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)

99. Mississippi - temperature trends COOLING

Mississippi has even better temperature data than Louisiana which I examined in Post 97. It has 46 long stations with over 1200 months of data and another 60 medium stations with over 480 months of data. Of these, 98 stations in total have over 600 months of data. The graph in Fig. 99.1 below shows the result of averaging the monthly temperature anomalies for the 100 longest station records in Mississippi. It shows that there was no warming in the 120 years before 2010. In fact the state's climate cooled by 0.5°C.

Fig. 99.1: The mean temperature change for Mississippi relative to the 1951-1980 monthly averages. The best fit is applied to the monthly mean data from 1891 to 2010 and has a negative gradient of -0.42 ± 0.13 °C per century.

The scatter plot graph in Fig. 99.1 of mean temperature anomalies (MTA) is the result of averaging the temperature anomalies from 100 separate temperature records. These anomalies were calculated by subtracting the raw monthly mean temperature of each station from its monthly reference temperature (MRT) using the method outlined in Post 47. In this case the MRTs for each station were determined for the period 1951-1980 as this corresponded to the thirty year period with the most station data as shown in Fig. 99.2 below.

Fig. 99.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Mississippi in Fig. 99.1.

The data in Fig. 99.2 indicates that after 1900 the MTA in Fig. 99.1 is the result of averaging over more than forty separate station records, and peaks at over ninety. However, before 1890 this number drops to less than fifteen, which as I have noted before in Post 57 seems to be the minimum needed in order to obtain reliable values for the MTA.

 

Fig. 99.3: The (approximate) locations of the 100 longest weather station records in Mississippi. Those stations with a high warming trend between 1911 and 2010 are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote stations with more than 480 months of data, although all but two have over 600 months of data.

The locations of the stations with the 100 longest temperature records are shown on the map in Fig. 99.3 above. This shows them to be evenly distributed across the state. This means that advanced numerical techniques of homogenization, gridding and Kriging used by Berkeley Earth (BE) and others are probably unnecessary for determining an accurate MTA.

Fig. 99.4: Temperature trends for Mississippi based on Berkeley Earth adjusted data from the 100 longest station data records. The best fit linear trend line (in red) is for the period 1891-2010 and has a gradient of +0.54 ± 0.04°C/century.

This is confirmed by a comparison of the data in Fig. 99.4 above and Fig. 99.5 below. The MTA in Fig. 99.4 above was determined by averaging the Berkeley Earth (BE) adjusted anomalies. These values are found in the data files for each station on the Berkeley Earth website along with the raw data that I use. This allows me to do two things: (i) compare the MTA based on raw data (Fig. 99.1) with the same based on BE adjusted data (Fig. 99.4); (ii) assess the accuracy of the averaging process by comparing the average without homogenization, gridding and Kriging (Fig. 99.4) with the BE average that uses all three (Fig. 99.5).

A comparison of Fig. 99.1 and Fig. 99.4 indicates that the BE adjustments have turned a strong negative (cooling) temperature trend into a significant warming trend. Yet this cannot be due to any fault in the averaging process that I use because that yields virtually identical results for the BE adjusted MTA in Fig. 99.4 as the official published version in Fig. 99.5. So it is the BE temperature adjustments, and only those adjustments that are making the difference.

Fig. 99.5: The temperature trend for Mississippi since 1750 according to Berkeley Earth.

Finally it is possible to determine the magnitude of the BE adjustments by subtracting the unadjusted data in Fig. 99.1 from the adjusted data in Fig. 99.4. The result is shown in Fig. 99.6 below and indicates that the BE adjustments add over 1.1°C of warming from 1890 onwards, and possibly as much as 1.4°C. This is a huge amount of warming to add to a trend that is otherwise generally cooling.

Fig. 99.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 99.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1891-2010 has a positive gradient of +0.949 ± 0.008 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Finally, we need to consider the discontinuity in the MTA data in 1957. This was discussed for Louisiana and Texas in Post 98 and it occurs again for Mississippi as shown by the arrow and red line in Fig. 99.7 below.

