Uncertainty propagation from climate change projections to impacts assessments:

water resource assessments in South America

Hideo Shiogama1, Seita Emori1 , 2, Naota Hanasaki1, Manabu Abe1, Yuji Masutomi3, Kiyoshi Takahashi1, and

Toru Nozawa1

1 National Institute for Environmental Studies2 Atmosphere and Ocean Research Institute, University of Tokyo

3 Center for Environmental Science in Saitama

　 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?

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.

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

Uncertainties in annual mean runoff changes

Ensem

ble

mea

n

• What kind of uncertainties in climate change projections did affect R?△

• Is the ensemble mean assessment the best estimate?

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

T0 P0 △T △P △R

SVD

Singular Value Decomposition Analysis

• Covariance matrix:

C=Cov[ R/ T△ △ gm, ( T / T△ △ gm, P / T△ △ gm)]• Singular value decomposition: C=UTΣV• This statistical method tells us pairs of R △ mode and

( T, P ) △ △ mode such that the covariance between their expansion coefficients is maximized.

1st modes　 (about 50%)

downward upward

2nd modes (about 20%)

downward upward

How to examine patterns of present climate simulations relating to the

uncertainties of impact assessment?

T0 P0 △T △P △R

SVD

Regressions between the present climate simulations and the expansion coefficients of

the runoff modes

Regression

Present climate patterns relating to the 1st runoff mode

downward upward downward upward

Vertical circulations in the present Vertical circulations in the future

Present climate patterns relating to the 2nd runoff mode

downward upward

Vertical circulations in the present Vertical circulations in the future

downward upward

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

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

Runoff modes vs. present climate biases

Constraining the uncertainty of runoff changes

Ensem

ble

mea

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More

plausi

ble

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.