133: UHI #6 - Buenos Aires (Argentina)

The second largest city in South America by population is Buenos Aires in Argentina with a population of over thirteen million people. The largest is Sao Paulo in Brazil. Both could be categorized as urban heat islands (UHIs); Sao Paulo in particular has warmed by about 3°C since 1887 and Buenos Aires by up to 2°C. However, as I have yet to fully analyse temperature data from Brazil it is not possible for me to compare the Sao Paulo data to the temperature change for the wider region, although this is unlikely to be more than about 1°C. So instead I will concentrate on Buenos Aires. Sao Paulo will come later.

A comparison of the temperature trends for Buenos Aires and Argentina shows that temperatures have risen far more in Buenos Aires than they have in Argentina as a whole. In fact as Fig. 133.1 below shows, they have risen almost three times faster in Buenos Aires since 1900 than they have in Argentina. Before 1900 temperatures were stable in both Buenos Aires and Argentina.

Fig. 132.1: The change to the 5-year average temperatures of Buenos Aires (red curve) and Argentina (blue curve) since 1900.

In Post 61 I examined the temperature trends for Argentina. The mean temperature change since 1900 is shown in Fig. 132.2 below and it indicates that Argentina has exhibited only modest warming over the last one hundred years. The best fit for 1901-2000 indicates a temperature rise of about 0.64°C while the 5-year average suggests a rise of about 0.52°C.

Fig. 132.2: The mean temperature change for Argentina since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 2000 and has a positive gradient of +0.64 ± 0.11 °C per century.

The oldest major weather station in Argentina is Buenos Aires Observatorio (Berkeley Earth ID: 151642). It is located in the heart of Buenos Aires and has continuous data stretching back as far as 1856, although there is a break in the data between 2006 and 2011. It is one of only two major stations within 20 km of the city centre, hence its significance as a case study of the urban heat island (UHI) effect. The other is Aeroparque (Berkeley Earth ID: 151640) which only has data from 1961 onwards but also exhibits strong warming.

In contrast to the rest of Argentina, Buenos Aires Observatorio shows strong and continuous warming since 1910 (see Fig. 132.3 below). Before 1910 the temperatures were stable. The best fit for 1901-2000 indicates a temperature rise of about 2.4°C in one hundred years while the 5-year average suggests a rise of 1.77°C.

Fig. 132.3: The mean temperature change for Buenos Aires Observatorio since 1900 relative to its 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 2000 and has a positive gradient of +2.40 ± 0.18 °C per century.

Summary

The following temperature changes were observed from 1901 to 2000.

Argentina: 0.52°C (trend 0.64°C).

Buenos Aires: 1.77°C (trend 2.40°C).

So Buenos Aires has warmed by almost 1.8°C more than the surrounding state of Argentina, or more than three times faster. A classic UHI!

113: The Guianas - temperature trends STABLE

The Guianas is the region of north-eastern South America that comprises the three territories of Guyana (formerly British Guiana), Suriname (formerly Dutch Guiana) and French Guiana. It sits on the Atlantic coast between Venezuela and Brazil, and as the data in Fig. 113.1 below shows, it does not appear to have experienced any significant climate change over the last 100 years, although the mean temperature has fluctuated significantly by up to 1°C (see yellow curve).

Fig. 113.1: The mean temperature change for the Guianas relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1896 to 2005 and has a slight positive gradient of +0.11 ± 0.04 °C per century.

The MTA in Fig. 113.1 was calculated by averaging the temperature anomalies from the fourteen longest temperature records for the region. Eight of these records were from medium stations with over 480 months of temperature data before the end of 2013, but there are only two long stations with more than 1200 months of data.

The anomalies for each station were determined using the usual method as outlined in Post 47. This involved first calculating the monthly reference temperatures (MRTs) for each station using a set reference period, in this case from 1961 to 1990, and then subtracting the MRTs from the raw temperature data to deliver the anomalies. If a station had at least twelve valid temperatures per month within the MRT interval then its anomalies were included in the MTA calculation. The total number of stations included in the MTA in Fig. 113.1 each month is indicated in Fig. 113.2 below. The peak just around 1975 suggests that the 1961-1990 interval was indeed the most appropriate.

Fig. 113.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for the Guianas in Fig. 113.1.

