Comments on Discussion paper Comments on Discussion paper ““Detecting, understanding, and Detecting, understanding, and

attributing climate change”attributing climate change”

Comments on Discussion paper Comments on Discussion paper ““Detecting, understanding, and Detecting, understanding, and

attributing climate change”attributing climate change”

David Karoly

School of Meteorology

University of Oklahoma

Why do detection and attribution?• To identify the causes of recent observed climate

variations• To evaluate the performance of climate models in

simulating the observed climate variations over the last century

• To constrain the projections of future climate change

IPCC Third Assessment (2001)• “The global average surface temperature has

increased over the 20th century by about 0.6°C”

• “Most of the observed warming over the last 50 years is likely to have been due to the increase in greenhouse gas concentrations”

• “Key uncertainties include … relating regional trends to anthropogenic climate change”

• “Surface temperature changes are detectable only on scales greater than 5,000 km”

Detection of regional warmingCalculate observed linear trend in each grid-box and test for 95% significance (marked with +) using model control simulations to provide estimate of natural variability of trends (Karoly and Wu, 2005). Similar results found by Knutson et al. (2006)

New approach to detection of anthropogenic temperature changes

• Reducing the noise associated with natural climate variations will increase the likelihood of detecting any anthropogenic climate change

• Optimal fingerprint method rotates the signal pattern away from the pattern of natural climate variability

• A large fraction of the interannual variability of surface temperatures is associated with rainfall variations (dry years are hot in Australia)

• Removing the rainfall-related temperature variations will reduce the noise and enhance the detection of any anthropogenic signal in the residual temperature variations

Interannual temperature variations• Scatterplot of interannual

variations of Tmax and precip for the southern Aust region

• Strong out-of-phase relationship in both obs and model

• Shift of this relationship to warmer temperatures during 1976-2003 in both obs and GS-forced model simulations (from Karoly and Braganza, 2005)

Observed annual southern Australia anomalies

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

-1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Maximum temperature

Rai

n (

mm

/day

)

1910-75

1976-2003

Linear (1910-75)

Linear (1976-2003)

HadCM2 annual southern Australia area-average anomalies

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Maximum temperature

Ra

in (

mm

/da

y)

HadCM2 control

GS 1976-2003

Linear (HadCM2 control)

Linear (GS 1976-2003)

Trends over last 50 yearsCompare observed trends over last 50 years with model estimates of natural variability of trends in each gridbox. Maps show probability of trend significant different from zero for Tmax (left) and residual Tmax after removing rainfall variations (right). From Karoly and Braganza (2005)

Continental-scale temperature projections

Uncertainty plume for changes relative to 1990s in Australian area-mean temperature using scalings based on continental-scale attribution. Probabilities are represented by the depth of shading. From Stott et al. (2006)

New references• Karoly, D.J., and Q. Wu (2005) Detection of regional surface

temperature trends. J. Climate, 18, 4337–4343.• Karoly, D.J., and K. Braganza (2005) A new approach to

detection of anthropogenic temperature changes in the Australian region. Meteor. Atmos. Phys., 89, 57-67.

• P. A. Stott, J. A. Kettleborough, and M. R. Allen (2006)

Uncertainty in continental-scale temperature predictions GRL, 33, L02708, doi:10.1029/2005GL024423

• T. R. Knutson et al. (2006) Assessment of Twentieth-Century Regional Surface Temperature Trends Using the GFDL CM2 Coupled Models. J Clim., 19, 1624-51.