Some complicating factors in
understanding climate change
Ross McKitrick
Dept of Economics
University of Guelph
October 2006
About me

Associate professor of economics, specializing in environmental
economics

Coauthor, Taken By Storm

Published in economics journals, as well as Climate Research,
Geophysical Research Letters, Journal of Non-Equilibrium
Thermodynamics

Participant in US National Academy of Science Review of
Paleoclimatology methods
Lines of argumentation for
global warming






Argument from basic physics
Radiative forcing summations
Increasing “global temperature” since
1900
Weather/oceanic/cryosphere changes
Millennial paleoclimate comparison
Projections of climate models
Argument from basic physics

More carbon dioxide in the air means
more infrared energy absorbed in the
atmosphere
Problem: this is a misleading
picture

It is not how the climate works

It leaves out everything that makes the
science difficult
The Climate

Energy balance mechanisms in the
Earth’s atmosphere:


Fluid Dynamics
Radiation
The Climate
 Scientists
understand
radiation very well.
 They can make exact
predictions from science
 But
they don’t understand fluid
dynamics nearly as well
 The math is too hard even for
computers to be able to make
accurate predictions
Background: CO2 and Climate
“The
Greenhouse
Effect”
Background: CO2 and Climate
“The
Greenhouse
Effect”
Background: CO2 and Climate
“The
Greenhouse
Effect”
Radiative Transfer
µ
d Iν
dz
 
κν I ν  j ν
Background: CO2 and Climate
“The
Greenhouse
Effect”
Fluid Dynamics
(Navier-Stokes)
Radiative Transfer
ρ
v
ρ v 
vη 2 v  p  ρ g

t
µ
d Iν
dz
 
κν I ν  j ν
Navier-Stokes

This is the equation which governs the flow of fluids such as
water and air. However, there is no proof for the most basic
questions one can ask: do solutions exist, and are they
unique? Why ask for a proof? Because a proof gives not only
certitude, but also understanding.

Clay Institute Millennium Prize: $1million
Lorenz Equations

Simplified 3-d model of convection
x   ( y  x)
y  rx  y  xz
z  xy  bz
Climate Forecasting

In climate research and modelling, we
should recognise that we are dealing with a
coupled non-linear chaotic system, and
therefore that the long-term prediction of
future climate states is not possible. The
most we can expect to achieve is the
prediction of the probability distribution of
the system’s future possible states by the
generation of ensembles of model solutions.

IPCC Third Assessment Report, Chapter 14.2.2.2
Another model

Standard Atmosphere:





Adding CO2 makes the atmosphere more
opaque in the infrared
Doubling CO2 raises the effective emissions
altitude ~300m
T must increase at that altitude to balance
radiation
Lapse rate of 6.5 oC/km forces T to increase at
surface
~2oC
Another model




But lapse rate is not constant at 6.5
Varies from 4—10 oC/km
Only has to change to 6.1 to eliminate
effect at surface
Emissions do not come from one
altitude
But what about the classical
Greenhouse Effect?

If not for infrared absorption by H2O, CO2
etc., the planet would be 30K cooler at the
surface.

Yes, but:



If not for convection, the planet would be 30K
warmer at the surface.
We could not live in a pure radiative equilibrium.
We live in a greenhouse that has giant air
conditioners running
Conclusion #1




“Basic physics” does not apply to the
climate problem
It is a problem in fluid dynamics
No known theoretical solution exists
No computational solution exists
Radiative Forcing Summations
What is “radiative forcing”

Have you ever seen someone out
measuring it?
Radiative forcing





A modeling concept
RF is not directly measured, instead it is calculated by
simplified climate models under abstract assumptions.
Measurement of RF in Watts/square meter is a convention,
but RF itself is not a measured physical quantity.
The various processes that it attempts to approximate are
themselves poorly quantified. (2.2)
An increase in radiative flux associated with changing
concentrations of CO2 and methane has been observed using
satellite data. This is what is meant by the term “enhanced
greenhouse effect”, but is not itself related to the “Radiative
Forcing” concept (2.3.8).
Global Temperature rising

NASA (GISS)
Is there a global temperature?

No, there is a temperature field

Spans 100K at any one time
We are looking for changes on the 0.1K scale

Non-equilibrium systems

Have no one temperature
Non-equilibrium systems

Take the average…
Which one?

The system is “warming” and “cooling” at the same time

Warming or Cooling?