The size of this discontinuity can be estimated by averaging the anomalies for a thirty year period either side of it. If the periods chosen are 1921-1950 and 1961-1990, then the difference in the mean anomalies for those periods is 0.81°C. This is the magnitude of the discontinuity (red curve) in Fig. 99.7.

Fig. 99.7: The mean temperature change for Mississippi relative to the 1951-1980 monthly averages. The arrow and red line indicate the position and size of the data discontinuity.

If we then correct for this discontinuity by adjusting upwards all the anomalies after 1957 by 0.81°C we get the MTA time series shown in Fig. 99.8 below. The best fit to the data after 1891 now has an upward slope indicating a warming of about 0.7°C in total. This is now almost identical to the trend in the BE adjusted data in Fig. 99.4, but is this correct? After all, the BE adjusted data in Fig. 99.4 shows the same discontinuity in 1957 as the unadjusted data in Fig. 99.1. In other words, the BE adjustments have failed to erase the most striking irregularity in the data.

Fig. 99.8: The mean temperature change for Mississippi after breakpoint adjustment in 1957. The best fit is applied to the monthly mean data from 1891 to 2010 and has a positive gradient of +0.58 ± 0.13 °C per century.

A further complication can be found in the data in 1919. Here there is also what appears to be an abrupt discontinuity, but this one adds warming of approximately 0.43°C. It therefore partially cancels the effect of the discontinuity in 1957. The result is that the warming trend is dramatically reduced to a mere 0.19°C per century, or about 0.23 °C in total from 1891 to 2010.

Fig. 99.9: The mean temperature change for Mississippi after breakpoint adjustments to data before 1919 of 0.43°C and after 1957 of 0.81°C. The best fit is applied to the monthly mean data from 1891 to 2010 and has a positive gradient of +0.19 ± 0.12 °C per century.

What this highlights is the difficulty in using breakpoint adjustments. In the two examples shown above the data discontinuities in 1919 and 1957 can be seen in many of the original data sets of the 100 individual station records used to determine the MTA. So these different stations act as corroboration for each other, and this also explains why the discontinuities are so pronounced. But that does not explain the cause of the discontinuities, which could be natural and so should just be accepted, or could be due to a systematic and simultaneous change in the data gathering methods which then needs to be adjusted for.

The Berkeley Earth breakpoint algorithm, on the other hand, instead identifies numerous potential breakpoints in most station records in addition to the ones I have outlined. These additional breakpoints are often unique to each station record and so have no way of being corroborated. The other issue here is whether the Berkeley Earth identification algorithm actually works. But even if it does, regression towards the mean would render many of these adjustments redundant as I demonstrated in Post 57 and Post 67. Only the coincident ones would then remain after averaging such as those at 1919 and 1957.

Conclusions

Analysis of the raw temperature data suggests that the climate of Mississippi has cooled by almost 0.5°C since 1890 (see Fig. 99.1).

This is totally at odds with the result presented by Berkeley Earth (Fig. 99.4 and Fig. 99.5) which claims that the climate warmed by over 0.6°C over the same period. This is still less than the mean temperature rise for land-based stations (see Fig. 98.1 in Post 98).

Two major discontinuities in the mean temperature anomaly (MTA) data remain unexplained. Compensating for either or both would change the temperature trend of the MTA from strong cooling in Fig. 99.1 to either moderate (Fig. 99.8) or slight (Fig. 99.9) warming.

Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Link to list of all stations and their raw data files.

 

98. What happened to Louisiana temperatures in 1957?

Fig. 98.1: Global average land temperatures since 1850 according to Berkeley Earth.

In my previous post looking at the temperature trend for Louisiana (Post 97) I showed that the mean temperature in the region had declined by almost 0.2°C in the last century or so. This is in sharp contrast to the claim from most climate scientists that average temperatures have increased by almost 1.2°C in that time, and that this increase is even greater on land. In fact Berkeley Earth claims the increase in land temperatures since 1850 to be in excess of 2°C (see Fig. 98.1 above). But while analysing the Louisiana data one feature stood out that makes me query both the results of my last post and the analysis processes of Berkeley Earth (BE). 

