64. Southern Hemisphere - temperature trends COOLING to 1970

Over the past year I have analysed most of the temperature data from the Southern Hemisphere as well as some data from Europe and the USA. Few if any of the resulting temperature trends that I have calculated have agreed with the global published trends of the IPCC, Hadley-CRU, NOAA, NASA-GISS, or the regional trends of Berkeley Earth. This may be because they are based on calculations for small regions rather than global averages, although this caveat does not explain the discrepancies seen when compared with the Berkeley Earth data. 

In this post I will make a first attempt at analysing the data for the entire Southern Hemisphere. I will do this by simply averaging the anomalies for the 1079 longest station records in the Southern Hemisphere, but without employing any regional weighting to the data. This will produce a first estimate of the temperature trend. A more accurate analysis will be done in a future post, where trends for the various regions will be combined using area weightings similar to those I used in Post 26 to calculate the overall trend for Australia, based on the trends from its individual states. Such an approach is, however, fraught with difficulty as the area of many regions (such as island archipelagos) are difficult to define exactly.

Fig. 64.1: The temperature trend for the Southern Hemisphere since 1820 derived by averaging the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1876 and 1975 and has a negative gradient of -0.12 ± 0.09 °C per century.

The overall temperature trend for the Southern Hemisphere since 1820 is shown in Fig. 64.1 above. This is the result of averaging over one thousand separate station records as indicated in Fig. 64.2 below. All the stations were either long stations with over 1200 months of data before the end of 2013, or medium stations with over 480 months of data.

The temperature profile from 1970 onwards appears to exhibit a clear upward trend with the mean temperature increasing by about 0.57°C from the 1960s to 2010. This data is also the most reliable as it is the result of averaging over 900 temperature records. 

In contrast, the data before 1970 exhibits a long modest cooling trend of over 0.1°C per century. The reliability of this data is also good, as it is the result of averaging over 100 temperature records from 1900 onwards. Before 1900, however, the data becomes less reliable due to its reliance on smaller numbers of stations that are also further apart and so less well correlated.

Fig. 64.2: The number of station records included each month in the mean temperature trend for the Southern Hemisphere when the MRT interval is 1981-2010.

What the data in Fig. 64.1 appears to indicate is that while there has been a significant warming of the Southern Hemisphere post-1970 of up to 0.57°C, this is partially offset by a noticeable cooling over the previous 100 years or more. So the total warming since pre-industrial times is likely to be less than 0.4°C. This is much less than the commonly quoted value of 1°C, or 1.5°C for the Northern Hemisphere. Yet this is not reflected in the Berkeley Earth adjusted data.

Fig. 64.3: Temperature trend for the Southern Hemisphere since 1840 derived by aggregating and averaging the Berkeley Earth adjusted data for over 1000 of the longest stations in the region. The best fit linear trend line (in red) is for the period 1951-2010 and has a gradient of +1.45 ± 0.10 °C/century.

An average of the Berkeley Earth adjusted time series temperature trends from the 1000 longest sets of station data in the Southern Hemisphere is presented in Fig. 64.3 above. This appears to indicate that the total temperature rise of the Southern Hemisphere since 1950 should be about 0.8°C. This is significantly more (between 0.1°C and 0.3°C depending on the time period you are considering) than is seen from the raw temperature data in Fig. 64.1, but it is in general agreement with the trend published by Berkeley Earth and shown in Fig. 64.4 below. 

However, what is even more prominent is the difference in the temperature trends before 1950. Whereas the raw data in Fig. 64.1 clearly indicates a cooling trend of 0.12°C per century, a simple average of the Berkeley Earth in Fig. 64.3 indicates a modest warming of 0.24°C per century. This, though, is still much less than the official trend shown in Fig. 64.4, which appears to claim an additional 0.5°C of warming has occurred between 1880 and 1950. This is almost the same as the warming since 1950, yet the atmospheric levels of carbon dioxide in 1950 were only 310 ppm, which is only about 30 ppm above pre-industrial levels. This means that the most recent increase in carbon dioxide of 100 ppm since 1950 has produced the same warming as the first 30 ppm did before 1950. If that is true, then it suggests further increases in carbon dioxide concentrations will have ever decreasing impacts on our climate, to the point where they are inconsequential.

Fig. 64.4: The temperature trend for the Southern Hemisphere since 1860 according to Berkeley Earth.

So what are the reasons for the differences in the trends before 1950? 

Well, we know that the differences between the trends in Fig. 64.1 and Fig. 64.3 are probably the result of the adjustments made to the data by Berkeley Earth. The statistical legitimacy of these adjustments I have already disputed in Post 57. This cannot explain the differences between the trends in Fig. 64.3 and Fig. 64.4, though, as these are both derived using the same adjusted data. These differences are likely to be the result of regional or station weightings, which would appear to be more important before 1950 due to the smaller number of stations and their uneven geographical distribution.

One way to examine the impact of these differences is to compare results from different samples of data. In the following five graphs I have split the stations used to construct the average in Fig. 64.1 into five separate random samples and compared their trends before and after 1975. In each case the temperature rise from the 1960s to 2010 is in the range 0.56 ±0.05°C while all but one of the samples has a negative trend before 1975. However, the range of trends for data before 1975 (or 1950) is much larger than the range for data after. This suggests that the data before 1950 is more sensitive to the impact that individual stations or regions may have on the average. The number of stations averaged each month for each sample is indicated in Fig. 64.10. This indicates that before 1940 each sample typically has significantly fewer than 70 stations in the average compared with over 150 after 1960.

