Does data coverage impact the HADCRUT4 and NASA GISS Temperature Anomalies?

Introduction

This post started with the title “HADCRUT4 and NASA GISS Temperature Anomalies – a Comparison by Latitude“.  After deriving a global temperature anomaly from the HADCRUT4 gridded data, I was intending to compare the results with GISS’s anomalies by 8 latitude zones. However, this opened up an intriguing issue. Are global temperature anomalies impacted by a relative lack of data in earlier periods? The leads to a further issue of whether infilling of the data can be meaningful, and hence be considered to “improve” the global anomaly calculation.

A Global Temperature Anomaly from HADCRUT4 Gridded Data

In a previous post, I looked at the relative magnitudes of early twentieth century and post-1975 warming episodes. In the Hadley datasets, there is a clear divergence between the land and sea temperature data trends post-1980, a feature that is not present in the early warming episode. This is reproduced below as Figure 1.

Figure 1 : Graph of Hadley Centre 7 year moving average temperature anomalies for Land (CRUTEM4), Sea (HADSST3) and Combined (HADCRUT4)

The question that needs to be answered is whether the anomalous post-1975 warming on the land is due to real divergence, or due to issues in the estimation of global average temperature anomaly.

In another post – The magnitude of Early Twentieth Century Warming relative to Post-1975 Warming – I looked at the NASA Gistemp data, which is usefully broken down into 8 Latitude Zones. A summary graph is shown in Figure 2.

Figure 2 : NASA Gistemp zonal anomalies and the global anomaly

This is more detail than the HADCRUT4 data, which is just presented as three zones of the Tropics, along with Northern and Southern Hemispheres. However, the Hadley Centre, on their HADCRUT4 Data: download page, have, under  HadCRUT4 Gridded data: additional fields, a file HadCRUT.4.6.0.0.median_ascii.zip. This contains monthly anomalies for 5o by 5o grid cells from 1850 to 2017. There are 36 zones of latitude and 72 zones of longitude. Over 2016 months, there are over 5.22 million grid cells, but only 2.51 million (48%) have data. From this data, I have constructed a global temperature anomaly. The major issue in the calculation is that the grid cells are of different areas. A grid cell nearest to the equator at 0o to 5o has about 23 times the area of a grid cell adjacent to the poles at 85o to 90o. I used the appropriate weighting for each band of latitude.

The question is whether I have calculated a global anomaly similar to the Hadley Centre. Figure 3 is a reconciliation with the published global anomaly mean (available from here) and my own.

Figure 3 : Reconciliation between HADCRUT4 published mean and calculated weighted average mean from the Gridded Data

Prior to 1910, my calculations are slightly below the HADCRUT 4 published data. The biggest differences are in 1956 and 1915. Overall the differences are insignificant and do not impact on the analysis.

I split down the HADCRUT4 temperature data by eight zones of latitude on a similar basis to NASA Gistemp. Figure 4 presents the results on the same basis as Figure 2.

Figure 4 : Zonal surface temperature anomalies a the global anomaly calculated using the HADCRUT4 gridded data.

Visually, there are a number of differences between the Gistemp and HADCRUT4-derived zonal trends.

A potential problem with the global average calculation

The major reason for differences between HADCRUT4 & Gistemp is that the latter has infilled estimated data into areas where there is no data. Could this be a problem?

In Figure 5, I have shown the build-up in global coverage. That is the percentage of 5o by 5o grid cells with an anomaly in the monthly data.

Figure 5 : HADCRUT4 Change in the percentage coverage of each zone in the HADCRUT4 gridded data. 

Figure 5 shows a build-up in data coverage during the late nineteenth and early twentieth centuries. The World Wars (1914-1918 & 1939-1945) had the biggest impact on the Southern Hemisphere data collection. This is unsurprising when one considers it was mostly fought in the Northern Hemisphere, and European powers withdrew resources from their far-flung Empires to protect the mother countries. The only zones with significantly less than 90% grid coverage in the post-1975 warming period are the Arctic and the region below 45S. That is around 19% of the global area.

Finally, comparing comparable zones in the Northen and Southern hemispheres, the tropics seem to have comparable coverage, whilst for the polar, temperate and mid-latitude areas the Northern Hemisphere seems to have better coverage after 1910.

This variation in coverage can potentially lead to wide discrepancies between any calculated temperature anomalies and a theoretical anomaly based upon one with data in all the 5o by 5o grid cells. As an extreme example, with my own calculation, if just one of the 72 grid cells in a band of latitude had a figure, then an “average” would have been calculated for a band right around the world 555km (345 miles) from North to South for that month for that band. In the annual figures by zone, it only requires one of the 72 grid cells, in one of the months, in one of the bands of latitude to have data to calculate an annual anomaly. For the tropics or the polar areas, that is just one in 4320 data points to create an anomaly. This issue will impact early twentieth-century warming episode far more than the post-1975 one. Although I would expect the Hadley centre to have done some data cleanup of the more egregious examples in their calculation, potentially lack of data in grid cells could have quite random impacts, thus biasing the global temperature anomaly trends to an unknown, but significant extent. An appreciation of how this could impact can be appreciated from an example of NASA GISS Global Maps.

NASA GISS Global Maps Temperature Trends Example

NASA GISS Global Maps from GHCN v3 Data provide maps with the calculated change in average temperatures. I have run the maps to compare annual data for 1940 with a baseline of 1881-1910, capturing much of the early twentieth-century warming. I have run the maps at both the 1200km and 250km smoothing.

Figure 6 : NASA GISS Global anomaly Map and average anomaly by Latitude comparing 1940 with a baseline of 1881 to 1910 and a 1200km smoothing radius

Figure 7 : NASA GISS Global anomaly Map and average anomaly by Latitude comparing 1940 with a baseline of 1881 to 1910 and a 250km smoothing radius. 

