Four Things
I Think I Know
About Climate
John R. Christy
University of Alabama in Huntsville
Alabama State Climatologist
We should always begin our scientific
assessments with the following
statement:
“At our present level of ignorance, we
think we know …”
Paraphrase of Richard Mallory
Hoover High School
Fresno CA
Physics Teacher 1968
Testing Hypothesis (assertions) about Climate
UAHuntsville builds datasets from scratch
1. Popular surface temperature datasets are poor
metrics for checking on the greenhouse effect - and
they are poorly measured as well
2. Warming is occurring but at a rate and in a manner
that is inconsistent with model projections of
enhanced greenhouse warming
3. Sensitivity research suggests the climate system is
less sensitive to CO2 increases as depicted in models
due to unaccounted-for negative cloud feedbacks
4. Impacts on global emissions of current legislative
actions are minuscule and will have no discernable
impact on whatever the climate is going to do
Testing Hypotheses on Global
Warming
1. Testing Assertions based on
Popular Surface Temperature
Datasets
Popular surface datasets tend to:
(a) overstate the warming, and (b)
serve as a poor greenhouse metric
"Global" Surface Temperature
Tmean is Average of Day and Night
HadCRUT3 (2010 Jan-Jun only)
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
1850
1870
1890
1910
1930
1950
1970
1990
CO2 up 38% at current rate of 0.6% per year
2010
Day vs. Night Surface Temp
Greenhouse signal
Warm air above inversion
Cold air near surface
Nighttime - disconnected
shallow layer/inversion. Temperature
affected by land-use changes, buildings,
farming, etc.
Daytime - deep layer mixing,
connected with levels impacted
by enhanced greenhouse effect
Night Surface Temp
Warm air above inversion
Warm air
Cold air near surface
Nighttime - disconnected
shallow layer/inversion. But this
situation can be sensitive to small
changes such as roughness or heat
sources.
Buildings, heat releasing
surfaces, aerosols, greenhouse
gases, etc. can disrupt the
delicate inversion, mixing warm
air downward - affecting TMin.
MODIS
21 Jul 2002
Jacques Descloitres
MODIS
Land Rapid Response Team
NASA GSFC
CA Valley and Sierra (Jun-Nov) 1910-2003
16
Valley TMin
°C
14
Sierra TMin
12
10
8
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Nighttime temperatures rising but
not because of greenhouse gas
warming, but nighttime readings
are included in popular datasets
Daytime temperatures tell more
accurate story
Christy 2002, Christy et al. 2006, 2007,
2009, Pielke et al 2008, Walters et al. 2007
CA Valley and Sierra (Jun-Nov) 1910-2003
16
Valley TMin
°C
14
Sierra TMin
12
10
8
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
CA Valley and Sierra Annual Avg TMax 1910-2003 .
28
26
°C
24
22
20
18
Valley TMax
Sierra TMax
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Nighttime temperatures rising but
not because of greenhouse gas
warming, but nighttime readings
are included in popular datasets
Daytime temperatures tell more
accurate story
Christy 2002, Christy et al. 2006, 2007,
2009, Pielke et al 2008, Walters et al. 2007
1. (c) Some surface data
sources are simply poor
1.5
East Africa TMax (Christy et al. 2009).
1.0
Obs: (HadCRUT3) +0.14 °C/decade
0.5
0.0
-0.5
-1.0
Kilimanjaro
-1.5
1900
1920
1940
1960
1980
2000
1.5
East Africa TMax (Christy et al. 2009).
