106. Bahamas and Key West - temperature trends STABLE to 1988

The islands of The Bahamas stretch over a distance of more than 800 km on the edge of the Atlantic Ocean southeast of Florida. Yet only four weather stations in the region have sufficient temperature data to be useful (for a list see here), and two of these are in Nassau (see map in Fig. 106.1 below). In addition, however, there are two stations in Key West (Key West and Key West airport) that are so far from the Florida coast as to be possibly more representative of the climate of The Bahamas than that of Florida (see Post 103). For this reason I will include them in this analysis.

Fig. 106.1: The (approximate) locations of the six longest weather station records in The Bahamas and Key West. 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.

In total there are two long stations with over 1200 months of data before 2014 and four medium stations with over 480 months in this analysis. The two long stations both had more or less continuous data that stretched from before 1900 to 2013. The four medium stations were more problematic. Three had virtually no data before 1950 while the station at Nassau had no data after. Added to that, the station at Freeport airport had no data before 1970.

The temperature anomalies for each station were determined using the usual method as outlined in Post 47. This involved first calculating the monthly reference temperatures (MRTs) for each station using a set reference period, in this case from 1961 to 1990, and then subtracting the MRTs from the raw temperature data to deliver the anomalies. If a station had at least twelve valid temperatures per month within the MRT interval then its anomalies were included in the calculation of the regional mean temperature anomaly (MTA). 

As no one single time interval for the monthly reference temperatures (MRTs) would allow all six stations to be included in the final average, the MRT interval was set to be 1961-1990. The one station to be excluded from the MTA calculation in this case was the Nassau station, but this exhibits virtually zero temperature change over its data range from 1900 to 1950.

Fig. 106.2: The mean temperature change for The Bahamas and Key West relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 1980 and has a slight positive gradient of +0.11 ± 0.12 °C per century.

The resulting MTA is shown in Fig. 106.2 above. It can be seen that before 1988 there is only a very slight upward temperature trend that is less than the uncertainty in the trend. Then in 1988 the temperature jumps suddenly by about 0.5°C. This is similar to the jump of about 1°C that was identified earlier in Post 44 for the MTA of Europe which also occurred in or around 1988. Is this coincidence, or did something happen to data collection methods in 1988?

The total number of stations included in the MTA in Fig. 106.2 each month is indicated in Fig. 106.3 below. The peak in the frequency around 1980 suggests that the 1961-1990 interval was indeed the most appropriate, but it also shows how much of the MTA trend in Fig. 106.2 relies on data from just two stations: Nassau airport and Key West airport.

Fig. 106.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for The Bahamas and Key West in Fig. 106.2.

Next I calculate the corresponding MTA result based on data that has been adjusted by Berkeley Earth (BE). The result is shown in Fig. 106.4 below.

Fig. 106.4: Temperature trends for The Bahamas and Key West based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1911-2010 and has a gradient of +0.79 ± 0.03°C/century.

 

Comparing the curves in Fig. 106.4 with the published Berkeley Earth (BE) version in Fig. 106.5 below indicates remarkably good agreement at least as far back as 1900 despite Fig. 103.4 also including data from Key West. This suggests that the simple averaging of anomalies I have used is effective and accurate, and adding the Key West stations was probably appropriate.

Fig. 106.5: The temperature trend for The Bahamas since 1750 according to Berkeley Earth.

The differences between the MTA in Fig. 106.2 and the BE versions using adjusted data in Fig. 106.4 and Fig. 106.5 are mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values seen in Fig. 106.2 and Fig. 106.4. The magnitudes of these adjustments are shown graphically in Fig. 106.6 below. The blue curve is the difference in MTA values between adjusted (Fig. 106.4) and unadjusted data (Fig. 106.2), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. Both are considerable and produce an additional warming since 1900 of about 0.4°C.

Fig. 106.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 106.4 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 1901-1980 has a positive gradient of +0.682 ± 0.018 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

 

Summary 

According to the raw unadjusted temperature data, over the ninety year period up to 1988 the climate of The Bahamas and Key West remained stable before experiencing a sudden jump in temperature of about 0.5°C (see Fig. 106.2).

