105 replies to this topic

[Rich C]

Now I will debunk the next paper colion proposed to show AGW is dead.  https://doi.org/10.1...G.2.1.4996.7840

 

From the paper:

 

"The theory of anthropogenic global warming is that since 1750, human activity, involving the use of

fossil fuels, the manufacture of cement, and changes in land use, has been injecting an artificial flow of

carbon dioxide (CO2) into the atmosphere at such an accelerated rate that it has overwhelmed nature’s

delicate carbon balance and caused a steadily rising unnatural and unprecedented accumulation of CO2

in the atmosphere. The change in atmospheric composition has enhanced its greenhouse effect causing

surface temperatures to rise unnaturally and dangerously and threaten catastrophic consequences in

terms of climate change (Hansen, 2006) (IPCC, 2007) (IPCC, 2014) (Plass, 1956). An important policy

implication is that since these changes were created by artificial means they can also be moderated by

artificial means simply by making significant reductions in our emissions of CO2 (IPCC, 2014).

 

 

Since the recent accumulation of CO2 in the atmosphere is ascribed solely to human emissions, a

testable implication of the theory of anthropogenic global warming is that there should be a close

correlation between the rate of anthropogenic emissions and the rate at which CO2 accumulates in the

atmosphere; and this correlation should be observable at the inter-annual frequency level (Patra, 2005)

(Raupach, 2008) (Keeling, 2001) (Plass, 1956) (Lorius, 1990). This means that, net of long term trends,

we should find that years of higher annual emissions should correspond with years of greater annual

increase in atmospheric CO2 and years of lower emissions should correspond with years of lower rates

of accumulation of atmospheric CO2. In this short note, we test this hypothesis by applying detrended

correlation analysis, a tool that is often used by financial analysts to detect higher frequency changes

net of long term trends (Prodobnik, 2008) (Granger, 1964) (Haan, 2002). The method tests the

relationship between two variables that share a common direction in their long term drift in time by

removing the drift component and comparing the detrended series in terms of correlation at shorter

intervals. When applied to atmospheric CO2, this procedure shows that the correlation between the

annual rate at which anthropogenic emissions are introduced into the atmosphere and the annual rate

at which CO2 accumulates in the atmosphere, though significant, does not survive into the detrended

series and is therefore likely to be spurious or an artifact of the common direction of their long term

drift in time to which no anthropogenic cause can be ascribed."

 

 

 

The first thing is that correlation does not imply causation.  It is also true that lack of correlation does not imply that there is no causation.  In both cases further investigation is required.

 

And yet we see Munshi reaching for causation in his conclusion:

"4. SUMMARY AND CONCLUSIONS

 

A necessary condition for the theory of anthropogenic global warming is that there should be a close

correlation between annual fluctuations of atmospheric CO2 and the annual rate of anthropogenic CO2

emissions. Data on atmospheric CO2 and anthropogenic emissions provided by the Mauna Loa

measuring station and the CDIAC in the period 1959-2011 were studied using detrended correlation

analysis to determine whether, net of their common long term upward trends, the rate of change in

atmospheric CO2 is responsive to the rate of anthropogenic emissions in a shorter time scale from year

to year. It was found that the observed correlation between these variables derives solely from a

common direction in their long term trends and not from a correspondence in their annual fluctuations.

As a corollary to this finding, a further study reveals that change in atmospheric CO2 is responsive to

surface temperature both in long term trends and in short term annual fluctuations. The results have

significant implications for interpreting the observed increase in atmospheric CO2 in terms of the

climate system and the theory of anthropogenic global warming.

 

 

 

The study is too simplistic.  The author is trying to analyze a very complex situation with numbers collected in a simplistic fashion.  All atmospheric measures are at Mauna Loa.  When a steel mill in Pennsylvania emits CO2, it is concentrated.  How long does it take to mix uniformly in the atomosphere and to REACH Manua Loa?  What about a steel mill in England, or Germany, or Russia or Japan or China.  The arrival rates are all different.  Yet the author ASSUMES there needs to be a close correlation on an annual basis?  He just made that up.  The author makes no mention nor any attempt to deal with sinks of CO2 that absorb man made CO2.  The study in no way overturns AGW.  The author fails to address the next question, if human activity is NOT the driver of atmospheric CO2, WHAT IS???

 

Below is a more rigorous study that concludes the opposite.  It does take into account the known factors involving the considerable variation of atmospheric CO2.

