1. Uncertainty or Sensitivity Analysis in Climate Change Impact Assessment? 2014. 06. 14. Jae-Kyoung LEE Weather Radar Center in KMA

2. Ⅱ. Methodology 1.Concept of Uncertainty Propagation 2.Uncertainty Quantification: Maximum Entropy Contents Ⅲ. Case Study 1.Application Outline 2.Projections of Precipitation and Streamflow 3.Uncertainty Quantification Ⅰ. Research Objective 2 Ⅳ. Conclusion

3. Ⅰ. Research Objective 3

4. Uncertainties in Everywhere over Climate Change Studies 4 Uncertainties from emission scenarios Uncertainty Grows!

5. Uncertainty Analysis in Previous Studies 5 1. Fix a single scenario of a certain stage being examined 2. Perturb the other stages using all the combinations of their scenarios 3. Calculate an average of output (e.g. flow) values of the scenario combinations 4. Repeat the above steps for another scenario of the examined stage 5. Calculate the final range using the output averages ESs GCMs DSsHMs Change in flow (cms) FIXED 3 3 4 =3×5×4=60 MAX MIN UNCERTAINTY 5 Avg3 Avg1 Avg2 FLOW

6. Uncertainty Analysis in Previous Studies 6 StudyESGCM/RCMDSHMNVIndex Jenkins et al. (2003) A29 GCMs SD /DD O (IC in RCM) % change mean monthly pre. Wilby et al. (2006) A2, B2 4 GCMs SD (2) 2(structural) (included HP) CDF of % change of Q95 Kay et al. (2009)A2 5 GCMs (included IC) SD /DD 2(structural) (included HP) O (resampling) % change in flood frequency Prudhomme et al. (2009) 3 GCMs SD /DD 2(structural) (included HP) O (black resampling) % change Mean monthly flow Chen et al. (2011) A2, B1 6 GCMs (included IC) SD (4) 3(structural) (included HP) CDF of % change of Q95 & peak discharge Poulin et al. (2011) modified climate condition 1 2(structural) (included HP) Streamflow, snow water groundwater Zhang et al. (2011) 2 RCMs SD (2) 1 change of water yield & ET & storage Dessai et al. (2011) A2, B2 9 GCMs 2 RCMs 1O additional water required

7. Uncertainty Analysis in Previous Studies 7 -Just a extension of the “Sensitivity Analysis” -Σ (Uncertainties 1, 2, …, N stages ) = Total Uncertainty ? -Meaningless comparing uncertainty i & uncertainty j stages -No systematic way to calculate contribution of stage i to the total uncertainty ESs GCMs DSsHMs Change in flow (cms) FIXED 3 3 4 =3×5×4=60 MAX MIN UNCERTAINTY of DSs 5 Averages of FLOW scenarios

8. Objective 8 Propose a proper uncertainty analysis methodology for the climate change impact assessments - Quantify total uncertainty - Estimate the contribution of each stage to the total uncertainty - Examine how uncertainty is propagated through the whole procedure

9. Ⅱ. Methodology 9

10. A Proposal: Uncertainty “Propagation” Concept 10 -Enable to examine how uncertainty is propagated through the whole procedure -Enable to quantify “total uncertainty” -Enable to estimate the contribution of each stage to the total uncertainty Uncertainty Related to ES Uncertainty Related to GCMs Uncertainty Related to DS Uncertainty Related to HM Emission scenario GCMs Down scaling Hydological model Uncertainty of models TOTAL UNCERTAINTY Uncertainty of emission scenario

11. History on Uncertainty Propagation 11 Cascade of uncertainty (Schneider, 1983) Uncertainty propagation (Henderson-Sellers, 1993) Uncertainty explosion (Jones, 2000; IPCC, 2001) Terminology The process whereby uncertainty accumulates throughout the process of climate change prediction and impact assessment has been described as a “cascade of uncertainty” or the “uncertainty propagation” (IPCC, 2001) Propagation of uncertainty is the effect of variables’ uncertainties on the uncertainty of a function based on them (Wikipedia) Definitions

12. A Vehicle for the Proposed Approach: Maximum Entropy 12 subject to moment-consistency constraint: normalization constraint: formal solution (the weight of ME probability distribution) Maximum entropy (Jones, 1957)

13. 13 Given data: a ≤ x ≤ b a is the max value and b is the min value of the ensemble results Assumption subject to normalization constraint: Maximum entropy solution Max (b) Min (a) uncertainty A Vehicle for the Proposed Approach: Max Entropy

14. Ⅲ. Case Study 14

15. Application Outline 15 Upstream of Chungju Dam (3 sub-basins) Flood seasons (June ~ September) - Precipitation: 808.0 mm (69.1 %) - Inflow: 1403.3 cms (72.8 %) Study basin Chungju Dam