1.0
Obs: (HadCRUT3) +0.14 °C/decade
0.5
0.0
-0.5
-1.0
Kilimanjaro
-1.5
1900
1920
Obs: (UAH) +0.02 °C/decade
1940
1960
1980
2000
Testing Hypothesis (assertions) about Climate
UAHuntsville builds datasets from scratch
1. Popular surface temperature datasets tend to be poor
metrics for checking on the greenhouse effect - and
they are often poorly measured as well
2. Warming is occurring but at a rate and in a manner
that is inconsistent with model projections of
enhanced greenhouse warming
3. Sensitivity research indicates the climate system is
less sensitive to CO2 increases as depicted in models
due to unaccounted-for negative cloud feedbacks
4. Impacts on global emissions of current legislative
actions are minuscule and will have no discernable
impact on whatever the climate is going to do
Testing Hypotheses on Global
Warming
2. (a) Testing Assertions based
on Climate Models for global
trend magnitude
Climate models tend to overstate
or misrepresent the warming
History Lesson 1988
1.8
1.6
GISS-A(88)
GISS-B(88)
GISS-C(88)
1.4
1.2
Predictions
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
1960
1970
1980
1990
2000
2010
2020
History Lesson 1988
1.8
1.6
GISS-A(88)
GISS-B(88)
GISS-C(88)
UAH-LT (SfcAdj)
RSS-LT (SfcAdj)
1.4
1.2
1.0
Predictions
0.8
0.6
0.4
Observations
0.2
2010 to July Only .
0.0
-0.2
1960
1970
1980
1990
2000
2010
2020
G. Schmidt NASA/GISS
Individual Model Surface Trends
1979-2010
17
BCCR
CCMaT47_avg
CCMaT63
GFDL_CM2.0
16
GFDL_CM2.1
HadGEM1
CSIROMk3. 0
CSIROMk3. 5
GISS EH (3)
15
GISS ER (4)
ECHAM5
MRI232a (5)
CCSM3. 0 (7)
PCM1 (2)
14
MIROC3.2HiRes (1)
CNRM CM3 (1)
INGV ECHAM4 (1)
INMCM3.0 (1)
13
IPSL CM4 (1)
MIROC3.2 MidRes (3)
ECHO G (3)
ESSENCE
UKMO HadCM3 (1)
12
QUMP HadCM3 (17)
1979
1983
1987
1991
1995
1999
2003
2007
Median
Trends ending in 2008 with various start years
IPCC AR4 Model Runs (22 models) vs. Obs.
HadCRUT3v
0.6
UAHLTsf c
R SSltsfc
BCCR
CCMaT47_avg
0.5
CCMaT63
GFDL_CM2.0
GFDL_CM2.1
HadGEM 1
0.4
CSIROMk3. 0
CSIROMk3. 5
GISS EH (3)
GISS ER (4)
0.3
ECHAM5
MR I232a (5)
CCSM 3. 0 (7)
PCM1 (2)
0.2
MIR OC3.2HiRes (1)
CNR M CM 3 (1)
INGV ECHAM4 (1)
INMCM3.0 (1)
0.1
IPSL CM4 (1)
MIR OC3.2 M idRes (3)
ECHO G (3)
0.0
ESSENCE
UKMO HadCM3 (1)
QUMP HadCM 3 (17)
Median
-0.1
mean w/oMRI, MIROC HiR es
1979
1985
1990
1995
2000
Start Year
Trends ending in 2008 with various start years
IPCC AR4 Model Runs (22 models) vs. Obs.
0.30
0.25
Mean of Model trends
(w/o Max,Min)
Median of Model
Trends
HadCRUT 3v
0.20
0.15
UAHLTsfc
0.10
RSSLTsfc
0.05
0.00
1979
1984
1989
1994
1999
Start Year
Trends ending in 2010 (Jun) with various start years
IPCC AR4 Model Runs (22 models) vs. Obs.
