Transcript Presentation - The Lyncean Group of San Diego

Nature – not Human Activity –
Rules the Climate
Lyncean Society, San Diego
Jan. 12, 2011
S. Fred Singer, Science &
Environmental Policy Project
<Singer@NIPCC report.org>
20th Cy Warming: AGW or Natural?
• Both are plausible. What says the evidence?
• Comparison of observations and models
• IPCC-4: Global mean sfc temp vs a composite
calculated curve, using adjustable parameters.
But this is simply an exercise in ‘curve-fitting.’
• IPCC-2, CCSP-1.1 and NIPCC use fingerprint
method (latitude&altitude patterns of trends):
Comparison of balloon/sat data vs GH-models
• Douglass et al (2007) vs Santer et al (2008)
CCSP 1.1 – Chapter 1, Figure 1.3F PCM Simulations of
Zonal-Mean Atmospheric Temperature Change
CCSP 1.1 – Chapter 5, Figure 7E
A more detailed view of the disparity:
Douglass, Christy, Pearson, Singer - 2007
Non-Governmental International
Panel on Climate Change -- NIPCC
• An independent examination of published
IPCC evidence by an international group of
some 30 climate experts from 16 nations
• Organized in 2003 by Prof. S. Fred Singer as
“Team B;” workshop in Vienna, April 2007
• Summary for Policy-Makers and Technical
Summary, published by Heartland Institute in
March 2008
Which set of temp observations ?
• Santer et al 2008 [S08] relies on visual overlap
between obs and modeled trends
• Direct sonde data: authors are not retracting
• Trends from re-analysis likely spurious -Christy
• But S08 shows both data sets agree with sat
• Hence, we need better discriminant: MT – LT
is sensitive to Upper Trop
Temperature Changes from more than 30 Years of Satellite Observations
Source: drroyspencer.com/latest-global-temperatures
MSU Weighting Functions
LT & MT Tropics Temp Trend
(degK/decade) (1979 to Yr xxxx)
0.15
0.1
LT Trend
DegK/Dec
0.05
MT Trend
0
-0.05
-0.1
-0.15
-0.2
1990
1995
2000
2005
Uncertainties of Modeled Trends
• Structural Uncertainties: model differences
– Choice of forcings
– Choice of parameters (clouds, etc)
• Chaotic Uncertainty: sensitive to initial values
– Typically an order of magnitude – or more
– How many model runs (“simulations”) are
necessary to get the ‘true’ temp trend: 5,10, 25?
– IPCC Practice: First form the “ensemble-means”
and then average over all models (but most
models trends are based on only one or two runs)
Figure 2.22
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 sims9
2 sims5
3 sims5
4 sims5
5 sims5
6 sims4
7 sims4
8 sims3
9 sims3
10 sims3
11 sims3
12 sims3
13 sims2
14 sims2
15 sims2
16 sims2
17 sims2
18 sims1
19 sims1
20 sims1
21 sims1
22 sims1
Santer trend
Grey left
Grey right
Empirical Study of Model
‘Ensemble-Mean’ (EM)
• Choose an Unforced 1000-yr Model
• Divide into 25 chunks of 40 years each
• Calculate ‘ensemble-mean’ vs no. of runs: its
value must be zero (for an unforced model)
– The EM reaches asymptotic value after ~10 runs
• Repeat: Create trends for 100 runs
– Trends show a Gaussian distribution
Temperatures of Unforced Model
Cumulative Mean Trend
Summing Up: Obs and GH Models
NOT Consistent
• Santer + 16 coauthors [Intl J Clim 2008] claim:
“Consistency of Modeled and Observed Temp
Trends…”
BUT
• New set of troposphere temp is spurious
• Analysis of model uncertainties is incomplete
Climate fears distort energy policy
• Climate change is a non-problem; not
amenable to human influence or control
• Our problem are politicians who claim to
“save the climate” and destroy the economy
• It takes real courage for politicians to resist
the urge and “do nothing”
• Example: RES (Renewable Electricity Standard)
– calls for 15%+ use of wind and solar
Renewable Electricity Standard
• Bi-partisan madness: Bingaman, Brownback et
al – high-cost, intermittent, unreliable power
• A FRAUD: Will not reduce CO2 emissions. Will
not reduce oil imports. Why not nuclear?
• A HOAX: CO2 is NOT a pollutant; has
negligible effect on climate.
• A RIP-OFF: Taxpayer pays for govt subsidies;
ratepayers milked by “feed-in tariff”
Two Economic Issues
• 1. Cost-Benefit Analysis
• 2. Cap & Trade
Dilemma for Politicians
When the facts change, I change my
opinion. What do you do, Sir?
John M. Keynes
Other Scientific Issues
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Sea Level rise
‘Hockeystick’ controversy
Climate models shortcomings;
IPCC-2 changes; IPCC-4 curve-fitting
Solar control of climate change; GCRclouds
IPCC efforts to simulate 20th century temps
D-O events; 1500-yr cycle; Temp-CO2 relation
SST problems: Skin effects; OHC; Buoys
Climate Sensitivity;Feedback issues; Saturation
Sea Level Rise to 2100
H
R
S
IPCC IPCC IPCC 2007
1990 1995 2001 Draft
IPCC
2007
Hansen Rahmst. Singer
(H)
(R)
(S)
Max 367 124 77
43
59
600
140
20
Min
14
18
600
50
18
10
3
11
b.
Temperature Deviation
(C)
a.
0.6
0.4
0.2
500
-0.2
-0.4
-0.6
1000
1500
2000
Source : http://climate.uah.edu/maps/30yrbig.jpg
Stalagmite Records in Oman
– a Proxy for Solar Activity
18O – a Proxy for Temperature
14C
The stalagmite record
shows a remarkably close
correlation between 14C
and 18O over a period of
more than 3,000 years.
Thus, a strong association
exists between solar
activity and temperature.
 One Century Duration!
Neff et al. (2001)
A Drastic Re-Consideration
• Contrary to IPCC, Santer, the ‘hot spot’ is due
to ‘moist adiabat’ [Riehl, Lindzen]--not GH gas
• Any sfc warming will lead to troposph hot spot
• But MSU-UAH sat data show no wmg (‘79-’97)
• Hence the reported (CRU) sfc wrmg is suspect
• This would explain many puzzling results [NAS
2000; Santer et al 2005; proxy data; etc]
• Could the cause be ‘Neg Feedback’? Unlikely;
any sfc wmg would create NF (incl 1910-40?)
a.
b.
Urban Heat Island Effect
Temperature in degF
Temperature Trends at 107 Californian Stations 1909 to 1994
Stratified by 1990 population of the county where station is located
(A) Large Counties:
More than 1 million people
Average 29 stations
(B) Midsized Counties:
100,000 to 1 million people
Average 51 stations
(C) Small Counties:
Less than 100,000 people
Average 27 stations
Temperature Changes from more than 30 Years of Satellite Observations
Source: drroyspencer.com/latest-global-temperatures