Fig. 64.5: The temperature trend for the Southern Hemisphere since 1820 based on the first sample average of 224 of the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1876 and 1975 and has a negative gradient of -0.02 ± 0.10 °C per century.

Fig. 64.6: The temperature trend for the Southern Hemisphere since 1820 based on the second sample average of 223 of the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1876 and 1975 and has a negative gradient of -0.19 ± 0.08 °C per century.

Fig. 64.7: The temperature trend for the Southern Hemisphere since 1820 based on the third sample average of 210 of the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1876 and 1975 and has a negative gradient of -0.37 ± 0.09 °C per century.

Fig. 64.8: The temperature trend for the Southern Hemisphere since 1820 based on the fourth sample average of 211 of the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1876 and 1975 and has a negative gradient of -0.09 ± 0.09 °C per century.

Fig. 64.9: The temperature trend for the Southern Hemisphere since 1820 based on the fifth sample average of 211 of the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1876 and 1975 and has a positive gradient of +0.15 ± 0.11 °C per century.

Fig. 64.10: The number of station records included each month in the mean temperature trend for each of the five samples in Fig. 64.5 - Fig. 64.9.

Summary

The temperature trend for the Southern Hemisphere, based on the raw temperature data, exhibits a warming of about 0.5°C since 1950.

Before 1950 there is strong evidence of a prolonged cooling period of over 100 years in duration that amounted to a cooling of at least 0.12°C in total.

Based on the available temperature data, the total warming seen in the Southern Hemisphere since pre-industrial times is likely to be less than 0.4°C. This is much less than the usually reported value. 

Final Thoughts

The data shown in Fig. 64.1 clearly shows no warming before 1980. However, the data before 1880 is not very reliable. As Fig. 64.2 indicates, the mean anomaly prior to 1880 is based on data from less than 50 temperature records. If these records were all from the same region, then this low amount of data would be less of a problem as the different stations would be strongly correlated. The result would be reliable - but only for that region. 

The data I have analysed so far for this blog suggests that, for a single region with a uniform climate, a good reliable average can be achieved from only about 15-20 sets of data. When dealing with an entire hemisphere, however, we need more data because the climate of South America will clearly be different from that of Australia. This means that the data before 1880 in Fig. 64.1 is likely to be misleading. So can we do better than the trend in Fig. 64.1? Well, yes we can.

Fig. 64.11: The temperature trend for the Southern Hemisphere since 1880 derived by averaging the 1079 longest temperature records for the region. The best fit is applied to the monthly mean data between 1881 and 1980 and has a slight positive gradient of 0.01 ± 0.09 °C per century.

If we re-scale the data in Fig. 64.1 we can create a graph that presents a truer picture of the historic temperature rise by ignoring the unreliable data before 1880. Such a graph is shown above in Fig. 64.11. The other change I have made is to the time interval of the best bit line. This fit now applies from 1881 to 1980 and its gradient is practically zero. The jump in temperature after 1980 still amounts to about 0.57°C, and this is still much less than the 1.5°C that is claimed by climate science for the rise in global land temperatures from 1900 to 2013. But what it also shows is that small changes to how data is analysed and presented can affect the results.

63. Peru - temperature trends PARABOLIC

What is striking about the temperature data for Peru is its variability. Not only is there a diverse mix of warming and cooling trends between stations, there are also a lot of extreme fluctuations within individual temperature records as illustrated by the time series for Arequipa Airport (Berkeley Earth ID:157461) in Fig. 63.1 below. This makes it very hard to assess what the true temperature trend for Peru really is.

Fig. 63.1: The temperature trend for Arequipa Airport since 1900. The best fit line has a positive gradient of +0.16 ± 0.14 °C per century. The monthly temperature changes are defined relative to the 1951-1980 monthly averages.

 

In all there are 42 stations in Peru with more than 300 months of data. Their locations are shown in Fig. 63.2 below. It can be seen that they are spread throughout most of Peru, but there is significant clustering in some regions and sparse coverage in others, particularly within the Amazon region to the north and east. Of these 42 stations, 24 are medium stations with over 480 months of data, the longest of which is Arequipa Airport (Berkeley Earth ID:157461) with 1163 months of data. There are no long stations with more than 1200 months of data. 

Fig. 63.2: The (approximate) locations of all stations in Chile with over 300 months of data. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

The station location map in Fig. 63.2 indicates that there is a fairly even mix of warming and cooling stations in Peru. However, stations with data before 1960 are more likely to exhibit cooling trends, while those with data after 1960 are more likely to be warming. The result is that the overall temperature trend from 1930 onwards comprises a sharp cooling period followed by a slow warming as shown in Fig. 63.3 below.

Fig. 63.3: The temperature trend for Peru since 1900. The best fit is applied to all the monthly mean data and has a positive gradient of +1.11 ± 0.06 °C per century. The monthly temperature changes are defined relative to the 1951-1980 monthly averages.

