Forest damage in a changing climate

Anna Maria Jönsson and Lars BärringDept. of Physical Geography and Ecosystem AnalysisGeobiosphere 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

1976

-200

5

1981

-201

0

2011

-204

0

2041

-207

0

2071

-210

0

0

50

100

150

200

250

300

m3

/ ha

North SwedenCentral SwedenSouth Sweden

Modelled without frost damageModelled with frost damage

Percentage of increase relative to 1976-2005 Reduction attributed to frost damage

1976

-200

5

1981

-201

0

2011

-204

0

2041

-207

0

2071

-210

0-30

-20

-10

0

10

20

30

40

50

%

North SwedenCentral SwedenSouth Sweden

Modelled without frost damageModelled with frost damageReduction attributed to frost damage

Simulated with RCA3-ECHAM4 A2-scenario data

Modelling the annual cycle of spruce bark beetle

Spring swarming

Egg development Summer swarming?

Winter hibernationhigh mortality for not completely

developed bark beetles

Egg development

>

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 SwedenChange *

(no. of days)

Spring swarming 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 2011-2040 2041-2070 2071-2100

 

     

-August

-July

-June

Percent of years with two generations: 1-3% 2-10% 8-18% 30-49% 63-81%

* RCA3-ECHAM4 A2

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

* 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.

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

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 time-scales, using two impact models:

Time-scales Impact modelsShort-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 requirementsCarry-over effects•Timing and occurrence of subsequent steps

Frost damage

Spruce bark beetle

Temperature increase

Summer swarming if Tmax >20oC and Tmean has not fallen below 15oC

for the first time during autumn

Spring swarmingTmax >20oC

Egg development Temperature sum 625-750 d.d.(+5oC)

Winter hibernationhigh mortality at low temperatures for not completely developed bark beetles

Egg development Temperature sum

625-750 d.d.(+5oC)

>

Temperature sums and thresholds affecting spruce bark beetle

Recover from hibernationTemperature sum>120 d.d.(+5oC)

Res

pons

e

Two generations of bark beetles

Growth period Temperature sum

Budburst Temperature sum spruce 120-220 d.d.(+5oC) Light and chilling requirements

Chilling Temperature sum Tmean >-3.4oC, <10.4oC

Cold hardinesslevel affected by ambient temperature

Cold hardening Light, Tmean, Tmin

>

Temperature sums and thresholds affecting tree phenology

Onset of photosynthesis and dehardening Tmean > +5oC, 4 consecutive days

Frost damage: any time when Tmin< cold hardiness

Res

pons

e

Temperature increase

Changes in risk for frost damage