Presentation on theme: "Forest damage in a changing climate Anna Maria Jönsson and Lars Bärring Dept. of Physical Geography and Ecosystem Analysis Geobiosphere Science Centre."— Presentation transcript:
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 , Klimatförändringens inverkan på svärmning och utveckling. at the conference Skogen, barkborrarna och framtiden, Swedish forest agency, Jönköping, September 6, 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: 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:
Simulated average stem wood volume using RCA3-ECHAM4 A2-scenario data m3 / ha North Sweden Central Sweden South Sweden Modelled without frost damage Modelled with frost damage
Percentage of increase relative to Reduction attributed to frost damage % North Sweden Central Sweden South Sweden Modelled without frost damage Modelled with frost damage Reduction attributed to frost damage Simulated with RCA3-ECHAM4 A2-scenario data
Modelling the annual cycle of spruce bark beetle Spring swarming Egg developmentSummer swarming? Winter hibernation high 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 minus Part of Sweden Change * (no. of days) Spring swarmingNorth13-19 Central16-20 South16-24 DevelopedNorth20-26 first generationCentral26-32 South26-33 * modelled with RCA3-ECHAM4 A2 and B2, RCA3-ECHAM5 A1b
Modelled extension of a second generation* 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: StepWeather requirement 1/ Dehardening4 consecutive days with Tmean>+5 o C 2/ Advancement of spring phenology If Tmean > +5 o C Degree-day = Tmean-5 o C 3/ Spring backlashTmin < -2 o C 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 ) and the common period ( ). 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 models 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 Frost damage Spruce bark beetle
Temperature increase Summer swarming if Tmax >20 o C and Tmean has not fallen below 15 o C for the first time during autumn Spring swarming Tmax >20 o C Egg development Temperature sum d.d.(+5 o C) Winter hibernation high mortality at low temperatures for not completely developed bark beetles Egg development Temperature sum d.d.(+5 o C) > Temperature sums and thresholds affecting spruce bark beetle Recover from hibernation Temperature sum>120 d.d.(+5 o C) Response Two generations of bark beetles
Growth period Temperature sum Budburst Temperature sum spruce d.d.(+5 o C) Light and chilling requirements Chilling Temperature sum Tmean >-3.4 o C, <10.4 o C Cold hardiness level affected by ambient temperature Cold hardening Light, Tmean, Tmin > Temperature sums and thresholds affecting tree phenology Onset of photosynthesis and dehardening Tmean > +5 o C, 4 consecutive days Frost damage: any time when Tmin< cold hardiness Response Temperature increase Changes in risk for frost damage