The magnitude of Early Twentieth Century Warming relative to Post-1975 Warming

I was browsing the Berkeley Earth website and came across their estimate of global average temperature change. Reproduced as Figure 1.

Figure 1 – BEST Global Temperature anomaly

What clearly stands out is the 10-year moving average line. It clearly shows warming from in the early twentieth century, (the period 1910 to 1940) being very similar warming from the mid-1970s to the end of the series in both time period and magnitude. Maybe the later warming period is up to one-tenth of a degree Celsius greater than the earlier one. The period from 1850 to 1910 shows stasis or a little cooling, but with high variability. The period from the 1940s to the 1970s shows stasis or slight cooling, and low variability.

This is largely corroborated by HADCRUT4, or at least the version I downloaded in mid-2014.

Figure 2 – HADCRUT4 Global Temperature anomaly

HADCRUT4 estimates that the later warming period is about three-twentieths of a degree Celsius greater than the earlier period and that the recent warming is slightly less than the BEST data.

The reason for the close fit is obvious. 70% of the globe is ocean and for that BEST use the same HADSST dataset as HADCRUT4. Graphics of HADSST are a little hard to come by, but KevinC at skepticalscience usefully produced a comparison of the latest HADSST3 in 2012 with the previous version.

Figure 3  – HADSST Ocean Temperature anomaly from skepticalscience 

This shows the two periods having pretty much the same magnitudes of warming.

It is the land data where the differences lie. The BEST Global Land temperature trend is reproduced below.

Figure 4 – BEST Global Land Temperature anomaly

For BEST global land temperatures, the recent warming was much greater than the early twentieth-century warming. This implies that the sea surface temperatures showed pretty much the same warming in the two periods. But if greenhouse gases were responsible for a significant part of global warming then the warming for both land and sea would be greater after the mid-1970s than in the early twentieth century. Whilst there was a rise in GHG levels in the early twentieth century, it was less than in the period from 1945 to 1975, when there was no warming, and much less than the post-1975 when CO2 levels rose massively. Whilst there can be alternative explanations for the early twentieth-century warming and the subsequent lack of warming for 30 years (when the post-WW2 economic boom which led to a continual and accelerating rise in CO2 levels), without such explanations being clear and robust the attribution of post-1975 warming to rising GHG levels is undermined. It could be just unexplained natural variation.

However, as a preliminary to examining explanations of warming trends, as a beancounter, I believe it is first necessary to examine the robustness of the figures. In looking at temperature data in early 2015, one aspect that I found unsatisfactory with the NASA GISS temperature data was the zonal data. GISS usefully divide the data between 8 bands of latitude, which I have replicated as 7 year centred moving averages in Figure 5.

Figure 5 – NASA Gistemp zonal anomalies and the global anomaly

What is significant is that some of the regional anomalies are far greater in magnitude

The most Southerly is for 90S-64S, which is basically Antarctica, an area covering just under 5% of the globe. I found it odd that there should a temperature anomaly for the region from the 1880s, when there were no weather stations recording on the frozen continent until the mid-1950s. The nearest is Base Orcadas located at 60.8 S 44.7 W, or about 350km north of 64 S. I found that whilst the Base Orcadas temperature anomaly was extremely similar to the Antarctica Zonal anomaly in the period until 1950, it was quite dissimilar in the period after.

Figure 6. Gistemp 64S-90S annual temperature anomaly compared to Base Orcadas GISS homogenised data.

NASA Gistemp has attempted to infill the missing temperature anomaly data by using the nearest data available. However, in this case, Base Orcadas appears to climatically different than the average anomalies for Antarctica, and from the global average as well. The result of this is to effectively cancel out the impact of the massive warming in the Arctic on global average temperatures in the early twentieth century. A false assumption has effectively shrunk the early twentieth-century warming. The shrinkage will be small, but it undermines the NASA GISS being the best estimate of a global temperature anomaly given the limited data available.

Rather than saying that the whole exercise of determining a valid comparison the two warming periods since 1900 is useless, I will instead attempt to evaluate how much the lack of data impacts on the anomalies. To this end, in a series of posts, I intend to look at the HADCRUT4 anomaly data. This will be a top-down approach, looking at monthly anomalies for 5o by 5o grid cells from 1850 to 2017, available from the Met Office Hadley Centre Observation Datasets. An advantage over previous analyses is the inclusion of anomalies for the 70% of the globe covered by ocean. The focus will be on the relative magnitudes of the early twentieth-century and post-1975 warming periods. At this point in time, I have no real idea of the conclusions that can be drawn from the analysis of the data.

