Tung and Zhou claim of constant decadal anthropogenic warming rates in last 100 years

Bishop Hill reports on

A new paper in PNAS entitled ‘Using data to attribute episodes of warming and cooling in instrumental records’ looks important. Ka-Kit Tung and Jiansong Zhou of the University of Washington report that anthropogenic global warming has been overcooked. A lot.

My comment was:-

My prediction is that this paper will turn out to have exaggerated the anthropogenic influence, rather than have under-estimated it.

The relevant quote:-

The underlying net anthropogenic warming rate in the industrial era is found to have been steady since 1910 at 0.07–0.08 °C/decade

Greenhouse gas emissions have not been increasing at a steady rate. The most important is CO2. A couple of years ago I tried to estimate from country data (filling in important gaps) how global CO2 emissions had increased. The increases per quarter century were

1900-1925 85%

1925-1950 60%

1950-1975 185%

1975-2000 45%

That meant global CO2 emissions increased more than 12 times (1100%) in 100 years. The conversion rate to retained CO2 seems to be roughly constant – 4Gt of carbon equivalent to increase CO2 levels by 1ppm. Furthermore, the C20th warming was nearly all in two phases. 1910-1945 and 1975-1998. Rather than temperature rise being related to CO2 emissions, it seems out of step. That would imply a combination of two things for the anthropogenic warming rate to be constant at 0.07–0.08 °C/decade. First is that CO2 has massively diminishing returns. Second is that CO2 emissions alone have a much smaller impact on the global average temperature changes (as reported in HADCRUT4), than this paper concludes.

Supplementary Information

This source of the emissions data is

Boden, T.A., G. Marland, and R.J. Andres. 2010. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi 10.3334/CDIAC/00001_V2010

The CO2 levels are for Mauna Loa back to 1959, and estimated backwards from there to 1780.


The above chart shows by estimated CO2 emissions (expressed in units of 10Gt of carbon equivalents) shown as against the HADCRUT3 data set. This shows a slow rate of increase in CO2 emissions in the first half of the twentieth century, with falls in emissions during the Great Depression (1929-1933) and at the end of the Second World War (1945). From 1950 to 1973 there was a huge upsurge in emissions with the post-war economic boom, then stalls in 1973 (The OPEC oil embargo) and 1980-83 (global recession). After 2000 there was another surge in emissions, mostly due to rapid growth in China.

The temperature increases followed a different pattern. There were two periods of increasing temperatures in the twentieth century – From 1910-1945 and 1975-1998. The decadal changes graph below shows clearly the change in emissions. The temperature changes by decade exaggerate the falls in temperature in the Edwardian decade and the 1940s.


What is clearly illustrated is why I believe the anthropogenic influence on temperature was not similar in every decade from 1910, as Ka-Kit Tung and Jiansong Zhou claim.

A Bet Won on the Warming Standstill

Congratulations to Dr David Whitehouse of the GWPF for winning a bet with Dr James Annan.

The bet, made in 2007, was that by 2011 that HADCRUT3 temperature record of 1998 would not be beaten by 2011. The bet was made at the instigation of the BBC Radio 4 program “More or Less”. Annan then provided data analysis to show why he was odds-on favourite to win the bet here and here. Both RealClimate and Mark Lynas had earlier weighed in with articles giving the mainstream viewpoint. I post on Dr Annan’s blog the following comment

The mark of good science is not to predict the obvious, but to predict the unlikely.

Dr Whitehouse has stated that it was going beyond the obvious that enabled him to take on the bet. His full analysis can be found at both the GWPF and wattsupwiththat.

Of course there are those who will point to the biased GISSTEMP to show that the warming is continuing. See my analysis here about why that dataset looks to be a little biased. There are of course those who will still maintain the warming is continuing (such as Roger Black of the BBC), but the true measure is the predictive ability.


A note on HADCRUT3 v GISSTEMP

Have just posted to WUWT the following on global temperature anomalies:-

Thanks Luboš for a well-thought out article, and nicely summarised by

“The “error of the measurement” of the warming trend is 3 times larger than the result!”

One of the implications of this wide variability, and the concentration of temperature measurements in a small proportion of the land mass (with very little from the oceans covering 70% of the globe) is that one must be very careful in the interpretation of the data. Even if the surface stations were totally representative and uniformly accurate (no UHI) and the raw data properly adjusted (Remember Darwin, Australia on this blog?), there are still normative judgements to be made to achieve a figure.

I have done some (much cruder) analysis comparing HADCRUT3 to GISSTEMP for the period 1880 to 2010, which helps illustrate these judgemental decisions.

1. The temperature series agree on the large fluctuations, with the exception of the post 1945 cooling – it happens 2 or 3 years later and more slowly in GISSTEMP.

2. One would expect greater agreement with recent data in more recent years. But since 1997 the difference in temperature anomalies has widened by nearly 0.3 celsius – GISSTEMP showing rapid warming and HADCRUT showing none.