Over the period 1901-2010 the adjusted temperature data from Berkeley Earth claims to show that the climate of The Bahamas and Key West has warmed by as much as 1.0°C (see Fig. 106.4).

Acronyms 

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

 

List of all stations

Nassau airport

Nassau

Abrahams Bay

Freeport airport

Key West

Key West airport

102. Georgia (US) - temperature trends COOLING

In a number of previous posts I have analysed the temperature data for all the US states along the Gulf of Mexico. None has experienced any global warming and the same is true for Georgia. In fact over the last 100 years the climate of Georgia has cooled by about 0.7°C as shown by the mean temperature anomaly (MTA) data for the state illustrated in Fig. 102.1 below.

Fig. 102.1: The mean temperature change for Georgia relative to the 1951-1980 monthly averages. The best fit is applied to the monthly mean data from 1911 to 2010 and has a negative gradient of -0.76 ± 0.16 °C per century.

The MTA in Fig. 102.1 was calculated by averaging the temperature anomalies from the 100 longest temperature records for the state. All these records had over 480 months of temperature data before the end of 2013 and 37 were long stations that each had more than 1200 months of data in total. For a full list of stations see here.

The anomalies for each station were determined using the usual method as outlined in Post 47. This involved first calculating the monthly reference temperatures (MRTs) for each station using a set reference period, in this case from 1951 to 1980, and then subtracting the MRTs from the raw temperature data to deliver the anomalies. If a station had at least twelve valid temperatures within the MRT interval then its anomalies were included in the MTA calculation. In total 98 stations were included with only two being excluded for lack of data between 1951 and 1980. These were Greensboro (Berkeley Earth ID: 28632) and Columbus (Berkeley Earth ID: 28632).

The total number of stations included in the MTA in Fig. 102.1 each month is indicated in Fig. 102.2 below. The broad peak from 1955 to 1990 suggests that the 1951-1980 interval was probably the most appropriate although a 1961-1990 interval could have been equally optimal.

Fig. 102.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Georgia in Fig. 102.1.

The locations of the one hundred stations is shown in the map in Fig. 102.3 below. This appears to show that the geographical spread is fairly uniform and in turn suggests that a simple average of all the anomalies should yield an accurate temperature trend for the state as a whole.

Fig. 102.3: The (approximate) locations of the 100 longest weather station records in Georgia. 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.

Next I compare the MTA based on raw unadjusted data with the MTA result based on data that has been adjusted by Berkeley Earth (BE). The result is shown in Fig. 102.4 below.

Fig. 102.4: Temperature trends for Georgia 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 1911-2010 and has a gradient of +0.35 ± 0.05°C/century.

Comparing the curves in Fig. 102.4 with the published Berkeley Earth (BE) version in Fig. 102.5 below indicates remarkably good agreement. This indicates that the 100 longest records are sufficient to determine the MTA and that simple averaging of anomalies is also highly effective and accurate.

Fig. 102.5: The temperature trend for Georgia since 1750 according to Berkeley Earth.

The differences between the MTA in Fig. 102.1 and the BE versions in Fig. 102.4 and Fig. 102.5 are mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values in Fig. 102.1 and Fig. 102.4. The magnitudes of these adjustments are shown graphically in Fig. 102.6 below. The blue curve is the difference in MTA between adjusted (Fig. 102.4) and unadjusted data (Fig. 102.1), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. Both are considerable with the former leading to an additional warming since 1935 of over 1°C.

Fig. 102.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 102.4 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 +1.018 ± 0.011 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Finally there is the question of the negative discontinuity in the temperature data in 1957 that was observed in Texas (Post 98), Louisiana (Post 98), Mississippi (Post 99) and Alabama (Post 101). It is again present in the data for Georgia and amounts to a temperature jump of 0.84°C. It can also be seen even more starkly in the BE adjusted data in Fig. 102.4. Correcting for this jump yields the MTA time series shown in Fig. 102.7 below.

