130: UHI #3 - Perth (Western Australia)

The population of Western Australia is only about 2.67 million but two million of that total live in the state capital Perth. So 75% of the state's population live in Perth even though Perth accounts for less than 0.25% of the area of Western Australia. Perhaps this is why temperatures in Perth appear to have risen at more than twice the rate of the rest of the state. By 1990 temperatures in Perth had risen more than 1.5°C since 1900 compared to less than 0.7°C in Western Australia as a whole (see Fig. 130.1 below). That looks like classic urban heat island (UHI) behaviour. The only caveat is that the main weather station for Perth at Perth Regional Office (Berkeley Earth ID: 4321) ceased operations in 1992 just as the UHI was taking off.

Fig. 130.1: The change to the 5-year average temperatures of Perth (red curve) and Western Australia (blue curve) since 1900.

In Post 22 I examined the temperature trends for Western Australia. The mean temperature change since 1900 is shown in Fig. 130.2 below and it indicates that Western Australia has warmed by about 1°C since 1990. The best fit for 1991-1990 indicates a temperature rise of less than 0.64°C in 90 years while the 5-year average suggests a rise of about 0.67°C for the same period.

Fig. 130.2: The mean temperature change for Western Australia since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 1990 and has a positive gradient of +0.71 ± 0.11 °C per century.

The mean temperature anomaly (MTA) for Western Australia shown in Fig. 130.2 above is the result of averaging monthly temperature anomalies from nearly hundred stations as Fig. 130.3 below demonstrates (see here for a full list of all stations). However, before 1900 there are less than ten available stations so the MTA is less reliable and more prone to error from statistical variability. For more details and analysis of the complete data for Western Australia see Post 22.

Fig. 130.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Western Australia in Fig. 130.2.

The oldest weather station in Western Australia is Perth Regional Office (Berkeley Earth ID: 4321). It has data stretching back as far as 1852, and continuous data from 1876 to 1992. In fact it is the only station within Perth with over 480 months of continuous data between 1876 to 1992, hence its significance as a case study of the urban heat island (UHI) effect.

Compared to the rest of Western Australia, Perth Regional Office shows much more significant and continuous warming since 1900 (see Fig. 130.3 below). The best fit for 1901-1990 indicates a temperature rise of more than 1.47°C in 90 years while the 5-year average suggests a rise of over 1.5°C.

Fig. 130.4: The mean temperature change for Perth Regional Office since 1900 relative to its 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 1990 and has a positive gradient of +1.63 ± 0.19 °C per century.

Summary

The following temperature changes were observed from 1901 to 1990.

Western Australia: 0.67°C (trend 0.64°C).

Perth: 1.53°C (trend 1.47°C).

So Perth warmed by almost 1°C more than the surrounding state of Western Australia in the ninety years up to 1990, or more than twice as fast. A classic UHI!

129: UHI #2 - Melbourne (Victoria)

The Australian state of Victoria has a total population of 6.7 million, of which 5.1 million live in the city of Melbourne. That means that 76% of the population of Victoria live in its capital city even though Melbourne accounts for only 4.4% of the area of Victoria. It is not really surprising then that the temperature trends for Melbourne and Victoria over the last 100 years are markedly different. In fact while the state of Victoria has cooled slightly for most of the last 120 years, Melbourne has warmed by over 2°C (see Fig. 129.1 below). So like Sydney in the previous post, Melbourne looks like a classic urban heat island (UHI).

Fig. 129.1: The change to the 5-year average temperatures of Melbourne (red curve) and Victoria (blue curve) since 1900.

 

In Post 19 I examined the temperature trends for Victoria. The mean temperature change since 1880 is shown in Fig. 129.2 below and it indicates that Victoria has exhibited no significant warming. In fact the best fit for 1886-2005 indicates that temperatures actually declined very slightly, although the 5-year average over the same period suggests that they may have risen slightly by about 0.47°C with most of this rise occurring after 1990.

Fig. 129.2: The mean temperature change for Victoria since 1880 relative to the 1966-1995 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a slight negative gradient of -0.02 ± 0.08 °C per century.

The mean temperature anomaly (MTA) for Victoria shown in Fig. 129.2 above is the result of averaging monthly temperature anomalies from over fifty stations as Fig. 129.3 below demonstrates (see here for a list of all stations). However, before 1900 there are less than twenty available stations so the MTA is less reliable and more prone to error from statistical variability. For more details and analysis of the complete data for Victoria see Post 19.

Fig. 129.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Victoria in Fig. 129.2.

The oldest weather stations in Victoria is Melbourne Regional Office (Berkeley Earth ID: 151813). It is located in the heart of Melbourne and has continuous data stretching back as far as 1855. It is also the only major station within 20 km of the city centre that has continuous data extending back before 1940 (the next best is Laverton Aerodrome), hence its significance as a case study of the urban heat island (UHI) effect.

In contrast to the rest of Victoria, Melbourne Regional Office shows significant and continuous warming since 1880 (see Fig. 129.4 below). The best fit for 1886-2005 indicates a temperature rise of more than 1.4°C in 120 years while the 5-year average suggests a rise of over 2.1°C.

Fig. 129.4: The mean temperature change for Melbourne Regional Office since 1880 relative to its 1966-1995 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a positive gradient of +1.23 ± 0.10 °C per century.

Summary

The following temperature changes were observed from 1886 to 2005.

Victoria: 0.47°C (trend -0.02°C).

Melbourne: 2.1°C (trend 1.48°C).

So Melbourne has warmed by at least 1.5°C more than the surrounding state of Victoria, or up to four times faster. A classic UHI!

Given that both Sydney and Melbourne appear to be great examples of UHIs, one might expect the same of similar large cities, Adelaide and Brisbane. Yet this appears not to be the case. Neither of these cities exhibits greater warming than the rest of their respective states even though over 70% of the South Australian population of 1.8 million live in Adelaide. For Brisbane and Queensland, though, the proportion is only 44%, although Brisbane is almost twice the size of Adelaide by population. The reason both Adelaide and Brisbane do not exhibit striking UHI properties could be that they are too small. Adelaide has a population that is less than a quarter of that of Sydney. That said, the population of Perth in Western Australia is just less than two million and yet as the next post will show, it too appears to be an urban heat island (UHI).