The temperature trend in Fig. 63.3 above was derived by averaging the temperature anomalies from all the stations with more than 300 months of data which also had at least ten years of data within the interval of 1951-1980. This amounted to 39 stations in total (for a list see here). The interval of 1951-1980 was used to determine the monthly reference temperatures (MRTs) against which the temperature anomalies were determined, as explained in Post 47. This period was chosen so as to maximize the number of stations included in the final mean trend.

If we perform a fit to all the data in Fig. 63.3, the result is a strong warming trend of 1.11°C per century as indicated in Fig. 63.3 above. Not only does this appear to closely follow the data from 1960 onwards, it also appears to fit with the data before 1925 as well.

However, the data before 1925 comes from at most two stations, as indicated in Fig. 63.4 below, while the data from 1930 to 1960 in Fig. 63.3 is the result of averaging at least fifteen different temperature records from different stations, and potentially as many as thirty. This suggests that the mean temperature trend after 1930 in Fig. 63.3 is far more reliable than the trend before 1925, a hypothesis that is confirmed by a study of the two datasets in question.

Fig. 63.4: The number of station records included each month in the mean temperature trend for Peru when the MRT interval is 1951-1980.

The two stations with data before 1925 have data that is discontinuous and that fluctuates enormously. One of the two stations is Arequipa Airport (Berkeley Earth ID:157461) shown in Fig. 63.1 above. The other is Lima-Callao Airport (Berkeley Earth ID:157469). For the former the temperatures before 1920 are comparable to those between 1970 and 2000. For the latter they are comparable with temperatures in the 1960-1980 period. Yet the result in both cases when this data is combined with the averaged data for 1929 onwards, is to produce a mean trend for 1900-1920 that is over 1°C lower than the temperatures seen in the rest of the trend between 1960 and 2000 (see Fig. 63.3). This indicates that the data for these two stations is clearly inconsistent with the overall trend for the region (compare the data from 1940-2000 in Fig. 63.1 with that in Fig. 63.3), and so the data before 1925 is highly unreliable.

If we therefore restrict our best fit to data that is from after 1929 and which is the result of averaging at least ten sets of station data, then the interpretation changes dramatically. The best fit line in Fig. 63.5 below now has a much smaller positive gradient of only 0.16°C per century. This is barely more than the uncertainty of ±0.11°C per century, and significantly less than the standard deviation of the data from 1931-2010 which is 0.63°C.

Fig. 63.5: The temperature trend for Peru since 1900. The best fit is applied to the monthly mean data from 1931-2010 and has a positive gradient of +0.16 ± 0.11 °C per century. The monthly temperature changes are defined relative to the 1951-1980 monthly averages.

Now if we compare these result with the results published by Berkeley Earth we once again see a number of major differences. The mean temperature trend becomes less variable and more linear as illustrated in Fig. 63.6 below. The trend in Fig. 63.6 was generated by performing a simple average on the Berkeley Earth adjusted data from the same 42 stations used to generate the temperature trend in Fig. 63.3.

Fig. 63.6: Temperature trend in Peru since 1900 derived by aggregating and averaging the Berkeley Earth adjusted data for all medium stations. The best fit linear trend line (in red) is for the period 1901-2012 and has a gradient of +0.84 ± 0.03 °C/century.

What is clear is that the trends in Fig. 63.6 above are very close to the trends published by Berkeley Earth and shown in Fig. 63.7 below. This comparison clearly shows that a simple average of the adjusted data from the Berkeley Earth data files (Fig. 63.6) gives almost the same result for the regional trend in Peru as the Berkeley Earth version does (Fig. 62.7), even though Berkeley Earth appears to use weighted averages for its regional averaging. This in turn also suggests that weighted averaging is probably not necessary in Peru, and simple averaging of stations is sufficient to generate a reliable trend even though the spread of stations across the county is far from ideal as Fig. 63.2 illustrates.

Fig. 63.7: The temperature trend for Peru since 1860 according to Berkeley Earth.

Clearly there are some significant differences between the temperature trend for Peru based on the original raw temperature data in Fig. 63.3 and that due to the adjusted data used by Berkeley Earth in Fig. 63.5. The exact magnitude of those differences are shown in Fig. 63.8 below.

The effect of the Berkeley Earth adjustments is to reduce the warming after 1990 and to flatten the curve between 1930 and 1950. The rationale for these adjustments is probably to correct for perceived bad data. However, the station frequency plot in Fig. 63.4 suggests that both these adjustments are being applied to data in Fig. 63.3 that should be highly robust, given that it is derived from averaging a large number (over fifteen) of independent datasets. As I have shown previously, averages of more than fifteen stations from the same local region will tend to cancel the errors from each dataset, and so produce a robust and accurate regional trend.

Fig. 63.8: The contribution of Berkeley Earth (BE) adjustments to the BE anomaly data shown in Fig. 63.6 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 1931-2010 has a positive gradient of +0.55 ± 0.06 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Conclusions

The data in Fig. 63.3 indicates that there has been a sustained but gentle warming of the climate in Peru of about 0.6°C since 1960. As this is the result of averaging between twenty and thirty different temperature records, this warming would appear to be a real effect and not one based on spurious data.

However, the evidence of Fig. 63.3 also stronly suggests that this warming is no greater than the cooling seen before 1960. So overall, temperatures today are no warmer than those of 100 years ago.

