96. Ecuador - temperature trends and the curious missing data

There are two major problems when it comes to analysing the temperature data of Ecuador. The first is that there is very little good data. The second is that what data there is is subject to major natural variations; not least from El Niño. 

In total there are only six medium stations in Ecuador with more than 480 months of data and only one with more than 800 months of data. That station is Quito Mariscal Sucre (Berkeley Earth ID: 13263) which has almost 1200 months of data up to the end of 2013 and is located in the capital city, but even it has no data after 2000. And based on evidence from other countries and states, it is reasonable to conclude that the temperature trend for this station is not indicative of the country as a whole (because of its growing urban environment), yet it is the only station with any significant data before 1960. There is a seventh medium station in Ecuador (San Cristobal radiosonde), but that is located in the Galapagos islands over 1000 km to the west and has already been included in my analysis of the South Pacific (see Post 34). For these reasons it will be excluded from this analysis.

Fig. 96.1: The (approximate) locations of the weather stations in Ecuador. 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 stations with over 480 months of data, while diamonds denote stations with more than 240 months of data.

Instead I have also included an additional ten stations with over 240 months of data, even though I generally feel that stations with less than 360 months of data generally add little to the overall trend. The locations of these and the six medium stations are shown on the map in Fig. 96.1 above (see here for a list of all stations with links to their original data). While these stations are fairly evenly distributed, it can be seen that almost all are in the western half of the country on the Pacific side of the Andes ridge. This, though does not seem to be a major issue as will be demonstrated in the analysis below. What is a major issue is the quantity and length of each dataset.

The result of averaging the monthly temperature anomalies from all the stations in Ecuador with over 240 months of data results in the set of mean temperature anomalies (MTA) shown in Fig. 96.2 below. 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 1961-1990 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.

Fig. 96.2: The mean temperature change for Ecuador relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 2010 and has a positive gradient of +0.98 ± 0.08 °C per century.

The MTA data in Fig. 99.2 clearly shows a positive temperature trend over time that equates to a warming of about 1.0°C over the last century. However, within this trend are fluctuations in the 5-year moving average (yellow curve) that are even greater than the overall rise in the trend (red curve). 

One of the principal causes of these fluctuations are El Niño events. These result in large positive spikes in the regional temperature, the most dramatic of which can be seen in 1957, 1972, 1982, 1987 and 1997. The events between 1982 and 1997 in particular appear to contribute significantly to the overall warming trend for Ecuador by leading to a consistent elevated warming in this period. However, after 1997 there is a clear reversal of this with a major dip in temperatures occurring. This appears to correspond to a major La Niña event where the region undergoes a sharp cooling. 

Yet curiously something else happens to the data in this period: a lot of it (~75%) appears to go missing. This can be seen in the graph below in Fig. 96.3 which shows the number of stations used to calculate the MTA for each month. Between 2001 and 2008 up to 75% of stations used to calculate the MTA suddenly have no data, just at the point where the mean temperatures of some of the few stations that do have data show a decline in their mean monthly temperatures of up to 4°C.

Fig. 96.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Ecuador in Fig. 96.2.

The station frequency data in Fig. 96.3 illustrates another deficiency in the data: the lack of it before 1960. In fact, as I pointed out at the start of this post, there is only one station with data pre-1960. Consequently it is plausible to assume that the trend seen in the data before 1960 will differ significantly from that thereafter. The best fit line in Fig. 96.4 below confirms this.

Fig. 96.4: The mean temperature change for Ecuador relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1961 to 2010 and has a positive gradient of +0.68 ± 0.17 °C per century.

The result of this is that we cannot with any certainty proclaim what the real temperature trend is. It could be that the climate is warming at over 1°C per century as the data fit in Fig. 96.2 suggests, or it could be less than 0.7°C as indicated in Fig. 96.4 above. And given the severity and frequency of El Niño and La Niña events in the period after 1950, it could be that the real underlying climate variation is even lower. Frankly, we just can't tell. 