Kevin Marshall



Ocean Impact on Temperature Data and Temperature Homgenization

Pierre Gosselin’s notrickszone looks at a new paper.

Temperature trends with reduced impact of ocean air temperature – Frank LansnerJens Olaf Pepke Pedersen.

The paper’s abstract.

Temperature data 1900–2010 from meteorological stations across the world have been analyzed and it has been found that all land areas generally have two different valid temperature trends. Coastal stations and hill stations facing ocean winds are normally more warm-trended than the valley stations that are sheltered from dominant oceans winds.

Thus, we found that in any area with variation in the topography, we can divide the stations into the more warm trended ocean air-affected stations, and the more cold-trended ocean air-sheltered stations. We find that the distinction between ocean air-affected and ocean air-sheltered stations can be used to identify the influence of the oceans on land surface. We can then use this knowledge as a tool to better study climate variability on the land surface without the moderating effects of the ocean.

We find a lack of warming in the ocean air sheltered temperature data – with less impact of ocean temperature trends – after 1950. The lack of warming in the ocean air sheltered temperature trends after 1950 should be considered when evaluating the climatic effects of changes in the Earth’s atmospheric trace amounts of greenhouse gasses as well as variations in solar conditions.

More generally, the paper’s authors are saying that over fairly short distances temperature stations will show different climatic trends. This has a profound implication for temperature homogenization. From Venema et al 2012.

The most commonly used method to detect and remove the effects of artificial changes is the relative homogenization approach, which assumes that nearby stations are exposed to almost the same climate signal and that thus the differences between nearby stations can be utilized to detect inhomogeneities (Conrad and Pollak, 1950). In relative homogeneity testing, a candidate time series is compared to multiple surrounding stations either in a pairwise fashion or to a single composite reference time series computed for multiple nearby stations. 

Lansner and Pederson are, by implication, demonstrating that the principle assumption on which homogenization is based (that nearby temperature stations are exposed to almost the same climatic signal) is not valid. As a result data homogenization will not only eliminate biases in the temperature data (such a measurement biases, impacts of station moves and the urban heat island effect where it impacts a minority of stations) but will also adjust out actual climatic trends. Where the climatic trends are localized and not replicated in surrounding areas, they will be eliminated by homogenization. What I found in early 2015 (following the examples of Paul Homewood, Euan Mearns and others) is that there are examples from all over the world where the data suggests that nearby temperature stations are exposed to different climatic signals. Data homogenization will, therefore, cause quite weird and unstable results. A number of posts were summarized in my post Defining “Temperature Homogenisation”.  Paul Matthews at Cliscep corroborated this in his post of February 2017 “Instability og GHCN Adjustment Algorithm“.

During my attempts to understand the data, I also found that those who support AGW theory not only do not question their assumptions but also have strong shared beliefs in what the data ought to look like. One of the most significant in this context is a Climategate email sent on Mon, 12 Oct 2009 by Kevin Trenberth to Michael Mann of Hockey Stick fame, and copied to Phil Jones of the Hadley centre, Thomas Karl of NOAA, Gavin Schmidt of NASA GISS, plus others.

The fact is that we can’t account for the lack of warming at the moment and it is a travesty that we can’t. The CERES data published in the August BAMS 09 supplement on 2008 shows there should be even more warming: but the data are surely wrong. Our observing system is inadequate. (emphasis mine)

Homogenizing data a number of times, and evaluating the unstable results in the context of strongly-held beliefs will bring the trends evermore into line with those beliefs. There is no requirement for some sort of conspiracy behind deliberate data manipulation for this emerging pattern of adjustments. Indeed a conspiracy in terms of a group knowing the truth and deliberately perverting that evidence does not really apply. Another reason for the conspiracy not applying is the underlying purpose of homogenization. It is to allow that temperature station to be representative of the surrounding area. Without that, it would not be possible to compile an average for the surrounding area, from which the global average in constructed. It is this requirement, in the context of real climatic differences over relatively small areas, I would suggest leads to the deletions of “erroneous” data and the infilling of estimated data elsewhere.

The gradual bringing the temperature data sets into line will beliefs is most clearly shown in the NASA GISS temperature data adjustments. Climate4you produces regular updates of the adjustments since May 2008. Below is the March 2018 version.

The reduction of the 1910 to 1940 warming period (which is at odds with theory) and the increase in the post-1975 warming phase (which correlates with the rise in CO2) supports my contention of the influence of beliefs.