The locations of the fourteen main stations are shown in the map in Fig. 113.3 below. This appears to show that the geographical spread is fairly uniform, although there does appear to be far more stations in Suriname than in either Guyana or French Guiana. This variation in station density is probably not sufficient to significantly distort the average in Fig. 113.1 from its true value though. In which case the simple average of the anomalies from all stations used to construct the MTA in Fig. 113.1 should still yield a fairly accurate temperature trend for the region as a whole. 

Overall there are more stations close to the coast than inland, and the coastal stations appear more likely to have warming trends. A warming trend is defined here as one where the temperature gradient for 1911-2010 is positive and exceeds twice the uncertainty in that trend.

Fig. 113.3: The (approximate) locations of the fourteen longest weather station records in the Guianas. Those stations with a high warming trend between 1911 and 2010 are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote stations with more than 300 months of data.

In contrast to Fig. 113.1, the corresponding MTA dataset based on data that has been adjusted by Berkeley Earth (BE) exhibits a strong warming trend with temperatures rising by over 1.5°C since 1890 (see Fig. 113.4 below).

Fig. 113.4: Temperature trends for the Guianas based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1896-2005 and has a gradient of +1.06 ± 0.03°C/century.

If we next compare the curves in Fig. 113.4 with the published Berkeley Earth (BE) version for Suriname in Fig. 113.5 below (where most stations are located) we see that there is remarkably good agreement between the two sets of data at least as far back as 1900. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 113.4 is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 113.5. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 113.1.

Fig. 113.5: The temperature trend for Suriname since 1820 according to Berkeley Earth.

The differences between the MTA in Fig. 113.1 and the BE versions using adjusted data in Fig. 113.4  are instead mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values seen in Fig. 113.1 and Fig. 113.4. The magnitudes of these adjustments are shown graphically in Fig. 113.6 below. The blue curve is the difference in MTA values between adjusted (Fig. 113.4) and unadjusted data (Fig. 113.1), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. Both are considerable with the former leading to an additional warming since 1900 of up to 1.6°C.

Fig. 113.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 113.4 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 1896-2005 has a positive gradient of +0.96 ± 0.03 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Summary

According to the raw unadjusted temperature data, over the past century the climate of the Guianas has remained stable (see Fig. 113.1).

Over the same period adjusted temperature data from Berkeley Earth appears to show that the climate of the Guianas has warmed by as much as 1.6°C (see Fig. 113.4).


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Link to list of all stations in Guyana and their raw data files.

Link to list of all stations in Suriname and their raw data files.

Link to list of all stations in French Guiana and their raw data files.

112: Venezuela - temperature trends WARMING

The climate of Venezuela is interesting because the country sits between Colombia to the west and the Lesser Antilles to the north. In this blog I have already examined the climate for both these regions and the results are not entirely consistent. The mean temperature of Colombia has remained fairly stable since 1940, increasing only slightly by about 0.1°C (see Fig. 95.2 in Post 95). The caveat to this is that there is no temperature data for the country before 1920 and only two stations of note with data before 1940. The Lesser Antilles, on the other hand, have more data but spread over a larger area, and this data shows much more warming, up to 2°C since 1890 (see Fig. 111.3 in Post 111). It turns out that the climate of Venezuela more closely resembles that of the Lesser Antilles than it does its neighbour Colombia as can be seen in Fig. 112.1 below.

Fig. 112.1: The mean temperature change for Venezuela relative to the 1976-2005 monthly averages. The best fit is applied to the monthly mean data from 1941 to 1980 and has a slight positive gradient of +0.28 ± 0.31 °C per century.

The main features of the data in Fig. 112.1 are very similar to those seen in Fig. 111.3 of Post 111. Between 1940 and 1980 the climate is stable, with the mean temperature rising by at most 0.1°C, but after 1980 there is a rapid temperature increase of over 0.5°C. This is consistent with other trends seen in the region such as for Puerto Rico (see Fig. 110.1 in Post 110) and the Dominican Republic (see Fig. 109.3 in Post 109). Yet the mean temperature anomaly (MTA) dataset in Fig. 112.1 also displays a large jump in temperatures of over 1.5°C before 1940. This is not seen in the Puerto Rico or the Dominican Republic data, nor is it seen in the data for Colombia (see Fig. 95.2 in Post 95), but it is seen in the data for the Lesser Antilles (see Fig. 111.3 in Post 111). In both cases the MTA before 1940 is based on data from only about five stations or less (see Fig. 112.2 below and Fig. 111.4 of Post 111), yet the fact that they corroborate each other suggests that the data may be more reliable than than I first thought and may be indicative of real climate change. The problem is that, if this is true, it poses a lot of difficult questions about the real nature of climate change.