The lack of good data before 1930 makes it difficult to assess the significance of the current temperature rise. It could be due to global warming, or it could be due to natural variations.

62. Chile - temperature trends COOLING

At over 3,600 km in length, the land border between Chile and Argentina is probably the longest continuous land border between the same two countries anywhere in the world (part of the USA-Canada border is through the Great Lakes). You might think, therefore, that the climates of these two countries should be very similar, and that their experiences of climate change should be the same. Except they are not. The reason, of course is that they are separated by the Andes mountains. So while the climate of Argentina over the last 100 years has been stable but with a sudden temperature rise of about 0.45°C in 1967 (see Post 61), the climate of Chile has been cooling steadily, just as most of the South Pacific has as well (see Post 34).

Overall there were 50 stations in Chile with over 300 months of data up until the end of 2013. The longest of these is Santiago, which with 1835 months of data is the second longest temperature record in South America behind Rio de Janeiro. There are also two other long stations with over 1200 months of data, and another 31 medium stations with over 480 months of data. It should be noted also that of the 50 stations being considered here, 20 have little or no data after 1960 and 16 have little or no data before 1960. This makes the choice of MRT interval problematic (monthly reference temperatures or MRTs are explained in Post 47).

Fig. 62.1: The temperature trend for Chile since 1860. The best fit is applied to the interval 1891-2010 and has a negative gradient of -0.30 ± 0.06 °C per century. The monthly temperature changes are defined relative to the 1961-1990 monthly averages.

The temperature trend in Fig. 62.1 above was derived by averaging the temperature anomalies from all the stations with more than 300 months of data which also had at least twelve years of data within the interval of 1961-1990. This amounted to 94 stations in total (for a list see here). The interval of 1961-1990 was used to determine the monthly reference temperatures (MRTs) against which the temperature anomalies are determined, as explained in Post 47. 

The trend in Fig. 62.1 is clearly strongly negative as indicated by the red best fit line. However, the temperature change is not uniform and there is considerable variability. The trend from 1960 onwards is the result of averaging over 20 different sets of temperature data, as shown in Fig. 62.2 below. This suggests the trend after 1960 is highly reliable, as I explained in Post 57 previously, while that before 1930 will probably be much less so.

Fig. 62.2: The number of station records included each month in the mean temperature trend for Chile when the MRT interval is 1961-1990.

The geographical distribution of the long and medium stations in Chile is illustrated in Fig. 62.3 below. These are classed as either warming stations (in red) or stable/cooling stations in blue. The criteria for determining if a station is warming are two-fold. First, the temperature trend must exceed twice the error in the trend in order to be statistically significant. Second, the overall temperature rise must exceed 0.25 °C in order for it to exceed the threshold below which it could be considered as merely a random fluctuation in the data. As I have pointed out previously, this threshold of 0.25°C may be on the low side as natural fluctuations in the long-term temperature trend may be much greater than this as the 5-year moving average in Fig. 62.1 appears to indicate.

Fig. 62.3: The (approximate) locations of long stations (large squares) and medium stations (small diamonds) in Chile. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

Clearly Fig. 62.3 shows that less than a third of stations in Chile have warmed over their history. It is also clear from Fig. 61.3 that there is a good spread of stations around Chile with little clustering, but a higher density of stations in the middle of the country south of Santiago. However, this does not appear to affect the simple averaging approach employed here to determine the regional temperature trend in Fig. 62.1 as the following graphs will show.

Fig. 62.4: Temperature trend in Chile since 1860 derived by aggregating and averaging the Berkeley Earth adjusted data for all medium stations. The best fit linear trend line (in red) is for the period 1891-2010 and has a gradient of +0.72 ± 0.02 °C/century.

The accuracy of the simple averaging process used in Fig. 62.1 can be tested by comparing two different trends that were each calculated using the same data but with different averaging techniques. The regional trend for Chile in Fig. 62.4 above was calculated using Berkeley Earth adjusted data and the simple averaging method. The trend shown in Fig. 62.5 below and published by Berkeley Earth was calculated using the same Berkeley Earth adjusted data, but with different weightings for each station based on station density and correlation with its neighbours. The data in the two graphs appear virtually identical, despite the fact that slightly fewer stations were used to generate the trends in Fig. 62.4.

This comparison of the trends in Fig. 62.4 and Fig. 62.5 clearly shows that a simple average of the adjusted data from the Berkeley Earth data files (Fig. 62.4) gives the same result for the regional trend in Chile as the Berkeley Earth version does (Fig. 62.5), even though Berkeley Earth appears to use weighted averages for its regional averaging. This in turn also suggests that weighted averaging is probably not necessary in Chile and simple averaging of stations is sufficient to generate a reliable trend.

Fig. 62.5: The temperature trend for Chile since 1840 according to Berkeley Earth.

However, what is also apparent is that the temperature trend produced by Berkeley Earth in Fig. 62.5 bares little or no resemblance to that which was derived from the original data in Fig. 62.1. A negative trend of -0.3°C per century in Fig. 62.1 has miraculously become a huge positive trend of +0.72°C per century in Fig. 62.4. The total difference between these two trends is illustrated in Fig. 62.6 below and amounts to an additional 1.02°C of warming over the last century. This is the result of adjustments made to the original data by Berkeley Earth. This explains the difference in trend gradient in Fig. 62.4 compared to that in Fig. 61.1. However, as I demonstrated in Post 57 previously, most of these temperature adjustments are unnecessary. Not only that, they are probably wholly unjustifiable from a statistical standpoint.