16. Application Outline 16 Projection Procedure ProcedureContent Emission scenarios (2)A2 and B1 scenarios Selected GCMs (4)CSIRO-Mk30, MPI-M-ECHAM5, MIUB-ECHO-G, and MRI-M-CGCM232 Downscaling (2)bilinear regression and ANN Hydrologic models (2)abcd models and GR2M models Scenario selection - 4 GCMs were selected among 19 GCMs based on 20C3M emission scenario using the PDF method proposed by Perkins et al. (2007) Calibration of hydrological model - R 2 of abcd model: 89.49 % ; R 2 of GR2M model: 92.30 %

17. Application Outline 17 Methods abcd model (Makblouf and Michel, 1994) GR2M model (Makblouf and Michel, 1994)

18. Projections of Precipitation and Streamflow: 2030~2059 18 Precipitation Emission ScenarioBilinear (mm/year)ANN (mm/year)Average (mm/year) A21176.08 (9.19 %)1282.19 (19.05 %)1229.14 (14.12 %) B11069.71 (-0.68 %)1142.61 (6.09 %)1106.16 (0.03 %) Ave.1122.90 (4.26 %)1212.40 (12.57 %)1167.65(8.41 %) Observation 1077.06 Based on emission scenarios, precipitation will increase from 0.03 to 14.12 % Based on downscaling, precipitation will increase from 4.26 to 12.57 % On average, precipitation will increase 8.41 % over observation

19. Projection of Precipitation and Streamflow: 2030~2059 19 Streamflow Emission Scenarioabcd (cms/year)GR2M (cms/year)Average (cms/year) A22299.31 (15.76 %)2361.90 (18.92 %)2330.61 (17.34 %) B12094.28 (5.44 %)2190.12 (10.27 %)2142.2 (7.9 %) Ave.2196.80 (10.60 %)2276.01 (14.59 %)2243.33 (12.95 %) Observation 1986.20 Based on emission scenarios, streamflow will increase from 7.90 to 17.34 % Based on hydrological model, streamflow will increase from 10.60 to 14.59 % On average, streamflow will increase 12.95 % over observation

20. Quantifying Uncertainty: by the Previous Approach The GCM stage is the largest contributor to total uncertainty (89.34 %) 20 The Approach based on the “Sensitivity Analysis” Procedure Average of streamflow scenarios over observation (%) Range (%) ES A214.94 9.29 B15.65 GCMs CSIRO66.56 89.34 ECHAM57.86 MIUB-ECHO-G-22.78 MRI-CGCM2.3.2-10.45 DS Bilinear regression4.75 11.09 NN15.84 HM abcd8.34 3.91 GR2M12.25

21. Quantifying Uncertainty: by the Proposed Approach The emission stage is the largest contributor to total uncertainty (57.07 %) The GCM stage is still a major contributor of the “model” uncertainty (26.50 %) 21 Streamflow Procedure MEAverage of MEIncrementalRatio (%) ES A2 3.32 3.23- 57.07 B1 3.13 GCMs CSIRO 5.22 4.731.5026.50 ECHAM5 4.83 MIUB-ECHO-G 4.56 MRI-CGCM2.3.2 4.31 DS Bilinear regression 4.80 5.180.457.95 NN 5.57 HM abcd 5.40 5.660.488.48 GR2M 5.66 Total 5.66 -100.00

22. Quantifying Uncertainty: The Proposed Approach The emission stage contribution become larger than in streamflow (70.05 %) However, the downscaling stage is the second largest (20.47 %) 22 Precipitation Procedure MEIncrementalRatio (%) ES 3.25-70.05 GCMs 3.690.449.48 DS 4.640.5220.47 Total 4.64 100.00

23. Uncertainty Propagation Result 23 ES uncertainty 3.23 (57.07 %) GCMs uncertainty 1.50 (26.50 %) DS uncertainty 0.45 (7.95 %) HM uncertainty 0.48 (8.48 %) Emission scenario GCMs Down scaling Hydological model Model uncertainty: 2.43 (40.63 %) TOTAL UNCERTAINTY 5.66 (100 %) Emission scenario uncertainty: 3.23 (57.07 %)

24. 24 Ⅳ. Conclusions

25. 25 The methodology used in most of previous studies for climate change uncertainty analysis was just a extension of the sensitivity analysis. no consensus expressing “total uncertainty” and “contribution of each stage” The proposed methodology with the maximum entropy theory enables us to quantify the total uncertainty estimate how uncertainty of each stage is propagated A Korean example warned the previous methodology could mislead us The GCM uncertainty is large, but which is dominated by the emission uncertainty in general

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