CO2 concentration trends and budgets

Before the Industrial Era, circa 1750, atmospheric carbon dioxide
(CO2) concentration was 280 ± 10 ppm for several thousand years.
It has risen continuously since then, reaching 367 ppm in 1999.
The present atmospheric CO2 concentration has not been
exceeded during the past 420,000 years, and likely not during the
past 20 million years. The rate of increase over the past century
is unprecedented, at least during the past 20,000 years.
The present atmospheric CO2 increase is caused by anthro-
pogenic emissions of CO 2 . About three-quarters of these
emissions are due to fossil fuel burning. Fossil fuel burning (plus
a small contribution from cement production) released on
average 5.4 ± 0.3 PgC/yr during 1980 to 1989, and 6.3 ± 0.4
PgC/yr during 1990 to 1999. Land use change is responsible for
the rest of the emissions.
The rate of increase of atmospheric CO2 content was 3.3 ±
0.1 PgC/yr during 1980 to 1989 and 3.2 ± 0.1 PgC/yr during 1990
to 1999. These rates are less than the emissions, because some of
the emitted CO2 dissolves in the oceans, and some is taken up by
terrestrial ecosystems. Individual years show different rates of
increase. For example, 1992 was low (1.9 PgC/yr), and 1998 was
the highest (6.0 PgC/yr) since direct measurements began in
1957. This variability is mainly caused by variations in land and
ocean uptake.
Statistically, high rates of increase in atmospheric CO2 have
occurred in most El Niño years, although low rates occurred
during the extended El Niño of 1991 to 1994. Surface water CO2
measurements from the equatorial Pacific show that the natural
source of CO2 from this region is reduced by between 0.2 and 1.0
PgC/yr during El Niño events, counter to the atmospheric
increase. It is likely that the high rates of CO2 increase during
most El Niño events are explained by reductions in land uptake,
caused in part by the effects of high temperatures, drought and
fire on terrestrial ecosystems in the tropics.
Land and ocean uptake of CO2 can now be separated using
atmospheric measurements (CO2, oxygen (O2) and 13CO2). For
1980 to 1989, the ocean-atmosphere flux is estimated as −1.9 ±
0.6 PgC/yr and the land-atmosphere flux as −0.2 ± 0.7 PgC/yr
based on CO2 and O2 measurements (negative signs denote net
uptake). For 1990 to 1999, the ocean-atmosphere flux is
estimated as −1.7 ± 0.5 PgC/yr and the land-atmosphere flux as
−1.4 ± 0.7 PgC/yr. These figures are consistent with alternative
budgets based on CO2 and 13CO2 measurements, and with
independent estimates based on measurements of CO2 and 13CO2
in sea water. The new 1980s estimates are also consistent with the
ocean-model based carbon budget of the IPCC WGI Second
Assessment Report (IPCC, 1996a) (hereafter SAR). The new
1990s estimates update the budget derived using SAR method-
ologies for the IPCC Special Report on Land Use, Land Use
Change and Forestry (IPCC, 2000a).

 

3.7.4 Conclusions
The differences among the CO2 concentrations projected with the
various SRES scenarios considered are larger than the differences
caused by inclusion or omission of climate-mediated feedbacks.
The range of uptake rates projected by process-based models for
any one scenario is, however, considerable, due to uncertainties
about (especially) terrestrial ecosystem responses to high CO2
concentrations, which have not yet been resolved experimentally,
and uncertainties about the response of global NPP to changes in
climate (Cramer et al., 1999). A smaller feedback would be
implied if, as some models indicate, global NPP increases with
warming throughout the relevant range of climates and no forest
die back occurs. Larger positive feedbacks would be implied if
regional drying caused partial die back of tropical forests, as some
of the DGVMs in Cramer et al. (2001), and one coupled climate-
carbon model study of Cox et al. (2000), suggest; however,
another coupled climate-carbon model study (Friedlingstein et al.,
2001) suggests a smaller feedback. Uncertainty also arises due to
differences in the climate responses of ocean models, especially as
regards the extent and effects (biological as well as physical) of
increased stratification in a warmer climate (Joos et al., 1999b).
In conclusion, anthropogenic CO2 emissions are virtually
certain to be the dominant factor determining CO2 concentrations
throughout the 21st century. The importance of anthropogenic
emissions is underlined by the expectation that the proportion of
emissions taken up by both ocean and land will decline at high
atmospheric CO2 concentrations (even if absolute uptake by the
ocean continues to rise). There is considerable uncertainty in
projections of future CO2 concentration, because of uncertainty
about the effects of climate change on the processes determining
ocean and land uptake of CO2. These uncertainties do not negate
the main finding that anthropogenic emissions will be the main
control.

https://www.ipcc.ch/...8/02/TAR-03.pdf

 

 

[colion]

"The first thing is that correlation does not imply causation."

