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?

European temperature projections

European temperature predictions

European temperature projections

European temperature projections

European temperature projections

European temperature projections

European temperature predictions

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

A different representation

Global mean temperature

Hawkins & Sutton, 2010, Clim. Dyn.

A different representation

British Isles mean temperature

Maps of uncertainty – temperature

Hawkins & Sutton, BAMS, 2009

Precipitation uncertainties

Global mean precipitation

Hawkins & Sutton, 2010, Clim. Dyn.

Precipitation uncertainties

Model uncertainty

Scenario uncertainty

Internal variability

Global, decadal mean

European DJF, decadal mean

Sahel JJA, decadal mean

SE Asia JJA, decadal mean

Maps of uncertainty – DJF precipitation

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.

Longer time means

Caveats

• Uncertainty estimates– only 3 scenarios used– only 15 models used– Internal variability estimate relies on

GCMs• Wide range in GCM estimates

Internal variability in CMIP3 GCMs

Discussion: www.met.reading.ac.uk/~ed/blog

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

Using ANOVA instead

Thanks to Stan Yip, Chris Ferro, David Stephenson

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/

Robustness of internal variability

CONTROL

TRANSIENT

Fractional uncertainty comparison

• Using CMIP3 data, model uncertainty is clearly the dominant contribution for decadal predictions

Cox & Stephenson schematic

Hawkins & Sutton, BAMS, 2009

Using CMIP3 projections

uncertaintymean signal