Some complicating factors in understanding climate change Ross McKitrick Dept of Economics...
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Transcript of Some complicating factors in understanding climate change Ross McKitrick Dept of Economics...
Some complicating factors in understanding climate change
Ross McKitrickDept of EconomicsUniversity of GuelphOctober 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
Background: CO2 and Climate
“The
Greenhouse
Effect”
Fluid Dynamics
(Navier-Stokes)
Radiative Transfer
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
bzxyz
xzyrxy
xyx
)(
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 havesteeper 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 post-
1979 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 TroposphereData (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
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
1950 1960 1970 1980 1990 2000
2000
4000
6000
8000
10000
12000
14000
16000
Average T No. Stations
Number of weather stations
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
1950 1960 1970 1980 1990 2000
2000
4000
6000
8000
10000
12000
14000
16000
Average T No. Stations
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: regional temperature trends under GHG warming do not correlate with
surface pattern of industrialization
Climate data: 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
Ice extent, 1979 Ice extent, 2003
?????(no measurements)
Ice extent, 1935
(Polyakov et al., 2002)
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
1400 1500 1600 1700 1800 1900 2000
-0.4
-0.2
0.0
0.2
de
g C
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 face-saving 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
At peak global population of 9 billion as of 2050, emissions peak at ~10 Gigatonnes
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1960 1965 1970 1975 1980 1985 1990 1995
Me
tric
to
ns
pe
r p
ers
on
Projection scenarios
This implies the lowest end of IPCC emission scenarios:
Emission Projections to 2050
0
5
10
15
20
25
1970 1980 1990 2000 2010 2020 2030 2040 2050
Gig
ato
nn
es C
Eq
uiv
alen
t
Tot-projB1T-MessageA2-AIMA1FI
Projection scenarios
…and IPCC warming scenarios
B1, B2, A1T
CO2: The Particular Challenge
Unlike smoke or sulphates, not a particle that can be scrubbed out
Unlike CO, NOx, 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?
0
100
200
300
400
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600
1998 2008 2018 2028 2038 2048 2058 2068 2078 2088 2098
With Kyoto Without Kyoto
Costs of Kyoto
Kaya Identity Approach:
PopulationPopulation
GDP
GDP
EmissionsEMISSIONSGHGTOTAL
% Growth in Emissions = [% change in emissions intensity] + [% change in average income] (2) + [% change in population]
Costs of Kyoto for Canada
80
100
120
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1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
GHG
GDP
Emissions intensity only changes slowly
GHG Emissions Intensity ($millions GDP / Megatonne Emissions)
0
200
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600
800
1000
1200
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
80
90
100
110
120
130
140
150
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
GHG emissions
Income
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