0.30
0.25
Mean of Model trends
(w/o Max,Min)
Median of Model
Trends
HadCRUT 3v
0.20
0.15
UAHLTsfc
0.10
RSSLTsfc
0.05
0.00
1979
1985
1990
1995
2000
Start Year
Global Bulk Atmospheric Temperatures
UAH Satellite Data
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
Warming rate 50% of model projections
Christy et al. 2007, 2009
-0.4
-0.6
1978
1982
1986
1990
1994
1998
2002
2006
2010
Testing Hypotheses on Global
Warming
Testing Assertions based on
Climate Models - Sierra Nevada
loses 80% of snow by 2100
Observations contradict this
Sierras
warm
faster than
Valley in
model
simulations
Snyder et al. 2002
1200
Southern Sierra Nevada Snowfall
1915-16 to 2009-10
1000
CM
800
600
400
200
Trend +1.1 cm/decade
So. Sierra Snowfall
0
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Christy and Hnilo 2010 .
Testing Hypotheses on Global
Warming
2. (b) Testing Assertions based on
Climate Models concerning Tropical
Upper Air Temperature trends
Climate models tend to
misrepresent the observed
relationship
A Climate Model
Simulation is a
Hypothesis
How does one define a
falsifiable test for a
model hypothesis?
Douglass, Christy, Pearson and Singer
2007
Select a prominent metric dependent
on the main perturbation in forcing - a
large signal - test against observations
One such signal is the vertical
structure of the tropical tropospheric
temperature trend - i.e. how surface
and upper air trends compare
Vertical Temperature Change due
to Greenhouse Forcing in Models
Model
Simulations of
Tropical
Troposphere
Warming:
About 2X surface
Lee et al. 2007
Models: Mean and Standard Error
100
200
300
400
500
600
Model -2SE
Model +2SE
Model Best Guess
700
800
900
1000
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
°C/decade
Best Estimate of Models - given surface trend close to observed
Observations: Four "corrected" Datasets
100
200
300
400
500
600
HadAT
IGRA
RATPAC
RICH
700
800
900
1000
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
°C/decade
Upper air trends of four observed datasets are significantly cooler in this
apples to apples comparison
Models vs. Obs
100
200
300
400
500
600
700
Model -2SE
Model +2SE
Model Best Guess
HadAT
IGRA
RATPAC
RICH
800
900
1000
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
°C/decade
Upper air trends of four observed datasets are significantly cooler in this
apples to apples comparison (Douglass et al. 2007).
Model Sfc-to-Upper Air Trends Normalized to Observed Sfc
100
200
300
400
500
600
700
Model Best Guess
HadAT
IGRA
RATPAC
RICH
800
900
Cool Ext
Warm Ext
1000
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
°C/decade
Putting the hot and cold extremes (thick red) of model trends tied to
actual surface trend, models are still too hot which in this case all models
have been tied to the actual observed surface trend.
A different test asks whether the
models and observational upper air
trends agree if NO restriction is placed
on the model surface trends (i.e.
apples to oranges.)
Extremely weak hypothesis to test and
does NOT address the sfc-to-upper air
relationship (a key signature of
greenhouse warming in models.)
Model Sfc-to-Upper Air Trends Normalized to Observed Sfc
100
200
300
400
500
600
700
800
900
Model Best Guess
HadAT
IGRA
RATPAC
RICH
S08-2SE
S08+2SE
1000
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
°C/decade
Putting the hot and cold extremes (thick red) of model trends tied to
actual surface trend, models are still too hot which in this case all models
have been tied to the actual observed surface trend.
Ratio of Lower Tropospheric Trend to Surface Trend
(Amplification Ratio)
2.0
1.8
1.6
Model
Median
1.4
1.2
1.0
0.8
0.6
Model AR
Obs AR
0.4
SS
)
(R
dA
RA T
TP
AC
RI
CH
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OB
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CC
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PC
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GF
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Me
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RM
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Me
dia
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0.0
UA
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0.2
Christy et al. 2007, 2010; Christy and Norris 2006, 2009; Randall and
Herman 2008; Klotzbach et al. 2009, 2010; McKitrick et al. 2010
Observations (Surface much more than upper air)
Land Surface Temperatures overstate warming
Model Expectation (Surface less than upper air)
Klotzbach et al. 2010
Table 2 displays the new per decade linear trend calculations [of
difference between global surface and troposphere using model
amplification factor] … over land and ocean. All trends are
significant at the 95% level.