Fig. 62.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 62.4 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 +1.02 ± 0.04 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Conclusion

The results here indicate that there has been no global warming in Chile in the last 100 years. In fact the climate there has cooled substantially in that time.

 

 

Addendum

As I noted above, the use of 1961-1990 for the MRT interval results in the exclusion of 20 of the available datasets from the trend in Fig. 62.1. However, if the MRT interval is instead chosen to be 1931-1960, most of these stations will be captured while about 16 stations with data mainly after 1960 are ejected from the average. This is illustrated in Fig. 62.7 below where the number of stations in the 1930s and 1940s increases to about 32 (compared to 14 in Fig. 62.2) while in the 1980s the number falls to about 14 from about 28.

 

Fig. 62.7: The number of station records included each month in the mean temperature trend for Chile when the MRT interval is 1931-1960.

 

The impact of this on the trend is shown in Fig. 62.8 below. It can be seen that the temperature trend is now even more strongly negative with almost 0.5°C of cooling occurring before 2010. What is certainly clear is that there is no global warming occurring in Chile.

 

Fig. 62.8: The temperature trend for Chile since 1860. The best fit is applied to the interval 1901-2010 and has a negative gradient of -0.44 ± 0.06 °C per century. The monthly temperature changes are defined relative to the 1931-1960 monthly averages.

 

61. Argentina - temperature trends WARMING 0.5°C

After Brazil, Argentina probably has the best temperature data in the whole of South America. And if judged on station density as well as length, it may even be better.

The longest dataset is for Buenos Aires Observatorio which is the third longest temperature record in South America behind Rio de Janeiro and Santiago. It has over 1800 months of data dating back to 1856. In addition there are another six long stations with over 1200 months of data, and another 76 with over 480 months of data.

 

Fig. 61.1: The temperature trend for Argentina since 1850. The best fit is applied to the interval 1856-2005 and has a positive gradient of +0.63 ± 0.07 °C per century. The monthly temperature changes are defined relative to the 1961-1990 monthly averages.

 

The temperature trend in Fig. 61.1 above was derived by averaging the temperature anomalies from all the stations with more than 300 months of data which also had at least twelve years of data within the interval of 1961-1990. This amounted to 94 stations in total (for a list see here). The interval of 1961-1990 was used to determine the monthly reference temperatures (MRTs) against which the temperature anomalies are determined, as explained in Post 47. 

The trend in Fig. 61.1 is clearly strongly positive as indicated by the red best fit line. However, the temperature rise is not uniform. In fact there is evidence of a sharp jump of about 0.4°C around 1967 that is both preceded and succeeded by about thirty years of temperature stability. Moreover, the trend from 1930 onwards is the result of averaging over 50 different sets of temperature data, as shown in Fig. 61.2 below. This suggests the trend after 1930 is highly reliable, as I explained in Post 57 previously, while that before 1900 will probably be much less so.

Before 1900 the data is very volatile, probably due to the combination of natural temperature variability and a shortage of stations that could reduce this variability through the averaging process. After 1900 the temperature rises, but not in a way that is correlated with carbon dioxide levels in the atmosphere. Nor is the temperature rise remotely close to the 1.0 °C claimed by climate scientists for the average in the Southern Hemisphere. The total temperature rise appears to be between 0.4°C and 0.7°C.

 

Fig. 61.2: The number of station records included each month in the mean temperature trend for Argentina when the MRT interval is 1961-1990.

 

The geographical distribution of the long and medium stations in Argentina is illustrated in Fig. 61.3 below. These are classed as either warming stations (in red) or stable/cooling stations in blue. The criteria for determining if a station is warming are two-fold. First, the temperature trend must exceed twice the error in the trend in order to be statistically significant. Second, the overall temperature rise must exceed 0.25 °C in order for it to exceed the threshold below which it could be considered as merely a random fluctuation in the data. As I have pointed out previously, this threshold of 0.25°C may be on the low side as natural fluctuations in the long-term temperature trend may be much greater than this as the 5-year moving average in Fig. 61.1 appears to indicate.

 

Fig. 61.3: The locations of long stations (large squares) and medium stations (small diamonds) in Argentina. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

Clearly Fig. 61.3 shows that the majority of stations in Argentina have warmed over their history, although a significant proportion (almost 40%) have not. It is also clear from Fig. 61.3 that there is a good spread of stations around Argentina, but with a much higher concentration of stations in the north of the country than in the south. However, this does not appear to affect the simple averaging approach employed here to determine the regional temperature trend in Fig. 61.1 as the following graphs will show. 

 

Fig. 61.4: Temperature trend in Argentina since 1850 derived by aggregating and averaging the Berkeley Earth adjusted data for all medium stations. The best fit linear trend line (in red) is for the period 1891-2010 and has a gradient of +0.86 ± 0.03 °C/century.