128: UHI #1 - Sydney (New South Wales)

In my previous post I explained the concept of the urban heat island (UHI). Unfortunately, finding clear examples in the global temperature records is not so easy. This is not because they don't exist, but because to demonstrate their existence beyond a reasonable doubt requires good data for both the urban area and the wider region within which the UHI sits. 

In the case of the urban data, this needs to be from a station that is located in the heart of the urban area and not on its perimeter such as at the local airport. Finding datasets from such locations is a lot harder than one might think because most weather stations are deliberately sited away from the centre of urban areas. Then the dataset needs to be sufficiently long with no gaps in the record in order for it to exhibit a definite trend over time. 

In the case of the regional data, this too needs to be based on long datasets with no gaps in their records. But in addition, a large number of these datasets are needed in order to establish an accurate trend for the region.

Satisfying these criteria is particularly difficult in the Southern Hemisphere where the data for most countries other than Australia is not particularly good. Nevertheless, I have identified six examples in the Southern Hemisphere so far (excluding Brazil which I have yet to examine in detail) where the quality of the temperature data for the UHI and its host country or state is sufficient to detect unambiguous differences in their temperature trends. Over the following six posts, including this one, I will examine each of these six examples in turn. So for Exhibit #1 I give you Sydney in New South Wales (NSW), Australia.

The city of Sydney has a population of about 5.3 million. That means that 65% of the New South Wales (NSW) population of 8.2 million live in Sydney even though Sydney accounts for only 1.5% of the area of NSW. It is not really surprising then that the temperature trends for Sydney and NSW over the last 100 years are markedly different. For while NSW has barely warmed at all in the last 140 years, Sydney has warmed by almost 2°C (see Fig. 128.1 below).

Fig. 128.1: The change to the 5-year average temperatures of Sydney (red curve) and New South Wales (blue curve) since 1900.

In Post 18 I examined the temperature trends for New South Wales. The mean temperature change since 1880 is shown in Fig. 128.2 below and it indicates that NSW has exhibited no significant warming. In fact the best fit for 1886-2005 indicates a temperature rise of less than 0.12°C in 120 years while the 5-year average suggests a rise of about 0.25°C.

Fig. 128.2: The mean temperature change for New South Wales since 1880 relative to the 1965-1994 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a slight positive gradient of +0.099 ± 0.077 °C per century.

The mean temperature anomaly (MTA) for NSW shown in Fig. 128.2 above is the result of averaging monthly temperature anomalies from over one hundred stations as Fig. 128.3 below demonstrates (see here for a list). However, before 1880 there are less than twenty available stations so the MTA is less reliable and more prone to error from statistical variability. For more details and analysis of the complete data for NSW see Post 18.

Fig. 128.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for New South Wales in Fig. 128.2.

One of the oldest weather stations in NSW is Sydney Observatory Hill (Berkeley Earth ID: 151986). It is located in the heart of Sydney, south of the opera house and harbour, and has continuous data stretching back as far as 1859. It is also the only major station within 20 km of the city centre, hence its significance as a case study of the urban heat island (UHI) effect. 

In contrast to the rest of NSW, Sydney Observatory Hill shows significant and continuous warming since 1880 (see Fig. 128.4 below). The best fit for 1886-2005 indicates a temperature rise of more than 1.2°C in 120 years while the 5-year average suggests a rise of over 1.5°C.

Fig. 128.4: The mean temperature change for Sydney Observatory Hill since 1880 relative to its 1965-1994 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a positive gradient of +1.01 ± 0.08 °C per century.

Summary

The following temperature changes were observed from 1886 to 2005.

NSW: 0.25°C (trend 0.12°C).

Sydney: 1.5°C (trend 1.2°C).

So Sydney has warmed by at least 1°C more than NSW, or up to ten times faster. A classic UHI!

125: Queensland revisited - temperature trends STABLE to 1980

In Post 24 I interrogated the temperature data of Queensland. This Australian state had 28 long stations and a further 85 medium stations in its Berkeley Earth (BE) dataset as listed here. Averaging the anomalies from these 113 stations resulted in a mean temperature anomaly (MTA) that appeared to exhibit a warming of 0.7°C since 1900 as shown in Fig. 125.1 below. But is this the true picture? In this post I will show how the data can be reinterpreted, and thus deliver different results without altering the actual data.

Fig. 125.1: The mean temperature change for Queensland since 1887. The best fit is applied to the monthly mean data from 1901 to 2004 and has a positive gradient of +0.74 ± 0.08 °C per century.

The first problem with the data in Fig. 125.1 is that not all the 113 stations used are of equal length. That means that the MTA before 1905 is dependent on data from less than twenty stations rather than over one hundred as was the case in the 1980s, as the graph in Fig. 125.2 below indicates. This suggests that the data after 1920 will be more reliable than the data before.

Fig. 125.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Queensland in Fig. 125.1.

Then there is the impact of the fitting range. Applying linear regression to the entire range of data is often inappropriate because the data may have different behaviours or trends at different times. This appears to be the case for the data in Fig. 125.1 as data after 1975 is clearly behaving differently to data before that date.

So suppose we look just at data after 1920 and only fit to data before 1980. Then the picture changes from that presented in Fig. 125.1. The best fit trend line to the data now rises less steeply by only 0.2°C or so before 1980 (see Fig. 125.3 below), and while there is a larger jump after 1975 of about 0.3°C again, this appears to be temporary as the temperature returns to trend after 2010 (although that may just be a temporary reversal). So changing the interval of the linear regression fit can also change the result, or at least change our perceptions, interpretations and conclusions.

Fig. 125.3: The mean temperature change for Queensland since 1920. The best fit is applied to the monthly mean data from 1921 to 1980 and has a positive gradient of +0.29 ± 0.19 °C per century.