Neither.
An average is rising or falling.
Only in special circumstances can this
be termed “warming” and “cooling”
Climate isn’t one of them
Focus on average T

Which one?
Problems of interpretation:
Satellites
Surface-satellite
discrepancy: USCCSP Report
Models
say top panel should have
steeper trend after 1979
“Previously reported discrepancies
between the amount of warming near the
surface and higher in the atmosphere have
been used to challenge the reliability of climate models and the reality of human-induced
global warming. Specifically, surface data showed substantial global-average warming,
while early versions of satellite and radiosonde data showed little or no warming above
the surface.”
Surface-Satellite Discrepancy

Tropospheric data since 1979 shows a trend in the mean of
between 0.04 and 0.20 oC/decade, and no data sets exhibit
significant warming in the tropical troposphere [3.4.1.2.1; Fig
3.4.3].

Data collected at the Earth’s surface shows, over the post1979 interval, trends of 0.1 to 0.4 oC/decade, with most data
sets indicating almost double the rate of warming in the
troposphere [Table 3-9].

Climate models project stronger warming in the troposphere
than at the surface, with the strongest warming in the tropical
troposphere, opposite to recent observations [10.3.2.1; Fig
10.3.4].
Problems of interpretation:
Satellites

Reconciliation…?
“…
larger surface warming (at least in the tropics)
would be inconsistent with our physical
understanding of the climate system, and with the
results from climate models.”
“…
[Since 1979] most data sets show slightly
greater warming at the surface.”
Problems of interpretation:
Satellites
Tropical Troposphere
Data (AR4 Fig 3.4.3)
Models (AR4 Fig 10.3.4)
Number of weather stations

Each dot represents a weather station
Number of weather stations
Average T
No. Stations
12.5
16000
12.0
14000
11.5
12000
11.0
10000
10.5
8000
10.0
6000
9.5
4000
9.0
1950
1960
1970
1980
1990
2000
2000
Number of weather stations
Average T
No. Stations
12.5
16000
12.0
14000
11.5
12000
11.0
10000
10.5
8000
10.0
6000
9.5
4000
9.0
1950
1960
1970
1980
1990
2000
2000
Problems of interpretation:
Surfaces

Anthropogenic surface processes


Land-use changes, urbanization, data quality
problems introduce false trends in data
Large literature shows these cause warming
bias in meteorological data, e.g.
Problems of interpretation:
Surfaces

Climate model predictions:


Climate data:


regional temperature trends under GHG warming do not correlate with
surface pattern of industrialization
observed regional temperature trends strongly correlate with surface
pattern of industrialization
From abstract:
Biases in surface record
Conclusions about 20th
century Temperatures

‘Global temperature’ not physically defined

Average temperature: rival definitions over
post-1980 period

Surface data sparse, poorly sampled,
contaminated by surface processes

Satellite data sets do not show predicted
tropospheric warming trend
Weather/Oceanic/Cryospheric
Changes

There is no globally-consistent pattern in snow-covered area
(SCA) or snow depth.

Since the 1920s and especially since the late 1970s, Northern
Hemisphere snow cover has declined in spring and summer but not
substantially in winter. [4.7:4—5]. In North America the trend in
SCA over the 20th century is upward overall, with a recent
downward trend [4.7:41—44]. SCA in mountainous areas of
Switzerland and Slovakia has declined since 1931, but not in
Bulgaria [4.8:7—9]. Lowland areas of central Europe have exhibited
decreased SCA, while increased snow depth has been recorded in
the former Soviet Union, Tibet and China [4.8:13—16]. In South
America a long term increasing trend in snow days has been
observed in the eastern central Andres [4.18:27—28]. In
Southeastern Australia, late-winter snow depth has declined
considerably, though winter precipitation has decreased only slightly
[4.8:41—45].
Weather/Oceanic/Cryospheric
Changes

There is no globally-consistent pattern in long-term
precipitation trends, though most places have observed
slight increases in rain and or snow cover.

Precipitation in North and South America has risen slightly
over the past century in many places, though in some regions
it has fallen.
The drying trend noted in the Sahel in the 1980s has since
reversed considerably. [p. 3-17, lines 17—28].
Rainfall in India increased from 1901 to 1979 then declined
through to the present [3-17 lines 28-29], and there is no
overall trend [3-18, lines 16-17].
Australian precipitation trends vary by region and are closely
linked to the El Nino cycle [3.17:30-31, 3.18:23-26].



Weather/Oceanic/Cryospheric
Changes

New York City: For the first time since records began in the 1860s,
Central Park reported four successive years of 40 inches of snow or
more ending in the winter of 2005/06.



On February 11-12, 2006, Central Park broke the ALL-TIME single
snowstorm record with 26.9 inches of snow.
Also in 1995/96, Central Park and most other cities in the central and
eastern US had ALL-TIME record seasonal snowfall. In Central Park, that
winter brought 75.6” of snow.
Boston, MA: the 12 year average snowfall in the winter ending
2004/05 was 51.3 inches, the highest in their entire record going
back into the 1800s.