Kevin Marshall


Climate Alarmist Bob Ward’s poor analysis of Research Data

After Christopher Booker’s excellent new Report for the GWPF “Global Warming: A Case Study In Groupthink” was published on 20th February, Bob Ward (Policy and Communications Director at the Grantham Research Institute on Climate Change and the Environment at the LSE) typed a rebuttal article “Do male climate change ‘sceptics’ have a problem with women?“. Ward commenced the article with a highly misleading statement.

On 20 February, the Global Warming Policy Foundation launched a new pamphlet at the House of Lords, attacking the mainstream media for not giving more coverage to climate change ‘sceptics’.

I will lead it to the reader to judge for themselves how misleading the statement is by reading the report or alternatively reading his summary at

At Cliscep (reproduced at WUWT), Jaime Jessop has looked into Ward’s distractive claims about the GWPF gender bias. This comment by Ward particularly caught my eye.

A tracking survey commissioned by the Department for Business, Energy and Industrial Strategy showed that, in March 2017, 7.6% answered “I don’t think there is such a thing as climate change” or “Climate change is caused entirely caused by natural processes”, when asked for their views. Among men the figure was 8.1%, while for women it was 7.1%.

I looked at the Tracking Survey. It is interesting that the Summary of Key Findings contains no mention of gender bias, nor of beliefs on climate change. It is only in the Wave 21 full dataset spreadsheet that you find the results of the question 22.

Q22. Thinking about the causes of climate change, which, if any, of the following best describes your opinion?
1. Climate change is entirely caused by natural processes
2. Climate change is mainly caused by natural processes
3. Climate change is partly caused by natural processes and partly caused by human activity
4. Climate change is mainly caused by human activity
5. Climate change is entirely caused by human activity
6. I don’t think there is such a thing as climate change.
7. Don’t know
8. No opinion

Note that the first option presented to the questionee is 5, then 4, then 3, then 2, then 1. There may, therefore, be an inbuilt bias in overstating the support for Climate Change being attributed to human activity. But the data is clearly presented, so a quick pivot table was able to check Ward’s results.

The sample was of 2180 – 1090 females and 1090 males. Adding the responses  to “I don’t think there is such a thing as climate change” or “Climate change is caused entirely caused by natural processes” I get 7.16% for females – (37+41)/1090 – and 8.17% for males – (46+43)/1090. Clearly, Bob Ward has failed to remember what he was taught in high school about roundings.

Another problem is that this is raw data. The opinion pollsters have taken time and care to adjust for various demographic factors by adding a weighting to each line. On this basis, Ward should have reported 6.7% for females, 7.6% for males and 7.1% overall.

More importantly, if males tend to be more sceptical of climate change than females, then they will be less alarmist than females. But the data says something different. Of the weighted responses, to those who opted for the most extreme “Climate change is entirely caused by natural processes“, 12.5% were female and 14.5% were male. Very fractionally at the extreme, men are proportionality more alarmist than females than they are sceptical. More importantly, men are slightly more extreme in their opinions on climate change (for or against) than women.

The middle ground is the response to “Climate change is partly caused by natural processes and partly caused by human activity“. The weighted response was 44.5% female and 40.7% male, confirming that men are more extreme in their views than women.

There is a further finding that can be drawn. The projections by the IPCC for future unmitigated global warming assume that all, or the vast majority of, global warming since 1850 is human-caused. Less than 41.6% of British women and 43.2% of British men agree with this assumption that justifies climate mitigation policies.

Below are my summaries. My results are easily replicated for those with an intermediate level of proficiency in Excel.

Learning Note

The most important lesson for understanding data is to analyse that data from different perspectives, against different hypotheses. Bob Ward’s claim of a male gender bias towards climate scepticism in an opinion survey, upon a slightly broader analysis, becomes one where British males are slightly more extreme and forthright in their views than British females whether for or against. This has parallels to my conclusion when looking at the 2013 US study The Role of Conspiracist Ideation and Worldviews in Predicting Rejection of Science – Stephan Lewandowsky, Gilles E. Gignac, Klaus Oberauer. Here I found that rather than the paper’s finding that conspiracist ideation being “associated with the rejection of all scientific propositions tested”, the data strongly indicated that people with strong opinions on one subject, whether for or against, tend to have strong opinions on other subjects, whether for or against. Like with any bias of perspective, (ideological, religious, gender, race, social class, national, football team affiliation etc.) the way to counter bias is to concentrate on the data. Opinion polls are a poor starting point, but at least they may report on perspectives outside of one’s own immediate belief systems. 

Kevin Marshall