Supporting Material for:
Climate Change Implications of Shifting Forest Management
Strategy in a Boreal Forest Ecosystem of Norway
Ryan M. Bright*, Clara Antón-Fernández, Rasmus Astrup, Francesco Cherubini, Maria Kvalevåg, & Anders
H. Strømman
*To
whom correspondence should be addressed: ryan.m.bright @ntnu.no
This supporting material file contains:
1) Additional information surrounding the methods and data used in our stand-level local
climate impact analysis
2) Uncertainty: Remote sensing data and empirical models
3) Modeling adjustments in RCP scenarios
4) Alternative results figures
1) Relative contributions to local climate change from changes to surface
intrinsic biophysical mechanisms
A 50-yr. deciduous-dominant (Betula pendula; 61.3N, 12.51E), 80-yr. mixed conifer (Picea abies and
Pinus sylvestris; 61.15N, 12.43E), and young clear-cut conifer site (61.18N, 12.57E) in close
proximity (~10km) to each other and to a meteorological station (“Trysil Vegstasjon”, 61.29N,
12.27E) are chosen for the analysis. The sites have a high level of structural homogeneity (i.e.,
canopy cover fraction > 85% for the forested sites and < 10% for the clear-cut site) and have
productivities that may be considered average for the region, with conifer sites yielding around
200 m3ha-1 (stem volume, under bark) and deciduous-dominant (>50% deciduous) sites typically
yielding <100 m3ha-1 at final harvest. The sites are assumed to share the same background
climate state; that is, they receive equal amounts of incoming short- and longwave radiation and
share identical ambient climate parameters like air temperature, vapor pressure, humidity, etc.
The analysis is performed at a single pixel level, and stands ≥ 1 km2 are selected in order to be
compatible with the spatial signature of all remote sensing products employed in the analysis (~1
km2). Ignoring minor terms such as ground heat flux and surface emissivity changes (Juang et al.,
2007), a change in surface temperature can be approximated by quantifying the response to both
1
the external (albedo change) forcing and the efficiency with which net radiation is internally
redistributed:
Ts 

(1  f )
SW 

Rn f
(1  f )2
(S1)
where  is the temperature sensitivity resulting from the longwave radiation feedback
( 1 / 4 Ts3 ), SW the net shortwave radiation change, Rn the apparent net radiation, and f the
energy redistribution factor formulated by Lee et al. (2011):
f 
C p
1
(1  )
3
4 Ts ra

(S2)
where  is air density, C p the specific heat of air at constant pressure, ra the aerodynamic
resistance,  the Stephan-Boltzmann constant, and  the Bowen ratio -- or the ratio of
sensible-to-latent heat flux. The change in the energy redistribution factor  f (Eq. (S2)) can be
decomposed into that associated with roughness changes f1 and changes to the Bowen ratio
f 2 ( f  f1  f 2 ):
f1  
C p
1 r
(1  ) a
3
4 Ts ra
 ra
(S3)
f 2  
 C p   


4 Ts3ra   2 
(S4)
where aerodynamic resistance ra is calculated using forest inventory, remote sensing and
meteorological data (elaborated more below) and where the Bowen ratio  is estimated with
sensible heat flux as the residual of the surface energy balance equation ( Rn  LE  H  G ):

( Rn  LE  G )
LE
(S5)
where LE is the latent heat flux from evaporation and transpiration estimated with an updated 8day MODIS global evapotranspiration algorithm (MOD16A2) (Mu et al., 2011), G the ground
heat flux estimated using the Fraction of Absorbed Photosynthetically Active Radiation (FPAR)
(MOD15A2) (Knyazikhin et al., 1999) as a surrogate for fractional vegetation coverage (Los et al.,
2000), a soil heat flux ratio for a fully vegetated canopy of 0.05 (Monteith & Unsworth, 2008),
2
and a soil heat flux ratio for bare soil of 0.315 (Kustas & Daughtry, 1990). Net radiation Rn is
the difference between incoming net shortwave radiation and outgoing net longwave radiation
calculated following Allen et al. (1998) using daily insolation and clearness index data for our sites
obtained from the NASA Langley Research Center POWER Project (NASA, 2013).
Aerodynamic resistance ( ra ) to heat and momentum transfer is estimated for neutral conditions
(stability parameters for heat and momentum equal zero) following refs. (Allen et al., 1998, Liu et
al., 2007):
ra 
1   z  d    z  d  
ln 
  ln 

k 2 u   z m    z h  
(S6)
where z is a reference height of 10 m, k is the von Karman constant ( 0.41), u the wind speed at
reference height (ms-1) , d the zero-plane displacement height (m), or the height at which wind
speed becomes essentially zero in the canopy, z m the roughness length for momentum transfer,
and zh the roughness length for heat transfer. Roughness lengths for momentum z m and zeroplace displacement heights d are calculated using empirical models developed by Nakai et al.
(2008) that are based on stand density SD (trees ha-1), LAI (m2 m-2), and mean tree height
h (m):
 1  exp(k1SD)  1  exp(k2 LAI )  
d  1  