Fig. 112.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Venezuela in Fig. 112.1.

If we assume that the temperature rises of 1.5°C from 1900 to 1940 that are seen in Venezuela (see Fig. 112.1 above) and the Lesser Antilles are real, then we need to ask the question, why?

Historical measurements of carbon dioxide (CO₂) levels suggest that atmospheric CO₂ levels increased from about 290 ppm in 1880 to about 310 ppm in 1940. But even with the best will in the world it is difficult to believe that a 7% rise in CO₂ would result in a 1.5°C temperature rise. In Fig. 87.3 of Post 87 I showed that the most it could lead to was a rise of 0.08°C, and even then three quarters of that rise is likely to be negated by the pre-existing presence of water vapour in the atmosphere, the absorption spectrum of which overlaps both edges of the 15 µm CO₂ absorption band. So the temperature rise seen before 1940 in Venezuela is actually nearly one hundred times greater than would be expected from CO₂ alone. So if CO₂ cannot explain the temperature rise, what does that say about our faith in climate stability? For if the climate can fluctuate by 1.5°C from time to time off its own bat, why should we care about CO₂?

Then there is the more practical issue: why did no-one even notice this temperature rise? We are constantly being told by climate scientists that a 1.5°C rise in global temperatures would be disastrous for the planet. Yet just such an increase appears to have occurred in Venezuela and the Caribbean over a century ago and nothing untoward happened. 

Fig. 112.3: The (approximate) locations of the 21 medium weather station records in Venezuela. 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 800 months of data, while diamonds denote stations with more than 480 months of data.

The mean temperature anomalies (MTA) in Fig. 112.1 were calculated by averaging the temperature anomalies from the 38 longest temperature records for the state. The anomalies for each station were determined using the usual method as outlined in Post 47. All the records used in calculating the MTA had over 240 months of temperature data before the end of 2013 and 21 were medium stations with over 480 months of data. Of these three had over 1000 months of data and a further ten had over 800 months of data. For a full list of stations see here. 

The locations of the medium stations are illustrated in Fig. 112.3 above. This map appears to show that the geographical spread of these stations is fairly uniform but confined to the northern half of the country. The variation in station density is probably not sufficient to significantly distort the average in Fig. 112.1 from its true value though. In which case the simple average of the anomalies from all stations used to construct the MTA in Fig. 112.1 should still yield a fairly accurate temperature trend for the country as a whole. This can be verified by calculating the equivalent MTA, but using Berkeley Earth (BE) adjusted data, and comparing the results with the official BE version. If they are the same then the averaging process should be sufficiently accurate.

Fig. 112.4: Temperature trends for Venezuela based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1941-2010 and has a gradient of +1.06 ± 0.07°C/century.

The corresponding MTA result based on data that has been adjusted by Berkeley Earth (BE) is shown in Fig. 112.4 above and, unlike the raw data in Fig. 112.1, it exhibits a strong warming trend that is more uniform in its gradient. The overall temperature rise from 1900 to 2010 is about 1.5°C and so is significantly less than the 2.2°C that is seen with the raw data in Fig. 112.1.

If we then compare the curves in Fig. 112.4 with the published Berkeley Earth (BE) version in Fig. 112.5 below we see that there is remarkably good agreement between the two sets of data at least as far back as 1920. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 112.4 is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 112.5. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 112.1.

Fig. 112.5: The temperature trend for Venezuela since 1820 according to Berkeley Earth.

The differences between the MTA in Fig. 112.1 and the BE versions using adjusted data in Fig. 112.4 and Fig. 112.5 are therefore mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values seen in Fig. 112.1 and Fig. 112.4. 