In 1957 the temperature appears to drop suddenly and permanently by about 0.615°C (see black arrow on Fig. 98.2 below). What makes this feature significant is that similar temperature falls at identical times can be seen in the most of the individual temperature records for Louisiana. But they can also be seen in the temperature trends of neighbouring states like Texas. 

So is this temperature drop due to a sudden and large, natural change in the local climate? Or is it due to a change in the data measurement and analysis? If it is the latter then it needs to be corrected for and that will change drastically the true temperature trend. If it is the former then it raises serious questions about how the climate changes over time. In this post I will look at this feature in more detail and try to answer those questions.

 

Fig. 98.2: The mean temperature change for Louisiana relative to the 1951-1980 monthly averages. The best fit (white line) is applied to the monthly mean data from 1911 to 2010 and has a negative gradient of -0.38 ± 0.15 °C per century. The arrow and red line indicate the position and size of the data discontinuity.

The data in Fig. 98.2 above is the part of the same data that was presented previously in Fig. 97.1 of Post 97. In this case I am concentrating only on data after 1910 which, as I pointed out in Post 97, is the most reliable as it all results from an averaging of over forty distinct temperature records (see Fig. 97.2). The white line in Fig. 98.2 is the best fit to the data from 1911 to 2010 and has a strong negative gradient of -0.38°C per century. This is somewhat more negative than the trend in Fig. 97.1 because the fitting range is different. This shows how the value of the best fit gradient can be strongly influenced by the data range, particularly when the data exhibits large fluctuations.

The point of interest in the data above is in 1957 (as indicated by the large black arrow) where the mean temperature appears to drop suddenly and permanently by about 0.615°C. This can be seen clearly in the yellow line which is the 5-year moving average of the monthly anomaly data. It is also illustrated by the red line which is effectively two separate lines: the average temperature for 1921-1960 and the average for 1961-1990. In both cases the discontinuity is clear. The magnitude of the vertical discontinuity can be estimated from the discontinuity in the red line and is 0.615°C. 

Fig. 98.3: The mean temperature change for Louisiana after breakpoint adjustment. The best fit is applied to the monthly mean data from 1911 to 2010 and has a positive gradient of +0.54 ± 0.15 °C per century.

The next step is to remove the discontinuity by shifting upwards all the data after the start of 1958 in Fig. 98.2 by the size of the discontinuity, 0.615°C. The result is shown in Fig. 98.3 above. Two things are striking about the result. First, the gradient of the best fit is now strongly positive (+0.54°C per century) suggesting that the climate is warming. And secondly, the data just looks better with a more consistent trend. Of course just because data looks nicer does not prove that it is more reliable or more accurate.

 

Fig. 98.4: The total contribution of Berkeley Earth (BE) adjustments to the Louisiana temperature data. The orange curve shows the contribution just from breakpoint adjustments. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to the total BE adjustments for the period 1911-2010 has a positive gradient of +0.731 ± 0.004 °C per century.

The process I have employed here is virtually identical in concept to the breakpoint adjustments used by Berkeley Earth (BE). The main difference is that I have only applied one adjustment to the final mean temperature data whereas Berkeley Earth apply multiple adjustments of differing magnitudes and times to almost every station dataset. The sum total of those BE adjustments for the Louisiana data is shown in Fig. 98.4 above and the result is a huge warming trend of +0.73°C per century. This is warming that is added to the original data as I showed in Post 97. Yet the 0.6°C discontinuity in the middle of 1957 still remains in the adjusted BE data even after their adjustments have been made as the arrow in Fig. 98.5 below indicates. So the BE adjustments have not corrected the most glaring issue with the original data, which does rather raise a lot of questions regarding the accuracy and validity of the BE adjustments that are made.

Fig. 98.5: Temperature trends for Louisiana based on Berkeley Earth adjusted data from the 90 longest station data records. The best fit linear trend line (in red) is for the period 1911-2010 and has a gradient of +0.37 ± 0.05°C/century.