One way to resolve this might be to compare the temperature data for Ecuador with that of its neighbours. Yet in the previous post (Post 95) I showed that there has been no warming in Colombia since 1940 while in Post 63 I showed that the same was probably true for Peru as well (see Fig. 63.5 in Post 65).

Fig. 96.5: Temperature trends for Ecuador based on Berkeley Earth adjusted data. The average is for anomalies from all stations with over 240 months of data. The best fit linear trend line (in red) is for the period 1901-2010 and has a gradient of +1.06 ± 0.03°C/century.

So how does this tally with the data presented by Berkeley Earth (BE)? Well averaging the BE adjusted data for each station yields the time series for the mean temperature shown in Fig. 96.5 above. This has a warming trend that is significantly larger than that determined using raw data and shown in Fig. 96.2. It is, however, almost identical to the BE published version shown in Fig. 96.6 below even though the official BE trend in Fig. 96.6 is constructed using a mixture of homogenization and station weighting, and incorporates data from stations with less than 240 months of data. 

The similarity of the data in Fig. 96.5 and Fig. 96.6 suggests that statistical techniques such as homogenization and station weighting have little influence on the overall trend in this case. That also means that these statistical techniques cannot account for the differences between the trend based on adjusted data in Fig. 96.5 and Fig. 96.6 and the trend resulting from an average of anomalies based on the raw data shown in Fig. 96.2. This difference can therefore only result from the temperature adjustments.

Fig. 96.6: The temperature trend for Ecuador since 1860 according to Berkeley Earth.

So what can we conclude about the overall trend in temperature for Ecuador? The lack of data before 1960 invalidates the trend before 1960 from the discussion, while the lack of data for the period 2001-2008 probably does likewise. The remaining data in Fig. 96.4 after 1960 also fluctuates too greatly for an accurate trend to be discerned, but suggests that the real trend could be anything between zero and 1.0°C per century. Another way to estimate the likely temperature trend might be to compare it with the trend in neighbouring countries.  As Colombia (Post 95) and Peru (Post 63) appear to show no evidence of warming after 1940 it would be reasonable to assume that the same is true for Ecuador.

Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Link to list of all stations.

95. Colombia - temperature trends STABLE

Like Central America the temperature data for Colombia is far from ideal. There are too few stations with little data before 1940, and very few stations in the east of the country (see Fig. 95.1 below). Nevertheless, the data that is available does allow us to determine the temperature trend since 1940 with a fair degree of certainty. That data indicates that Colombia has experienced no global warming so far.

Fig. 95.1: The (approximate) locations of the weather stations in Colombia. 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 stations with over 600 months of data, while diamonds denote medium stations with more than 480 months of data.

Overall, Colombia has only 22 medium station temperature records with over 480 months of data (before 2014) and no long stations with over 1200 months of data. Of these medium stations, ten have more than 600 months of data, with the two longest datasets containing just over 1000 months of data each. In addition there are another 13 station datasets with over 360 months of data. Most of these stations are located on the Cordillera mountain ranges in the west of the country, with a few also being found on the Caribbean coast but only three being located in the eastern half of the country (see Fig. 95.1 above). There is no temperature data before 1920.

The change in the mean monthly temperature of Colombia since 1920 is shown in Fig. 95.2 below. This was determined by first calculating the monthly temperature anomalies for each station dataset and then averaging them to produce a mean temperature anomaly (MTA) for the region. The temperature anomalies for each station were determined by calculating the twelve monthly reference temperatures (MRTs) for each station using the method described previously in Post 47 with the reference period being 1971-2000. The MRTs for each station were then subtracted from that station's raw temperature data to produce the anomalies for that station. These anomalies are therefore a measure of the change in the monthly temperature relative to the average for that month between 1971 and 2000.

Fig. 95.2: The mean temperature change for Colombia relative to the 1951-1980 monthly averages. The best fit is applied to the monthly mean data from 1946 to 2005 and has a slight positive gradient of +0.17 ± 0.10 °C per century.