Fig. 102.7: The mean temperature change for Georgia after breakpoint adjustment in 1957. The best fit is applied to the monthly mean data from 1911 to 2010 and has a positive gradient of +0.48 ± 0.16 °C per century.

The net result of making this correction is that the temperature trend since 1910 changes from a negative value of -0.76°C per century in Fig. 102.1 to a positive one of 0.48°C per century. Yet the origin of this discontinuity is still unclear. So the validity of this correction is therefore not known either.

Summary 

According to the raw unadjusted temperature data, over the past century the climate of Georgia has cooled by around 0.76°C (see Fig. 102.1).

Over the same period adjusted temperature data from Berkeley Earth claims to show that the climate of Georgia has warmed by over 1°C (see Fig. 102.4 and Fig. 102.5).

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.

101. Alabama - temperature trends COOLING

Like most of its neighbours Alabama has not experienced any global warming. In fact over the last ninety years the climate has cooled by about 0.7°C as shown by the mean temperature anomaly (MTA) data for the state illustrated in Fig. 101.1 below.

Fig. 101.1: The mean temperature change for Alabama relative to the 1951-1980 monthly averages. The best fit is applied to the monthly mean data from 1911 to 2010 and has a negative gradient of -0.72 ± 0.17 °C per century.

The MTA in Fig. 101.1 was calculated by averaging the temperature anomalies from the 100 longest temperature records for the state. All these records had over 480 months of temperature data before the end of 2013 and seventeen were long stations that each had more than 1200 months of data in total. For a full list of stations see here.

The anomalies for each station were determined using the usual method as outlined in Post 47. This involved first calculating the monthly reference temperatures (MRTs) for each station using a set reference period, in this case from 1951 to 1980, and then subtracting the MRTs from the raw temperature data to deliver the anomalies. If a station had at least twelve valid temperatures within the MRT interval then its anomalies were included in the MTA calculation. In total 92 stations were included with eight being excluded for lack of data between 1951 and 1980. The total number of stations included in the MTA in Fig. 101.1 each month is indicated in Fig. 101.2 below. The peak just after 1960 suggests that the 1951-1980 interval was indeed the most appropriate.

Fig. 101.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Alabama in Fig. 101.1.

The locations of the one hundred stations is shown in the map in Fig. 101.3 below. This appears to show that the geographical spread is fairly uniform and in turn suggests that a simple average of all the anomalies should yield an accurate temperature trend for the state as a whole.

Fig. 101.3: The (approximate) locations of the 100 longest weather station records in Alabama. 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.

Next I compare the MTA based on raw unadjusted data with the MTA result based on data that has been adjusted by Berkeley Earth (BE). The result is shown in Fig. 101.4 below.

Fig. 101.4: Temperature trends for Alabama 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 1911-2010 and has a positive gradient of +0.32 ± 0.05°C/century.

Comparing the curves in Fig. 101.4 with the published Berkeley Earth (BE) version in Fig. 101.5 below indicates remarkably good agreement. This indicates that the 100 longest records are sufficient to determine the MTA and that simple averaging of anomalies is also highly effective and accurate.

Fig. 101.5: The temperature trend for Alabama since 1750 according to Berkeley Earth.

The differences between the MTA in Fig. 101.1 and the BE versions in Fig. 101.4 and Fig. 101.5 are mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values in Fig. 101.1 and Fig. 101.4. The magnitudes of these adjustments are shown graphically in Fig. 101.6 below. The blue curve is the difference in MTA between adjusted (Fig. 101.4) and unadjusted data (Fig. 101.1), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. Both are considerable with the former leading to an additional warming since 1935 of over 1°C.

Fig. 101.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 101.4 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 +1.018 ± 0.011 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Finally there is the question of the negative discontinuity in the temperature data in 1957 that was observed in Texas (Post 98), Louisiana (Post 98) and Mississippi (Post 99). It is again present in the data for Alabama and amounts to a temperature jump of 0.92°C. It can also be seen in the BE adjusted data in Fig. 101.4. Correcting for this jump yields the MTA time series shown in Fig. 101.7 below.