 

True 

 

"It is also true that lack of correlation does not imply that there is no causation."

 

Wrong.  Correlation is required for causation and in its absence it negates the causation proposal.

 

IPCC AR3 is very dated and must be evaluated relative to subsequent studies.

[Rich C]

"The first thing is that correlation does not imply causation."

 

True 

 

"It is also true that lack of correlation does not imply that there is no causation."

 

Wrong.  Correlation is required for causation and in its absence it negates the causation proposal.

 

IPCC AR3 is very dated and must be evaluated relative to subsequent studies.

Let me be more specific.  Lack of correlation in improperly collected detrended data does not imply there is no causation.

 

IPCC AR3 is twenty years old, and the finding that atmospheric CO2 concentration is a complex process has not changed.  Nor has the IPCC findings more recently differed in the opinion that atmospheric CO2 rise is driven by human activity, primarily burning fossil fuel.  That has not changed.

[Rich C]

According to scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York, July 2023 was hotter than any other month in the global temperature record.

 

Overall, July 2023 was 0.43 degrees Fahrenheit (F) (0.24 degrees Celsius ©) warmer than any other July in NASA’s record, and it was 2.1 F (1.18 C) warmer than the average July between 1951 and 1980. The primary focus of the GISS analysis are long-term temperature changes over many decades and centuries, and a fixed base period yields anomalies that are consistent over time. Temperature "normals" are defined by several decades or more - typically 30 years.

 

“NASA data confirms what billions around the world literally felt: temperatures in July 2023 made it the hottest month on record. In every corner of the country, Americans are right now experiencing firsthand the effects of the climate crisis, underscoring the urgency of President Biden’s historic climate agenda,” said NASA Administrator Bill Nelson. “The science is clear. We must act now to protect our communities and planet; it’s the only one we have.”

This chart shows global temperature anomalies for every July since the 1880s, based on NASA's GISTEMP analysis. Anomalies reflect how much the global temperature was above or below the 1951-1980 norm for July.

Credits: NASA’s Goddard Institute for Space Studies

 

Parts of South America, North Africa, North America, and the Antarctic Peninsula were especially hot, experiencing temperatures increases around 7.2 F (4 C) above average. Overall, extreme heat this summer put tens of millions of people under heat warnings and was linked to hundreds of heat-related illnesses and deaths. The record-breaking July continues a long-term trend of human-driven warming driven primarily by greenhouse gas emissions that has become evident over the past four decades. According to NASA data, the five hottest Julys since 1880 have all happened in the past five years.

 

https://www.nasa.gov...ever-since-1880

 

[colion]

Munshi's data is not improperly collected.  CO2 concentration is quite uniform in contrast to other GHGs such as H2O.  ML has been used for numerous studies including C19 effect.

 

IPCC's CO2 position assumes the Bern model which has been debunked and is flavored by their mandate to assess human-induced (not all forcings) climate change.

[colion]

 

According to scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York, July 2023 was hotter than any other month in the global temperature record.

 

 

Global T data for the past 150 years is quite unreliable for a variety of reasons - missing/estimated data, margin of error, urban heat effect, land/sea measurements inadequate, corrupt manipulation (e.g., Climategate), etc.  The 30s (dust bowl period) were much worse than today.  Satellite data is the gold standard and it shows a slight increase in 45 years with no warming for various periods for the past decade.  NOAA's surface gold standard, USCRN, agrees.  If you can't explain the pause you do not know the cause.

 

 

 

 

[Rich C]

Munshi's data is not improperly collected.  CO2 concentration is quite uniform in contrast to other GHGs such as H2O.  ML has been used for numerous studies including C19 effect.

Yes, his data is improperly collected for his stated intent.  Munshi says he expects to find "close correlation" between human CO2 production and CO2 level in the atmosphere measured at Mauna Loa on an annual basis.  If you are looking for close correlation, you need fine measurement of the man made CO2 AND you need to track where it goes, such as into the ocean or into plants.  Munshi does none of that.

 

Here is his collection:

 

The data for carbon emissions from fossil fuels and cement manufacture (Boden, 2013) are provided by

the CDIAC on an annual basis from 1750 to 2010 (CDIAC, 2015). The value for 2011 was taken from the

IPCC AR5 Chapter 6 (IPCC, 2014). Values provided as millions of metric tons of carbon equivalent were

multiplied by 0.0036667 and converted to gigatons of CO2. Carbon flux to the atmosphere from land

use changes (Houghton, 2008) are provided by the CDIAC on an annual basis from 1850 to 2005. Values

from 2006 to 2011 are estimated from the information in the IPCC AR5 Chapter 6 that these emissions

had remained constant in the decade 2002-2011 at 0.9±0.8 gigatons of carbon equivalent (IPCC, 2014).

