Transcript Document

Forest damage in a changing climate
Anna Maria Jönsson and Lars Bärring
Dept. of Physical Geography and Ecosystem Analysis
Geobiosphere Science Centre
Ongoing activities within ENSEMBLES
Modelling the risk for frost damage to Norway spruce (RT 6.2)
• Rammig A., Jönsson A.M., Hickler, T., Smith B., Bärring L., Sykes M.T. (in prep.) Simulating
acclimatization of Norway spruce: Linking a cold hardiness model to an ecosystem model.
• Rammig, A., Jönsson, A.M.,Smith, B., Bärring, L., Sykes, M. (2007)
Simulating the impact of extreme climatic events in ecosystem models.
Marie Curie iLeaps-Workshop “Towards a process-based description of
trace gas emissions in land surface models”, Helsingborg.
• Rammig, A., Jönsson, A.M.,Smith, B., Bärring, L., Sykes, M. (2007)
Impact of climate change on frost hardiness of Norway spruce – A
predisposing factor for susceptibility to other stressors? Proceedings
of the German Ecological Society 37, Marburg.
• Rammig, A., Jönsson, A.M., Smith, B., Bärring, L., Sykes, M. (2006).
Projecting ecosystem response to climate extremes. Proceedings of the
German Ecological Society, Bremen 36, p.16.
Ongoing activities within ENSEMBLES
Modelling of the spruce bark beetle Ips typographus (RT 6.2)
• Jönsson, A.M., Appelberg, G. , Harding, S., and Bärring, L. (in prep.) The impact of climate change on the
temperature dependent swarming and development of the spruce bark beetle, Ips typographus, in Sweden
• Oral presentation: Jönsson, A.M., ”Granbarkborren – en scenarioanalys för 2008-2009,
Klimatförändringens inverkan på svärmning och utveckling.” at the conference ”Skogen, barkborrarna och
framtiden, Swedish forest agency, Jönköping, September 6, 2007.
• Jönsson, A.M., Harding, S., Bärring., L and Ravn, H.P. 2007: Impact of climate change on the population
dynamics of Ips typographus in southern Sweden. Agricultural and Forest Meteorology 146:70-81.
Evaluation of RCM impact on impact model projections (RT 2b)
• Jönsson, A.M. et al. (in prep). Warming up for spring frost damage in Europe.
Modelling the risk for frost damage to Norway spruce
• Incorporated a cold hardiness model *
in the Ecosystem model LPJ-GUESS
• Calculated the impact of frost damage
on forest productivity
* Jönsson, A.M., Linderson, M.-L., Stjernquist, I., Schlyter, P. and Bärring, L. 2004: Climate change and the effect of
temperature backlashes causing frost damage in Picea abies. Global and Planetary Change 44:195-207.
Simulated average stem wood volume
using RCA3-ECHAM4 A2-scenario data
300
200
150
North Sweden
Central Sweden
South Sweden
100
50
2071-2100
2041-2070
2011-2040
1981-2010
0
Modelled without frost damage
Modelled with frost damage
1976-2005
m3 / ha
250
Percentage of increase relative to 1976-2005
Reduction attributed to frost damage
50
North Sweden
Central Sweden
South Sweden
40
30
Modelled without frost damage
Modelled with frost damage
Reduction attributed to frost damage
10
0
Simulated with RCA3-ECHAM4
A2-scenario data
-10
2071-2100
2041-2070
2011-2040
-30
1981-2010
-20
1976-2005
%
20
Modelling the annual cycle of spruce bark beetle
Egg development
Spring
swarming
>
Summer swarming?
Egg
development
Winter hibernation
high mortality for not completely
developed bark beetles
Jönsson, A.M., Harding, S., Bärring., L and Ravn, H.P. 2007: Impact of climate change on the population dynamics of
Ips typographus in southern Sweden. Agricultural and Forest Meteorology 146:70-81.
