Different temperature response of
broadleaf and conifer species alters
productivity predictions of boreal
forests under climate change
Tapio Linkosalo1), Pasi Kolari2), Raisa Mäkipää1),
Jukka Pumpanen2), Annikki Mäkelä2)
1) Finnish Forest Research Institute, Vantaa research unit, Finland,
2) Dept. of Forest Ecology, University of Helsinki, Finland.
•
The CC-TAME (Climate Change: Terrestrial Adaptation & Mitigation in
Europe) project concentrates on assessing the impacts of agricultural,
climate, energy, forestry and other associated land-use policies considering
the resulting feed-backs on the climate system in the European Union.
•
Effect of land use on carbon sequestration
•
Sub-projects for agricultural and forest land use; divided for boreal, temperate
and mediterranean climate zones
•
Continental level simulation for species, rotation length, management
•
Local models provide maximum of Mean Annual Increment for different
species -> essentially a GPP model needed
•
For boreal zone we used PreLuEd to calculate GPP -> NPP -> stem growth
Gross Primary Production (GPP) -model
Potential GPP calculated with PreLuEd model(*) :
P = fAPAR·[β · f(I)] · f(T) · f(H2O) · f(CO2), where
β max. potential photosynthetic rate
f(I) irradiance modifier
fAPAR absorbed fraction of radiation [varies for species and sites]
f(T) temperature/phenology modifier
f(H2O) VPD/soil water modifier
f(CO2) CO2 modifier
(*) Model from: A. Makela et al., 2008. Developing an empirical model of stand GPP with the LUE
approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Global
Change Biology 14:92-108
Calculation for 3 subzones of the
boreal zone;
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•
•
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Hemiboreal, TS >1250 DD
South boreal, 1100 < TS < 1250
Mid-boreal, 900 < TS < 1100
North boreal, TS < 900 DD
Daily weather data for 100 years, for
• (Simulated) Current Climate
• A1B scenario
• B1 scenario
Results for the boreal zone:
change in productivity (trend over 100 years )
Results indicate that temperature / phenology is the
major contributor resulting in difference in productivity
between conifers and broadleaf species
Therefore, lets take a closer look where the difference
comes from!
Temperature modifier combines the effect of :
– Photosynthetic rate depending on daily temperature
– Phenology
For conifers, a rate function following air temperature
with delay (phenology = frost resistance)
For broadleaves, a rate function following immediate air
temperature,
In addition, a temperature sum model to predict timing of
leaf bud burst
F(T) –function for (both) conifers and broadleaf species:
xk =
TMean − Tmin
Tmax − Tmin
∆X =
xk − X k
τ
 0, X < 0

f (T ) =  X ,0 ≤ X ≤ 1
 1, X > 1

Variable: TMean = daily mean air temperature (°C)
Parameters:
Conifer:
Broadleaf:
Tmax = 13.2 (°C), Tmin = -3.9 (°C), τ = 6.4 (days)
Tmax = 17.0 (°C), Tmin = 0 (°C), τ = 1 (days)
Current climate:
Annual development of
f(T) averaged over years,
for a site in Southern
Finland (Hyytiälä), A1B
-scenario
– higher values for conifers
(blue line) meet the
”ceiling”, and for changed
climate there is less
headroom to increase
Changed climate:
Difference between changed and current climate = potential
increase in f(T) for climate change
Conclusion:
– Temperature function is the main difference between conifers
and broadleaf species
– The difference comes from the different value for saturating
temperature:
Parameters:
Conifer:
Tmax = 13.2 (°C), Tmin = -3.9 (°C), τ = 6.4 (days)
Broadleaf:
Tmax = 17.0 (°C), Tmin = 0 (°C), τ = 1 (days)
Are the parameter values valid?
Momentarily values:
From Hyytiälä:
from literature:
– 15-> °C for pine
– up to 25 °C for aspen
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•
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15 °C for spruce (Bergh et al. 1997)
18 °C for pine (Kellomäki in Bergh 2003)
22 °C for cottonwood (Sigurdsson 2001)
24 °C beech (Freeman 1998)
Take-home message:
Broadleaf species show a larger potential for growth
increase with climatic warming, due to their higher
optimal temperature for photosynthesis
Thank you
Precipitation,
snow melt
Soil water -model
• Soil water model(*) is a simple "open bucket" -type
model,
• Soil water holding capacity is the (metric) difference
between wilting point and field capacity
• The difference translates to the maximum water layer
thickness that the soil can hold
• Soil water holding capacity depends on soil thickness,
soil type, texture etc.
• for modeling purposes volume of retended water is
sufficient!
(*) based loosely on Duursma, R. A. et al. 2006. Predicting the
decline in daily maximum transpiration rate of two pine stands
during drought based on constant minimum leaf water potential
and plant hydraulic conductance, Tree Physiology 28, 265–276.
Evapotranspiration
Runoff,
drainage
Soil water -model
• Precipitation comes from meteorological data
• Evapotranspiration calculated as:
Et = (E0 ⋅ f ( I ) ⋅ f (T ) ⋅ f ( D) + a1 ⋅ PAR + a2 ) ⋅ f (θ )
300
280
260
Soil moisture (mm)
•f(I) = PAR - does not
saturate!
•separate functions for VPD
and soil water
•constant and PAR-related
term for wintertime
evaporation
240
220
200
180
160
140
120
Observed
100
80
01-98
Predicted
01-99
01-00
01-01
01-02
01-03
Date
01-04
01-05
01-06
01-07