uncertainty propagation from climate change projections to impacts assessments: water resource...
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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
n
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.