 

That is the grossest level possible of data collection, when you expect close correlation.  Look at the last item, carbon flux to the atmosphere from land use changes, 0.9 plus or minus 0.8 gigatons, that is nearly 100% variance in the data itself.

 

With data collection that gross of a level, the author should have no expectation of "close correlation".

 

I have already shown the care Dr. Prentice took in his analysis and reached the opposite conclusion of Munshi's little correlation study.

 

3.7.4 Conclusions
The differences among the CO2 concentrations projected with the
various SRES scenarios considered are larger than the differences
caused by inclusion or omission of climate-mediated feedbacks.
The range of uptake rates projected by process-based models for
any one scenario is, however, considerable, due to uncertainties
about (especially) terrestrial ecosystem responses to high CO2
concentrations, which have not yet been resolved experimentally,
and uncertainties about the response of global NPP to changes in
climate (Cramer et al., 1999). A smaller feedback would be
implied if, as some models indicate, global NPP increases with
warming throughout the relevant range of climates and no forest
die back occurs. Larger positive feedbacks would be implied if
regional drying caused partial die back of tropical forests, as some
of the DGVMs in Cramer et al. (2001), and one coupled climate-
carbon model study of Cox et al. (2000), suggest; however,
another coupled climate-carbon model study (Friedlingstein et al.,
2001) suggests a smaller feedback. Uncertainty also arises due to
differences in the climate responses of ocean models, especially as
regards the extent and effects (biological as well as physical) of
increased stratification in a warmer climate (Joos et al., 1999b).
In conclusion, anthropogenic CO2 emissions are virtually
certain to be the dominant factor determining CO2 concentrations
throughout the 21st century. The importance of anthropogenic
emissions is underlined by the expectation that the proportion of
emissions taken up by both ocean and land will decline at high
atmospheric CO2 concentrations (even if absolute uptake by the
ocean continues to rise). There is considerable uncertainty in
projections of future CO2 concentration, because of uncertainty
about the effects of climate change on the processes determining
ocean and land uptake of CO2. These uncertainties do not negate
the main finding that anthropogenic emissions will be the main
control.

[colion]

 

Munshi's data is not improperly collected.  CO2 concentration is quite uniform in contrast to other GHGs such as H2O.  ML has been used for numerous studies including C19 effect.

Yes, his data is improperly collected for his stated intent.  Munshi says he expects to find "close correlation" between human CO2 production and CO2 level in the atmosphere measured at Mauna Loa on an annual basis.  If you are looking for close correlation, you need fine measurement of the man made CO2 AND you need to track where it goes, such as into the ocean or into plants.  Munshi does none of that.

 

Wrong.  ML data satisfies the statistical analysis that he is doing.  Tracking is straw man.  You have failed to knock the mathematical papers off the rails and have not attempted to do so.  Mathematics is nice but in science the bottom line is empirical evidence whether mathematical analyses agree or not.

 

 

The data for carbon emissions from fossil fuels and cement manufacture (Boden, 2013) are provided by

the CDIAC on an annual basis from 1750 to 2010 (CDIAC, 2015). The value for 2011 was taken from the

IPCC AR5 Chapter 6 (IPCC, 2014). Values provided as millions of metric tons of carbon equivalent were

multiplied by 0.0036667 and converted to gigatons of CO2. Carbon flux to the atmosphere from land

use changes (Houghton, 2008) are provided by the CDIAC on an annual basis from 1850 to 2005. Values

from 2006 to 2011 are estimated from the information in the IPCC AR5 Chapter 6 that these emissions

had remained constant in the decade 2002-2011 at 0.9±0.8 gigatons of carbon equivalent (IPCC, 2014).

 

That is the grossest level possible of data collection, when you expect close correlation.  Look at the last item, carbon flux to the atmosphere from land use changes, 0.9 plus or minus 0.8 gigatons, that is nearly 100% variance in the data itself.

 

Irrelevant.  CDIAC is not ML.  They simply gather whatever data they find including much before CO2 was measured accurately by ML by a variety of means which will inherently be quite imprecise, including using "estimates" from IPCC which is BS as their carbon cycle is a gross estimate.  IPCC stills hangs onto the AGW myth, as required by their charter, without ever providing empirical evidence which should raise a red flag for you.  

 

I have already shown the care Dr. Prentice took in his analysis and reached the opposite conclusion of Munshi's little correlation study.