With respect to the maps in figures 6 & 7

  • There is no apparent difference in the sea data between the 1200km and 250km smoothing radius, except in the polar regions with more cover in the former. The differences lie in the land area.
  • The grey areas with insufficient data all apply to the land or ocean areas in polar regions.
  • Figure 6, with 1200km smoothing, has most of the land infilled, whilst the 250km smoothing shows the lack of data coverage for much of South America, Africa, the Middle East, South-East Asia and Greenland.

Even with these land-based differences in coverage, it is clear that from either map that at any latitude there are huge variations in calculated average temperature change. For instance, take 40N. This line of latitude is North of San Francisco on the West Coast USA, clips Philidelphia on the East Coast. On the other side of the Atlantic, Madrid, Ankara and Beijing are at about 40N. There are significant points on the line on latitude with estimate warming greater than 1C (e.g. California), whilst at the same time in Eastern Europe, cooling may have exceeded 1C in the period. More extreme is at 60N (Southern Alaska, Stockholm, St Petersburg) the difference in temperature along the line of latitude is over 3C. This compares to a calculated global rise of 0.40C.

This lack of data may have contributed (along with a faulty algorithm) to the differences in the Zonal mean charts by Latitude. The 1200km smoothing radius chart bears little relation to the 250km smoothing radius. For instance:-

  •  1200km shows 1.5C warming at 45S, 250km about zero. 45S cuts through South Island, New Zealand.
  • From the equator to 45N, 1200km shows rise from 0.5C to over 2.0C, 250km shows drop from less than 0.5C to near zero, then rise to 0.2C. At around 45N lies Ottowa, Maine, Bordeaux, Belgrade, Crimea and the most Northern point in Japan.

The differences in the NASA Giss Maps, in a period when available data covered only around half the 2592 5o by 5o grid cells, indicate quite huge differences in trends between different areas. As a consequence, trying to interpolate warming trends from one area to adjacent areas appears to give quite different results in terms of trends by latitude.

Conclusions and Further Questions

The issue I originally focussed upon was the relative size of the early twentieth-century warming to the Post-1975. The greater amount of warming in the later period seemed to be due to the greater warming on land covering just 30% of the total global area. The sea temperature warming phases appear to be pretty much the same.

The issue that I focussed upon was a data issue. The early twentieth century had much less data coverage than after 1975. Further, the Southern Hemisphere had worse data coverage than the Northern Hemisphere, except in the Tropics. This means that in my calculation of a global temperature anomaly from the HADCRUT4 gridded data (which in aggregate was very similar to the published HADCRUT4 anomaly) the average by latitude will not be comparing like with like in the two warming periods. In particular, in the early twentieth-century, a calculation by latitude will not average right the way around the globe, but only on a limited selection of bands of longitude. On average this was about half, but there are massive variations. This would be alright if the changes in anomalies were roughly the same over time by latitude. But an examination of NASA GISS global maps for a period covering the early twentieth-century warming phase reveals that trends in anomalies at the same latitude are quite different over time. This implies that there could be large, but unknown, biases in the data.

I do not believe the analysis ends here. There are a number of areas that I (or others) can try to explore.

  1. Does the NASA GISS infilling of the data get us closer or further away from a what a global temperature anomaly would look like with full data coverage? My guess, based on the extreme example of Antartica trends (discussed here) is that the infilling will move away from the more perfect trend. The data could show otherwise.
  2. Are the changes in data coverage on land more significant than the global average or less? Looking at CRUTEM4 data could resolve this question.
  3. Would anomalies based upon similar grid coverage after 1900 give different relative trend patterns to the published ones based on dissimilar grid coverage?

Whether I get the time to analyze these is another issue.

Finally, the problem of trends varying considerably and quite randomly across the globe is the same issue that I found with land data homogenisation discussed here and here. To derive a temperature anomaly for a grid cell, it is necessary to make the data homogeneous. In standard homogenisation techniques, it is assumed that the underlying trends in an area is pretty much the same. Therefore, any differences in trend between adjacent temperature stations will be as a result of data imperfections. I found numerous examples where there were likely differences in trend between adjacent temperature stations. Homogenisation will, therefore, eliminate real but local climatic trends. Averaging incomplete global data where missing data could contain regional but unknown data trends may cause biases at a global scale.

Kevin Marshall

 

 

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

 

President Trumps Tweet on record cold in New York and Temperature Data

As Record-breaking winter weather grips North-Eastern USA (and much of Canada as well) President Donald Trump has caused quite a stir with his latest Tweet.

There is nothing new in the current President’s tweets causing controversy. This is a hard-hitting one has highlights a point of real significance for AGW theory. After decades of human-caused global warming, record cold temperatures are more significant than record warm temperatures. Record cold can be accommodated within the AGW paradigm by claiming greater variability in climate resultant on the warming. This would be a portent of the whole climate system being thrown into chaos once some tipping point had been breached. But that would also require that warm records are
(a) far more numerous than cold records and
(b) Many new warm records outstrip the old records of a few decades ago by a greater amount than the rise in average temperatures in that area.
I will illustrate with three temperature data sets I looked at a couple of years ago – Reykjavík, Iceland and Isfjord Radio and Svalbard Airport on Svalbard.

Suppose there had been an extremely high and an extremely low temperature in 2009 in Reykjavík. For the extreme high temperature to be a record it would only have to be nominally higher than a record set in 1940 to be a new record. The unadjusted average anomaly data is the same. If the previous record had been set in say 1990, a new high record would only be confirmation of more extreme climate if it was at least 1C higher than the previous record. But a new cold record in 2009 could be up to 1C higher than a 1990 low record to count as greater climate extremes. Similarly in the case of Svalbard Airport, new warm records in 2008 or 2009 would need to be over 4C higher than records set around 1980, and new cold records would need to be up to 4C higher than records set around 1980 to count as effective new warm and cold records.
By rebasing in terms of unadjusted anomaly data (and looking at monthly data) a very large number of possible records could be generated from one temperature station. With thousands of temperature stations with long records, it is possible to generate a huge number of “records” to analyze if the temperatures are becoming more extreme. But absolute record cold records should be few and far between. However, if relative cold records outstrip relative warm records, then there are questions to be asked of the average data. Similarly, if there were a lack of absolute records or a decreasing frequency of relative records, then the beliefs in impending climate chaos would be undermined.

