
Federation data portal (https://pcmdi.llnl.gov/projects/esgf-llnl/ )). Ts is the difference in
simulated mean Ts between the 1934-1959 and 1989-2005 period – or 1.31 K for the global
mean. This value is higher than the observed global mean air temperature change ( Ta ) of 0.9 K
reported in ref.28 over the 1951-2010 record, which is not unexpected given the difference in
metric. Attribution to CO2 is then done by multiplying Ts by the ratio of the reconstructed CO2
to total reconstructed WMGHG radiative forcings over the same 1951-2010 period 26 – or
1.13/1.92 Wm-2. This result is presented in Figure S21 and used in the normalization calculation
presented as Figure 4 of the main article.
Additional FLUXNET site detail
Table S5 provides an overview of the FLUXNET sites comprising our final in-situ dataset.
Additional acknowledgements are provided in Table S6.
Table S5 | FLUXNET site names and Principle Investigators
Site Name
IGBP
Principle Investigators
ENF
ENF
Derek Eamus, James Cleverly
Hank Margolis
CA-SF1
CA-SF2
ENF
ENF
CH-Dav
ENF
Bily Kriz –
Mountains
Lackenberg
Beskidy CZ-BK1
ENF
Andrew Black
Andrew Black, Alan Barr, Harry
McCaughey
Werner Eugster, Lukas Hörtnagi, Nina
Buchmann, Sabina Keller
Marian Pavelka, Radek Czerny
DE-Lkb
ENF
Oberbarenburg
Tharandt
–
Station
Hyytiala
Lavarone
Renon/Ritten
DE-Obe
Anchor DE-Tha
ENF
ENF
Alice Springs
Eastern
Old
Spruce
SK-1977 Fire
SK-1989 Fire
Short
Name
AU-ASM
Black CA-Qfo
Davos – Seehorn Forest
FY-Hyy
IT-Lav
IT-Ren
ENF
ENF
ENF
Rainer Steinbrecher, Matthias Mauder,
Elisabeth Eckart, Hans Schmid, Burkhard
Beudert
Christian Bernhofer, J. Gruenwald
Christian Bernhofer, Barbara Köstner,
Thomas Grünwald
Timo Vesala, Ivan Mammarella, Pasi Kolari
Damiano Gianelle, Matteo Sottocornola
Stefano Minerbi, Leonardo Montagnani
San Rossore 2
IT-SR2
ENF
San Rossore
IT-SRo
Loobos
NL-Loo
Fedorovskoje – drained RU-Fyo
spruce
Zotino
RU-Zot
GLEES
US-GLE
ENF
ENF
ENF
Metolius
Intermediate US-Me2
Pine
Niwot Ridge
US-NR1
Wetzstein
DE-Wet
ENF
Le Bray
FR-LBr
ENF
Yatir
Norunda
IL-Yat
SE-Nor
ENF
ENF
Hainich
DE-Hai
DBF
Soroe – Lille Bogeskov
Castel D’Asso 1
DK-Sor
IT-CA1
DBF
DBF
Castel D’Asso 3
IT-CA3
DBF
Collelongo – Selva Piana
IT-Col
DBF
Ispra ABC-IS
IT-Isp
DBF
Roccarespampani 1
IT-Ro1
DBF
Roccarespampani 2
IT-Ro2
DBF
Morgan
Forest
Monroe
ENF
ENF
ENF
ENF
State US-MMS
DBF
University of Michigan US-UMB
Biological Station
DBF
Willow Creek
US-WCr
DBF
Mongo
Missouri Ozark
ZM-Mon
US-MOz
DBF
DBF
Alessandro Cescatti, Carsten Grüning,
Ignacio Goded, Olga Pokorska
Alessandro Cescatti, Carsten Grüning
Eddy Moors, Jan Elbers, Wilma Jans
Andrej Varlagin, Juliya Kurbatova
Corinna Rebmann
William Massman, Linda Joyce, Anna
Schoettle, John Frank, Jose Negron
Beverly Law, Larry Mahrt, Dean Vickers,
Christoph Thomas, Chad Hanson
Peter Blanken, Russ Monson
Corinna Rebmann, Olaf Kolle, Werner
Kutsch
Denis Loustau, Nathalie Jarosz, Jean-Marc
Bonnefond
Dan Yakir, Eyal Rotenberg
Anders Lindroth, Harry Lankreijer, Fredrik
Lagergren
Alexander Knohl, Olaf Kolle, Lukas
Siebicke, Matthias Herbst
Kim Pilegaard, Andreas Ibrom
Dario Papale, Nicola Arriga, Simone
Sabbatini, Michele Tomassucci, Alessio
Boschi
Giorgio Matteucci, Dario Papale, Nicola
Arriga,
Simone
Sabbatini,
Michele
Tomassucci
Giorgio Matteucci, Giuseppe Scarascia,
Francesco Mazzenga
Alessandro Cescatti, Eleonora Canfora,
Carsten Gruening, Ignacio Goded, Olga
Pokorska
Dario Papale, Paolo Stefani, Nicola Arriga,
Luca Belelli, Raffaele Casa, Domenico
Vitale
Dario Papale, Paolo Stefani, Nicola Arriga,
Luca Belelli, Raffaele Casa, Simone
Sabbatini, Michele Tomassucci, Alessio
Boschi, Claudia Consalvo
Danilo Dragoni, Sue Grimmond, J.C.