What is more, this temperature rise from 1921 to 1980 seen in Fig. 125.3 is more consistent with that seen for the longest temperature record for the state, Brisbane Regional Office (ID 152224), as shown in Fig. 125.4 below. Unusually, this record shows only modest warming despite coming from the middle of the largest urban area in the state. As I will show in future posts, the urban heat island (UHI) effect, where large urban areas lead to a greater warming of the local environment than is seen in more rural areas, or for the region as a whole, can be a serious issue. It usually results in greater warming for stations in large, dense, urban environments compared to the regional average, not less. But not here.

Fig. 125.4: The mean temperature change for Brisbane since 1887. The best fit is applied to the monthly mean data from 1901 to 2004 and has a positive gradient of +0.22 ± 0.08 °C per century.

All this indicates the difficulties in interpreting temperature data correctly. Not all times in history have equal quality of data, and even if they did, the natural variability in that data means that you need long time intervals to see the true trend. And even then your conclusions will be affected by your choices in how the data is analysed.

So which is the better interpretation of the data, Fig. 125.1 or Fig 125.3? My opinion is Fig. 125.3 because it focuses on the better data. The data analysis also fits to data that is less variable, and therefore more reliable. It is too early to know if the temperature rise after 1975 is part of a trend or whether it is just temporary, so the better approach is to treat it almost as a separate dataset and compare it with what went before. 

The temperature dips in Fig. 125.1 before 1910 are also of questionable veracity. Are they the latter part of an upward trend or are they just just natural variability? Without extra data before 1880 we don't know, and even if that upward trend exists, then why does it exist? Because it can't be caused by rises in CO₂ because those rises were negligible before 1910. In fact CO₂ levels in 1910 are estimated to be less than 300 ppm which is a rise of only 6% since 1800. That is nowhere near enough to produce temperature rises of 1°C or more. In fact it would barely result in rises of 0.1°C (see Fig. 87.3 in Post 87).

But of course not everyone sees things this way. One problem with climate science is the amount of data adjustments that are used to correct for perceived data flaws in the temperature data. But as I have shown repeatedly throughout this blog, those adjustments appear hard to justify from any statistical perspective. The raw data is far more reliable than is often assumed, and this can be evidenced by the repeated behaviours seen in temperature trends based on raw data from neighbouring regions that consistently correlate. Many (but not all) of these adjustments also appear to add warming more often than they reduce it, and so appear to exaggerate the amount of climate change that is occurring.

But perhaps one of the most concerning aspects of temperature adjustments is that they are not permanent. The same data often continues to be readjusted over time, and more often each adjustment makes the claims for the warming trend even greater. As exhibit #1 I give you the Australian Bureau of Meteorology (BoM). According to the BoM the climate of Queensland has warmed by about 1.65°C since 1910 as shown in Fig. 125.5 below. Yet the raw data in Fig. 125.1 suggests that the warming is less than half this value and Fig. 125.3 suggests it may be less than 0.3°C. The problem is that the data shown in Fig. 125.5, which is the official BoM version for July 2022, is rather different from the version published in 2010.

Fig. 125.5: The mean temperature change for Queensland since 1910 according to BoM in 2022. The best fit line has a positive gradient of 1.5 °C per century.

In 2010 the temperature trend for Queensland according to the BoM was as shown in Fig. 125.6 below (h/t Ken's Kingdom). Yes it has twelve years less data, but that is not the only difference. Many of the temperature anomalies before 2010 have rather different values compared to now, and so too does the linear trend which was only 1.0°C per century; this despite there being no change in the 30-year reference period of 1961-1990. Now some of the change in linear trend may be due to the extra data after 2010, but not all. It is quite clear that most of the annual anomalies before 1980 in Fig. 125.6 are larger or less negative than was the case for anomalies for the same year in Fig. 125.5 above, while anomalies for most years after 1980 in Fig. 125.6 have smaller values when compared to the corresponding anomaly in Fig. 125.5.

Fig. 125.6: The mean temperature change for Queensland since 1910 according to BoM in 2010. The best fit line has a positive gradient of 1.0 °C per century.

It may be difficult for some readers to spot the difference because we are talking of changes of less than 0.2°C in the height of the bars, but if we overlay the data from Fig. 125.6 on top of that from Fig. 125.5 the differences become more apparent. This is done in Fig. 125.7 below with the 2010 data from Fig. 125.6 coloured green (for positive vales) or sea-green (for negative values) and being slightly translucent so that the red and blue coloured bars from 2022 can be seen underneath.

Fig. 125.7: A comparison of BoM annual temperature anomalies for Queensland from 2022 (red and blue) and 2020 (green).

What Fig. 125.7 shows quite clearly is that the temperature anomalies before 1980 were up to 0.2°C greater back in 2010, while those after 1980 were up to 0.2°C smaller in value. In other words, the extra adjustments made to the data since 2010 have added up to 0.4°C of warming. And yet neither set of data is comparable to the unadjusted data in Fig. 125.3 where the warming is estimated at less than 0.3°C.

Summary

Re-analysis of the unadjusted Queensland temperature data from Post 24 shows that the state may have warmed by as little as 0.3°C since 1920 (see Fig. 125.3).

The most extreme analysis of the unadjusted data indicates that the warming since 1900 is less than 0.8°C (see Fig. 125.1).

According to the Australian Bureau of Meteorology (BoM) in 2010, there had been 1.0°C of warming from 1910 to 2010 (see Fig. 125.6).

In 2022 the BoM now claims that warming since 1910 has increased to 1.65°C (see 125.5).

Adjustments made to the 1910-2010 data by the BoM since 2010 appear to have added up to 0.4°C of warming (see Fig. 125.7). So up to 60% of the 0.65°C temperature rise claimed by the BoM since 2010 could be due to data readjustments for data before 2010.

100. List of completed temperature analyses by country

 

As this is my 100th post on this blog I thought it would be a good moment to summarize the results that have emerged from the temperature data I have analysed so far. Below is a list of all the countries and regions that I have investigated to date with links to the relevant post. This amounts to about 60 countries, states and territories in total, which is roughly one third of all the countries in the world. 