The magnitudes of these adjustments are shown graphically in Fig. 112.6 below. The blue curve is the difference in MTA values between adjusted (Fig. 112.4) and unadjusted data (Fig. 112.1), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. The vertical offset between the two curves is due to the difference in MRT intervals used by Berkeley Earth (1961-1990) and for Fig. 112.1 in this blog (1976-2005). What is clear is that after 1960 any adjustments made by Berkeley Earth to the data have little effect on the overall trend. However, before 1940 these adjustments appear to reduce the magnitude of the temperature rise by about 0.5°C. Overall the adjustments tend to make the MTA curve more linear.

Fig. 107.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 112.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1891-2010 has a positive gradient of +0.19 ± 0.10 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Summary

According to the raw unadjusted temperature data, over the past century the climate of Venezuela has warmed by over 2°C (see Fig. 112.1).

The climate change seen for Venezuela appears to be very similar to that of the Lesser Antilles (see Fig. 111.3 of Post 111) with 75% of the warming occurring before 1940 and very little warming between 1940 and 1980. This does not correlate with changes to atmospheric carbon dioxide concentrations over the same period. 

The origin of the 1.5°C warming before 1940 remains unexplained but its similarity to data from the Lesser Antilles suggests that the temperature change is real and not the result of measurement biases or errors.

The adjusted temperature data from Berkeley Earth appears to show that the climate of Venezuela has warmed more continuously (or linearly) and by about 1.4°C (see Fig. 112.4 and Fig. 112.5) since 1880.


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

List of all stations in Venezuela and their raw data files.

100. List of completed temperature analyses by country

 

As this is my 100th post on this blog I thought it would be a good moment to summarize the results that have emerged from the temperature data I have analysed so far. Below is a list of all the countries and regions that I have investigated to date with links to the relevant post. This amounts to about 60 countries, states and territories in total, which is roughly one third of all the countries in the world. 

The main areas that so far remain to be studied are the Arctic, Canada, Russia, UK, Scandinavia, Mediterranean, North Africa, Middle East, China and Japan. In the Southern Hemisphere only Brazil, Venezuela, Guyana, Suriname, French Guiana and the South Atlantic remain. However, most of these Southern Hemisphere regions are already included in the analysis of South America in Post 35.

Europe

Europe has the longest temperature records available with several in Germany, Sweden and the Netherlands stretching back to the early 18th century. The average of the 109 longest records yields a mean temperature anomaly (MTA) that shows a small but continuous warming of about 0.1°C per century for over 200 years until 1988. Then the temperature jumps suddenly by over 1°C. The reason for this jump is unclear. It is certainly not related directly to carbon dioxide emissions. The only countries that appear to have strong warming trends are the Benelux countries, Denmark and Switzerland. The Baltic states and most of central Europe appear to cool before 1980 and then warm suddenly.

109 longest station records (Post 44)

Austria (Post 55)

Baltic States (Post 51)

Belgium and Luxembourg (Post 40)

Central Europe average (Post 57)

Czechoslovakia (Post 53)

Denmark (Post 48)

Germany (Post 49)

Hungary (Post 54)

Netherlands (Post 41)

Poland (Post 50)

Switzerland (Post 56)

 

USA

The USA may not have any temperature records that are as long as the longest that Europe can boast, but its temperature data from 1850 onwards is the best there is. Virtually every state has over 100 station records with over 50 years of data and over 50 records with over 100 years of data. An average of the 400 longest temperature records appears to indicate that the climate warmed by more than 2°C from 1780 to 1920 when carbon dioxide levels barely increased, and then cooled by over 0.5°C when carbon dioxide levels took off. The early warming cannot therefore be due to CO2 and is therefore generally attributed to urbanization and deforestation in the north and east. The cooling seen after 1920 is also seen in most southern states like Louisiana, Mississippi and Texas.

400 longest station records (Post 66)

Louisiana (Post 97)

Mississippi (Post 99)

Texas (Post 52)

 

Central America

The temperature data for Central America can basically be split between Mexico and the rest, however, even then there are more than four times as many stations in Mexico as there are in the rest of Central America. The picture in Mexico is also complicated by the stations there falling into two distinct types from two different sources. On balance it is likely that the overall climate was stable until 1980 and then warmed over the following twenty years by about 1°C.

Mexico (Post 93)

Rest of Central America (Post 94)

South America

Of the countries in South America studied so far, only Argentina (0.6°C), Ecuador (1°C) and Uruguay (1°C) show significant warming, although the Ecuador data is far from reliable. In Paraguay and Chile the climate has cooled while in most other countries it has remained stable. The average of all medium and long stations in South America yields a warming of about 0.5°C since 1900.