 

The accuracy of the simple averaging process used in Fig. 61.1 can be tested by comparing two different trends that were each calculated using the same data but with different averaging techniques. The regional trend for Argentina in Fig. 61.4 above was calculated using Berkeley Earth adjusted data and the simple averaging method. The trend shown in Fig. 61.5 below and published by Berkeley Earth was calculated using the same Berkeley Earth adjusted data, but with different weightings for each station based on station density and correlation with its neighbours. The data in the two graphs appear virtually identical, despite the fact that slightly few stations were used to generate the trends in Fig. 61.4.

This comparison of the trends in Fig. 61.4 and Fig. 61.5 clearly shows that a simple average of the adjusted data from the Berkeley Earth data files (Fig. 61.4) gives the same result for the regional trend in Argentina as the Berkeley Earth version does (Fig. 61.5), even though Berkeley Earth appears to use weighted averages for its regional averaging. This in turn also suggests that weighted averaging is probably not necessary in Argentina.

 

Fig. 61.5: The temperature trend for Argentina since 1850 according to Berkeley Earth.

What is also apparent is that there are some distinct differences between the temperature trend produced by Berkeley Earth in Fig. 61.5 and that which can be derived from the original data in Fig. 61.1. The total difference is illustrated in Fig. 61.6 below and amounts to an additional 0.26°C of warming over the last century. This is the result of adjustments made to the original data by Berkeley Earth. This explains the difference in trend gradient in Fig. 61.4 compared to that in Fig. 61.1. However, as I demonstrated in Post 57 previously, most of these temperature adjustments are unnecessary.

 

Fig. 61.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 61.4 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 +0.26 ± 0.04 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

 

 

Conclusion

The results here indicate that global warming in Argentina has probably been modest (less than 0.5°C in the last 100 years) and may have occurred mainly in the 1960s.

 

60. Uruguay - temperature trends WARMING 0.4°C to 1°C

Like Paraguay, Uruguay has even less temperature data than Bolivia, but it does have some data from before 1900 and it does have one long station with over 1200 months of data (Montevideo/Prado - Berkeley Earth ID: 165708). In addition there are ten medium stations with over 480 months of data and another eight with over 180 months of data.

 

Fig. 60.1: The temperature trend for Uruguay since 1880. The best fit is applied to the interval 1933-2012 and has a positive gradient of +0.96 ± 0.13 °C per century. The monthly temperature changes are defined relative to the 1981-2010 monthly averages.

 

The temperature trend in Fig. 60.1 above was derived by averaging the temperature anomalies from all the stations with more than 180 months of data. This amounted to only nineteen stations in total (for a list see here) all of which had at least twelve years of data within the interval of 1981-2010 that was used to determine the monthly reference temperatures (MRTs). The use of MRTs is explained in Post 47.

The trend in Fig. 60.1 is clearly strongly positive but there is only one significant set of station data before 1950, and only about ten between 1950 and 1980 (see Fig. 60.2 below). This means that we can only have a high degree of confidence in the trend shown in Fig. 60.1 after 1950, as I explained in Post 57 previously. So while it is possible that Uruguay has warmed at the same rate since 1880, it is also possible that the only significant temperature rise has occurred after 1970. If so, this would amount to a total warming of only about 0.4°C.

 

Fig. 60.2: The number of station records included each month in the mean temperature trend for Uruguay when the MRT interval is 1981-2010.

The geographical distribution of the long and medium stations in Uruguay is illustrated in Fig. 60.3 below. These are classed as either warming stations (in red) or stable/cooling stations in blue. The criteria for determining if a station is warming are two-fold. Firstly, the temperature trend must exceed twice the error in the trend in order to be statistically significant. Secondly, the overall temperature rise must exceed 0.25 °C in order for it to exceed the threshold below which it could be considered as merely a random fluctuation in the data. As I pointed out previously, this threshold may be on the low side as natural fluctuations in the long-term temperature trend may be much greater than 0.25°C as the 5-year moving average in Fig. 60.1 appears to indicate. 

 

Fig. 60.3: The locations of long stations (large squares) and medium stations (small diamonds) in Uruguay. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

 

Clearly Fig. 60.3 shows that the majority of stations in Uruguay have warmed over their history. Out of the eleven long and medium stations, only three are stable or cooling. It is also clear from Fig. 60.3 that there is a good, even spread of those stations around Uruguay with very little clustering of stations other than near the capital Montevideo. This suggests that the simple averaging approach employed here to determine the regional temperature trend in Fig. 60.1 is appropriate and highly likely to give results that are very close to the true result. Evidence for this is shown below.

 

Fig. 60.4: Temperature trend in Uruguay since 1880 derived by aggregating and averaging the Berkeley Earth adjusted data for all medium stations. The best fit linear trend line (in red) is for the period 1901-2010 and has a gradient of +1.00 ± 0.04 °C/century.

 

This hypothesis that a simple averaging process is sufficient to determine the regional trend is confirmed by comparing the regional trend in Fig. 60.4 above, which was calculated using Berkeley Earth adjusted data and a simple averaging method, with that published by Berkeley Earth and shown in Fig. 60.5 below. This comparison shows that a simple average of the adjusted data from the Berkeley Earth data files (Fig. 60.4) gives the same result for the regional trend in Uruguay as the Berkeley Earth version (Fig. 60.5), even though Berkeley Earth appears to use weighted averages for its regional averaging. This in turn also suggests that weighted averaging is not necessary in Uruguay.