A new ALL-TIME single snowstorm record was set on February 17-18,
2003 with 27.5 inches and a new ALL-TIME seasonal snowfall record of
107.6 inches was set in 1995/96.
In the last dozen years, Boston has recorded their 1st, 3rd, 5th, 7th and
12th snowiest winters.
Long-term Arctic Temperature History
(Polyakov et al., 2002)
?????
(no measurements)
Ice extent, 1935
Ice extent, 1979
Ice extent, 2003
Millennial Paleoclimate
Comparisons

Are the recent trends large in a climatic sense?
No:

(IPCC 1991)
Millennial Paleoclimate
Comparisons

Are the recent trends large in a climatic sense?
No:

(IPCC 1991)
Yes:

(IPCC 2001)
Millennial Paleoclimate
Comparisons
2006: No
0.0
-0.2
-0.4
deg C
0.2

1400
1500
1600
1700
1800
1900
2000
Millennial Paleoclimate
Comparisons

Since 2003, Steve McIntyre and I have worked at figuring out
how the hockey stick graph was constructed

Our claims:





Hockey stick depends on use of bristlecone pine ring widths, which
should not be used as temperature proxies
Hockey stick PCA method is biased towards producing false hockey
stick shapes in this type of data
Hockey stick results do not pass standard tests of statistical significance
Hockey stick methods systematically underestimated the uncertainties
March 2006: presented these findings to the National Academy
of Sciences Expert Panel on Paleoclimate Reconstruction
Millennial Paleoclimate
Comparisons

NAS Conclusions, June 2006:

Hockey stick depends on use of bristlecone pine
ring widths, which should not be used as
temperature proxies

Hockey stick PCA method is biased towards
producing false hockey stick shapes in this type of
data

Hockey stick results do not pass standard tests of
statistical significance

Hockey stick methods systematically
underestimated the uncertainties
Millennial Paleoclimate
Comparisons

The IPCC used the hockey stick to assert it was “likely” that the 1990s
were the warmest decade, and 1998 the warmest year, in the
millennium

The NAS concluded:

it is “plausible that the Northern Hemisphere was warmer during the last
few decades of the 20th century than during any comparable period over
the preceding millennium. The substantial uncertainties currently present in
the quantitative assessment of large-scale surface temperature changes
prior to about A.D. 1600 lower our confidence in this conclusion compared
to the high level of confidence we place in the Little Ice Age cooling and
20th century warming. Even less confidence can be placed in the
original conclusions by Mann et al. (1999) that “the 1990s are likely
the warmest decade, and 1998 the warmest year, in at least a
millennium”

“…Some of these [McIntyre & McKitrick] criticisms are more relevant than
others, but taken together, they are an important aspect of a more general
finding of this committee, which is that uncertainties of the published
reconstructions have been underestimated.”
Barton Letters

US House Energy and Commerce
initiated ad hoc panel under leadership
of Edward Wegman

Prof. of Statistics at George Mason and
Chair, National Academy of Sciences
Committee on Theoretical and Applied
Statistics
Wegman Panel: July 2006

Edward Wegman, George Mason
University




David W. Scott, Rice University
Yasmin Said, Johns Hopkins University
John T. Rigsby III, Naval Warfare Center
Denise M. Reeves, MITRE Corp.
Findings: very similar to NAS
without the political correctness

(P. 4) In general, we found MBH98 and MBH99 to be
somewhat obscure and incomplete and the criticisms of
MM03/05a/05b to be valid and compelling.

…authors in the area of paleoclimate studies are closely
connected and thus ‘independent studies’ may not be as
independent as they might appear on the surface.
Findings: very similar to NAS
without the political correctness

(p. 4)… we judge that the sharing of research
materials, data and results was haphazardly
and grudgingly done. In this case we judge
that there was too much reliance on peer review,
which was not necessarily independent.
Moreover, the work has been sufficiently
politicized that this community can hardly
reassess their public positions without losing
credibility.
Wegman Panel Findings

(P. 26): “While the work of Michael Mann and colleagues
presents what appears to be compelling evidence of global
temperature change, the criticisms of McIntyre and McKitrick,
as well as those of other authors mentioned are indeed valid
…The papers of Mann et al. in themselves are written in a
confusing manner, making it difficult for the reader to discern
the actual methodology and what uncertainty is actually
associated with these reconstructions.”
Wegman Panel Findings

(p. 28) “The description of the work in MBH98 is both
somewhat obscure and as others have noted incomplete… It is
not clear that Dr. Mann and his associates even realized that
their methodology was faulty at the time of writing the MBH
paper. …