This is not the first time I have encountered these sudden jumps in temperature data. A similar upward jump in temperature of over 1°C can be seen in the temperature trend for Europe in 1988 (see Fig. 44.1 in Post 44). So what is the cause? At the moment I can only think of two explanations: a natural phenomenon that suddenly changes the local climate, or a sudden change in measurement equipment or methodology that is applied across all stations in a region simultaneously. But so far I can find no evidence for either. Of course the natural phenomenon may not have occurred in 1957 or at any other recent time before that. The complex dynamics of the Earth's climate could mean we are seeing the ripples now of forcing events many centuries ago. In Post 9 and Post 17 I have investigated chaotic effects in the temperature record and found evidence of fractal behaviour that can persist for centuries.

Fig. 98.6: The mean temperature change for Texas relative to the 1961-1990 monthly averages. The best fit (white line) is applied to the monthly mean data from 1911 to 2010 and has a negative gradient of -0.15 ± 0.15 °C per century. The arrow and red line indicate the position and size of the data discontinuity.

What is clear is that this temperature discontinuity is not restricted to Louisiana. The same data anomaly can be seen in the temperature trend for Texas that I analysed in Post 52. This is shown in Fig. 98.6 above with the breakpoint adjusted temperatures shown in Fig. 98.7 below.

 

 Fig. 98.7: The mean temperature change for Texas after breakpoint adjustment. The best fit is applied to the monthly mean data from 1911 to 2010 and has a positive gradient of +0.56 ± 0.15 °C per century.

After the breakpoint adjustment the temperature trend for Texas is now positive and virtually identical to that of Louisiana in Fig. 98.3. There also appears to be a strong correlation between the 5-year moving average (yellow curves) of each. This suggests that the region could have warmed by about 0.5°C over the last one hundred years. However, as I pointed out in Post 52, direct anthropogenic surface heating (DASH) or waste heat equating to about 0.7 W/m² is probably currently warming Texas by up to 0.3 °C compared to 1850. That only leaves about 0.2°C for carbon dioxide induced climate change. This in line with the temperature rise I estimated in Post 87 and a long way short of the 2°C claimed by Berkeley Earth and others. So even with this adjustment there is little evidence to support severe carbon dioxide induced climate change in Louisiana or Texas.

97. Louisiana - temperature trends COOLING

In Post 52 I calculated the mean temperature anomalies for Texas and showed that there had been no warming in the state since 1840. In this post I will repeat the analysis for the neighbouring state of Louisiana. The result (SPOILER ALERT) is that once again there is no evidence of the climate warming. In fact over the last 120 years it appears to have cooled by almost 0.2°C.

Louisiana has less temperature data than Texas but considerably more than most states in Europe, Africa or South America. It has 26 long stations with over 1200 months of data (before 2014) and another 62 medium stations with over 480 months of data. In Fig. 97.1 below I have plotted the mean temperature anomaly (MTA) over time for the state by combining the monthly anomalies from the ninety longest temperature records in Louisiana. As can be seen, the trend over time is clearly negative suggesting the local climate is cooling not warming.

Fig. 97.1: The mean temperature change for Louisiana relative to the 1951-1980 monthly averages. The best fit is applied to the monthly mean data from 1896 to 2010 and has a negative gradient of -0.17 ± 0.13 °C per century.

The procedure for calculating the monthly anomalies for each station was the same as that used in all my previous regional temperature trend analyses. The anomalies for each station were determined by first calculating the twelve monthly reference temperatures (MRT) for each station. The method for calculating the MRTs, and then the anomalies for each station dataset has been described previously in Post 47. In this case the time interval used to determine the MRTs was 1951-1980 as almost all the stations had at least 40% data coverage in this interval. The MRTs for each station were then subtracted from the station's raw temperature data to produce the anomalies for that station. These were then averaged to obtain the MTA for each month shown in Fig. 97.1 above.

Fig. 97.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Louisiana in Fig. 97.1.