Randolph, Hans Peter Schmid, Hong-Bing
Su
Peter Curtis, Christoph Vogel, Hans Peter
Schmid, Damiano Gianelle, Christopher
Gough, Gil Bohrer
Paul Bolstad, Bruce Cook, Kenneth Davis,
Weiguo Wang, Ankur Desai
Werner Kutsch, Lutz Merbold
Lianhong Gu, Paul Hanson, Tilden Meyers,
Stan Wullschleger, Stephen Pallardy, Bai
Cumberland Plains
Robson Creek
Tumbarumba
Wallaby Creek
Whroo
Wombat
EBF
EBF
EBF
EBF
EBF
EBF
Santarem-Km83
AU-Cum
AU-Rob
AU-Tum
AU-Wac
AU-Whr
AUWom
BR-Sa3
Puechabon
FR-Pue
EBF
Guyaflux
Neustift/Stubai Valley
Daly River Pasture
Emerald
Riggs Creek
Sturt Plains
GF-Guy
AT-Neu
AU-DaP
AU-Emr
AU-Rig
AU-Stp
EBF
GRA
GRA
GRA
GRA
GRA
Chamau Grassland
CH-Cha
GRA
Fruebuel Grassland
CH-Fru
GRA
Önsingen 1
Changling
Bily Kriz Grassland
Grillenburg
Monte Bondone
CH-Oe1
CN-Cng
CZ-BK2
DE-Gri
IT-MBo
GRA
GRA
GRA
GRA
GRA
Torgnon
IT-Tor
GRA
Horstermeer
NL-Hor
GRA
Woodward Switchgrass 1
US-AR1
GRA
Woodward Switchgrass 2
US-AR2
GRA
Santa Rita Grassland
Vaira Ranch
US-SRG
US-Var
GRA
GRA
Walnut Gulch Kendall
Grasslands
Ghanzi Grassland
Vall d’Alinya
Dripsey
Amplero
US-Wkg
GRA
BW-Ghg
ES-VDA
IE-Dri
IT-Amp
GRA
GRA
GRA
GRA
Cabauw
NL-Ca1
GRA
EBF
Yang
Elise Pendall
Michael Liddell
Eva van Gorsel
Jason Beringer
Jason Beringer
Jason Beringer, Anne Griebel
Mike Goulden, Humberto da Rocha, Scott
Miller
Richard Joffre, Serge Rambal, Jean-Marc
Ourcival, Jean-Marc Limousin, Karim
Piquemal
Damien Bonal, Eleonora Canfora
Georg Wohlfahrt
Jason Beringer, Lindsay Hutley
Ivan Schroder, Steve Zegelin
Jason Beringer
Jason Beringer, Lindsay Hutley, Shaun
Cunningham
Nina Buchmann, Kathrin Fuchs, Sabina
Keller, Lutz Merbold, Lukas Hörtnagl
Nina Buchmann, Lutz Merbold, Sabina
Keller, Lukas Hörtnagl
Christoph Ammann
Gang Dong
Marian Pavelka, Radek Czerny
Christian Bernhofer, Thomas Grünwald
Damiano Gianelle, Fondazione Mach,
Barbara Marcolla, Matteo Sottocornola
Edoardo Cremonese, Eleonora Canfora,
Mirco Migliavacca, Umberto Morradicella,
Marta Galvagno
Han Dolman, Luca Belelli, Johannes
Hachmann, Ko Huissteden, You Wang
Jan Elbers, James Bradford, Marc Fischer,
Chris Zou
Jan Elbers, James Bradford, Marc Fischer,
Chris Zou
Russell Scott
Dennis Baldocchi, Ted Hehn, Nancy
Kiang, Francesca Ponti, Jorge Curiel-Yuste,
Siyan Ma
Russell Scott
Christopher Williams
Arnaud Carrara, Cristina Gimeno
Gerard Kiely, Paul Leahy, Nelius Foley
Dario Papale, Paolo Stefani, Manuela
Balzarolo
Eddy Moors, Jan Elbers, Wilma Jans
Mitra IV Tojal
Brookings
PT-Mi2
US-Bkg
GRA
GRA
Fort Peck
Goodwin Creek
Yanco
US-FPe
US-Goo
AU-Ync
Lonzee
BE-Lon
Önsingen 2
CH-Oe2
GRA
GRA
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
Gebesee
DE-Geb
Klingenberg
DE-Kli
Grignon
FR-Gri
Castel d’Asso 2
IT-CA2
ARM Southern Great US-ARM
Plains
Mead – Rainfed Maize- US-Ne3
Soybean
Langerak
NL-Lan
Lutjewad
NL-Lut
Molenweg
NL-Mol
Bondville
US-Bo1
Borgo Cioffi
IT-BCi
Castellaro
IT-Cas
Mead
–
Irrigated US-Ne1
Continuous Maize
Mead – Irrigated Maize- US-Ne2
Soybean
Twitchell Corn
US-Tw2
Twitchell Alfalfa
US-Tw3
El Saler-Sueca
ES-ES2
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Rain)
CRO
(Irr.)