Fig. 101.7: The mean temperature change for Alabama after breakpoint adjustment in 1957. The best fit is applied to the monthly mean data from 1921 to 2010 and has a positive gradient of +0.52 ± 0.19 °C per century.

The net result of making this correction is that the temperature trend since 1910 changes from a negative value of -0.72°C per century in Fig. 101.1 to a positive one of 0.52°C per century. Yet the origin of this discontinuity is still unclear. So the validity of this correction is therefore not known either.

Summary

According to the raw unadjusted temperature data, over the past century the climate of Alabama has cooled by around 0.6°C (see Fig. 101.1).

Over the same period adjusted temperature data from Berkeley Earth claims to show that the climate of Alabama has warmed by as much as 1°C (see Fig. 101.4 and Fig. 101.5).

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.

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.

73. Sri Lanka - temperature trends STABLE before 1975

The striking thing about the temperature data for Sri Lanka is the lack of medium length weather station records (i.e. those with over 480 months of data). It only has two. Yet the country has ten long weather station records with over 1200 months of data, which considering its area is fifty times less than that of India, means it has over twelve times as many long stations per square kilometre as does India. Even if we combine the number of long and medium stations, Sri Lanka still has six times as many per square kilometre as India. Unfortunately, with only twelve temperature records in total (for a full list see here), any average temperature trend derived from them could potentially be less accurate than that calculated for India in the previous post, despite the higher station density. This is because, as I pointed out in a previous post, in order for regression towards the mean to be able to eradicate random data errors that are present in all temperature series, it appears that there needs to be at least twenty datasets in the average. That said, the temperature trend for Sri Lanka is still informative, not least because of its similarities to those of India and Pakistan.

Fig. 73.1: The (approximate) locations of the long and medium temperature records in Sri Lanka. Those stations with a high warming trend between 1876 and 1975 are marked in red while those with cooling or stable trends are marked in blue. Those denoted with squares are long stations with over 1200 months of data.

The locations of the long and medium stations in Sri Lanka are shown in Fig. 73.1 above. Their geographical spread is fairly even, although most are on the coast. Nevertheless, this suggests any simple average of their temperature anomalies should yield a fairly accurate approximation to the true mean temperature anomaly for the region. It can also be seen that the vast majority of stations have stable or cooling trends for the 100 years up to 1975.

Just as for previous regional analyses, the monthly temperature anomalies for each station were calculated by subtracting the monthly reference temperature (MRT) for the relevant month from the unadjusted mean temperature for that month. The MRTs are different and specific to each station, but were always calculated using data from the same time period (in this case 1951-1980) for each station using the method described previously in Post 47. This ensures that the baseline reference temperature for each station is consistent so that all temperature changes over time are measured relative to the same point in time.

 

Fig. 73.2: The temperature trend for Sri Lanka based on an average of anomalies from all long and medium stations. The best fit is applied to the monthly mean data from 1876 to 1975 and has a positive gradient of +0.11 ± 0.03 °C per century. The monthly temperature changes are defined relative to the 1951-1980 monthly averages.

 

If we calculate the monthly temperature anomalies for the twelve stations in Fig. 73.1 and then average them, the resulting times series exhibits a trend that is slightly warming, as shown in Fig. 72.2 above. However, this warming occurs in two distinct phases. Before 1975 the warming is negligible with temperatures rising barely more than 0.1°C in over a century. This is much less than the natural variation in the 5-year average temperature. Then, after 1975, the temperature appears to jump abruptly by about 0.4°C. Similar jumps have been seen in the trends for South Africa, Botswana, Europe, India and Pakistan. 