The main areas that so far remain to be studied are the Arctic, Canada, Russia, UK, Scandinavia, Mediterranean, North Africa, Middle East, China and Japan. In the Southern Hemisphere only Brazil, Venezuela, Guyana, Suriname, French Guiana and the South Atlantic remain. However, most of these Southern Hemisphere regions are already included in the analysis of South America in Post 35.

Europe

Europe has the longest temperature records available with several in Germany, Sweden and the Netherlands stretching back to the early 18th century. The average of the 109 longest records yields a mean temperature anomaly (MTA) that shows a small but continuous warming of about 0.1°C per century for over 200 years until 1988. Then the temperature jumps suddenly by over 1°C. The reason for this jump is unclear. It is certainly not related directly to carbon dioxide emissions. The only countries that appear to have strong warming trends are the Benelux countries, Denmark and Switzerland. The Baltic states and most of central Europe appear to cool before 1980 and then warm suddenly.

109 longest station records (Post 44)

Austria (Post 55)

Baltic States (Post 51)

Belgium and Luxembourg (Post 40)

Central Europe average (Post 57)

Czechoslovakia (Post 53)

Denmark (Post 48)

Germany (Post 49)

Hungary (Post 54)

Netherlands (Post 41)

Poland (Post 50)

Switzerland (Post 56)

 

USA

The USA may not have any temperature records that are as long as the longest that Europe can boast, but its temperature data from 1850 onwards is the best there is. Virtually every state has over 100 station records with over 50 years of data and over 50 records with over 100 years of data. An average of the 400 longest temperature records appears to indicate that the climate warmed by more than 2°C from 1780 to 1920 when carbon dioxide levels barely increased, and then cooled by over 0.5°C when carbon dioxide levels took off. The early warming cannot therefore be due to CO2 and is therefore generally attributed to urbanization and deforestation in the north and east. The cooling seen after 1920 is also seen in most southern states like Louisiana, Mississippi and Texas.

400 longest station records (Post 66)

Louisiana (Post 97)

Mississippi (Post 99)

Texas (Post 52)

 

Central America

The temperature data for Central America can basically be split between Mexico and the rest, however, even then there are more than four times as many stations in Mexico as there are in the rest of Central America. The picture in Mexico is also complicated by the stations there falling into two distinct types from two different sources. On balance it is likely that the overall climate was stable until 1980 and then warmed over the following twenty years by about 1°C.

Mexico (Post 93)

Rest of Central America (Post 94)

South America

Of the countries in South America studied so far, only Argentina (0.6°C), Ecuador (1°C) and Uruguay (1°C) show significant warming, although the Ecuador data is far from reliable. In Paraguay and Chile the climate has cooled while in most other countries it has remained stable. The average of all medium and long stations in South America yields a warming of about 0.5°C since 1900.

All long and medium stations (Post 35)

Argentina (Post 61)

Bolivia (Post 58)

Chile (Post 62)

Colombia (Post 95)

Ecuador (Post 96)

Paraguay (Post 59)

Peru (Post 63)

Uruguay (Post 60)

Asia

My analysis so far of temperatures in Asia has focused on the countries of Indochina and the Indian subcontinent. The overall temperature trend for Indochina is one of cooling before 1980 and warming thereafter. The result is that temperatures in 2010 are barely any higher than they were in 1890. This is also reflected in the individual temperature records of Burma, Malaysia and Vietnam, while those of the Philippines and Thailand remain stable from 1920 onwards. In India and Pakistan there is little warming before 1990 and then a sudden jump in temperatures of about 0.5°C in the mid-1990s. For Sri Lanka the jump in temperature occurs in 1978 while Bangladesh sees a continuous warming of only 0.3°C per century. 

Bangladesh (Post 74)

Burma/Myanmar (Post 69)

India including Nepal (Post 71)

Indian subcontinent (Post 75)

Indochina (Post 70)

Malaysia and Singapore (Post 69)

Pakistan (Post 72)

Philippines (Post 69)

Sri Lanka (Post 73)

Thailand (Post 69)

Vietnam (Post 69)

Africa

Most of southern Africa has exhibited some significant warming of over 1°C since 1980 but the overall picture before 1980 is varied. Angola, Mozambique and South Africa show no warming before 1980 while Malawi, Zambia, Zimbabwe and Madagascar all cool significantly by as much as they later warm. The data for Namibia and Botswana is not great but may indicate a slight warming before 1980 as well as much larger warming thereafter. Of all the countries listed below, Madagascar, Mozambique, South Africa and Zimbabwe have the best quality data and none of these countries appear to exhibit any warming before 1980.

Angola (Post 82)

Botswana (Post 38)

Madagascar (Post 77)

Mozambique (Post 78)

Namibia (Post 39)

South Africa including Lesotho and Eswatini/Swaziland (Post 37)

Zambia and Malawi (Post 81)

Zimbabwe (Post 79)

Australia

Analysis of temperature data for Australia indicates that the mean temperature trend is parabolic with the climate cooling from 1875 to 1960 and then warming. Overall temperatures in 2010 are only about 0.1-0.2°C warmer than in 1875 with temperatures having increased by about 0.5°C since 1960. This pattern in seen in most states such as South Australia, New South Wales and Victoria. It is harder to be conclusive for Tasmania and Western Australia due to a lack of data before 1900 while the trend in Northern Territory is one of consistent cooling. Only Queensland shows constant warming of about 1°C since 1990.