The data in Fig. 95.2 above clearly shows that there has been no significant climate change in Colombia since 1940, while the frequency graph in Fig. 95.3 below shows that before 1940 there is too little data to make a reliable judgement. Generally I have found that at least fifteen active stations in a region of under 500 km in extent are needed to provide a reliable MTA. This condition is really only satisfied for Colombia after 1960, and even then only for the west of the country. This suggests that the maximum warming seen in Colombia is likely to be 0.1°C at most. This, of course, does not conform to the established narrative on climate change.

Fig. 95.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Colombia.

According to Berkeley Earth (BE) the climate in Colombia has warmed by over 1.5°C since 1890. If we average the BE adjusted anomalies for Colombia we get the temperature trend shown in Fig. 95.4 below which indicates a similar result and clearly implies a warming of over 0.8°C since 1920. This is clearly completely different from the trend shown in Fig. 95.2 at the start of this blog. So why the difference?

Fig. 95.4: Temperature trends for Colombia based on Berkeley Earth (BE) adjusted data. The average is for anomalies from all stations with over 360 months of data. The best fit linear trend line (in red) is for the period 1926-2010 and has a gradient of +0.95 ± 0.05°C/century.

Critics might claim that the difference is down to the averaging process. Berkeley Earth use gridding, Kriging and homogenization in their process in order to account for variations in local station density: I do not. But if that were the sole or principal explanation then the graph I have constructed in Fig. 95.4 using a simple average would differ significantly from the official Berkeley Earth (BE) plot shown in Fig. 95.5 below. Yet it does not. In fact the two plots are virtually identical even though I have also excluded all stations will less than 360 months of data from the MTA in Fig. 95.2. It is also interesting that the BE graph in Fig. 95.5 claims to be able to estimate the mean temperature in Colombia as far back as 1850 (admittedly with some greater uncertainty) even though the country has no temperature data that I can find before 1920.

Fig. 95.5: The temperature trend for Colombia since 1820 according to Berkeley Earth.

Instead what this shows is that the averaging process is sufficiently accurate to yield the correct result and that the processes of homogenization etc. are not needed. It also shows that stations with small amounts of data (i.e. less than 360 months) add nothing to the overall MTA trend and are therefore nigh on useless. 

We are therefore left with the only other explanation, namely that the differences between the trends in Fig. 95.2 and Fig. 95.4 (or Fig. 95.5) are mainly down to the adjustments made to the data by Berkeley Earth. In short, these adjustments have turned a temperature trend with no intrinsic warming (in Fig. 95.2) into one with almost 1°C of warming in a century (in Fig. 95.4).

Fig. 95.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 95.4 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 1926-2010 has a positive gradient of +0.62 ± 0.03 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

We can quantify the difference between the climate change seen in the raw data and that claimed by climate science by subtracting the data in Fig. 95.2 from the data in Fig. 95.4. The result is the blue curve in Fig. 95.6 above. The warming it represents clearly amounts to at least 0.6°C over the last century. Conveniently Berkeley Earth also detail the magnitude of their breakpoint adjustments in their station data files. These can easily be averaged separately and are indicated by the orange curve in Fig. 95.6. Clearly these adjustments account for the majority of the added warming.

Summary and conclusions

1) There is no evidence of any meaningful rise in temperatures in Colombia since 1940 (see Fig. 95.2).

2) The difference between the temperature trend based on the unadulterated raw data (Fig. 95.2) and the trend based on Berkeley Earth (BE) adjusted data (Fig. 95.4) can probably only be explained by the BE adjustments (see Fig. 95.6) and not some other factors such as the irregular geographical distribution of stations or missing data. This is the most reasonable conclusion in my opinion based on the similarity of the data time series in Fig. 95.4 and Fig. 95.5. 



Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Link to list of all stations.

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.

 

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.