I would not want to jump ahead with the conclusions. The most important element is to mine the temperature data and then analyze the results in multiple ways. There are likely to be surprises that could enhance understanding of climate in quite novel ways.

Kevin Marshall

Climate Delusions 2 – Use of Linear Warming Trends to defend Human-caused Warming

This post is part of a planned series about climate delusions. These are short pieces of where the climate alarmists are either deluding themselves, or deluding others, about the evidence to support the global warming hypothesis; the likely implications for changing the climate; the consequential implications of changing / changed climate; or associated policies to either mitigate or adapt to the harms. The delusion consists is I will make suggestions of ways to avoid the delusions.

In the previous post I looked at how for the Karl el al 2015 paper to be a pause-buster required falsely showing a linear trend in the data. In particular it required the selection of the 1950-1999 period for comparing with the twenty-first century warming. Comparison with the previous 25 years would shows a marked decrease in the rate of warming. Now consider again the claims made in the summary.

Newly corrected and updated global surface temperature data from NOAA’s NCEI do not support the notion of a global warming “hiatus.”  Our new analysis now shows that the trend over the period 1950–1999, a time widely agreed as having significant anthropogenic global warming, is 0.113°C decade−1 , which is virtually indistinguishable from the trend over the period 2000–2014 (0.116°C decade−1 ). …..there is no discernable (statistical or otherwise) decrease in the rate of warming between the second half of the 20th century and the first 15 years of the 21st century.

…..

…..the IPCC’s statement of 2 years ago—that the global surface temperature “has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years”—is no longer valid.

The “pause-buster” linear warming trend needs to be put into context. In terms of timing the Karl reevaluation of the global temperature data was published in the run-up to the COP21 Paris meeting which aimed to get global agreement on reducing global greenhouse gas emissions to near zero by the end of the century. Having a consensus of the World’s leading climate experts admitting that warming was not happening strongly implied that there was no big problem to be dealt with. But is demonstrating a linear warming trend – even if it could be done without the use of grossly misleading statements like in Karl paper – sufficient to show that warming is caused by greenhouse gas emissions?

The IPCC estimates that about three-quarters of all greenhouse emissions are of carbon dioxide. The BBC’s recently made a graphic of the emission types, reproduced as Figure 1.

 

There is a strong similarity between the rise in CO2 emissions and the rise in CO2 levels. Although I will not demonstrate this here, the emissions data estimates are available from CDIAC where my claim an be verified. The issue arises with the rate of increase in CO2 levels. The full Mauna Loa CO2 record shows a marked increase in CO2 levels since the end of the 1950s, as reproduced in Figure 2.

What is not so clear is that the rate of rise is increasing. In fact in the 1960s CO2 increased on average by less than 1ppm per annum, whereas in the last few years it has exceeded over 2ppm per annum. But the supposed eventual impact of the impact of the rise in CO2 is though a doubling. That implies that if CO2 rises at a constant percentage rate, and the full impact is near instantaneous, then the rate of warming produced from CO2 alone will be linear. In Figure 3 I have shown the percentage annual increase in CO2 levels.

Of note from the graph

  • In every year of the record the CO2 level has increased.
  • The warming impact of the rise in CO2 post 2000 was twice that of the 1960s.
  • There was a marked slowdown in the rate of rise in CO2 in the 1990s, but it was only for a few years below the long term average.
  • After 1998 CO2 growth rates increased to a level greater for any for any previous period.

The empirical data of Mauna Loa CO2 levels shows what should be an increasing impact on average temperatures. The marked slowdown, or pause, in global warming post 2000, is therefore inconsistent with CO2 having a dominant, or even a major role, in producing that warming. Quoting a linear rate of warming over the whole period is people deluding both themselves and others to the empirical failure of the theory.

Possible Objections

You fail to isolate the short-term and long-term effects of CO2 on temperature.

Reply: The lagged, long-term effects would have to be both larger and negative for a long period to account for the divergence. There has so far been no successful and clear modelling, just a number of attempts that amount to excuses.

Natural variations could account for the slowdown.

Reply: Equally natural variations could account for much, if not all, of the average temperature rise.in preceding decades. Non-verifiable constructs that contradict real-world evidence, are for those who delude themselves or others.  Further, if natural factors can be a stronger influence on global average temperature change for more than decade than human-caused factors, then this is a tacit admission that human-caused factors are not a dominant influence on global average temperature change.

Kevin Marshall

 

Climate Delusions 1 – Karl et al 2015 propaganda

This is the first is a planned series of climate delusions. These are short pieces of where the climate alarmists are either deluding themselves, or deluding others, about the evidence to support the global warming hypothesis; the likely implications for changing the climate; the consequential implications of changing / changed climate; or associated policies to either mitigate or adapt to the harms. The delusion consists is I will make suggestions of ways to avoid the delusions.

Why is the Karl et al 2015 paper, Possible artifacts of data biases in the recent global surface warming hiatus proclaimed to be the pause-buster?

The concluding comments to the paper gives the following boast

Newly corrected and updated global surface temperature data from NOAA’s NCEI do not support the notion of a global warming “hiatus.”  …..there is no discernable (statistical or otherwise) decrease in the rate of warming between the second half of the 20th century and the first 15 years of the 21st century. Our new analysis now shows that the trend over the period 1950–1999, a time widely agreed as having significant anthropogenic global warming (1), is 0.113°C decade−1 , which is virtually indistinguishable from the trend over the period 2000–2014 (0.116°C decade−1 ). Even starting a trend calculation with 1998, the extremely warm El Niño year that is often used as the beginning of the “hiatus,” our global temperature trend (1998–2014) is 0.106°C decade−1 —and we know that is an underestimate because of incomplete coverage over the Arctic. Indeed, according to our new analysis, the IPCC’s statement of 2 years ago—that the global surface temperature “has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years”—is no longer valid.