Australia (Post 26)

New South Wales and ACT (Post 18)

Northern Territory (Post 23)

Queensland (Post 24)

South Australia (Post 21)

Tasmania (Post 20)

Victoria (Post 19)

Western Australia (Post 22)

Oceania

Most of the countries and regions of Oceania show little of no warming. In Antarctica the only warming is found around the peninsula. New Zealand cools slightly from 1860 until 1960 then warms by about 0.5°C, rather like much of Australia. Yet despite this, temperatures in 2010 are barely above those in 1860. In Indonesia only the capital city Jakarta shows any strong warming but the average temperature for the country remains stable, although data quality and quantity before 1960 is poor. This is also true for Papua New Guinea where there is some evidence of warming after 1960 by about 0.5°C. In the South Pacific there is a contrast between east and west with the eastern half cooling significantly while the west cools slightly until 1970 before warming again by about 0.5°C. In fact of all the regions listed below, only the Indian Ocean shows significant warming of about 1°C.

Antarctica (Post 30)

Indian Ocean (Post 76)

Indonesia (Post 31)

New Zealand (Post 8)

Papua New Guinea (Post 32)

South Pacific Islands - East (Post 34)

South Pacific Islands - West (Post 33)

Southern Hemisphere

An average of the temperature anomalies from the 1000 longest records in the Southern Hemisphere shows a slight cooling of about 0.1°C until 1975 followed by a modest warming of only about 0.6°C.

Southern Hemisphere station average (Post 64)

45. Review of the year 2020

I started this blog in May, in part to occupy my time during the Covid-19 lockdown. But I was also motivated by a growing dissatisfaction with the quality of data analysis I was witnessing in climate science, and in particular the lack of any objectivity in the way much of the data was being presented and reported. My concerns were twofold. 

The first was the drip-drip of selective alarmism with an overt confirmation bias that kept appearing in the media with no comparable reporting of events that contradicted that narrative. The worry here is that extreme events that are just part of the natural variation of the climate were being portrayed as the new normal, while events of the opposite extreme were being ignored. It appeared that balance was being sacrificed for publicity.

The second was the over-reliance of much of the climate analysis on complex statistical analysis techniques of doubtful accuracy or veracity. To paraphrase Lord Rutherford: if you need to use complex statistics to see any trends in your data, then you would be better off using better data. Or to put it more simply, if you can't see a trend with simple regression analysis, then the odds are there is no trend to see.

The purpose of this blog has not been to repeat the methods of climate scientists, nor to improve on them. It has merely been to set a benchmark against which their claims can be measured and tested.

My first aim has been to go back to basics, to examine the original temperature data, look for trends in that data, and to apply some basic error analysis to determine how significant those trends really are. Then I have sought to compare what I see in the original data with what climate scientists claim is happening. In most cases I have found that the temperature trends in the real data are significantly less than those reported by climate scientists. In other words, much of the reported temperature rises, particularly in Southern Hemisphere data, result from the data manipulations performed by the climate scientists on the data. This implies that many of the reported temperature rises are an exaggeration.

In addition, I have tried to look at the physics and mathematics underpinning the data in order to test other possible hypotheses that could explain the observed temperature trends that I could detect. Below I have set out a summary of my conclusions so far.

1) The physics and mathematics

There are two alternative theories that I have considered as explanations of the temperature changes. The first is natural variation. The problem here is that in order to conclusively prove this to be the case you need temperature data that extends back in time for dozens of centuries, and we simply do not have that data. Climate scientists have tried to solve this by using proxy data from tree rings and sediments and other biological or geological sources, but in my opinion these are wholly inadequate as they are badly calibrated. The idea that you can measure the average annual temperature of an entire region to an accuracy of better than 0.1 °C simply by measuring the width of a few tree rings, when you have no idea of the degree of linearity of your proxy, or the influence of numerous external variables (e.g. rainfall, soil quality, disease, access to sunlight), is preposterous. But there is another way.

i) Fractals and self-similarity

If you can show that the fluctuations in temperature over different timescales follow a clear pattern, then you can extrapolate back in time. One such pattern is that resulting from fractal behaviour and self-similarity in the temperature record. By self-similarity I mean that every time you average the data you end up with a pattern of fluctuations that looks similar to the one you started with, but with amplitudes and periods that change according to a precise mathematical scaling function.

In Post 9 I applied this analysis to various sets of temperature data from New Zealand. I then repeated it for data from Australia and then again in Post 42 for data from De Bilt in the Netherlands. In virtually all these cases I found a consistent power law for the scaling parameter indicative of a fractal dimension of between 0.20 and 0.30, with most values clustered close to 0.25. The low magnitude of this scaling term suggests that the fluctuations in long term temperatures are much greater in amplitude than conventional statistical analysis would predict. 

For example, in the case of De Bilt it suggests that the standard deviation in the average 100-year temperature is more than 0.2 °C. This means that there is a 16% probability of the mean temperature for any century being more than 0.3°C more (or less) than the mean temperature for the previous century, and therefore a one in six possibility of a 0.6 °C temperature rise in any given century. So a 0.6 °C temperature rise over a century could occur once every 600 years purely because of natural variations in temperature. It also suggests that similar temperature variations that we have seen in temperature data in the last 50 or 100 years might have been repeated frequently in the not so distant past.

ii) Direct anthropogenic surface heating (DASH) and the urban heat island (UHI)

Another possible explanation for any observed rise in temperature is the heating of the environment that occurs due to human industrial activity. All energy use produces waste heat. Not only that, but all energy must end up as heat and entropy in the end. The Second Law of Thermodynamics tells us that. It is therefore inevitable that human activity must heat the local environment. The only question is by how much.

Most discussions in this area focus on what is known as the urban heat island (UHI). This is a phenomenon whereby urban areas either absorb extra solar radiation because of changes made to the surface albedo by urban development (e.g. concrete, tarmac, etc), or tall buildings trap the absorbed heat and reduce the circulation of warm air, thereby concentrating the heat. But there is another contribution that continually gets overlooked - direct anthropogenic surface heating (DASH). 

When humans generate and consume energy they liberate heat or thermal energy. This energy heats up the ground, and the air just above it, in much the same way that radiation from the Sun does. In so doing DASH adds to the heat that is re-emitted from the Earth's surface, and therefore increases the Earth's surface temperature at that location.

In Post 14 I showed that this heating can be significant - up to 1 °C in countries such as Belgium and the Netherlands with high levels of economic output and high population densities. In Post 29 I extended this idea to look at suburban energy usage and found a similar result. 