 

Fig. 60.5: The temperature trend for Uruguay since 1830 according to Berkeley Earth.

What is also apparent is that there some distinct differences between the temperature trend produced by Berkeley Earth in Fig. 60.5 and that which can be derived from the original data in Fig. 60.1. The total difference is illustrated in Fig. 60.6 below and amounts to an additional 0.11°C per century of warming overall. This additional warming is greatest between 1950 and 2000, but it appears that the result of many of the biggest adjustments is to smooth the trend curve rather than to add additional warming. However, as I demonstrated in Post 57 previously, most of these temperature adjustments are unnecessary.

 

Fig. 60.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 60.4 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 1901-2010 has a positive gradient of +0.11 ± 0.08 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

 

Conclusion

The results here indicate that global warming in Uruguay has probably been modest (about 0.4°C) and may have only occurred in the 1970s. Without more early data from before 1950 it is difficult to tell for certain.

59. Paraguay - temperature trends COOLING

Paraguay has even less temperature data than Bolivia, but it does have some data from before 1900 and it does have one long station with over 1200 months of data (Asuncion Aeropuerto - Berkeley Earth ID: 157448). Despite this lack of data there is still a clear trend in the data that we do have, and that trend is negative. There has been no global warming in Paraguay either in the last 100 years.

 

Fig. 59.1: The temperature trend for Paraguay since 1893. The best fit is applied to the interval 1933-2012 and has a negative gradient of -1.78 ± 0.21 °C per century. The monthly temperature changes are defined relative to the 1981-2010 monthly averages.

 

The temperature trend in Fig. 59.1 above was derived by averaging the temperature anomalies from all the medium stations with more than 480 months of data together with the single long station. This amounted to only thirteen stations in total (for a list see here) all of which had at least twelve years of data within the interval of 1981-2010 that was used to determine the monthly reference temperatures (MRTs). The use of MRTs is explained in Post 47.

The trend in Fig. 59.1 is clearly strongly negative from about 1940 onwards. This corresponds to the period with the greatest number of active stations, as shown in Fig. 59.2 below, with more than ten sets of station data being available for most months between 1950 and 2013. This confers a high degree of confidence to the trend in Fig. 59.1 as I illustrated in Post 57 previously. In contrast, the trend before 1940 is much less reliable as it is based on the fragmented data from a single station: Asuncion Aeropuerto (Berkeley Earth ID: 157448). For this reason the best fit trend line in Fig. 59.1 is only calculated using the eighty years of data after 1933.

 

Fig. 59.2: The number of station records included each month in the mean temperature trend for Paraguay when the MRT interval is 1981-2010.

 

The geographical distribution of the long and medium stations in Paraguay is illustrated in Fig. 59.3 below. These are classed as either warming stations (in red) or stable/cooling stations in blue. The criteria for determining if a station is warming are two-fold. Firstly, the temperature trend must exceed twice the error in the trend in order to be statistically significant. Secondly, the overall temperature rise must exceed 0.25 °C in order for it to exceed the threshold below which it could be considered as merely a random fluctuation in the data. As I pointed out previously, this threshold may be on the low side as natural fluctuations in the long-term temperature trend may be much greater than 0.25°C. In fact in some cases they may exceed 1°C.

 

Fig. 59.3: The locations of long stations (large squares) and medium stations (small diamonds) in Paraguay. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

 

It is clear from Fig. 59.3 that there is a good, even spread of stations in the east of Paraguay with very little clustering of stations other than near the capital Asuncion and in the middle of the country near Concepcion. However, there is very little coverage in the north-west. Nevertheless, overall this suggests that the simple averaging approach employed here to determine the regional temperature trend is still appropriate and is likely to give results that are close to the true result as will be shown below. What is also apparent is the almost complete lack of warming. Only two of the thirteen stations exhibit any significant warming.

 

 

Fig. 59.4: Temperature trend in Paraguay since 1893 derived by aggregating and averaging the Berkeley Earth adjusted data for all medium stations. The best fit linear trend line (in red) is for the period 1934-2012 and has a gradient of +1.01 ± 0.07 °C/century.

This hypothesis is confirmed by comparing the regional trend using Berkeley Earth adjusted data in Fig. 59.4 above, and which was also constructed using a simple averaging method, with that published by Berkeley Earth and shown in Fig. 59.5 below. This comparison shows that a simple average of the adjusted data from the Berkeley Earth data files gives the same result for the regional trend in Paraguay as the Berkeley Earth version, even though Berkeley Earth appears to use weighted averages for its regional averaging. This in turn also suggests that weighted averaging is not necessary in Paraguay.

 

Fig. 59.5: The temperature trend for Paraguay since 1830 according to Berkeley Earth.

What is also apparent is that there is a clear difference between the temperature trend produced by Berkeley Earth in Fig. 59.5 and that which can be derived from the original data in Fig. 59.1, not only in the magnitudes of the two different temperature trends, but also in their directions. The total difference is illustrated in Fig. 59.6 below and amounts to an additional 2.7°C per century of warming that has been added to a regional trend that has actually been cooling at -1.78°C per century since 1940. That is over 2°C in total. What is more, most of these temperature adjustments are due to the very breakpoint adjustments that I demonstrated in Post 57 were unnecessary.