All long and medium stations (Post 35)

Argentina (Post 61)

Bolivia (Post 58)

Chile (Post 62)

Colombia (Post 95)

Ecuador (Post 96)

Paraguay (Post 59)

Peru (Post 63)

Uruguay (Post 60)

Asia

My analysis so far of temperatures in Asia has focused on the countries of Indochina and the Indian subcontinent. The overall temperature trend for Indochina is one of cooling before 1980 and warming thereafter. The result is that temperatures in 2010 are barely any higher than they were in 1890. This is also reflected in the individual temperature records of Burma, Malaysia and Vietnam, while those of the Philippines and Thailand remain stable from 1920 onwards. In India and Pakistan there is little warming before 1990 and then a sudden jump in temperatures of about 0.5°C in the mid-1990s. For Sri Lanka the jump in temperature occurs in 1978 while Bangladesh sees a continuous warming of only 0.3°C per century. 

Bangladesh (Post 74)

Burma/Myanmar (Post 69)

India including Nepal (Post 71)

Indian subcontinent (Post 75)

Indochina (Post 70)

Malaysia and Singapore (Post 69)

Pakistan (Post 72)

Philippines (Post 69)

Sri Lanka (Post 73)

Thailand (Post 69)

Vietnam (Post 69)

Africa

Most of southern Africa has exhibited some significant warming of over 1°C since 1980 but the overall picture before 1980 is varied. Angola, Mozambique and South Africa show no warming before 1980 while Malawi, Zambia, Zimbabwe and Madagascar all cool significantly by as much as they later warm. The data for Namibia and Botswana is not great but may indicate a slight warming before 1980 as well as much larger warming thereafter. Of all the countries listed below, Madagascar, Mozambique, South Africa and Zimbabwe have the best quality data and none of these countries appear to exhibit any warming before 1980.

Angola (Post 82)

Botswana (Post 38)

Madagascar (Post 77)

Mozambique (Post 78)

Namibia (Post 39)

South Africa including Lesotho and Eswatini/Swaziland (Post 37)

Zambia and Malawi (Post 81)

Zimbabwe (Post 79)

Australia

Analysis of temperature data for Australia indicates that the mean temperature trend is parabolic with the climate cooling from 1875 to 1960 and then warming. Overall temperatures in 2010 are only about 0.1-0.2°C warmer than in 1875 with temperatures having increased by about 0.5°C since 1960. This pattern in seen in most states such as South Australia, New South Wales and Victoria. It is harder to be conclusive for Tasmania and Western Australia due to a lack of data before 1900 while the trend in Northern Territory is one of consistent cooling. Only Queensland shows constant warming of about 1°C since 1990.

Australia (Post 26)

New South Wales and ACT (Post 18)

Northern Territory (Post 23)

Queensland (Post 24)

South Australia (Post 21)

Tasmania (Post 20)

Victoria (Post 19)

Western Australia (Post 22)

Oceania

Most of the countries and regions of Oceania show little of no warming. In Antarctica the only warming is found around the peninsula. New Zealand cools slightly from 1860 until 1960 then warms by about 0.5°C, rather like much of Australia. Yet despite this, temperatures in 2010 are barely above those in 1860. In Indonesia only the capital city Jakarta shows any strong warming but the average temperature for the country remains stable, although data quality and quantity before 1960 is poor. This is also true for Papua New Guinea where there is some evidence of warming after 1960 by about 0.5°C. In the South Pacific there is a contrast between east and west with the eastern half cooling significantly while the west cools slightly until 1970 before warming again by about 0.5°C. In fact of all the regions listed below, only the Indian Ocean shows significant warming of about 1°C.

Antarctica (Post 30)

Indian Ocean (Post 76)

Indonesia (Post 31)

New Zealand (Post 8)

Papua New Guinea (Post 32)

South Pacific Islands - East (Post 34)

South Pacific Islands - West (Post 33)

Southern Hemisphere

An average of the temperature anomalies from the 1000 longest records in the Southern Hemisphere shows a slight cooling of about 0.1°C until 1975 followed by a modest warming of only about 0.6°C.

Southern Hemisphere station average (Post 64)

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.