Impact of climate change on spruce bark beetle
2071-2100 minus 1961-1990
Part of Sweden
Spring swarming
Change *
(no. of days)
North
13-19
Central
16-20
South
16-24
Developed
North
20-26
first generation
Central
26-32
South
26-33
* modelled with RCA3-ECHAM4 A2 and B2, RCA3-ECHAM5 A1b
Modelled extension of a second generation*
1961-1990
1981-2010
1-3%
2011-2040
2041-2070
Percent of years with two generations:
2-10%
8-18%
30-49%
2071-2100
63-81%
-August
-July
* RCA3-ECHAM4 A2
-June
RCM impact on biological impact assessments
Increased awareness of climate change has created need for using climate
model data in combination with biological models for assessing the potential
impact of climate change.
Assessments of biological impacts of future climate change depend on the
representativity and quality of regional climate model (RCM) data.
Climate model data deviate from observed climate due to properties of gridded
data, model biases and uncertainties from a range of sources.
The weather impact on biological systems is often complex, involving
cumulative effects and thresholds. This increases the risk for amplification of
otherwise modest systematic errors.
Spring backlash index *
– an example of a biological impact model
Step
Weather requirement
1/ Dehardening
4 consecutive days with
Tmean>+5oC
2/ Advancement of
spring phenology
If Tmean > +5oC
Degree-day = Tmean-5oC
3/ Spring backlash
Tmin < -2oC
4/ Severity of
vegetation damage
Accumulated daily mean
temperatures (sum of degree-days) in
combination with a frost episode
* Jönsson, A.M., Linderson, M.-L., Stjernquist, I., Schlyter, P. and Bärring, L. 2004: Climate change and the effect of
temperature backlashes causing frost damage in Picea abies. Global and Planetary Change 44:195-207.
Spring backlash index
The maps show changes in severity of
spring frost damage between future
scenario A2 (year 2071-2100) and the
common period (1961-1990).
The spring backlash index was calculated
with data from regional climate models in
the PRUDENCE data-set.
All RCMs were forced by lateral boundary
conditions from the HadAM3H global
model.
Conclusions of RCM impact on impact model projections
Assessments of climate change impact on biological systems can be highly
sensitive to the choice of regional climate model.
It is often not possible to account for RCM biases simply by calculating a climate
change signal:
1.
Timing and response magnitude are commonly based on sharp
thresholds and non-linear relationships, respectively.
2.
Calculations of processes dependent on accumulated weather impact
may be highly sensitive to accumulation of climate data biases.
3.
The more complex models, the higher the risk for systematic errors
caused by carry-over effects.
Work within ENSEMBLES
RCM-downscaled ERA40 data will be used to calibrate for systematic errors and
we will explore statistical downscaling methods for reaching site-specific spatial
resolution. Focus will be on in biological impact assessments at different timescales, using two impact models:
Time-scales
Short-term calculations (daily values)
•Response magnitude
•Above or below thresholds
•Combination of weather impact
(precipitation & temperature etc)
•Seasonal effects
•Accumulation of weather impact
a) response magnitude
b) timing of fulfilled requirements
Carry-over effects
•Timing and occurrence of subsequent steps
Impact models
Frost damage
Spruce bark beetle
Temperature sums and thresholds affecting spruce bark beetle
Egg development
Temperature sum
625-750 d.d.(+5oC)
Summer swarming if Tmax >20oC and
>
Tmean has not fallen below 15oC
for the first time during autumn
Egg development
Spring swarming
Temperature sum
625-750 d.d.(+5oC)
Tmax >20oC
Recover from hibernation
Temperature sum>120 d.d.(+5oC)
Winter hibernation
Response
high mortality at low temperatures for
not completely developed bark beetles
Two generations
of bark beetles
Temperature increase
Response
Temperature sums and thresholds affecting tree phenology
Growth period
Budburst Temperature sum
spruce 120-220 d.d.(+5oC)
Temperature sum
>
Changes in risk for
frost damage
Temperature increase
Light and chilling requirements
Cold hardening
Light, Tmean, Tmin
Chilling Temperature sum
Onset of photosynthesis and
dehardening Tmean > +5oC,
4 consecutive days
Tmean >-3.4oC, <10.4oC
Cold hardiness
level affected by ambient temperature
Frost damage: any time when Tmin< cold hardiness