An opinion piece in Science, Much-touted global warming pause never happened, basically repeats these claims.

In their paper, Karl’s team sums up the combined effect of additional land temperature stations, corrected commercial ship temperature data, and corrected ship-to-buoy calibrations. The group estimates that the world warmed at a rate of 0.086°C per decade between 1998 and 2012—more than twice the IPCC’s estimate of about 0.039°C per decade. The new estimate, the researchers note, is much closer to the rate of 0.113°C per decade estimated for 1950 to 1999. And for the period from 2000 to 2014, the new analysis suggests a warming rate of 0.116°C per decade—slightly higher than the 20th century rate. “What you see is that the slowdown just goes away,” Karl says.

The Skeptical Science Temperature trend data gives very similar results. 1950-1999 gives a linear trend of 0.113°C decade−1 against 0.112°C decade−1 and for 2000-2014 gives 0.097°C decade−1 against 0.116°C decade−1. There is no real sign if a slowdown,

However, looking at any temperature anomaly  chart, whether Karl. NASA Gistemp, or HADCRUT4, it is clear that the period 1950-1975 showed little or no warming, whilst the last quarter of the twentieth century show significant warming.  This is confirmed by the Sks trend calculator figures in Figure 1.

What can be clearly seen is the claim of no slowdown in the twenty-first century compared with previous years is dependent on the selection of the period. To repeat the Karl et. al concluding claim.

Indeed, according to our new analysis, the IPCC’s statement of 2 years ago—that the global surface temperature “has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years”—is no longer valid.

The period 1976-2014 is in the middle of the range, and from the Sks temperature trend is .160. The trend is significantly higher than 0.097, so a slowdown has taken place. Any remotely competent peer review would have checked what is the most startling claim. The comparative figures from HADCRUT4 are shown in Figure 2.

With the HADCRUT4 temperature trend it is not so easy to claim that there is no significant slowdown. But the full claim in the Karl et al paper to be a pause-buster can only be made by a combination of recalculating the temperature anomaly figures and selection of the 1950-1999 period for comparing the twenty-first century warming. It is the latter part that makes the “pause-buster” claims a delusion.

Kevin Marshall

 

Climate Experts Attacking a Journalist by Misinformation on Global Warming

Summary

Journalist David Rose was attacked for pointing out in a Daily Mail article that the strong El Nino event, that resulted in record temperatures, was reversing rapidly. He claimed record highs may be not down to human emissions. The Climate Feedback attack article claimed that the El Nino event did not affect the long-term human-caused trend. My analysis shows

  • CO2 levels have been rising at increasing rates since 1950.
  • In theory this should translate in warming at increasing rates. That is a non-linear warming rate.
  • HADCRUT4 temperature data shows warming stopped in 2002, only resuming with the El Nino event in 2015 and 2016.
  • At the central climate sensitivity estimate of doubling of CO2 leads to 3C of global warming, HADCRUT4 was already falling short of theoretical warming in 2000. This is without the impact of other greenhouse gases.
  • Putting a linear trend lines through the last 35 to 65 years of data will show very little impact of El Nino, but has a very large visual impact on the divergence between theoretical human-caused warming and the temperature data. It reduces the apparent impact of the divergence between theory and data, but does not eliminate it.

Claiming that the large El Nino does not affect long-term linear trends is correct. But a linear trend neither describes warming in theory or in the leading temperature set. To say, as experts in their field, that the long-term warming trend is even principally human-caused needs a lot of circumspection. This is lacking in the attack article.

 

Introduction

Journalist David Rose recently wrote a couple of articles in the Daily Mail on the plummeting global average temperatures.
The first on 26th November was under the headline

Stunning new data indicates El Nino drove record highs in global temperatures suggesting rise may not be down to man-made emissions

With the summary

• Global average temperatures over land have plummeted by more than 1C
• Comes amid mounting evidence run of record temperatures about to end
• The fall, revealed by Nasa satellites, has been caused by the end of El Nino

Rose’s second article used the Met Offices’ HADCRUT4 data set, whereas the first used satellite data. Rose was a little more circumspect when he said.

El Nino is not caused by greenhouse gases and has nothing to do with climate change. It is true that the massive 2015-16 El Nino – probably the strongest ever seen – took place against a steady warming trend, most of which scientists believe has been caused by human emissions.

But when El Nino was triggering new records earlier this year, some downplayed its effects. For example, the Met Office said it contributed ‘only a few hundredths of a degree’ to the record heat. The size of the current fall suggests that this minimised its impact.

There was a massive reaction to the first article, as discussed by Jaime Jessop at Cliscep. She particularly noted that earlier in the year there were articles on the dramatically higher temperature record of 2015, such as in a Guardian article in January.There was also a follow-up video conversation between David Rose and Dr David Whitehouse of the GWPF commenting on the reactions. One key feature of the reactions was claiming the contribution to global warming trend of the El Nino effect was just a few hundredths of a degree. I find particularly interesting the Climate Feedback article, as it emphasizes trend over short-run blips. Some examples

Zeke Hausfather, Research Scientist, Berkeley Earth:
In reality, 2014, 2015, and 2016 have been the three warmest years on record not just because of a large El Niño, but primarily because of a long-term warming trend driven by human emissions of greenhouse gases.

….
Kyle Armour, Assistant Professor, University of Washington:
It is well known that global temperature falls after receiving a temporary boost from El Niño. The author cherry-picks the slight cooling at the end of the current El Niño to suggest that the long-term global warming trend has ended. It has not.

…..
KEY TAKE-AWAYS
1.Recent record global surface temperatures are primarily the result of the long-term, human-caused warming trend. A smaller boost from El Niño conditions has helped set new records in 2015 and 2016.

…….

2. The article makes its case by relying only on cherry-picked data from specific datasets on short periods.

To understand what was said, I will try to take the broader perspective. That is to see whether the evidence points conclusively to a single long-term warming trend being primarily human caused. This will point to the real reason(or reasons) for downplaying the impact of an extreme El Nino event on record global average temperatures. There are a number of steps in this process.

