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.
No comments:
Post a Comment