Christy et al. 2010
[Our] result is inconsistent with model projections which show
that significant amplification of the modeled surface trends
occurs in the modeled tropospheric trends.
McKitrick et al. 2010
Over the interval 1979-2009, model-projected temperature
trends are two to four times larger than observed trends in both
the lower and mid-troposphere and the differences are
statistically significant at the 99% level. [Note: recalculated
Santer et al. 2008 method, and even with surface trend variation
found Santer et al.’s result is not verified.]
Testing Hypothesis (assertions) about Climate
UAHuntsville builds datasets from scratch
1. Popular surface temperature datasets tend to be poor
metrics for checking on the greenhouse effect - and
they are often poorly measured as well
2. Warming is occurring but at a rate and in a manner that
is inconsistent with model projections of enhanced
greenhouse warming
1. Sensitivity research suggests the climate system is
less sensitive to CO2 increases (as depicted in
models) due to unaccounted-for negative cloud
feedbacks
4. Impacts on global emissions of current legislative
actions are minuscule and will have no discernable
impact on whatever the climate is going to do
Testing Hypotheses on Global
Warming
3. Testing Assertions on the
sensitivity of the climate to
increases in greenhouse gas forcing
Climate models tend to overstate
the sensitivity due to missing
negative cloud feedbacks
Response of Clouds and Water Vapor (shortwave
and longwave) to Increasing CO2
Negative Feedback?
(mitigates CO2 impact)
Positive Feedback?
(enhances CO2 impact - models)
100
80
60
Water Vapor and Clouds
CO2
Other
40
20
0
Relative Temperature Effect
A Climate Model
Simulation is a
Hypothesis
How does one define a
falsifiable test for a
model hypothesis?
Select a prominent metric dependent
on the main perturbation in forcing a large signal - test against
observations
One such test is to calculate the
climate feedback parameter during
monthly and annual scale variations
Spencer and Braswell
Global Warming in Models is
Greatly Magnified by Positive Feedbacks
Warming in models
amplified by clouds
& vapor
(~3 deg. C by 2100)
Warming from
CO2 only
Satellite data
suggests clouds
reduce warming
(~0.5°C by 2100)
Spencer and Braswell .
Testing Hypothesis (assertions) about Climate
UAHuntsville builds datasets from scratch
1. Popular surface temperature datasets tend to be poor
metrics for checking on the greenhouse effect - and they
are often poorly measured as well
2. Warming is occurring but at a rate and in a manner that is
inconsistent with model projections of enhanced
greenhouse warming
3. Sensitivity research suggests the climate is less
sensitive to CO2 increases than depicted in models due
to unaccounted-for negative cloud feedbacks
4. Impacts on global emissions of current legislative
actions are minuscule and will have no discernable
impact on whatever the climate is going to do
Testing Hypotheses on Global
Warming
4. Testing Assertions the impact of
regulations on climate
Regulations will have a minuscule
impact on whatever the climate is
going to do
What did California do?
• Force a limit on emissions of Light Duty
Vehicles
• California AB 1493 seeks to reduce tailpipe emissions of CO2 by 26% by 2016
• 11 NE States adopted AB 1493
• Trial in Federal Court (Burlington VT) to
address the engineering, legal and
climate issues of AB 1493, April-May
2007
What did California do?
• Force a limit on emissions of Light Duty
Vehicles
• California AB 1493 seeks to reduce tailpipe emissions of CO2 by 26% by 2016
• 11 NE States adopted AB 1493
• Trial in Federal Court (Burlington VT) to
address the engineering, legal and
climate issues of AB 1493, April-May
2007
What did California do?