  h
k
SD
k
LAI

1


2


(S7)
where k1 and k2 are parameters and LAI is leaf area index, also taken from MOD15A2
(Knyazikhin et al., 1999). For the deciduous site, a stand density of ~1400 trees ha-1 and average
tree height of 9 m are used; for the conifer site, a stand density of ~750 trees ha -1 and average
tree height of 15 m are used; the clear-cut site has a stand density of ~10 trees ha-1 (due to tree
retention policy) with a mean tree height from 2004-2009 of 2 m. Roughness length for
momentum is then estimated with the zero-plane displacement height, tree height, and an
empirical constant:
d
zm  0.264(1  )h
h
(S8)
Following Allen (1998) we assume the roughness length of heat, zh , is 1/10th of z m .
3
We take the diurnal mean radiometric surface temperature Ts from MODIS 1 km2 product 11A2
(Wan, 1999, Wan & Li, 2008) for use in estimating the climate sensitivity (  ) and energy
redistribution expressions ( f ). MODIS 8-day Albedo/BRDF 1 km2 product MCD43B (Schaaf
et al., 2002) provides estimates of black-sky shortwave broadband albedo and is used to compute
changes in surface shortwave radiation ( SW ) and net radiation ( Rn ).
Uncertainties
surrounding the remote sensing products employed are discussed further in subsequent sections.
The expanded form of Eq. (S1) is a reproduction of Eq. (2) in the main article, given as:
Ts 

(1  f )
SW 


Rn f1 
Rn f 2
2
(1  f )
(1  f ) 2
(S9)
where the first, second, and third term on the right-hand side gives us the contributions to the
surface temperature change from the albedo change forcing, roughness change, and Bowen ratio
change, respectively.
Figure S1 presents the 2004-2009 8-day means of the MODIS observations utilized in the Ts
contribution analysis. The full LAI and albedo time series is presented in Figure S2 along with
fractional vegetation coverage ( FC , used to estimate the ground heat fluxes, G), aerodynamic
resistance ( ra ), roughness length ( z0 ), and Bowen ratio (  ) at each of the three sites. Table S1
provides an overview of how meteorological and remotely sensed data are utilized in the analysis
together with their sources.
4
Table S1. Overview of meteorological and remote sensing variables employed in the standlevel/local impact analysis.
Parameter
ra
Equation
Eq. (S6)
Name
Aerodynamic
Meteorological
Variable
Variable(s)
Description
u
Windspeed at
(Norwegian
reference height
Meteorological
(ms-1)
Institute, 2013)
resistance
Rn
(Equation
Net radiation
not shown)
K clr , Rs , VP, Clear-sky clearness
Tair
Data Source
(NASA,
2013,
index (unitless),
Norwegian
Downwelling solar
Meteorological
radiation at surface
Institute, 2013)
(Wm-2), Vapor
pressure (kPa), Air
temperature at
reference height (K)
Parameter
Equation
Name
Remote
Variable
Sensing
Description
Data Source
Variable(s)
f
Eq. (S2-4)
Energy
Ts
redistribution
Surface temperature
(ORNL DAAC,
(K)
2013)
Fraction of
(ORNL DAAC,
2013)
expression
FC
(Eq. not
Fractional
shown)
vegetation
absorbed
coverage
photosynthetically
FPAR
active radiation

