Transcript ppt file - School of Meteorology

Comments on Discussion paper
“Detecting, understanding, and
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 warming
Calculate 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
0.6
1910-75
1976-2003
0.4
Rain (mm/day)
Linear (1910-75)
Linear (1976-2003)
0.2
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
0.0
-0.2 0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
-0.2
-0.4
-0.6
Maximum temperature
HadCM2 annual southern Australia area-average anomalies
0.6
HadCM2 control
GS 1976-2003
0.4
Linear (HadCM2 control)
Rain (mm/day)
• 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
0.2
Linear (GS 1976-2003)
0.0
-2.0
-1.5
-1.0
-0.5
0.0
0.5
-0.2
-0.4
-0.6
Maximum temperature
1.0
1.5
2.0
2.5
Trends over last 50 years
Compare 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 continentalscale 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 TwentiethCentury Regional Surface Temperature Trends Using the
GFDL CM2 Coupled Models. J Clim., 19, 1624-51.