• Force a limit on emissions of Light Duty
Vehicles
• California AB 1493 seeks to reduce tailpipe emissions of CO2 by 26% by 2016
• 11 NE States adopted AB 1493
• Trial in Federal Court (Burlington VT) to
address the engineering, legal and
climate issues of AB 1493, April-May
2007
What did California do?
• Force a limit on emissions of Light Duty
Vehicles
• California AB 1493 seeks to reduce tailpipe emissions of CO2 by 26% by 2016
• 11 NE States adopted AB 1493
• Trial in Federal Court (Burlington VT) to
address the engineering, legal and
climate issues of AB 1493, April-May
2007
9
9
2 0
0
0
2 0
0
1
0
2
0
2
2 0
0
3
2 0
0
4
0
2
0
5
2 0
0
6
2 0
0
7
0
2
0
8
2 0
0
9
2 0
1
0
0
1
IPCC “Best Estimate”
3.0
2.5
2.0
A1B
1.5
1.0
0.5
0.0
California AB 1493
26% CO2 reduction LDV 2016
3.0
2.5
2.0
A1B
CA
No. East
USA
1.5
1.0
0.5
1
9
9
2 0
0
0
2 0
0
1
2 0
0
2
2 0
0
3
2 0
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2
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5
2 0
0
6
2 0
0
7
2 0
0
8
2 0
0
9
2 0
1
0
0
0.0
Net Impact if all US 0.01°C 2100
The temperature impact on global
temperatures if the entire world adopted
AB 1493 is an undetectable 0.03°C.
Latest sensitivity results suggest the
impact is even smaller.
Judge William Sessions III Ruling 12 Sept 2007
AB 1493 is legal
Pg 46
“Plaintiffs’ expert Dr. Christy
estimated that implementing the
regulations across the entire United
States would reduce global
temperature by about 1/100th (.01)
of a degree by 2100. Hansen did not
contradict that testimony.”
Questions
• What could make a “dent” in
forecasted global temperatures?
• What would be the impact of
building 1000 nuclear power plants
and putting them on-line by 2020?
– (average 1.4 gigawatt output each)
Questions
• What could make a “dent” in
forecasted global temperatures?
• What would be the impact of
building 1000 nuclear power plants
and putting them on-line by 2020?
– (average 1.4 gigawatt output each)
9
9
2 0
0
0
2 0
0
1
0
2
0
2
2 0
0
3
2 0
0
4
0
2
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2 0
0
6
2 0
0
7
0
2
0
8
2 0
0
9
2 0
1
0
0
1
IPCC “Best Estimate”
3.0
2.5
2.0
A1B
1.5
1.0
0.5
0.0
Net Effect of 10% CO2 emission
reduction to A1B Scenario
(~1000 Nuclear Plants by 2020)
3.0
2.5
A1B Emissions
10% Reduction A1B
2.0
1.5
1.0
0.5
Net Impact 0.07°C 2050
1
9
9
2 0
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0
2 0
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2
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3
2 0
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2
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2 0
0
6
2 0
0
7
0
2
0
8
2 0
0
9
2 0
1
0
0
0.0
Testing Hypothesis (assertions) about Climate
UAHuntsville builds datasets from scratch
1. Popular surface temperature datasets tend to be poor
metrics for checking on the greenhouse effect - and they
are often poorly measured as well
2. Warming is occurring but at a rate and in a manner that is
inconsistent with model projections of enhanced
greenhouse warming
3. Sensitivity research suggests the climate is less sensitive to
CO2 increases than depicted in models due to
unaccounted-for negative cloud feedbacks
4. Impacts on global emissions of current legislative
actions are minuscule and will have no discernable
impact on whatever the climate is going to do
Kenya, East Africa
Energy System
Energy Source
Energy Transmission
Energy Use
Consensus is not Science
Michael Crichton
Consensus is not Science
Michael Crichton
All Science is numbers
William Thomson (Lord Kelvin)