1. The potential of historical simulations Katja Frieler

2. Design of the historical simulations Challenges with regard to the model evaluations: Observed impact indicators may not only depend on weather fluctuations but to a varying extent also on human interventions not necessarily implemented in the models Structure of the paper

3. Examples of smart ways to extract the signal from the observations that should be reproduced by the models Maize rainfedSoy rainfed Observed relationship, Schlenker et al. 2009 Schauberger et al.

4. Quantification of the weather induced historical variability instead of model evaluations Explained variances by all models countries ordered according to the highest individual explained variance Multi-model median Empirical model Frieler et al.

5. Differences between observations and simulations could be used to estimate historical adaptation levels Reported people affected by floodings Simulated people affected by floodings based on naturalized historical simulations To reproduce the observed number of affected people on national level you have to assume that people are only affected by > 100 year events

6. “It is virtually certain that globally the troposphere has warmed since the mid-20th century.“ (IPCC, AR5, SPM) We know there is climate change… “Limiting the warming caused by anthropogenic CO2 emissions alone with a probability of >66% to less than 2°C since the period 1861–1880, will require cumulative CO2 emissions from all anthropogenic sources to stay between 0 and about 1000 GtC[…]” (IPCC, AR5, SPM) “It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20 th century.” (IPCC, AR5, SPM) We know it is anthropogenic… We know how to stop it…

7. 7 … we know that stopping it at 2°C is more ambitious than stopping it at 3°C… Knopf et al., 2011

8. ... However, how good are we at telling the difference between a 2°C and a 3°C world ?

9. 9 IPCC AR4, WGII, Table 20.8 Global Mean Annual Temperature Change Relative to 1980-1999 102345°C Huge amount of literature behind it Studies are usually based on individual impact models No consistent scenario set-up

10. It was six men of Indostan To learning much inclined, Who went to see the Elephant (Though all of them were blind). John Godfrey Saxe, “The Blind Men and the Elephant” We are trying to identify pieces… “A tree” “A wall” “A fan” “A snake” “A spear” “A rope”

11. But may miss the true nature of the object…

12. There is a better way! Problem 1: False interpretation of the individual parts Solution: Examination of the individual parts by an entire group sharing experiences. Problem 2: Drawing conclusions about the whole after examining only single components. Solution: Knowledge integration

13. 13 ISI-MIP: The Fast Track RCP climate projections Global: CMIP5 (5 GCMs x 4 RCPs) Socio-economic input SSP population and GDP (mainly SSP2) Synthesis of Impacts at different levels of warming Impact Models Global Water (11) Agriculture (7) Ecosystems (7) Infrastructure (1) Health (5) The ISI-MIP agriculture sector is coordinated by

14. Our invitation was sucessfull! Meanwhile more than 30 global impact models from 11 countries joined ISI-MIP 9 Water 5 Biomes 6 Crop models (+2 agro-economic models) 3 Cross-sectoral 5 Health 1 Coastal Infrastructure 12/2011 Invitation to modeling teams

15. Provision of bias correction of GCM data Kick-off workshop at PIK Work on simulation protocol 2/2012 1/2013 Submission of Publications Dead- line of WG2 9/2012 Results workshop Reading Approval of PNAS Special Issue Impact simulations start to come in Invitation of contributing authors 6/2012

16. Results of the ISI-MIP Fast Track 1/2013 Dead- line of WG2 Submission of Publications Sectoral overview papers Schewe et al., PNAS (water scarcity) Friend et al., PNAS (biomes) Warszawski et al., ERL (biomes) Rosenzweig et al., PNAS (agriculture) Kovats et al., (malaria) Hinkel et al., (coastal infrastructure) Cross-sectoral synthesis papers Piontek et al., PNAS (multisectoral hotspots) Frieler et al., (cross sectoral interactions)) Extreme events (drougths and floods) Dankers et al., PNAS (floods) Prudhomme et al., PNAS (droughts) water supply vs. water demand Elliott et al., PNAS (production potential of available irrigation water) Wada et al, ERL (irrigation water demand) … www.isi-mip.org