CRO
(Irr.)
CRO
(Irr.)
CRO
(Irr.)
CRO
(Irr.)
CRO
(Irr.)
CRO
(Irr.)
Casimiro Pio
Tilden Meyers, Tagir Gilmanov, Bruce
Wylie
Tilden Meyers
Tilden Meyers
Jeffrey Walker
Bernard Heinesch, Anne Ligne, Tanguy
Manise
Nina Buchmann, Carmen Emmel, Werner
Eugster, Sabina Keller, Lutz Merbold,
Lukas Hörtnagl
Antje Moffat, Olaf Kolle, Mathias Herbst,
Corinna Rebmann
Christian Bernhofer, Thomas Grünwald
Pierre Cellier, Benjamin Loubet, Nicolas
Mascher
Dario Papale, Beniamino Gioli, Nicola
Arriga,
Simone
Sabbatini,
Michele
Tomassucci, Alessio Boschi
Marc Fischer, Dave Billesbach, Margaret
Torn
Shashi Verma, Andrew Suyker, Todd
Schimelfenig
Eddy Moors, Jan Elbers, Wilma Jans
Eddy Moors, Jan Elbers, Wilma Jans
Eddy Moors, Jan Elbers, Wilma Jans
Tilden Meyers, Stephen Ogle,, Mark Heuer
Enzo Magliulo, Paul Di Tommasi, Luca
Vitale, Daniela Famulari, Anna Tedeschi
Alessandro Cescatti, Carsten Grüning
Shashi Verma, Andrew Suyker, Todd
Schimelfenig
Shashi Verma, Andrew Suyker, Todd
Schimelfenig
Dennis Baldocchi, Joe Verfaillie, Sara
Knox, Laurie Koteen, Cove Sturtevant
Dennis Baldocchi, Joe Verfaillie, Sara
Knox, Laurie Koteen, Cove Sturtevant
Arnaud Carrara, Maria Sanz
Additional data citations or acknowledgements
This work used eddy covariance data acquired and shared by the FLUXNET community,
including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP,
CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC,
OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by
ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and
harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux
Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and
ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices.
Table S6| Citations for individual sites or additional acknowledgements.
Site Name
CA-Qfo
FR-LBr
IT-Ro1
US-MMS
CH-Oe1
Citation/Acknowledgment
Bergeron, O., Margolis, H.A., Black, T.A., Coursolle, C., Dunn, A.L., Barr, A.G., Wofsy,
S.C. 2007. Comparison of CO2 fluxes over three boreal black spruce forests in Canada.
Global Change Biol. 13: 89-107, doi:10.1111/j.1365-2486.2006.01281.x.
Berbigier P., Bonnefond J.M., Mellmann P., 2001. CO2 and water vapour fluxes for 2
years above Euroflux forest site. Agricultural and Forest Meteorology, 108 (3), 183-197.
Rey, A. et al. Annual variation in soil respiration and its components in a coppice oak
forest in central italy. Global Change Biology 9, 851-866 (2002).
Research at the MMSF site was supported by the Office of Science (BER), U.S.
Department of Energy, Grant No. DE-FG02-07ER64371
Ammann C., Flechard C., Leifeld J., Neftel A., Fuhrer J., 2007. The carbon budget of
newly established temperate grassland depends on management intensity. Agriculture
Ecosystems and Environment 121, 5-20.
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