Download Politics and Greenhouse Climate Change

A brief history of time
the detection and attribution of
climate change
David Karoly
School of Meteorology
University of Oklahoma
What is detection and attribution?
Detection of significant observed climate change
and attribution of this observed change to one or
more causes is a signal-in-noise problem:
identifying possible signals in the noise of natural
internal climate variations in the chaotic climate
system.
Detection is the process of demonstrating that
an observed change is significantly different (in a
statistical sense) than can be explained by
natural internal climate variability.
What is detection and attribution?
Attribution of climate change to specific causes
involves statistical analysis and the careful assessment
of multiple lines of evidence to demonstrate that the
observed changes are:
• unlikely to be due entirely to internal climate variability;
• consistent with the estimated responses to a given
combination of anthropogenic and natural forcing; and
• not consistent with alternative, physically plausible
explanations of recent climate change
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
Senator James Inhofe (R, Oklahoma), Chair, Senate
Env. and Public Works Comm., in a speech to the US
Senate on Jan 4, 2005 “I called the threat of
catastrophic global warming the ‘greatest hoax ever
perpetrated on the American people’ ”
History
Global Mean Temperature
0.6
Anomaly (deg C)
0.4
0.2
Madden and Ramanathan (1980)
“surface warming due to increasing
carbon dioxide … should be
detectable … (by) the year 2000”
0
1860
-0.2
-0.4
-0.6
1880
1900
1920
1940
1960
1980
2000
History
Global Mean Temperature
0.6
Anomaly (deg C)
0.4
0.2
IPCC First Assessment Report in 1990
had no attribution chapter; general
statement on consistency of observed
warming with model projections
0
1860
-0.2
-0.4
-0.6
1880
1900
1920
1940
1960
1980
2000
History
0.6
Anomaly (deg C)
0.4
0.2
Global Mean Temperature
IPCC SAR (1995) had detection
and attribution chapter;
“the balance of evidence suggests a
discernable human influence on climate”
0
1860
-0.2
-0.4
-0.6
1880
1900
1920
1940
1960
1980
2000
History
0.6
Anomaly (deg C)
0.4
0.2
Global
Mean(2001);
Temperature
IPCC TAR
“Most of the observed warming over the
last fifty years is likely to have been due
to the increase in greenhouse gas
concentrations”
0
1860
-0.2
-0.4
-0.6
1880
1900
1920
1940
1960
1980
2000
History
Global Mean Temperature
0.6
IPCC Fourth Assessment
Report (2007): ??
Anomaly (deg C)
0.4
0.2
0
1860
-0.2
-0.4
-0.6
1880
1900
1920
1940
1960
1980
2000
Initial studies to SAR (1995)
• Univariate analysis of global mean temperature
comparing change with internal variability
• Difficult to separate different causes that affect
global radiation balance; increasing greenhouse
gases, increasing solar irradiance
• Use the spatial pattern of the temperature response
to differentiate between different causes –
fingerprint analysis
• Initially consider contrast between troposphere and
stratosphere using pattern correlation –
Karoly et al (1994), Santer et al (1995)
Changes in zonal mean atmospheric temperature (C),
1960-1995: Modelled and observed
From Tett et al (1996), following Santer et al (1995)
Further studies to TAR (2001)
• Use optimal fingerprint analysis to reduce noise by
rotating detection vector away from noise direction
• Use linear regression to estimate amplitude of
forced signal from model pattern and observational
data
• Applied to spatial pattern of surface temperature
change
• Use more models, including models without flux
correction between the atmosphere and ocean
All factors
Global mean surface
temperature (C)
HadCM3 model (black)
Observations (red)
Natural only
Anthropogenic only
Stott et al. (2000)
Apply optimal fingerprint analysis to large-scale variations of
surface temperature at decadal timescales
Bars show 5-95% uncertainty limits
Allen et al. (2000)
Optimised predictions of temperature change (C), from
1990 to 2040 under IS92a emissions (diamonds)
Allen et al, 2000
Studies since the TAR
• Use other variables: anthropogenic signal found in
ocean heat content, mean sea level pressure, and
temperature extremes
• Evaluate anthropogenic signal in temperature
changes at smaller scales
• “Key uncertainties include … relating regional trends
to anthropogenic climate change” IPCC TAR
Continental-scale studies
Stott (2003) showed that most of the observed warming over
the last 50 years in six separate continents, including North
America, Eurasia and Australia, was likely to have been due to
the increase in greenhouse gases
Decadal variations of North American mean temperatures
1.2
1.0
Karoly et al (2003)
Anomaly (K)
0.8
0.6
0.4
0.2
0.0
1900
-0.2
1920
1940
1960
1980
2000
-0.4
-0.6
Observed
HadCM2 GS
PCM GS
GFDL GS
HadCM3 GS
ECHAM4 GS
HadCM2 NAT
PCM NAT
GFDL NAT
HadCM3 NAT
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)
Simple indices of climate variability & change
• Select a small number of indices of surface
temperature variations that represent different
aspects of natural climate variability but represent
key features of patterns of anthropogenic climate
change (following Braganza et al., 2003, 2004)
• Want indices that show a common signal due to
greenhouse climate change but are nearly
independent for natural climate variations
–
–
–
–
Global mean surface temperature (GM)
Mean land – ocean temp difference (LO)
Mean magnitude of the annual cycle over land (AC)
Mean meridional temperature gradient in the NH (MTG)
Describing climate variability
Decadal standard deviations
0.25
Temperature (K)
0.20
Observed
CCSM
CSIRO
ECHAM5
GFDL 2.1
GISS E-R
HadCM3
MIROC Med
PCM
0.15
0.10
0.05
0.00
GM
LO
AC
MTG
Assessing climate change
Temperature Trends over last 50 years
1.5
Trend (K/100 yrs)
1.0
Observed
CCSM
CSIRO MkIII
ECHAM5
GFDL 2.1
GISS E-R
HadCM3
MIROC Med
PCM
0.5
0.0
-0.5
-1.0
-1.5
GM
LO
AC
MTG
Detection of regional warming: California
Compare observed area-mean
temperature change with model
simulations for 20th century
from NCAR CCSM3 and GFDL
CM2
Attribution of regional warming: California
Probability distributions of 90-year trends in California
temperature from control model simulations (solid line) and
20th century simulations with increasing greenhouse gases
and aerosols (dash-dot line, 20C3M).
The observed
trend agrees well
with the 20C3M
simulations and
can’t be
explained by
natural climate
variations
Attribution of regional warming:
Central England temperature
Probability distributions of 50-year trends in CET from control
model simulations (solid line) and 20th century simulations with
anthropogenic (ANT) and natural (NAT) external forcing
50-year trends in Central England Temperature
30
25
20
Probability (%)
The observed
trend agrees
well with the
ANT simulations
and can’t be
explained by
NAT simulations
HadCM3 Control
Obs trend 1950-99
Obs trend 1955-04
HadCM3 ANT 1950-99
HadCM3 NAT 1950-99
15
10
5
0
-0.3
-0.2
-0.1
0
0.1
0.2
Temperature trend (deg C/decade)
0.3
0.4
Continental-scale temperature projections
Uncertainty plumes for changes relative to 1990s using scalings
based on continental-scale attribution. Probabilities are
represented by the depth of shading. From Stott et al. (2006)
Conclusions
• There have been significant advances in the
methods used for attribution of the causes of
observed climate change over the past two
decades
• The predictions of Madden and Ramanathan
(1980) proved to be uncannily correct
• A clear anthropogenic signal can be identified
in observed climate changes over the last 50
years in many variables and in temperature in
almost all regions