17. Agriculture

18. Exemplary model output from the agriculture sector 7 Global Gridded Crop Models: EPIC, GEPIC, IMAGE, LPJ-GUESS, LPJmL, pDSSAT, PEGASUS 4 first priority crops: wheat, maize, rice, soy (LPJmL and EPIC also provide simulations for additional crops) Main output variables Crop yields, annual (0.5°x0.5°), t/ha/yr Crop‐specific irrigation water demand, annual (0.5°x0.5°), kg/m2/s

19. Scenario set up

20. Exemplary model output from the agriculture sector Production changes (%, relative to 1980-2010) 60 40 20 0 -20 -40 -60 60 40 20 0 -20 -40 -60 2020 20802020 2080 Time Rosenzweig et al, PNAS 2013

21. Rosenzweig et al., 2013 < -50% > +50% Declines in the (sub-) tropics Increases in higher latitudes Strong signal for maize and wheat, less pronounced changes for rice and soy Accounting for CO2 fertilization Yield changes under the high emission scenario RCP8.5 Rosenzweig et al, PNAS 2013

22. N stress and CO2 fertilization Rosenzweig et al., 2013 N stress means an important constraint for the CO2 fertilization effect Models accounting for N limitations show significantly stronger losses Losses of 25% are comparible to the drought induced losses in 2012 < -50% > +50%

23. Rosenzweig et al., 2013 Maize Wheat Rice Soy Yield Changes [%] Regional Mean Temperature Change [°C] Models without explicit representation of the nitrogen cycle Models accounting for N stress Previous collection of site specific simulations used in the IPCC AR4 Mid to High LatitudesLow Latitudes

24. Water

25. Multi-model mean of relative change in annual mean discharge Schewe et al., PNAS 2013 Discharge: Runoff accumulated along the river network Runoff: Rainfall + Snowfall = Evapotranspiration + Runoff

26. Water Scarcity Schewe et al., 2013 Effect of population growth and climate change : About 12% of the world’s populations will live in countries under “absolute water scarcity” Global Warming relative to 1980-2010, °C Strongest effect of climate change already at low levels of global warming Climate change will increase the number of people living under absolute water scarcity (<500m 3 /capita/year) by another 40% compared to the effect of population growth alone. RCP8.5 + SSP2

27. Impact of Climate Change on Renewable Groundwater Resources Portmann et al., 2013 1 hydrological model, 5 climate models Projected change in groundwater recharge for the 2080 for RCP8.5 Relative Change [%]

28. GWR > 70% decreaseGWR > 30% decrease GWR > 10% decrease GWR > 10% change Changes in Terms of Global Mean Temperature Change Global Mean Temperature Change [°C] Percentage of Global Land Area

29. Hydrological Droughts RCP8.5, end of century All Models, mean change All Models, S/N ratio Relative Change [%] in Drought Days Strong signal-to-noise ratio in Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia The uncertainty due to GIMs is greater than that from global climate models, Prudhomme et al., 2013

30. Hydrological Droughts and the effect of CO2 fertilization Inclusion of the CO2 fertilization effect reduces the area under „drought conditions“ JULES that accounts for the dynamic response of plants to CO2 and climate, simulates little or no increase in drought frequency. Possible explanations: Stomata opening less widely in a CO2-enriched atmosphere, leading to less water loss through transpiration Dynamic vegetation changes Fraction of the total land area under drought conditions

31. Changes in Flood Hazard Dankers et al., 2013 RCP8.5, end of the century Indicator of flood hazard: 30-y return level of 5-d average peak flows Current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. Large-scale patterns of change are remarkably consistent among impact and climate models… … but at local scale there can be disagreement even on the sign of change, Average change in the magnitude of Q30 across all experiments.

32. Biomes

33. Uncertainty in Changes in Vegetation Carbon Friend et al., PNAS, 2013 At 4 °C of global land surface warming vegetation carbon increases by 52–477 Pg C, mainly due to CO2 fertilization of photosynthesis.