Firstly to look at the data of rising CO2 levels. Secondly to relate that to predicted global average temperature rise, and then expected warming trends. Thirdly to compare those trends to global data trends using the actual estimates of HADCRUT4, taking note of the consequences of including other greenhouse gases. Fourthly to put the calculated trends in the context of the statements made above.

 

1. The recent data of rising CO2 levels
CO2 accounts for a significant majority of the alleged warming from increases in greenhouse gas levels. Since 1958 CO2 (when accurate measures started to be taken at Mauna Loa) levels have risen significantly. Whilst I could produce a simple graph either the CO2 level rising from 316 to 401 ppm in 2015, or the year-on-year increases CO2 rising from 0.8ppm in the 1960s to over 2ppm in in the last few years, Figure 1 is more illustrative.

CO2 is not just rising, but the rate of rise has been increasing as well, from 0.25% a year in the 1960s to over 0.50% a year in the current century.

 

2. Rising CO2 should be causing accelerating temperature rises

The impact of CO2 on temperatures is not linear, but is believed to approximate to a fixed temperature rise for each doubling of CO2 levels. That means if CO2 levels were rising arithmetically, the impact on the rate of warming would fall over time. If CO2 levels were rising by the same percentage amount year-on-year, then the consequential rate of warming would be constant over time.  But figure 1 shows that percentage rise in CO2 has increased over the last two-thirds of a century.  The best way to evaluate the combination of CO2 increasing at an accelerating rate and a diminishing impact of each unit rise on warming is to crunch some numbers. The central estimate used by the IPCC is that a doubling of CO2 levels will result in an eventual rise of 3C in global average temperatures. Dana1981 at Skepticalscience used a formula that produces a rise of 2.967 for any doubling. After adjusting the formula, plugging the Mauna Loa annual average CO2 levels into values in produces Figure 2.

In computing the data I estimated the level of CO2 in 1949 (based roughly on CO2 estimates from Law Dome ice core data) and then assumed a linear increased through the 1950s. Another assumption was that the full impact of the CO2 rise on temperatures would take place in the year following that rise.

The annual CO2 induced temperature change is highly variable, corresponding to the fluctuations in annual CO2 rise. The 11 year average – placed at the end of the series to give an indication of the lagged impact that CO2 is supposed to have on temperatures – shows the acceleration in the expected rate of CO2-induced warming from the acceleration in rate of increase in CO2 levels. Most critically there is some acceleration in warming around the turn of the century.

I have also included the impact of linear trend (by simply dividing the total CO2 increase in the period by the number of years) along with a steady increase of .396% a year, producing a constant rate of temperature rise.

Figure 3 puts the calculations into the context of the current issue.

This gives the expected temperature linear temperature trends from various start dates up until 2014 and 2016, assuming a one year lag in the impact of changes in CO2 levels on temperatures. These are the same sort of linear trends that the climate experts used in criticizing David Rose. The difference in warming by more two years produces very little difference – about 0.054C of temperature rise, and an increase in trend of less than 0.01 C per decade. More importantly, the rate of temperature rise from CO2 alone should be accelerating.

 

3. HADCRUT4 warming

How does one compare this to the actual temperature data? A major issue is that there is a very indeterminate lag between the rise in CO2 levels and the rise in average temperature. Another issue is that CO2 is not the only greenhouse gas. More minor greenhouse gases may have different patterns if increases in the last few decades. However, the change the trends of the resultant warming, but only but the impact should be additional to the warming caused by CO2. That is, in the long term, CO2 warming should account for less than the total observed.
There is no need to do actual calculations of trends from the surface temperature data. The Skeptical Science website has a trend calculator, where one can just plug in the values. Figure 4 shows an example of the graph, which shows that the dataset currently ends in an El Nino peak.

The trend results for HADCRUT4 are shown in Figure 5 for periods up to 2014 and 2016 and compared to the CO2 induced warming.

There are a number of things to observe from the trend data.

The most visual difference between the two tables is the first has a pause in global warming after 2002, whilst the second has a warming trend. This is attributable to the impact of El Nino. These experts are also right in that it makes very little difference to the long term trend. If the long term is over 40 years, then it is like adding 0.04C per century that long term trend.

But there is far more within the tables than this observations. Concentrate first on the three “Trend in °C/decade” columns. The first is of the CO2 warming impact from figure 3. For a given end year, the shorter the period the higher is the warming trend. Next to this are Skeptical Science trends for the HADCRUT4 data set. Start Year 1960 has a higher trend than Start Year 1950 and Start Year 1970 has a higher trend than Start Year 1960. But then each later Start Year has a lower trend the previous Start Years. There is one exception. The period 2010 to 2016 has a much higher trend than for any other period – a consequence of the extreme El Nino event. Excluding this there are now over three decades where the actual warming trend has been diverging from the theory.

The third of the “Trend in °C/decade” columns is simply the difference between the HADCRUT4 temperature trend and the expected trend from rising CO2 levels. If a doubling of CO2 levels did produce around 3C of warming, and other greenhouse gases were also contributing to warming then one would expect that CO2 would eventually start explaining less than the observed warming. That is the variance would be positive. But CO2 levels accelerated, actual warming stalled, increasing the negative variance.

 

4. Putting the claims into context

Compare David Rose

Stunning new data indicates El Nino drove record highs in global temperatures suggesting rise may not be down to man-made emissions

With Climate Feedback KEY TAKE-AWAY

1.Recent record global surface temperatures are primarily the result of the long-term, human-caused warming trend. A smaller boost from El Niño conditions has helped set new records in 2015 and 2016.

The HADCRUT4 temperature data shows that there had been no warming for over a decade, following a warming trend. This is in direct contradiction to theory which would predict that CO2-based warming would be at a higher rate than previously. Given that a record temperatures following this hiatus come as part of a naturally-occurring El Nino event it is fair to say that record highs in global temperatures ….. may not be down to man-made emissions.