What this shows is that you don't need to invoke the Greenhouse Effect to find a plausible mechanism via which humans are heating the planet. Simple thermodynamics will suffice. Of course climate scientists dismiss this because they assume that this heat is dissipated uniformly across the Earth's surface - but it isn't. And just as significant is the fact that the majority of weather stations are in places where most people live, and therefore they also tend to be in regions where the direct anthropogenic surface heating (DASH) is most pronounced. So this direct heating effect is magnified in the temperature data.

iii) The data reliability

It is taken as read that the temperature data used to determine the magnitude of the observed global warming is accurate. But is it? Every measurement has an error. In the case of temperature data it appears that these errors are comparable in magnitude to many of the effects climate scientists are trying to measure.

In Post 43 I looked at pairs of stations in the Netherlands that were less than 1.6 km apart. One might expect that most such pairs would exhibit identical datasets for the two stations in the pair, but they don't. In virtually every case the fluctuations in the difference in their monthly average temperatures was about 0.2 °C. While this was consistent with the values one would expect based on error analysis, it does highlight the limits to the accuracy of this data. It also raises questions about how valid techniques such as breakpoint adjustment are, given that these techniques depend on detecting relatively small differences in temperature for data from neighbouring stations.

iv) Temperature correlations between stations

In Post 11 I looked at the product moment correlation coefficients (PMCC) between temperature data from different stations, and compared the correlation coefficients with the station separation. What became apparent was evidence for a strong negative linear relationship between the maximum correlation coefficient for temperature anomalies between pairs of station and their separation. For station separations of less than 500 km positive correlations of better than 0.9 were possible, but this dropped to a maximum correlation of about 0.7 for separations of 1000 km and 0.3 at 2000 km.

There were also clear differences between the behaviour of the raw anomaly data and the Berkeley Earth adjusted data. The Berkeley Earth adjustments appear to reduce the scatter in the correlations for the 12-month averaged data, but do so at the expense of the quality of the monthly data. This suggests that these adjustments may be making the data less reliable not more so. The improvement in the scatter of the Berkeley Earth 12-month averaged data is also curious. Is it because it is this data that is used to determine the adjustments and not the monthly data, or is this not the case and instead there is some other reason? And what of the scatter in the data? Can we use this to measure the quality and reliability of the original data? This clearly warrants further study.

Fig. 45.1: Correlations (PMCC) for the period 1971-2010 between temperature anomalies for all stations in New Zealand with a minimum overlap of 200 months. Three datasets were studied: a) the monthly anomalies; b) the 12-month average of the monthly anomalies; c) the 5-year average of the monthly anomalies. Also studied were the equivalent for the Berkeley Earth adjusted data.

2) The data

Over the last eight months I have analysed most of the temperature data in the Southern Hemisphere as well as all the data in Europe that predates 1850. The results are summarized below.

i) Antarctica

In Post 4 I showed that the temperature at the South Pole has been stable since the 1950s. There is no instrumental temperature data before 1956 and there are only two stations of note near the South Pole (Amundsen-Scott and Vostok). Both show stable or negative trends.

Then in Post 30 I looked at the temperature data from the periphery of the continent. This I divided into three geographical regions: the Atlantic coast, the Pacific coast and the Peninsula. The first two only have data from about 1950 onwards. In both cases the temperature data is also stable with no statistically significant trend either upwards or downwards. Only the Peninsula exhibited a strong and statistically significant upward trend of about 2 °C since 1945.

ii) New Zealand

Fig. 45.2: Average warming trend of for long and medium stations in New Zealand. The best fit to the data has a gradient of +0.27 ± 0.04 °C per century.

In Posts 6-9 I looked at the temperature data from New Zealand. Although the country only has about 27 long or medium length temperature records, with only ten having data before 1880, there is sufficient data before 1930 to suggest temperatures in this period were almost comparable to those of today. The difference is less than 0.3 °C.

iii) Australia

Fig. 45.3: The temperature trend for Australia since 1853. The best fit is applied to the interval 1871-2010 and has a gradient of 0.24 ± 0.04 °C per century.

The temperature trend for Australia (see Post 26) is very similar to that of New Zealand. Most states and territories exhibited high temperatures in the latter part of the 19th century that then declined before increasing in the latter quarter of the 20th century. The exceptions were Queensland (see Post 24) and Western Australia (see Post 22), but this was largely due to an absence of data before 1900. While there is much less temperature data for Australia before 1900 compared to the latter part of the 20th century, there is sufficient to indicate that, as in New Zealand, temperatures in the late 19th century were similar to those of the present day.

iv) Indonesia

Fig. 45.4: The temperature trend for Indonesia since 1840. The best fit is applied to the interval 1908-2002 and has a negative gradient of -0.03 ± 0.04 °C per century.

The temperature data for Indonesia is complicated by the lack of quality data before 1960 (see Post 31). The temperature trend after 1960 is the average of between 33 and 53 different datasets, but between 1910 and 1960 it generally comprises less than ten. Nevertheless, this is sufficient data to suggest that temperatures in the first half of the 20th century were greater than those in the latter half. This is despite the data from Jakarta Observatorium which exhibits an overall warming trend of nearly 3 °C from 1870 to 2010 (see Fig. 31.1 in Post 31).

It is also worth noting that the temperature data from Papua New Guinea (see Post 32) is similar to that for Indonesia for the period from 1940 onwards. Unfortunately Papua New Guinea only has one significant dataset that predates 1940, so conclusions regarding the temperature trend in this earlier time period are difficult to ascertain.

v) South Pacific

Most of the temperature data from the South Pacific comes from the various islands in the western half of the ocean. This data exhibits little if any warming, but does exhibit large fluctuations in temperature over the course of the 20th century (see Post 33). The eastern half of the South Pacific, on the other hand, exhibits a small but discernible negative temperature trend of between -0.1 and -0.2 °C per century (see Post 34).

vi) South America

Fig. 45.5: The temperature trend for South America since 1832. The best fit is applied to the interval 1900-1999 and has a gradient of +0.54 ± 0.05 °C per century.