“the fact that their paper fit some policy agendas has greatly
enhanced their paper’s visibility. … The ‘hockey stick’
reconstruction of temperature graphic dramatically illustrated
the global warming issue and was adopted by the IPCC and
many governments as the poster graphic. The graphics’
prominence together with the fact that it is based on incorrect
use of PCA puts Dr. Mann and his co-authors in a difficult facesaving position.”
Wegman Panel Findings

(p. 49) “Generally speaking, the paleoclimatology
community has not recognized the validity of the
MM05 papers and has tended dismiss their results
as being developed by biased amateurs. The
paleoclimatology community seems to be tightly
coupled as indicated by our social network analysis,
has rallied around the MBH98/99 position, and has
issued an extensive series of alternative
assessments most of which appear to support the
conclusions of MBH98/99.”
Millennial Paleoclimate
Comparisons
Millennial Paleoclimate
Comparisons

Moberg:
Millennial Paleoclimate
Comparisons

Divergence problem
Conclusions: Millennial
comparison




Data too noisy to support conclusions
Stats methods are widely flawed
Too much data recycling
Tree ring proxy data not reliable
temperature recorders
Climate Model Projections



GCMs are the focus of thinking about climate change
‘Modelers’ do not speak for climate/atmospheric science!
“At least at the time of my fieldwork, close users and potential close
users at NCAR (mostly synoptically trained meteorologists who would
like to have a chance to validate the models) complained that
modelers had a ‘fortress mentality’. In the words of one such user I
interviewed, the model developers had ‘built themselves into a shell
into which external ideas do not enter’. His criticism suggests that
users who were more removed from the sites of GCM development
sometimes have knowledge of model limitations that modelers
themselves are unwilling, and perhaps unable, to countenance.”

Lahsen, 2005. Seductive Simulations? Uncertainty Distribution Around
Climate Models, Social Studies of Science, 35, 895-922.
Climate Model Projections

US National
Assessment 2001, precip projections
Is there a consensus that humans
are causing climate change?

In 2003 a German lab surveyed 530
climate scientists.
“[The] consensus is not all that strong and only 9.4% of the respondents
‘strongly agree’ that climate change is mostly the result of anthropogenic
causes.
“… In fact, the results of the two surveys even question the Oreskes’ claim
that the majority of climate scientists agree with the IPCC”
http://w3g.gkss.de/G/mitarbeiter/bray/BrayGKSSsite/BrayGKSS/WedPDFs/Science2.pdf
Projection scenarios

Global average C emissions are very stable
at 1.1 tonnes/person
1.4
1.2
Metric tons per person
1
0.8
0.6
0.4
0.2
0
1960

1965
1970
1975
1980
1985
1990
1995
At peak global population of 9 billion as of
2050, emissions peak at ~10 Gigatonnes
Projection scenarios
This implies the lowest end of IPCC
emission scenarios:
Emission Projections to 2050
25
20
Gigatonnes C Equivalent

Tot-proj
B1T-Message
A2-AIM
A1FI
15
10
5
0
1970
1980
1990
2000
2010
2020
2030
2040
2050
Projection scenarios
…and IPCC warming scenarios
B1, B2, A1T
CO2: The Particular Challenge

Unlike smoke or sulphates,


Unlike CO, NOx,


not a particle that can be scrubbed out
not a gas that forms due to incomplete
combustion
If you burn fuel, you release it, period.
Projection scenarios

What if we all did Kyoto?
600
500
400
With Kyoto
W ithout Kyoto
300
200
100
0
1998 2008 2018 2028 2038 2048 2058 2068 2078 2088 2098
Costs of Kyoto

Kaya Identity Approach:
 Emissions   GDP 
  Population
TOTAL GHG EMISSIONS  
  
 GDP   Population
% Growth in Emissions =
[% change in emissions intensity]
+
[% change in average income]
+
[% change in population]
(2)
Costs of Kyoto for Canada
200
180
GHG
GDP
160
140
120
100
80
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Emissions intensity only changes
slowly
GHG Emissions Intensity ($millions GDP / Megatonne Emissions)
1200
1000
800
600
400
200
0
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Factors behind emissions
growth
Change in Emissions Intensity
+Change in Population
+Change in Income (GDP per person)
=Change in Emissions
Factors behind emissions
growth
Change in Emissions Intensity
+Change in Population
+Change in Income (GDP per person)
=Change in Emissions
Factors behind emissions
growth
150
140
130
120
110
GHG emissions
100
Income
90
80
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
No country is serious about
Kyoto once they understand it

Not even the
UK
Some conclusions







Climate is complicated
No one knows what effect, if any, increased CO2 will
have on the weather you will experience in your life
There is no basic physics to look to
We’re not sure how to measure what we’re looking
for
Popular graphs should be read with skepticism
CO2 cannot be controlled easily the way other air
pollutants could
Emissions will be at the low end of IPCC scenarios