105. US southern states - summary of BE temperature adjustments

In my previous post I summarized the temperature trends since 1900 of the six US states closest to the Gulf of Mexico (Texas, Louisiana, Mississippi, Alabama, Georgia and Florida). All the trends were constructed using data from the longest available temperature records in the state, all involved averaging the temperature anomalies from over 90 different station records, and none exhibited a significant positive warming trend.

Yet in every case the official Berkeley Earth (BE) trend does exhibit warming, often lots of it. The difference of course is largely down to the adjustments that Berkeley Earth make to the data via homogenization, Kriging, gridding and of course breakpoint alignment. In the post for each state (the links are here: Texas, Louisiana, Mississippi, Alabama, Georgia and Florida) I have quantified the magnitude of these adjustments, but I thought it would also be instructive to summarize them in one post just so that their full impact can be seen and compared.

The adjustments shown in the graphs below are of two types. The orange curve is the mean adjustment each month solely from breakpoint adjustments while the blue curve is the mean adjustment relative to unadjusted data from all sources of correction. This will also include homogenization, Kriging and gridding in addition to breakpoints, but it will also be affected by any difference in the chosen period for calculating the monthly reference temperatures (MRTs). The last of these will, however, only change the offset of the blue curve in the vertical direction relative to the orange one, not its slope or total change over time.

The graphs below indicate that the BE adjustments to the temperature data add between 0.5°C and 1.2°C to the final BE temperature trends. Given that we are constantly being told by climate scientists that the total global warming experienced so far is about 1.2°C, I would suggest that this is a bit of a problem.

Fig. 105.1: The Berkeley Earth (BE) temperature adjustments for Texas since 1900. The linear best fit (red line) to these adjustments for the period 1911-2010 has a positive gradient of +0.568 ± 0.003 °C per century.

Fig. 105.2: The Berkeley Earth (BE) temperature adjustments for Louisiana since 1900. 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.

Fig. 105.3: The Berkeley Earth (BE) temperature adjustments for Mississippi since 1900. The linear best fit (red line) to these adjustments for the period 1931-2010 has a positive gradient of +1.300 ± 0.007 °C per century.

Fig. 105.4: The Berkeley Earth (BE) temperature adjustments for Alabama since 1900. The linear best fit (red line) to these adjustments for the period 1931-2010 has a positive gradient of +1.231 ± 0.012 °C per century.

Fig. 105.5: The Berkeley Earth (BE) temperature adjustments for Georgia since 1900. The linear best fit (red line) to these adjustments for the period 1911-2010 has a positive gradient of +1.087 ± 0.006 °C per century.

Fig. 105.6: The Berkeley Earth (BE) temperature adjustments for Florida since 1900. The linear best fit (red line) to these adjustments for the period 1941-2010 has a positive gradient of +0.611 ± 0.010 °C per century.

104. US southern states - summary of temperature trends

Over the last month I have examined the temperature trends of five different US states (Louisiana, Mississippi, Alabama, Georgia and Florida) that surround, or are within 100km of (in the case of Georgia), the Gulf of Mexico. These all appear to have similar trends to that of Texas that I examined in Post 52. All have negative or stable temperature trends over the last 100 years. For comparison their temperature trends are republished here with identical data ranges (from 1900) and fitting ranges (1911-2010). What is clear is that none of these trends is remotely similar to either the Berkeley Earth (BE) versions for each state based on adjusted data, or the global trends published by NOAA, NASA-GISS, BE, HadCRU etc.

Fig. 104.1: The mean temperature change for Texas. The best fit has a slight negative gradient of -0.15 ± 0.15 °C per century.

Fig. 104.2: The mean temperature change for Louisiana. The best fit has a negative gradient of -0.38 ± 0.15 °C per century.

Fig. 104.3: The mean temperature change for Mississippi. The best fit has a negative gradient of -0.76 ± 0.17 °C per century.

Fig. 104.4: The mean temperature change for Alabama. The best fit has a negative gradient of -0.72 ± 0.17 °C per century.

Fig. 104.5: The mean temperature change for Georgia. The best fit has a negative gradient of -0.76 ± 0.16 °C per century.

Fig. 104.6: The mean temperature change for Texas. The best fit has a slight positive gradient of +0.08 ± 0.13 °C per century.

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.