 

 

Fig. 59.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 59.4 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 1934-2012 has a positive gradient of +2.70 ± 0.08 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

 

Conclusion

The results here indicate that there has been no global warming in Paraguay in the last 100 years. The regional temperature is either stable or cooling, as shown in Fig. 59.1.

 

Addendum

 

Fig. 59.7: The temperature trend for Paraguay since 1893 including short stations with over 300 months of data. The best fit is applied to the interval 1933-2012 and has a negative gradient of -1.64 ± 0.22 °C per century. The monthly temperature changes are defined relative to the 1981-2010 monthly averages.

 

There are only thirteen long and medium stations in Paraguay. However, there are another five with over 300 months of data. Including these could improve slightly the accuracy of the trend after 1980 where most of the new data exists. This is shown in Fig. 59.7 above. The trend in Fig. 59.7 is very similar to that shown in Fig. 59.1, with only a slight increase in the overall trend.

58. Bolivia - temperature trends COOLING

The biggest problem with assessing climate change in Bolivia is the relative lack of data. There is no data from before 1900 and no long stations with over 1200 months of data. Despite this there is a clear trend in the data that we do have, and that trend is negative. There has been no global warming in Bolivia in the last 100 years.

Fig. 58.1: The temperature trend for Bolivia since 1910. The best fit is applied to the interval 1953-2012 and has a negative gradient of -0.16 ± 0.17 °C per century. The monthly temperature changes are defined relative to the 1981-2010 monthly averages.

The temperature trend in Fig. 58.1 above was derived by averaging the temperature anomalies from all the medium stations with more than 480 months of data. This amounted to 25 stations in total (for a list see here). However, one station at La Paz (Berkeley Earth ID: 5644) was excluded because it had no data within the interval of 1981-2010 that was used to determine the monthly reference temperatures (MRTs). The use of MRTs is explained in Post 47.

The trend in Fig. 58.1 is clearly negative from about 1950 onwards. This corresponds to the period with the greatest number of active stations, as shown in Fig. 58.2 below, with more than 20 sets of station data being available for most months between 1950 and 2013. This confers a high degree of confidence to the trend in Fig. 58.1 as I illustrated in Post 57 previously. In contrast, the trend before 1950 is much less reliable. For this reason the best fit trend line in Fig. 58.1 is only calculated using the sixty years of data after 1953.

Fig. 58.2: The number of station records included each month in the mean temperature trend for Bolivia when the MRT interval is 1981-2010.

The geographical distribution of the medium stations in Bolivia is illustrated in Fig. 58.3 below. These are classed as either warming stations (in red) or stable/cooling stations in blue, and there appears to be a fairly even split between the two groups. The criteria for determining if a station is warming are two-fold. Firstly, the temperature trend must exceed twice the error in the trend in order to be statistically sound. Secondly, the overall temperature rise must exceed 0.25 °C in order for it to exceed the threshold for it to be regarded as merely a random fluctuation in the data. I should point out that even this threshold may be on the low side as natural fluctuations in the long-term temperature trend may be much greater than 0.25°C. 

Fig. 58.3: The locations of the medium stations (small diamonds) in Bolivia. Those stations with a high warming trend are marked in red. Those with cooling or stable trends are marked in blue.

It is clear from Fig. 58.3 that there is a good, even spread of stations in Bolivia with very little clustering of stations other than near the capital La Paz. The only area of Bolivia with sparse coverage is the mountainous Andes region in the south-west. This suggests that the simple averaging approach employed here to determine the regional temperature trend is highly appropriate and is likely to give results that are close to the true result.

Fig. 58.4: Temperature trend in Bolivia since 1910 derived by aggregating and averaging the Berkeley Earth adjusted data for all medium stations. The best fit linear trend line (in red) is for the period 1914-2012 and has a gradient of +0.66 ± 0.04 °C/century.

This hypothesis is confirmed by the regional trend in Fig. 58.4 above which was also constructed using a simple averaging method, and which is virtually identical to the trend published by Berkeley Earth and shown in Fig. 58.5 below. This shows that a simple average of the adjusted data from the Berkeley Earth data files gives the same result for the regional trend in Bolivia as the Berkeley Earth version, even though Berkeley Earth appears to use weighted averages for its regional averaging. This in turn also suggests that weighted averaging is not necessary, except possibly in cases of extreme clustering of stations in urban areas, of which there is none in Bolivia.

Fig. 58.5: The temperature trend for Bolivia since 1850 according to Berkeley Earth.

What is apparent is that there is a clear difference between the temperature trend produced by Berkeley Earth in Fig. 58.5 and that which can be derived from the original data in Fig. 58.1, not only in the magnitudes of the two different temperature trends, but also in their directions. The total difference is illustrated in Fig. 58.6 below and amounts to an additional 0.72°C per century of warming that has been added to a regional trend that is actually cooling at -0.16°C per century. What is more, most of these temperature adjustments are due to the very breakpoint adjustments that I demonstrated in my last post were unnecessary.

Fig. 58.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 58.4 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 1914-2012 has a positive gradient of +0.72 ± 0.07 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Conclusion

The results here indicate that there has been no global warming in Bolivia in the last 100 years. The regional temperature is either stable or cooling, as shown in Fig. 58.1.