1. Uncertainty propagation from climate change projections to impacts assessments: water resource assessments in South America Hideo Shiogama 1, Seita Emori 1, 2, Naota Hanasaki 1, Manabu Abe 1, Yuji Masutomi 3, Kiyoshi Takahashi 1, and Toru Nozawa 1 1 National Institute for Environmental Studies 2 Atmosphere and Ocean Research Institute, University of Tokyo 3 Center for Environmental Science in Saitama

2. AOGCMs Impact model Biases of current climate Uncertainty of future climate projections Uncertainty of impact assessments Uncertainty of climate change projections propagates to impact assessments. Impact researchers have often investigated relations between regional impact assessments and regional climate changes. However large-scale climate changes can affect regional impacts. How to examine relations between large-scale climate changes and regional impact assessments? How to constrain the uncertainty of impact assessments?

3. Toward more consistent analysis and communications between climate scientists and impact researchers. Moss et al. (2010, Nature) Parallel approach in the IPCC AR5 We have developed a method to examine uncertainty propagation from climate to impact and to determine metrics relating to impact assessments.

4. A global hydrological model (Hanasaki et al. 2008) Inputs: △ T and △ P from 14 AOGCMs of CMIP3. Outputs: 14 assessments of annual mean runoff changes ( △ R). Changes from 1980-1999 to 2080-2099 (SRES A2). Normalized by the global mean △ T of each AOGCM. Water resource impact assessments in South America

5. Uncertainties in annual mean runoff changes Ensemble mean What kind of uncertainties in climate change projections did affect △ R? Is the ensemble mean assessment the best estimate?

6. How to examine relations between large-scale climate change patterns and △ R in SA?

7. T0P0 △T△T △P△P △R△R SVD Singular Value Decomposition Analysis Covariance matrix: C=Cov[ △ R/ △ T gm, ( △ T / △ T gm, △ P / △ T gm )] Singular value decomposition: C=U T ΣV This statistical method tells us pairs of △ R mode and ( △ T, △ P ) mode such that the covariance between their expansion coefficients is maximized.

8. 1st modes (about 50%) downwardupward

9. 2nd modes (about 20%) downwardupward

10. How to examine patterns of present climate simulations relating to the uncertainties of impact assessment?

11. T0P0 △T△T △P△P △R△R SVD Regressions between the present climate simulations and the expansion coefficients of the runoff modes Regression

12. Present climate patterns relating to the 1 st runoff mode downwardupward downwardupward Vertical circulations in the presentVertical circulations in the future

13. Present climate patterns relating to the 2nd runoff mode downwardupward Vertical circulations in the presentVertical circulations in the future downwardupward

14. How to determine metrics relating to the uncertainties of impact assessments?

15. How to determine metrics? Biases of surface air temperature (from ERA40) Biases of precipitation (from CMAP) Present climate patterns associated with the leading runoff modes. Inner products

16. Runoff modes vs. present climate biases

17. Constraining the uncertainty of runoff changes Ensemble mean More plausible

18. Conclusions The ensemble mean is not always the best estimate. A naive overreliance on consensus assessments could lead to inappropriate adaptation policies. Our new approach could help find a target- oriented metric for a particular aspect of climate change projections and impact assessments over a particular region. This approach can help promote more communications between climate scientists and impact researchers.