the potential to narrow uncertainty in regional climate predictions ed hawkins, rowan sutton...
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The potential to narrow uncertainty in regional climate predictions
Ed Hawkins, Rowan Sutton
NCAS-Climate, University of Reading
IMSC 11 – July 2010
Motivation
Adaptation planners would like quantitative projections of future climate on regional scales, especially for the next few decades—these projections exist but have (large)
uncertainties
Questions:—what are the largest sources of climate
uncertainty on regional scales? —does this vary with region, lead time and
climate variable?—are the dominant uncertainties potentially
reducible?
Uncertainty in temperature projections
Model uncertainty
Scenario uncertainty
Internal variability
Global mean temperature
Hawkins & Sutton, BAMS, 2009 – also see Cox & Stephenson (2007)
CMIP3 projections
Internal variability – spread in residuals from smooth fits to projectionsScenario uncertainty – spread between multi-model mean of smooth fitsModel uncertainty – spread around multi-model means of smooth fits
Relative to 1971-2000
Hawkins & Sutton, BAMS, 2009 – also see Cox & Stephenson (2007)
Uncertainty in temperature projections
Model uncertainty
Scenario uncertainty
Internal variability
CMIP3 projections
British Isles (UK) mean temperature
Internal variability – spread in residuals from smooth fits to projectionsScenario uncertainty – spread between multi-model mean of smooth fitsModel uncertainty – spread around multi-model means of smooth fits
Relative to 1971-2000
Precipitation uncertainties
Model uncertainty
Scenario uncertainty
Internal variability
Global, decadal mean
European DJF, decadal mean
Sahel JJA, decadal mean
SE Asia JJA, decadal mean
Signal-to-noise ratios
Signal-to-noise ratio (S/N) for JJA projections
Hawkins & Sutton, 2010, Clim. Dyn.
Signal-to-noise ratios
Signal-to-noise ratio (S/N) for JJA projections
without model uncertainty
with model uncertainty
Hawkins & Sutton, 2010, Clim. Dyn.
Caveats
• Uncertainty estimates– only 3 scenarios used– only 15 models used– Internal variability estimate relies on
GCMs• Wide range in GCM estimates
Caveats
• Uncertainty estimates– only 3 scenarios used– only 15 models used– Internal variability estimate relies on
GCMs• Wide range in GCM estimates
• Spread ≠ skill
• Progress in climate science may increase uncertainty– carbon cycle feedbacks, ice sheet and
land-use change uncertainties…• Simple trend model used
Uncertainty in global ozone recovery
Charlton-Perez et al. (2010), ACPD
Global mean ozone
CCMVal-2 intercomparison
Uncertainty in tropical evergreen tree cover for the Amazon
Poulter et al., (2010), Glob. Change Bio.
Reducing uncertainty – decadal climate prediction
June 1991 June 2001June 1995
Thanks to Jon Robson
Retrospectively predicting North Atlantic upper ocean heat content
Decadal climate prediction allows us to test our climate models in making predictions, to identify processes causing errors and may help predict some internal variability for up to a decade
Observations
GCM predictions
Summary
Model uncertainty and internal variability are the dominant sources of uncertainty in regional climate projections for next few decades.
—Uncertainty is potentially reducible with progress in climate science
— Internal variability more important for precipitation than temperature
—Scenario uncertainty is almost negligible in the tropics for precipitation
Potential for reduction in uncertainty for precipitation appears smaller
—Adaptation decisions will need to be made with low S/N predictions for precipitation, even with a perfect model!
Climate impact modellers need to use more than one GCM!
Could estimate potential value of climate science investments to reduce uncertainty, compared to economic savings from less costly adaptation
Interactive website: http://ncas-climate.nerc.ac.uk/research/uncertainty/