 

Red Herring.  Prentice ignores anti-AGW hypothesis empirical evidence available to him (consistent with WG3 scope).  This evidence and subsequent that was e provided to you rejects the AGW hypothesis.  Even IPCC now assigns "low confidence" to extreme weather being caused by anthropogenic influences and has "low confidence" that the weather in the past 30 years exceeds the pre-instrumental post-LIA period; CO2 keeps going up and T does almost nothing.

 

You cannot refute the anti-AGW hypothesis empirical evidence provided to you and cannot provide any empirical evidence supporting the AGW hypothesis. 

 

 

 

 

[colion]

 

Munshi's data is not improperly collected.  CO2 concentration is quite uniform in contrast to other GHGs such as H2O.  ML has been used for numerous studies including C19 effect.

Yes, his data is improperly collected for his stated intent.  Munshi says he expects to find "close correlation" between human CO2 production and CO2 level in the atmosphere measured at Mauna Loa on an annual basis.  If you are looking for close correlation, you need fine measurement of the man made CO2 AND you need to track where it goes, such as into the ocean or into plants.  Munshi does none of that.

 

Wrong.  ML data satisfies the statistical analysis that he is doing.  Tracking is straw man.  You have failed to knock the mathematical papers off the rails and have not attempted to do so for the physical ones.  Mathematics is nice but in science the bottom line is empirical evidence whether mathematical analyses agree or not.

 

 

The data for carbon emissions from fossil fuels and cement manufacture (Boden, 2013) are provided by

the CDIAC on an annual basis from 1750 to 2010 (CDIAC, 2015). The value for 2011 was taken from the

IPCC AR5 Chapter 6 (IPCC, 2014). Values provided as millions of metric tons of carbon equivalent were

multiplied by 0.0036667 and converted to gigatons of CO2. Carbon flux to the atmosphere from land

use changes (Houghton, 2008) are provided by the CDIAC on an annual basis from 1850 to 2005. Values

from 2006 to 2011 are estimated from the information in the IPCC AR5 Chapter 6 that these emissions

had remained constant in the decade 2002-2011 at 0.9±0.8 gigatons of carbon equivalent (IPCC, 2014).

 

That is the grossest level possible of data collection, when you expect close correlation.  Look at the last item, carbon flux to the atmosphere from land use changes, 0.9 plus or minus 0.8 gigatons, that is nearly 100% variance in the data itself.

 

Irrelevant.  CDIAC is not ML.  They simply gather whatever data they can find including much before CO2 was measured accurately by ML by a variety of means which will inherently be quite imprecise, including using "estimates" from IPCC which is BS as their carbon cycle is a gross estimate.  IPCC stills hangs onto the AGW myth, as required by their charter, without ever providing empirical evidence which should raise a red flag.  

 

I have already shown the care Dr. Prentice took in his analysis and reached the opposite conclusion of Munshi's little correlation study.

 

Red Herring.  Prentice ignores anti-AGW hypothesis empirical evidence available to him (consistent with WG3 scope).  This evidence and subsequent that was provided to you rejects the AGW hypothesis.  Even IPCC now assigns "low confidence" to extreme weather being caused by anthropogenic influences and has "low confidence" that the weather in the past 30 years exceeds the pre-instrumental post-LIA period; CO2 keeps going up and T does almost nothing.

 

You cannot refute the anti-AGW hypothesis empirical evidence provided to you and cannot provide any empirical evidence supporting the AGW hypothesis. 

 

 

 

 

[Rich C]

 

 

According to scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York, July 2023 was hotter than any other month in the global temperature record.

 

 

Global T data for the past 150 years is quite unreliable for a variety of reasons - missing/estimated data, margin of error, urban heat effect, land/sea measurements inadequate, corrupt manipulation (e.g., Climategate), etc.  The 30s (dust bowl period) were much worse than today.  Satellite data is the gold standard and it shows a slight increase in 45 years with no warming for various periods for the past decade.  NOAA's surface gold standard, USCRN, agrees.  If you can't explain the pause you do not know the cause.

 

 

 

 

 

Your explanation of why July 2023 was not the hottest global temperature for July on record fails.  The statement is "global" but your "dust bowl" graph is US only.  Then you show temperature anomalies, not temperature.  An anomaly is something that you add to a base to get to temperature.  If the base was lower in the past, an equal high anomaly for instance in 1988, when added to what was a lower base back then, would not exceed todays TEMPERATURE.

 

So, you have not shown a July in the past with a higher global temperature than July 2023.

 

The NASA statement stands as correct.