I have yet to determine what is causing these jumps. Are they natural or are they man-made? Are they the result of climate changes, economic changes, or changes to data collection methods? In the case of Sri Lanka, one possible cause is the civil war that raged from 1983 to 2009. This may be why there is a large dip between 1980 and 2010 in the station frequency plot in Fig. 73.3 below. And as so many of the station records are consequently discontinuous between 1983 to 2009, this may have led to bad data being generated after 1983 from station moves and equipment changes. That, though, is pure speculation.

 

Fig. 73.3: The number of station records included each month in the mean temperature trend for Sri Lanka in Fig. 73.2.

 

Of course the general temperature stability seen in Fig. 73.2 before 1975, and the temperature jump that occurs after 1975, are not what is claimed by climate scientists. Below in Fig. 73.4 is the trend for Sri Lanka according to Berkeley Earth which shows a more or less continuous warming trend since 1830 of about 1.5°C in total. Clearly this is at odds with the trend based on the raw data in Fig. 73.2, and the reasons for the differences are the same as they were for Pakistan. They are mainly due to the adjustments made to the original raw data by Berkeley Earth through the use of breakpoints and homogenization.

 

Fig. 73.4: The temperature trend for Sri Lanka since 1800 according to Berkeley Earth.

 

The temperature anomalies used to calculate the trend in Fig. 73.2 were determined using the raw data from each station, using the method that I have used throughout this blog. However, if we average the anomaly data for the same twelve long and medium stations using not the original raw data, but the adjusted data that Berkeley Earth create from the raw data (both adjusted and unadjusted data sets are listed in the data files on their website), the result is the temperature time series shown below in Fig. 73.5. These trends are very similar to the official Berkeley Earth trends shown in Fig. 73.4 above. This once again suggests that gridding and homogenization may be unnecessary steps in creating regional trends. Simple averages of all station times series appear to work just as well in most cases, particularly if the stations are fairly evenly spread.

 

Fig. 73.5: Temperature trends for Sri Lanka based on the average of anomalies for all long and medium stations using Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1876-1975 and has a gradient of +0.28 ± 0.02°C/century.

 

The difference between the Berkeley Earth temperature trend in Fig. 73.5 that is based on adjusted data, and the trend derived solely from raw data can be calculated by subtraction. The result is shown is shown in Fig. 73.6 below (blue curve). Also shown are the breakpoint adjustments (orange curve) that are added to the data by Berkeley Earth. It can be seen that most of the difference between the data in Fig. 73.5 and that in Fig. 73.2 is due to the breakpoint adjustments in this case. In fact they add over 0.5°C of warming between 1890 and 2010. This explains why the official Berkeley Earth trends in Fig. 73.4 are so different from the trends that result from the actual raw data in Fig. 73.2.

 

Fig. 73.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 73.5 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 1876-1975 has a positive gradient of +0.171 ± 0.007 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

 

Finally, a word of caution. It is also clear that the data in Fig. 73.2 before 1875 is much less reliable as an indicator of the true mean temperature in the region due to insufficicient stations in the average. If we ignore that data, then the trend for Sri Lanka will be as shown in Fig. 73.7 below.

 

Fig. 73.7: A rescaled plot of the data in Fig. 73.2. The best fit is applied to the monthly mean data from 1876 to 1975 and has a positive gradient of +0.11 ± 0.03 °C per century. The monthly temperature changes are defined relative to the 1951-1980 monthly averages.

 

 

Summary

It is clear from the raw data that there was no climate change in Sri Lanka before 1975. Temperatures were actually stable for over 100 years prior to 1975 (see Fig. 73.7), just as in India.

Since 1975 there has been a modest temperature rise of about 0.4°C (see Fig. 73.7), but this is a long way short of the almost 1.5°C claimed by Berkeley Earth (see Fig. 73.4) or the similar value currently being touted by climate science for the mean temperature change for the entire Northern Hemisphere.

The temperature rise of 0.4°C since 1975 could be due to increased greenhouse gas emissions. Carbon dioxide levels in the atmosphere have increased from 330 ppm in 1975 to nearly 420 ppm now. But there could be other reasons, such as the impact of the civil war.