34. Uncertainty in Changes in Vegetation Carbon Friend et al., PNAS, 2013 Uncertainties in carbon residence time explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone (increasing to 151% for non-HYBRID4 models). HYBRID4 Carbon residence time  depends on the turnover rates of plant parts and the mortality rates of individuals

35. Risk of severe ecosystem changes The extent of regions at risk of severe ecosystem change is projected to rise with global warming; median value of 36% at 4 ° C, ~ doubling between 2 ° C and 3 ° C. Warszawski et al., ERL, 2013

36. Cross-sectoral Integration

37. Multi-sectoral hotspots of climate change impacts Water + Agriculture + Biomes + Malaria

38. Multi-sectoral hotspots of climate changes impacts Piontek et al., PNAS, 2013 wateragriculture biomes malaria When do we leave the world as we know it?

39. Multi-sectoral Impact Hotspots Piontek et al., 2013 Multisectoral overlap starts to be seen robustly at a mean global warming of 3°C above the 1980-2010 mean 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4°C. Piontek et al., PNAS, 2013

40. Irrigation Potential Water + Agriculture

41. Agriculture: Irrigation potential Petacalories Expansion of irrigated areas + CO2 fertilization effect Current irrigation + CO2 fertilization effect Expansion of irrigated areas, constant CO2 Current irrigation, constant CO2 Potential production increase due to the expansion of irrigated areas accounting for water availability Global production of maize, wheat, rice, and soy under RCP8.5 Additional irrigation may help to mitigate the effects of climate change but will not be sufficient to balance the increasing demand due to population growth

42. Cross sectoral trade-offs of land use changes Water + Agriculture + Biomes

43. Change in global production under fixed present day land use and irrigation patterns Inner 90% of the Uncertainty distribution Relative change in global demand [%] Inter-crop-model spread much larger than inter- GCM spread Disagreement with regard to the sign CO2 effect unlikely to balance the demand increases

44. Irrigation potential Production change due to potential extra-irrigation (40% of runoff) Estimates based on 3 GCMs x 11 water models x 6 crop models

45. Illustrative land use changes Production change due to an illustrative land use pattern + increased irrigation

46. Reduction of C sink due to loss of natural Loss reaches up to 50% of the simulated “present day” C sink Loss reaches up to 50% of the simulated present day C sink The direct reduction of the vegetation carbon stock reaches a multi-model median of about 85 Pg (about 8.5 years of current CO 2 emission) by the end of the century

47. 47 The process Outlook: ISI-MIP2

48. 48 The ISI-MIP2 RCP climate projections Global: CMIP5 Regional: CORDEX Socio-economic input SSP population and GDP Impact Models Global Regional Synthesis of Impacts at different levels of global warming Quantification of uncertainties Impact emulators Comparison of regional and global impact model simulations Model validation and improvement with a focus on the representation of the variability and extreme events

49. 49 The process Focus regions of ISI-MIP2

50. Representation of variability and extreme events Wheat “Observed” de-trended yields (Food and Agriculture Organization of the UN) De-trended simulated yield variations for the crop model and simulations setting providing the highest country specific correlation with the FAO time series De-trended simulated yield variations with the second highest correlation Maize For some countries individual crop models explain a large fraction of the observed variability

51. 51 Which part of the „observed“ variance of the yields can be explained by whether?

52. 52 The process Inclusion of new sectors Impacts on the energy supply: Hydropower Wind energy Cooling capacity Impacts on energy demand Cooling / heating

53. Conclusion There is an urgent need to join our forces to produce a clearer picture of the impacts of climate change at different levels of global warming by: 1. Multi model projections 2. A cross-sectoral consistent scenario framework allowing for the analysis of interactions (crop yield reduction and malnutrition, vegetation changes and their effect of malaria etc.) The Fast Track ISI-MIP archive is now open! Data are available via www.isi-mip.orgwww.isi-mip.org