Eq. (S5)
Bowen ratio
LE
5
Latent heat of
(ORNL DAAC,
vaporization (Wm-2)
2013)
Figure S1. Six-year mean 8-day MODIS ET, Black-sky Albedo, LAI, and FPAR observations at
each stand in our local-scale analysis, shown with standard deviation as indicator of inter-annual
variability. Biophysical variables are grouped by color and row, and sites are grouped by column.
Data are for a single MODIS pixel of ~1 km2. Six-year annual means and standard deviations are
shown in legends.
Differences in important radiative, aerodynamic, and physiological attributes across the three sites
employed in the factor analysis are presented in Figure S2 for the full 2004-2009 time series.
6
Figure S2. Full six-year time series of key radiative, aerodynamic, and physiological parameters
applied in the stand-scale local climate impact analysis. Top: Roughness lengths for momentum
(left) and aerodynamic resistances to heat and momentum transfer (right); Middle: Black-sky
albedo at local solar noon (left) and Bowen ratios (right); Bottom: Leaf Area Index (left) and
Fractional Vegetation Coverage (right).
2) Uncertainty
Remote sensing data
Albedo products based on MODIS sensors have been thoroughly validated with field
observations and other satellite data (Jin et al., 2003, Salomon et al., 2006, Tsvetsinskaya et al.,
2006), with the accuracy of the Collection 5 shortwave albedo reported generally at <0.03
(Román et al., 2009, Wang et al., 2010). Only data passing quality control filters (Justice et al.,
1998) were included in our analysis. For the remote sensing datasets, for any composite date
having unacceptable data quality, values are filled by averaging values of acceptable quality from
an 11-year time series at the same composite date, which has the benefit of constraining the local
information provided by the interpolation with phenological and climatological information
7
included in the ensemble average (O'Halloran et al., 2012). Regarding albedo, this helps reduce
interpolation errors from the discontinuities caused at the edges of snowy periods, where the
albedo changes abruptly.
Land surface temperature products from MODIS have a reported accuracy to within 1 degree C
(0.5C in most cases) (Bosilovich, 2006, Wan & Li, 2008, Wang et al., 2008).
Serbin et al. (2013) recently evaluated the spatial and temporal performance of MODIS
LAI/FPAR over a boreal forest chronosequence in Manitoba, Canada, and found that MODIS
LAI/FPAR Collection 5 generally outperformed the Collection 4 products when compared to insitu observations. For Collection 5 LAI, they report an uncertainty (RMSE) of 0.63 m2m-2, and
for FPAR, they report an uncertainty (RMSE) of 0.07. Although in general the performance of
MODIS LAI and FPAR (C5) products is good with respect to capturing the general phenological
trajectory in boreal forests, Serbin et al. (2013) show that they tended to overestimate and
underestimate the LAI and FPAR for the youngest (1 yr.) and oldest (154 yr.) sites, respectively.
This implies that surface temperature contributions from aerodynamic roughness and Bowen
ratio at our young/clear-cut site could be overestimated since computations are based on
vegetation coverage fraction, aerodynamic resistance, and evapotranspiration (ET) – which all
rely to some extent on MODIS LAI and FPAR (C5) products (MOD15A2).
The recently updated MODIS 16 evapotranspiration (ET) algorithm of Mu et al. (2011) is applied
in this study to estimate the latent heat ( LE ) flux at each of our sites, where results from
AmeriFlux ground validation experiments show improvements over the old algorithm (Mu et al.,
2007), with error ranges within the 10-30% mean acceptable range (Allen et al., 1998, Gowda et
al., 2008, Kalma et al., 2008, Li et al., 2009, Wang & Dickinson, 2012). Although validation
studies for boreal Norway do not exist, Velpuri et al. (2013) recently report good agreement
between MODIS ET (MOD162) and in-situ ET for similar climate zones to those spanning our
study region -- Koppen-Geiger “Dfb” and “Dfc” – with R2s of 0.72 and 0.59 and RMSEs of 31
and 25 (mm month-1), respectively.
Empirical Models
Parametric uncertainty in the models used to predict thinning and final biomass harvests, soil
carbon, and albedo in each of the four forest management scenarios was not presented in the
8
main article (Figure 3). Figure S3 presents the Monte Carlo simulation mean outcomes after
1,000 runs (as shown in Figure 3) together with one standard deviation.
Figure S3. Monte Carlo simulation mean (1,000 replications) NEE flux, HP flux, and surface
albedo for each scenario shown with one standard deviation.
3) Modeling Adjustments: RCP Scenarios
Radiative efficiency adjustments in emission Scenarios
Carbon cycle-climate impacts presented in Figures 5 & 6 of the main manuscript are calculated
using both a fixed radiative efficiency kCO2 based on the 2010 atmospheric CO2 concentration
and one that evolves over time with the projected evolution of atmospheric CO2 concentration
associated with RCP4.5 and 8.5 scenarios. Projected concentrations in these two scenarios are
CMIP5 multi-model ensemble means (Meinshausen et al., 2011) which are fed into Eq.’s (7) and
(8) of the main manuscript.
Figure S4 shows the resulting values applied in the main article.
9
Figure S4. Radiative efficiency per kg CO2 emitted to the atmosphere for the two emission
scenarios linked to representative concentration pathways (RCPs) 4.5 and 8.5 compared to the
2010 concentration of 389 ppm.
4) Alternative Figures: Results
Local impact, full time series
Figure S5 is an alternative version of Figure 2 of the main article that presents the full time series
results from the local/stand-level analysis based, in part, on the data presented in Figure S2.
10
Figure S5. Full 6-year time series contributions to daily local surface temperature across three
managed sites due to differences in surface albedo (“∆Albedo RF”), aerodynamic roughness
(“∆Roughness”), and the partitioning of net radiation into sensible and latent turbulent heat
fluxes (“∆Bowen”). A) Clear-cut site – Deciduous site; B) Clear-cut – Conifer site; C) Deciduous
– Conifer forest.
11
Temperature-albedo feedback
Figure S6 is an alternate version of Figure 4 of the main article showing absolute albedo
trajectories rather than normalized.
Figure S6. Simulation means of absolute spring and autumn albedo trajectories (lefthand y-axis,
blue line colors) in the BAU scenario under projected regional climate warming (righthand y-axis,
green line colors).
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