Probabilistic projections of climate change impacts: sub-arctic palsa mires and crop yields

Transcription

1 Probabilistic projections of climate change impacts: sub-arctic palsa mires and crop yields ACCLIM seminar Helsinki, 8 March 211 Stefan Fronzek, Timothy R. Carter & Nina Virtanen Finnish Environment Institute

2 Outline Probabilistic projections of climate change Impact response surface approach Sub-arctic palsa mires Crop yields Summary

3 Probabilistic projections of climate change Distribution of an ensemble of GCM simulation; Jylhä et al. 29, ACCLIM Resampling an ensemble of GCM simulation; Räisänen & Ruokalainen 26

4 Probabilistic projections of climate change Harris et al. 21, Hadley Centre Joint frequency distributions for annual temperature and annual precipitation anomalies SRES A1B emission scenario Quantify uncertainties in the leading physical, chemical and biological feedbacks Combine information from perturbed physics ensembles, multi-model ensembles and observations.

5 Impact response surfaces to determine probabilistic climate change impacts A method was developed to assess the risk of impacts with probabilistic projections of climate change: an impact response surface is constructed based on a sensitivity analysis of the impact model with respect to changes in key climatic variables, probabilistic projections of future climate are superimposed onto the response surface to assess the risk of impact. Two case studies: permafrost in sub-arctic palsa mires, crop yields simulated with a dynamic crop model

6 Sub-arctic palsa mires Palsa mires are northern mire complexes with permanently frozen peat hummocks, located at the outer limit of the permafrost zone. Palsa mire in Paistunturit area, Utsjoki, northern Finland, August Photo taken by Maria Rönkkö, University of Helsinki Palsa mires create heterogeneous landscape supports rich species diversity (Luoto, Heikkinen and Carter, 24) Sensitive to small changes in climate (Fronzek et al., 26) Thawing palsa mires source of CH 4 (Christensen et al., 24) Palsas west of Järämä, 5 km north of Kiruna, 5-6 m.a.s.l. Photo taken by Britta Sannel,

7 Modelling the distribution of palsa mires in northern Fennoscandia Climate envelope modelling Climate variables: Annual precipitation Continentality (=max min of all monthly mean temperatures) Thawing degree days (accumulated daily temperature sum above C) Freezing degree days (below C) Source: Luoto, Fronzek & Zuidhoff 24

8 Modelling the distribution of palsa mires Source: Fronzek, Luoto & Carter, 26.

9 relative P change (%) Total areal loss of palsa mires Impact response surface and climate PDF (A1B) Sensitivity analysis of the impact model wrt. to changes in temperature and precipitation T ( C) Source: Fronzek et al. (21), adopted

10 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted 1 Probability (%) Probability of palsa mire loss

11 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

12 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

13 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

14 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

15 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

16 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

17 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

18 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

19 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

20 relative P change (%) Total areal loss of palsa mires -1 Impact response surface and climate PDF (A1B) T ( C) Source: Fronzek et al. (21), adopted Probability (%) Probability of palsa mire loss Half area All area

21 Impact model uncertainty Parameter uncertainty of palsa distribution model: calibrating alternative models by randomly selecting a sub-set of the data 25 models for year model type Structural uncertainty of the impact model using 8 alternative statistical modelling techniques Initial conditions, Moving baseline, using 3 periods ( , , ) to calibrate models Alltogether 25 x 8 x 3 = 6 impact models considered

22 Palsa impact response surfaces of 2 models ( baseline period) Overfitting of some models Model rejected if palsa area increases with warming

23 Risk of permafrost loss in sub-arctic palsa mires in Fennoscandia Source: adopted from Fronzek et al. 21, Climatic Change. P-change (%) % % -75% -5% <95% <75% <5% Grand ensemble, A1B <25% +15% -1% 1% areas loss Probability (% ) Initial conditions, GAM (n=3) Initial conditions, ANN (n=3) Model class (n=6) Parameter, GAM (n=25) Parameter, ANN (n=16) All (n=38) 5% areas loss 1% areas loss T ( C) Impact response surface of change in area suitable for palsas and probabilistic projections of for Estimated probability of permafrost thawing in all current palsa areas assuming a perfect impact model Estimated probability for different sources of impact model uncertainty

24 Crop yield simulation with dynamic model WOFOST collaboration with Reimund Rötter, MTT Daily weather data Crop parameters Spring barley Scarlett Soil parameters Min & Max temp Global radiation Vapour pressure Wind speed Precipitation Simulation model WOFOST World Food Studies (Wageningen-Amsterdam) Total Biomass production Crop Yield Gross Assimilation Respiration LAI Field water balance (ET, DP, SM) Management data Sowing date Fertilizer input Yield/biomass for different N,P,K Nutrient balances

25 Spring barley yields in Jokioinen Crop simulations with weather data from the M&Rfi weather generator Simulated grain yields for current and possible climate change variants at Jokioinen for a clay soil. Source: Rötter et al. (submitted)

26 Impact response surface of average spring barley yield in Jokioinen (in kg/ha, dry matter) Clay soil Sandy soil Precipitation change (%) Precipitation change (%) Temperature change (oc) Temperature change (oc) ACCLIM

27 Plans for probabilistic assessment of climate change effect on crop yields Superimposing probabilistic projections of climate change to derive risk estimates of e.g. yield losses Impact response surfaces with respect to changes in other climate variables, e.g. describing the interannual variability or extremes CO 2 effect, different locations Possibly extend the analysis to the grid system nationally to evaluate the spatial pattern of risk. Incorporating impact model uncertainty

28 Summary Impact response surfaces (IRS) can be used to depict the sensitivity of impacts to climate change IRS in combination with probabilistic projections of climate change can indicate risks of future impacts Example of the risk of disappearance of ecologically valuable permafrost features (palsa mires) in northern Fennoscandia Risk of total loss of palsa mires was estimated to be ~8% by 21 (A1B); impact model uncertainties reduce this estimate (preliminary analysis) Examples of IRS with a dynamic crop model was presented