In Post 35 I analysed over 300 of the longest temperature records from South America, including over 20 with more than 100 years of data. The overall trend suggests that temperatures fluctuated significantly before 1900 and have risen by about 0.5 °C since. The high temperatures seen before 1850 are exclusively due to the data from Rio de Janeiro and so may not be representative of the region as a whole.

vii) Southern Africa

Fig. 45.6: The temperature trend for South Africa since 1840. The best fit is applied to the interval 1857-1976 and has a gradient of +0.017 ± 0.056 °C per century.

In Posts 37-39 I looked at the temperature trends for South Africa, Botswana and Namibia. Botswana and Namibia were both found to have less than four usable sets of station data before 1960 and only about 10-12 afterwards. South Africa had much more data, but the general trends were the same. Before 1980 the temperature trends were stable or perhaps slightly negative, but after 1980 there was a sudden rise of between 0.5 °C and 2 °C in all three trends, with the largest being found in Botswana. This does not correlate with accepted theories on global warming (the rises in temperature are too large and too sudden, and do not correlate with rises in atmospheric carbon dioxide), and so the exact origin of these rises appears to be unexplained.

 

viii) Europe

Fig. 45.7: The temperature trend for Europe since 1700. The best fit is applied to the interval 1731-1980 and has a positive gradient of +0.10 ± 0.04 °C per century.

In Post 44 I used the 109 longest temperature records to determine the temperature trend in Europe since 1700. The resulting data suggests that temperatures were stable from 1700 to 1980 (they rose by less than 0.25 °C), and then rose suddenly by about 0.8 °C after 1986. The reason for this change is unclear, but one possibility is that it has occurred due to a significant improvement in air quality that reduced the amount of particulates in the atmosphere. These particulates, that may have been present in earlier years, could have induced a cooling that compensated for the underlying warming trend. Once removed, the temperature then rebounded. Even if this is true, it suggests a maximum warming of about 1 °C since 1700, much of which could be the result of direct anthropogenic surface heating (DASH) as discussed in Post 14. In countries such as Belgium and the Netherlands the temperature rise is even less than that expected from such surface heating. It is also much less than that expected from an enhanced Greenhouse Effect due to increasing carbon dioxide levels in the atmosphere (i.e. about 1.5 °C in the Northern Hemisphere since 1910). In fact the total temperature rise should exceed 2.5 °C. So here is the BIG question? Where has all that missing temperature rise gone?

32. Papua New Guinea - temperature trends 0.4°C WARMING (moderate)

I had thought about combining the temperature data for Papua New Guinea (PNG) with that of Indonesia, just as I did with East Timor (Timor Leste) in the previous post. Like East Timor, PNG shares an island (in this case Papua) with Indonesia, so from that point of view it would be logical. However, in the end I decided there was enough data in Indonesia, and extending the analysis to PNG would not only increase the data analysis complexity, but also the geographical area of coverage, and that would be too much. 

Like Indonesia, PNG has only one long station with a temperature record longer than 1200 month (Port Moresby AP - Berkeley Earth ID: 157418). It also has seven medium stations with records of more than 480 months of temperature data, and there are approximately 30 other shorter records that are too small to be useful. One of the medium stations (Port Moresby - Berkeley Earth ID: 19383) is excluded from the following analysis even though it contains data that suggests temperatures in the late 1800s were up to 1.0 °C higher than in the early 20th century. This is because: a) it is close to another long station (Port Moresby AP - Berkeley Earth ID: 157418) which has longer and more complete data in the 20th century; and b) because it has no data after 1941, and so its monthly reference temperatures (MRTs) cannot be calculated for the same time period (1961-1990) as the other stations. For an explanation of MRTs, and how they are used to calculate the monthly temperature anomaly, see Post 4.

Fig. 32.1: Temperature trend for all long and medium stations in Papua New Guineasince 1900 derived using the Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1912-1999 and has a gradient of +0.83 ± 0.03 °C/century.

Averaging the Berkeley Earth adjusted anomaly data from the eight long and medium stations yields the temperature trends shown in Fig. 32.1 above. These are very similar to the versions published by Berkeley Earth and shown below in Fig. 32.2, which suggests that the weightings for each station used by Berkeley Earth in their averaging process were fairly equal.

 

 Fig. 32.2: Temperature trend for Papua New Guinea since 1880 according to Berkeley Earth.

 

The high level of agreement between the data in Fig. 32.1 and Fig. 32.2 allows us to repeat the process for the raw anomaly data without the need for different station weighting coefficients. The result is shown below in Fig. 32.3. 

 

Fig. 32.3: The temperature trend for Papua New Guinea since 1900. The best fit is applied to the interval 1912-1999 and has a gradient of 0.44 ± 0.07 °C per century. The temperature changes are relative to the 1961-1990 average.

It can be seen that once again, the temperature trend derived from the raw anomaly data in Fig. 32.3 is significantly different in its degree of warming compared to that derived using the Berkeley Earth adjusted data in Fig. 32.1 and Fig. 32.2. While there are qualitative similarities (the peaks at 1910 and 2000, and the local minimum around 1965), the overall temperature rise seen in the raw data is much less. At worst, the temperature rise seen in the raw data in Fig. 32.3 is less than 0.4 °C, while the 5-year average in 2010 is barely higher than the peaks in the same curve before 1940.

The 5-year average in 2010 is also only 0.3 °C higher than the 80-year average for 1903-1982. This is hardly conclusive evidence of cataclysmic global warming. In fact the 5-year mean in 2010 is less than two standard deviations above the pre-1982 mean. It is, therefore, within the expected range for natural fluctuations for the given timescale of 110 years.

The data in Fig. 32.3 is also noticeably noisier before 1950 than it is after 1950. This is because there are only two temperature records with data before 1950, and only one of those, Port Moresby AP (Berkeley Earth ID: 157418), is reasonably continuous.