It is important to note, however, that not all data points in Fig. 97.1 are equally reliable. This is because the accuracy of the MTA for any given month depends in large part on the number of stations included in the average for that month. As Fig. 97.2 above shows, most months after 1910 have over forty different sets of station data available to be included in the MTA, but before 1890 that number is less than ten. This means that the trend in Fig. 97.1 is likely to be very unreliable before 1895.

Fig. 97.3: The (approximate) locations of the weather stations in Louisiana. Those stations with a high warming trend between 1911 and 2010 are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

Then there is the issue of the geographical distribution of the stations. Ideally this should be as even as possible, with all stations being equally separated. This is never the case as Fig. 97.3 above illustrates for Louisiana. But previous analyses on this blog have shown that any inhomogeneity in the station density is usually of minor importance. As a result the simple averaging process of station anomalies generally gives the correct answer (or as close as we can be reasonably sure of) as I will demonstrate below.

In contrast, climate groups like Berkeley Earth use gridding and homogenization to construct idealized networks of stations. Not only is this unnecessary in my opinion, but it can also introduce additional biases via temperature adjustments. It is also worth noting that while climate scientists try to initiate regular square grids of nodes or points in order to perform their numerical models, in real physics and engineering finite element modellers favour highly irregular lattices in order to improve the accuracy of their simulations in fields such as thermodynamic heat flow and microelectronic device operation. So regular grids are not only unnecessary, but can be less accurate, particularly in regions of the model with large gradients.

Fig. 97.4: Temperature trends for Louisiana based on Berkeley Earth adjusted data from the 90 longest station data records. The best fit linear trend line (in red) is for the period 1896-2010 and has a gradient of +0.49 ± 0.04°C/century.

To prove my point I invite you to compare the two graphs in Fig. 97.4 above and Fig. 97.5 below. Both use the same adjusted data from Berkeley Earth, but the monthly average in Fig. 97.4 is a plot I derived by simply averaging the adjusted anomalies from Berkeley Earth (BE) of the ninety longest stations, while Fig. 97.5 below is the result Berkeley Earth obtained, probably by weighting each station in the average based on local station density. Yet it is clear that the results from 1910 onwards are virtually identical, thus indicating that the weighting process (and by extension the non-uniform station density) have minimal impact on the final result.

Fig. 97.5: The temperature trend for Louisiana since 1750 according to Berkeley Earth.

It is also clear that the BE adjusted anomaly data gives completely different results for the MTA in Fig. 97.4 (and Fig. 97.5) compared to the MTA based on unadjusted raw anomaly data in Fig. 97.1. The main reason for this difference is the data adjustments made by Berkeley Earth. Subtracting the data in Fig. 97.1 from that in Fig. 97.4 yields the total adjustments in Fig. 97.6 below (blue curve). Also shown is the contribution from breakpoint adjustments alone (in orange). These breakpoint adjustments can be determined from data in Berkeley Earth's own data files. It is clear from Fig. 97.6 that the BE adjustments are huge and add at least 0.7°C of warming to the BE trend.

Fig. 97.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 97.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1911-2010 has a positive gradient of +0.731 ± 0.004 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Summary and conclusions

Once again an analysis of raw temperature data for a state or region yields results that are less alarming from a warming perspective than the official narrative.

In this case the raw unadjusted data for Louisiana shows a significant cooling of up to 0.2°C (see Fig. 97.1).

The Berkeley Earth adjusted data (Fig. 97.4), on the other hand, shows a warming since 1896 of between 0.6°C (red trend line) and 1.2°C (orange 10-year moving average). The difference is largely down to the adjustments made to the original data by Berkeley Earth (see Fig. 97.6).

There is, however, one intriguing complication: the data discontinuity or sudden temperature decline in 1957 in Fig. 97.1. I will examine this more closely in my next post, not least because it occurs not just in the Louisiana data, but in data for most of Louisiana's neighbours as well. It is also in the BE data in Fig. 97.4. Its importance though is in its origin. If it is a systematic measurement error, then correcting for it changes everything.

Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Link to list of all stations.