3. If you take the absolute change in anomaly from month to month and average from 1880 to 2010, GISSTEMP is nearly double that of HADCRUT3 – 0.15 degrees v 0.08. The divergence in volatility reduced from 1880 to the middle of last century, when GISSTEMP was around 40% more volatile than HADCRUT3. But since then the relative volatility has increased. The figures for the last five years are respectively about 0.12 and 0.05 degrees. That is GISSTEMP is around 120% more volatile that HADCRUT3.

This all indicates that there must be greater clarity in the figures. We need the temperature indices to be compiled by qualified independent statisticians, not by those who major in another subject. This is particularly true of the major measure of global warming, where there is more than a modicum of partisan elements.

These graphs help illustrate the points made. Please note that I use overlapping moving averages, so it is for illustrative purposes only.

NB. Luboš Motl’s article was cross-posted from his blog here

Show Warming After it Has Stopped Part 2

Last week I posted how Miles Allen had pulled off a trick to show warming in the 21st century after that trend had stopped in 1998. According to David Middleton at Watts up with That, the BBC’s Richard Black is using a similar decadal comparison to show that warming has continued. There are two Richard Black’s claim that the GWPF are cherry-picking the data. First, that an employee of the UK state broadcaster should choose to use a foreign temperature record over the UK one. Second, why the switch to decadal comparisons, when the IPCC has long used the norm.

Let me break this down with two graphs. Like with the previous posting, I see no scientific reason to necessitate why the starting point for the earth’s orbit of the sun has to be on 1st January. I therefore include all 12 month moving averages. That is Jan-Dec, Feb-Jan, Mar-Feb etc. I have also included three lines on my analysis. First the NASA GISSTEMP; second the HADCRUT3 and third the difference between the two.

The first graph shows the decadal change in the NASA GISS figures that Richard Black is talking about. Sure enough the only period where the 12 month average temperature anomaly is lower than a decade before is in 2008. Using the HADCRUT3 data reveals a similar pattern, but the negative period is much longer. If The HADCRUT3 decadal change is subtracted from the GISSTEMP, there is shown to be a greater decadal warming trend in the NASA than in the UK figures. This might suggest the reason for Richard Black’s preference for foreign data over that paid for by the UK taxpayer’s.

The second graph shows the 12 month moving average data – and clearly shows the reasons for both using decadal temperature changes over annual, and foreign data over British. From 1988 to 1997, there was no real warming trend if the Pinatubo cooling is removed from 1995. However the NASA anomaly seems to be around twice as volatile is the Hadley. But in 1998 the position reverses. The natural 1998 El Nino effect is twice according to the British scientists, as it is to Dr Hansen and his team. Post 1998 the story diverges. According to NASA, the warming resumes on an upward trend. According to the Hadley scientists, the 1998 El Nino causes a step change in average temperatures and the warming stops. As a result the NASA GISS warming trend is mirrored by its divergence from the more established and sober British series.

Showing Warming after it has Stopped

Bishop Hill points to an article by Miles Allen that

“examines how predictions he made in 2000 compare to outturn. The match between prediction and outturn is striking…..”

Bishop Hill points out that this using HADCRUT decadal data. Maybe a quick examination of the figures will reveal something? Using the HADCRUT3 data here is are the data for the last five decade.

This shows that the decadal rate of warming has been rising at a pretty constant rate for the last three decades. So all those sceptics who claim that global warming has stopped must have got it wrong then?

Let us examine the data a bit more closely.

The blue line is the Hadcrut annual anomaly figures from 1965 to 2010. The smoother red line is the 10 year average anomaly, starting with the 1956-1965 average and finishing with the 2001-2010 average. The decadal averages are highlighted by the red triangles.

The blue would indicate to me that there was a warming trend from 1976 to 1998, since then it has stopped. This is borne out by the 10 year moving average, but (due to the averaging) the plateau arrives five years later. But the story from the decadal figures is different, simply due to timing.

So what scientific basis is there for using the decadal average? Annual data seems reasonable, at it is the time for the earth make one rotation around the sun. But the calendar is fixed where it is because 1500 years ago Dionysius Exiguus devised a calendar with a mistaken estimate of the birth (or conception) of Jesus Christ as Year 1, and we have number base 10 possibly to the number of fingers we have. Both are a human artefact. Further, the data is actually held in months, so it is only due to the Christian calendar that we go from January to December. This means of the 120 possible periods for decadal averages, Myles Allen shows a cultural prejudice, and in choosing decadal averages, he shows a very human bias, over real world selectivity.

How does this affect the analysis of the performance of the models? The global temperature averages showed a sharp uptick in 1998. Therefore, if the models simply predicted a continuation of the trend of the previous twenty years, they would have been quite accurate. The fact was the prediction was higher than the outturn, so the models overestimated. It is only by exploiting the arbitrary construct of decadal data that the difference appears insignificant. Drop to 5 years moving average, and you will get a bigger divergence. Wait a couple of years, and you will get a bigger divergence. Use annual figures and you will get a bigger divergence. The result is not robust.

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