A final point of interest is the qualitative similarity between the data for PNG in Fig. 32.3 above, and that for Queensland shown in Fig. 24.4 previously. The biggest difference appears to be the overall temperature rise which is significantly higher in the case of Queensland (0.74 °C per century compared to 0.44 °C per century for PNG).

Fig. 32.4: The contribution of Berkeley Earth (BE) adjustments to the anomaly data after smoothing with a 12-month moving average. The linear best fit to the data is for the period 1904-2012 (red line) and the gradient is +0.34 ± 0.03 °C per century. The orange curve represents the contribution made to the BE adjustment curve by breakpoint adjustments only.

It is clear that the Berkeley Earth adjusted data for PNG results in almost double the temperature rise since 1900 compared to that found using the raw data. The actual difference is shown in Fig. 32.4 above and amounts to about 0.34 °C per century, most of which is due to breakpoint adjustments.

Conclusions

1) Papua New Guinea has experienced a modest temperature rise since 1960 (perhaps 0.5°C), but overall, temperatures have barely risen by more than 0.3 °C since 1900 (see Fig. 32.3).

2) The temperature trend for Papua New Guinea from 1900 to 2013 is broadly similar to that seen in neighbouring countries and regions (e.g. Indonesia, Australia and New Zealand).

3) The fluctuations in temperature for Papua New Guinea appear broadly consistent with natural variability. The magnitude of these temperature changes clearly challenge the current prevailing paradigm regarding anthropogenic global warming of more than 1.0 °C.

4) The adjustments made to the temperature data by Berkeley Earth have once again had a material and significant impact on the overall temperature trend. It is only with the inclusion of these adjustments that the temperature trend for Papua New Guinea resembles that of the IPCC HadCRUT4 temperature record.

5) The lack of data means that the temperature record of Papua New Guinea before 1950 is extremely uncertain. It can only be speculated upon based on similarities with neighbouring countries.

 

Addendum

The maximum number of temperature records used to derive the mean temperature trend in Fig. 32.3 is seven but before 1940 this reduces to two or less (see Fig. 32.5 below). See here for a complete list of all stations in Papua New Guinea.

 

Fig. 32.5: The number of station records included each month in the mean temperature anomaly (MTA) trend for Papua New Guinea in Fig. 32.3.

 

28. No AGW in Australia? A summary of trends.

 

Fig. 28: 10-year average temperature trends for Australia based on actual raw data (blue curve) and Berkeley Earth adjusted data (orange curve). The gradient of the best fit to the actual raw data (red line) is +0.18 ± 0.02 °C per century. The temperature change is relative to the 1961-1990 average.

 

 

 

My previous ten posts have examined the temperature records of Australia, state by state, and then also examined the combined result. The final results, based on my analytical methods, are summarized as follows.

1) The mean temperatures in Australia since 2000 are at most 0.2 °C higher, and probably less than 0.1 °C higher than those seen in the latter part of the 19th century (see Fig. 26.1).

2) The average temperature in Australia over the course of the entire 20th century was 0.063 °C lower than the equivalent value for the last 50 years of the 19th century.

3) The average temperature in Australia from 1950-1999 was only 0.1 °C higher than the average for the last 50 years of the 19th century.

4) The fluctuations in the temperature of Australia show a scaling behaviour with a fractal dimension of 0.26 (see Fig. 27.2). This suggests that most of the features in the smoothed data, or data averaged over long timescales, are just low frequency noise. Similar effects are seen in the data for most states, and also in the data for New Zealand (see Post 9).

5) The scaling behaviour of the anomalies implies that the 100-year average temperature for Australia would still have fluctuations with a standard deviation of more than 0.10 °C. This is more than the temperature difference observed between the values for the mean temperature of the latter half of the 19th century and that of the latter half of the 20th century. Thus, the temperature rise seen in the latter half of the 20th century is within the range that would be expected based on random chaotic fluctuations.

6) Only Western Australia and Queensland appear to have had noticeably higher temperatures after the year 2000 compared to the late 19th century. This is partly explained by the fact that both states have little or poor data before 1890.

7) The various adjustments made to the individual temperature records by climate groups like Berkeley Earth appear to have had a significant impact on the overall warming trend for Australia when compared with my more simplified (but in my view more justifiable) statistical methodology. This means that the statistical methods used to analyse the data, and their rationale, are of critical importance and need to be thoroughly tested, evaluated, and justified. The first step in doing this should always be to compare the results based on the adjustments with those obtained without the adjustments. That has always been the primary raison d'être of this blog.

8) The overall effect of adjustments made to the individual temperature records of Australia by Berkeley Earth, when compared to my results, has been to partially flatten the curve in Fig. 26.1 before 1900 and to increase the warming trend by up to 0.3 °C after 1900 (see Fig. 26.5). These adjustments are not neutral and completely change the shape of the curve.

9) The overall temperature trend for Australia looks more like a parabola or low frequency oscillation when the raw data is averaged according to my statistical procedure. The effect of the adjustments made to the data by Berkeley Earth is to make the temperature trend look more like a hockey stick (see Fig. 26.4).

Given the shape of the overall instrumental temperature record illustrated in Fig. 26.1, it is difficult to see how this could constitute unambiguous evidence for anthropogenic global warming (AGW). The best (or worst) that can be said about the data is that it is ambiguous. However, it also represents an alternative self-consistent narrative that raises profound questions about the current climate warming zeitgeist.

If my averaging methods for the anomaly data were simplistic to the point of being erroneous, the result would be a mean temperature trend in Fig. 26.1 that was totally uncorrelated with the majority of the individual records from which it was formed. Yet there is no evidence that this is the case. In fact the majority of long temperature records for Australia look very similar to the mean trend shown in Fig. 26.1.

But it is the scaling behaviour that is the killer application. If this phenomenon is real and ubiquitous, then it implies that (almost) everything that is seen in the temperature record is just chaotic noise. The only exception might be the urban heating I described here, and which is clearly important in those parts of the world that have high levels of industry and high population densities. But that is unlikely to be important in most of the Southern Hemisphere.