Transcript Politics and Greenhouse Climate Change

What has caused recent climate
change?
How statistical analysis has
helped to provide the answer
Global warming alarmism
Intergovernmental Panel
on Climate Change (IPCC)
• Joint body of UN Environment Program and World
Meteorological Organization, established in 1988
• Every 5-6 years, carries out a comprehensive assessment of
climate change science, impacts, and approaches for
mitigation and adaptation to climate change
• Includes representatives from all countries
• Fourth Assessment Report prepared by more than 500
scientists over the last three years
• Summaries for Policy Makers approved by consensus
(including representatives of the Australian govt) at meetings
in Paris (Feb 07), Brussels (Apr 07) and Bangkok (May 07)
• Received the 2007 Nobel Peace Prize jointly with Al Gore
• Available from www.ipcc.ch
Causes of recent climate change
Radiative forcing components
IPCC WGI
WGIFig
FigSPM.2
SPM.2
What is the most
likely cause?
‘Most of the observed
increase in global average
temperatures since the mid20th century is very likely
(more than 90% certain)
due to the observed increase
in anthropogenic greenhouse
gas concentrations.’
IPCC(2007)
Figure TS.23
IPCC WGI Fig TS.23
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
Requirements of detection and attribution?
• Variable with high signal-to-noise ratio
• Long observational record
• Long control model simulations and ensembles of
forced climate model simulations
• Consistent response to specified forcings between
different models – consistent signals
• Separable signals between different forcings
• Statistical analysis methods that enhance signal
relative to noise and for identifying signals in
observed changes
Detection and attribution methods
• Greater confidence when
– We are able to separate the contributions to
observed change from individual sources
• Decompose the observed global surface temperature
change into contributions from GHG forcing, aerosol
forcing, natural forcing, internal variability
– Account for multiple known sources of uncertainty
– Models and observations agree on the amplitude of
the contributions
– Able to demonstrate that competing explanations
are not viable
– Models simulate similar levels of internal variability
as observed
Observations
Model
1946-56
1986-96
Filtering
and projection
onto reduced
dimension space
Y
Evaluate
amplitude
estimates
X
Y  X  
ˆ
Weaver and Zwiers, 2000
Total least squares regression
in reduced dimension space
ˆ
Evaluate
goodness of
fit
Y  (X  ξ)β  ε
• Observations represented in a
dimension-reduced space
– Typically filtered
• Spatially (to retain large
scales)
• Temporally (to retain decadal
variability - 5-10 decades)
• Projected onto low-order
space-time EOFs
• Signals estimated from
– Multi-model ensembles of 20th
century simulations
• With different combinations of
external forcings
– Anthropogenic (GHG,
aerosols, etc)
– Natural (Volcanic, solar)
• Multiple models, ensemble
sizes from 1-9 runs
• Assume linearity of response
IPCC WG1 AR4 Fig. TS-23
Examples
of signals
Solar
Volcanic
20th
century
response
to forcing
simulated
by PCM
GHGs
Ozone
Direct SO4 aerosol
All
IPCC WG1 AR4 Fig. 9.1
Y  (X  ξ)β  ε
• Signal error term represents effects of
– Internal variability (ensemble sizes are finite)
– Structural error
• Know that multi-model mean often a better presentation of current climate
• Do not know how model space has been sampled
• Scaling factor
– Alters amplitude of simulated response pattern
• Error term
– Sampling error in observations (likely small)
– Internal variability (substantial, particular at smaller scales)
– Misfit between model-simulated signal and real signal (hopefully
small … a scaling factor near unity would support this)
• Ultimate small sample inference problem:
Observations provide very little information
about the error variance-covariance structure
We think models
adequately
represent
internal surface
temperature
variability on
global scales …
Variability of observed
and simulated
annual global mean
surface temperature
(1901-2005)
ALL forcings
58 simulations
14 models
IPCC WG1 AR4 Fig. 9.7
… and also on
continental
scales
IPCC WG1 AR4
Fig. 9.8
Figure 9.8
Observations
Model
1946-56
1986-96
Filtering
and projection
onto reduced
dimension space
Y
Evaluate
amplitude
estimates
X
Y  X  
ˆ
Total least squares regression
in reduced dimension space
ˆ
Weaver and Zwiers, 2000
Evaluate
goodness of
fit
‘Most of the observed increase in global average
temperatures since the mid-20th century is very likely
due to the observed increase in anthropogenic
greenhouse gas concentrations.’
1900-99
1950-99
Estimated contribution
from greenhouse gas
(red), other
anthropogenic (green)
and natural (blue)
components to observed
global mean surface
temperature changes,
based on ‘optimal’
detection analyses
Summary
• There have been significant advances in the
methods used for attribution of the causes of
observed climate change over the past two decades
• 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
• Most of the observed increase in global average
temperatures since the mid-20th century is very
likely due to the observed increase in anthropogenic
greenhouse gas concentrations.
IPCC AR4 WGI chapt 9 (www.ipcc.ch)