The so-called long-term warming trend encompasses both the late twentieth century warming and the twenty-first century hiatus. As the later flatly contradicts theory it is incorrect to describe the long-term warming trend as “human-caused”. There needs to be a more circumspect description, such as the vast majority of academics working in climate-related areas believe that the long-term (last 50+ years) warming  is mostly “human-caused”. This would be in line with the first bullet point from the UNIPCC AR5 WG1 SPM section D3:-

It is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together.

When the IPCC’s summary opinion, and the actual data are taken into account Zeke Hausfather’s comment that the records “are primarily because of a long-term warming trend driven by human emissions of greenhouse gases” is dogmatic.

Now consider what David Rose said in the second article

El Nino is not caused by greenhouse gases and has nothing to do with climate change. It is true that the massive 2015-16 El Nino – probably the strongest ever seen – took place against a steady warming trend, most of which scientists believe has been caused by human emissions.

Compare this to Kyle Armour’s statement about the first article.

It is well known that global temperature falls after receiving a temporary boost from El Niño. The author cherry-picks the slight cooling at the end of the current El Niño to suggest that the long-term global warming trend has ended. It has not.

This time Rose seems to have responded to the pressure by stating that there is a long-term warming trend, despite the data clearly showing that this is untrue, except in the vaguest sense. There data does not show a single warming trend. Going back to the skeptical science trends we can break down the data from 1950 into four periods.

1950-1976 -0.014 ±0.072 °C/decade (2σ)

1976-2002 0.180 ±0.068 °C/decade (2σ)

2002-2014 -0.014 ±0.166 °C/decade (2σ)

2014-2016 1.889 ±1.882 °C/decade (2σ)

There was warming for about a quarter of a century sandwiched between two periods of no warming. At the end is an uptick. Only very loosely can anyone speak of a long-term warming trend in the data. But basic theory hypotheses a continuous, non-linear, warming trend. Journalists can be excused failing to make the distinctions. As non-experts they will reference opinion that appears sensibly expressed, especially when the alleged experts in the field are united in using such language. But those in academia, who should have a demonstrable understanding of theory and data, should be more circumspect in their statements when speaking as experts in their field. (Kyle Armour’s comment is an extreme example of what happens when academics completely suspend drawing on their expertise.)  This is particularly true when there are strong divergences between the theory and the data. The consequence is plain to see. Expert academic opinion tries to bring the real world into line with the theory by authoritative but banal statements about trends.

Kevin Marshall

Temperature Homogenization at Puerto Casado

Summary

The temperature homogenizations for the Paraguay data within both the BEST and UHCN/Gistemp surface temperature data sets points to a potential flaw within the temperature homogenization process. It removes real, but localized, temperature variations, creating incorrect temperature trends. In the case of Paraguay from 1955 to 1980, a cooling trend is turned into a warming trend. Whether this biases the overall temperature anomalies, or our understanding of climate variation, remains to be explored.

 

A small place in Mid-Paraguay, on the Brazil/Paraguay border has become the centre of focus of the argument on temperature homogenizations.

For instance here is Dr Kevin Cowtan, of the Department of Chemistry at the University of York, explaining the BEST adjustments at Puerto Casado.

Cowtan explains at 6.40

In a previous video we looked at a station in Paraguay, Puerto Casado. Here is the Berkeley Earth data for that station. Again the difference between the station record and the regional average shows very clear jumps. In this case there are documented station moves corresponding to the two jumps. There may be another small change here that wasn’t picked up. The picture for this station is actually fairly clear.

The first of these “jumps” was a fall in the late 1960s of about 1oC. Figure 1 expands the section of the Berkeley Earth graph from the video, to emphasise this change.

Figure 1 – Berkeley Earth Temperature Anomaly graph for Puerto Casado, with expanded section showing the fall in temperature and against the estimated mean station bias.

The station move is after the fall in temperature.

Shub Niggareth looked at the metadata on the actual station move concluding

IT MOVED BECAUSE THERE IS CHANGE AND THERE IS A CHANGE BECAUSE IT MOVED

That is the evidence of the station move was vague. The major evidence was the fall in temperatures. Alternative evidence is that there were a number of other stations in the area exhibiting similar patterns.

But maybe there was some, unknown, measurement bias (to use Steven Mosher’s term) that would make this data stand out from the rest? I have previously looked eight temperature stations in Paraguay with respect to the NASA Gistemp and UHCN adjustments. The BEST adjustments for the stations, along another in Paul Homewood’s original post, are summarized in Figure 2 for the late 1960s and early 1970s. All eight have similar downward adjustment that I estimate as being between 0.8 to 1.2oC. The first six have a single adjustment. Asuncion Airport and San Juan Bautista have multiple adjustments in the period. Pedro Juan CA was of very poor data quality due to many gaps (see GHCNv2 graph of the raw data) hence the reason for exclusion.

GHCN Name

GHCN Location

BEST Ref

Break Type

Break Year

 

Concepcion

23.4 S,57.3 W

157453

Empirical

1969

 

Encarcion

27.3 S,55.8 W

157439

Empirical

1968

 

Mariscal

22.0 S,60.6 W

157456

Empirical

1970

 

Pilar

26.9 S,58.3 W

157441

Empirical

1967

 

Puerto Casado

22.3 S,57.9 W

157455

Station Move

1971

 

San Juan Baut

26.7 S,57.1 W

157442

Empirical

1970

 

Asuncion Aero

25.3 S,57.6 W

157448

Empirical

1969

 

  

  

  

Station Move

1972

 

  

  

  

Station Move

1973

 

San Juan Bautista

25.8 S,56.3 W

157444

Empirical

1965

 

  

  

  

Empirical

1967

 

  

  

  

Station Move

1971

 

Pedro Juan CA

22.6 S,55.6 W

19469

Empirical

1968

 

  

  

  

Empirical

3 in 1970s

 
           

Figure 2 – Temperature stations used in previous post on Paraguayan Temperature Homogenisations

 

Why would both BEST and UHCN remove a consistent pattern covering and area of around 200,000 km2? The first reason, as Roger Andrews has found, the temperature fall was confined to Paraguay. The second reason is suggested by the UHCNv2 raw data1 shown in figure 3.

Figure 3 – UHCNv2 “raw data” mean annual temperature anomalies for eight Paraguayan temperature stations, with mean of 1970-1979=0.

There was an average temperature fall across these eight temperature stations of about half a degree from 1967 to 1970, and over one degree by the mid-1970s. But it was not at the same time. The consistency is only show by the periods before and after as the data sets do not diverge. Any homogenisation program would see that for each year or month for every data set, the readings were out of line with all the other data sets. Now maybe it was simply data noise, or maybe there is some unknown change, but it is clearly present in the data. But temperature homogenisation should just smooth this out. Instead it cools the past. Figure 4 shows the impact average change resulting from the UHCN and NASA GISS homogenisations.

Figure 4 – UHCNv2 “raw data” and NASA GISS Homogenized average temperature anomalies, with the net adjustment.

A cooling trend for the period 1955-1980 has been turned into a warming trend due to the flaw in homogenization procedures.

The Paraguayan data on its own does not impact on the global land surface temperature as it is a tiny area. Further it might be an isolated incident or offset by incidences of understating the warming trend. But what if there are smaller micro climates that are only picked up by one or two temperature stations? Consider figure 5 which looks at the BEST adjustments for Encarnacion, one of the eight Paraguayan stations.

Figure 5 – BEST adjustment for Encarnacion.

There is the empirical break in 1968 from the table above, but also empirical breaks in the 1981 and 1991 that look to be exactly opposite. What Berkeley earth call the “estimated station mean bias” is as a result of actual deviations in the real data. Homogenisation eliminates much of the richness and diversity in the real world data. The question is whether this happens consistently. First we need to understand the term “temperature homogenization“.

Kevin Marshall

Notes

  1. The UHCNv2 “raw” data is more accurately pre-homogenized data. That is the raw data with some adjustments.

Understanding GISS Temperature Adjustments

A couple of weeks ago something struck me as odd. Paul Homewood had been going on about all sorts of systematic temperature adjustments, showing clearly that the past has been cooled between the UHCN “raw data” and the GISS Homogenised data used in the data sets. When I looked at eight stations in Paraguay, at Reykjavik and at two stations on Spitzbergen I was able to corroborate this result. Yet Euan Mearns has looked at groups of stations in central Australia and Iceland, in both finding no warming trend between the raw and adjusted temperature data. I thought that Mearns must be wrong, so when he published on 26 stations in Southern Africa1, I set out to evaluate those results, to find the flaw. I have been unable to fully reconcile the differences, but the notes I have made on the Southern African stations may enable a greater understanding of temperature adjustments. What I do find is that clear trends in the data across a wide area have been largely removed, bringing the data into line with Southern Hemisphere trends. The most important point to remember is that looking at data in different ways can lead to different conclusions.

Net difference and temperature adjustments

I downloaded three lots of data – raw, GCHNv3 and GISS Homogenised (GISS H), then replicated Mearns’ method of calculating temperature anomalies. Using 5 year moving averages, in Chart 1 I have mapped the trends in the three data sets.

There is a large divergence prior to 1900, but for the twentieth century the warming trend is not excessively increased. Further, the warming trend from around 1900 is about half of that in the GISTEMP Southern Hemisphere or global anomalies. Looked in this way Mearns would appear to have a point. But there has been considerable downward adjustment of the early twentieth century warming, so Homewood’s claim of cooling the past is also substantiated. This might be the more important aspect, as the adjusted data makes the warming since the mid-1970s appear unusual.

Another feature is that the GCHNv3 data is very close to the GISS Homogenised data. So in looking the GISS H data used in the creation of the temperature data sets is very much the same as looking at GCHNv3 that forms the source data for GISS.

But why not mention the pre-1900 data where the divergence is huge?

The number of stations gives a clue in Chart 2.

It was only in the late 1890s that there are greater than five stations of raw data. The first year there are more data points left in than removed is 1909 (5 against 4).

Removed data would appear to have a role in the homogenisation process. But is it material? Chart 3 graphs five year moving averages of raw data anomalies, split between the raw data removed and retained in GISS H, along with the average for the 26 stations.

Where there are a large number of data points, it does not materially affect the larger picture, but does remove some of the extreme “anomalies” from the data set. But where there is very little data available the impact is much larger. That is particularly the case prior to 1910. But after 1910, any data deletions pale into insignificance next to the adjustments.

The Adjustments

I plotted the average difference between the Raw Data and the adjustment, along with the max and min values in Chart 4.

The max and min of net adjustments are consistent with Euan Mearns’ graph “safrica_deltaT” when flipped upside down and made back to front. It shows a difficulty of comparing adjusted, where all the data is shifted. For instance the maximum figures are dominated by Windhoek, which I looked at a couple of weeks ago. Between the raw data and the GISS Homogenised there was a 3.6oC uniform increase. There were a number of other lesser differences that I have listed in note 3. Chart 5 shows the impact of adjusting the adjustments is on both the range of the adjustments and the pattern of the average adjustments.

Comparing this with this average variance between the raw data and the GISS Homogenised shows the closer fit if the adjustments to the variance. Please note the difference in scale on Chart 6 from the above!

In the earlier period has by far the most deletions of data, hence the lack of closeness of fit between the average adjustment and average variance. After 1945, the consistent pattern of the average adjustment being slightly higher than the average variance is probably due to a light touch approach on adjustment corrections than due to other data deletions. The might be other reasons as well for the lack of fit, such as the impact of different length of data sets on the anomaly calculations.

Update 15/03/15

Of note is that the adjustments in the early 1890s and around 1930 is about three times the size of the change in trend. This might be partly due to zero net adjustments in 1903 and partly due to the small downward adjustments in post 2000.

The consequences of the adjustments

It should be remembered that GISS use this data to create the GISTEMP surface temperature anomalies. In Chart 7 I have amended Chart 1 to include Southern Hemisphere annual mean data on the same basis as the raw data and GISS H.

It seems fairly clear that the homogenisation process has achieved bringing the Southern Africa data sets into line with the wider data sets. Whether the early twentieth century warming and mid-century cooling are outliers that have been correctly cleansed is a subject for further study.

What has struck me in doing this analysis is that looking at individual surface temperature stations becomes nonsensical, as they are grid reference points. Thus comparing the station moves for Reykjavik with the adjustments will not achieve anything. The implications of this insight will have to wait upon another day.

Kevin Marshall

Notes

1. 26 Data sets

The temperature stations, with the periods for the raw data are below.

Location

Lat

Lon

ID

Pop.

Years

Harare

17.9 S

31.1 E

156677750005

601,000

1897 – 2011

Kimberley

28.8 S

24.8 E

141684380004

105,000

1897 – 2011

Gwelo

19.4 S

29.8 E

156678670010

68,000

1898 – 1970

Bulawayo

20.1 S

28.6 E

156679640005

359,000

1897 – 2011

Beira

19.8 S

34.9 E

131672970000

46,000

1913 – 1991

Kabwe

14.4 S

28.5 E

155676630004

144,000

1925 – 2011

Livingstone

17.8 S

25.8 E

155677430003

72,000

1918 – 2010

Mongu

15.2 S

23.1 E

155676330003

< 10,000

1923 – 2010

Mwinilunga

11.8 S

24.4 E

155674410000

< 10,000

1923 – 1970

Ndola

13.0 S

28.6 E

155675610000

282,000

1923 – 1981

Capetown Safr

33.9 S

18.5 E

141688160000

834,000

1880 – 2011

Calvinia

31.5 S

19.8 E

141686180000

< 10,000

1941 – 2011

East London

33.0 S

27.8 E

141688580005

127,000

1940 – 2011

Windhoek

22.6 S

17.1 E

132681100000

61,000

1921 – 1991

Keetmanshoop

26.5 S

18.1 E

132683120000

10,000

1931 – 2010

Bloemfontein

29.1 S

26.3 E

141684420002

182,000

1943 – 2011

De Aar

30.6 S

24.0 E

141685380000

18,000

1940 – 2011

Queenstown

31.9 S

26.9 E

141686480000

39,000

1940 – 1991

Bethal

26.4 S

29.5 E

141683700000

30,000

1940 – 1991

Antananarivo

18.8 S

47.5 E

125670830002

452,000

1889 – 2011

Tamatave

18.1 S

49.4 E

125670950003

77,000

1951 – 2011

Porto Amelia

13.0 S

40.5 E

131672150000

< 10,000

1947 – 1991

Potchefstroom

26.7 S

27.1 E

141683500000

57,000

1940 – 1991

Zanzibar

6.2 S

39.2 E

149638700000

111,000

1880 – 1960

Tabora

5.1 S

32.8 E

149638320000

67,000

1893 – 2011

Dar Es Salaam

6.9 S

39.2 E

149638940003

757,000

1895 – 2011

2. Temperature trends

To calculate the trends I used the OLS method, both from the formula and using the EXCEL “LINEST” function, getting the same answer each time. If you are able please check my calculations. The GISTEMP Southern Hemisphere and global data can be accessed direct from the NASA GISS website. The GISTEMP trends are from the skepticalscience trends tool. My figures are:-

3. Adjustments to the Adjustments

Location

Recent adjustment

Other adjustment

Other Period
Antananarivo

0.50

 

 
Beira

 

0.10

Mid-70s + inter-war
Bloemfontein

0.70

 

 
Dar Es Salaam

0.10

 

 
Harare

 

1.10

About 1999-2002
Keetmanshoop

1.57

 

 
Potchefstroom

-0.10

 

 
Tamatave

0.39

 

 
Windhoek

3.60

 

 
Zanzibar

-0.80

 

 

Windhoek Temperature adjustments

At Euan Mearn’s blog I made reference to my findings, posted in full last night, that in the Isfjord Radio weather station had adjustments that varied between +4.0oC in 1917 to -1.7oC in the 1950s. I challenged anyone to find bigger adjustments than that. Euan came back with the example of Windhoek in South Africa, claiming 5oC of adjustments between the “raw” and GISS homogenised data.

I cry foul, as the adjustments are throughout the data set. J

That is the whole of the data set has been adjusted up by about 4 oC!

However, comparing the “raw” with the GISS homogenised data, with 5 year moving averages, (alongside the net adjustments) there are some interesting features.

The overall temperatures have been adjusted up by around 4oC, but

  • From the start of the record in 1920 to 1939 the cooling has been retained, if not slightly amplified.
  • The warming from 1938 to 1947 of 1.5oC has been erased by a combination of deleting the 1940 to 1944 data and reducing the 1945-1948 adjustments by 1.4oC.
  • The 1945-1948 adjustments, along with random adjustments and deletion of data mostly remove the near 1.5oC of cooling from the late 1940s to mid-1950s and the slight rebound through to the early 1960s.
  • The early 1970s cooling and the warming to the end of the series in the mid-1980s is largely untouched.

The overall adjustments leave a peculiar picture that cannot be explained by a homogenisation algorithm. The cooling in the 1920s offsets the global trend. Deletion of data and the adjustments in the data counter the peak of warming in the early 1940s in the global data. Natural variations in the raw data between the late 1940s and 1970 appear to have been removed, then the slight early 1970s cooling and the subsequent warming in the raw data left alone. However, the raw data shows average temperatures in the 1980s to be around 0.8oC higher than in the early 1920s. The adjustments seem to have removed this.

This removal of the warming trend tends to disprove something else. There appears to be no clever conspiracy, with a secret set of true figures. Rather, there are a lot of people dipping in to adjusting adjusted data to their view of the world, but nobody really questioning the results. They have totally lost sight of what the real data actually is. If they have compared the final adjusted data with the raw data, then they realised that the adjustments had managed to have eliminated a warming trend of over 1 oC per century.

Kevin Marshall