139: Alaska - temperature trends WARMING (probably)

The US state most often linked to climate change is Alaska. This is probably because it is seen as having an Arctic climate even though only about a third of the state actually lies within the Arctic Circle. In fact Alaska is no more northerly than Norway and its Aleutian Island chain stretches further south than London and Berlin. It has an area three times that of France but its population is less than that of Marseille, yet it has an extensive network of weather stations that is greater in data quality than that seen in many industrialized countries. Ordinarily this should be sufficient to determine the temperature change for Alaska to a high level of precision but it isn't. In fact the data is so inconclusive it is difficult to determine whether Alaska has warmed at all over the last one hundred years let alone quantify that warming and discern when exactly it occurred. This is because the natural variation in the long term temperature averages is far greater than the likely warming.

There are one hundred stations in Alaska with over 480 months of data before 2014 including seven long stations with over 1200 months of data. Of the 93 medium stations with over 480 months of data twenty have over 1000 months of data (for a full list of stations see here). The locations of these stations are shown in Fig. 139.1 below.

Fig. 139.1: The (approximate) locations of the 100 longest weather station records in Alaska. Those stations with a high warming trend between 1911 and 2010 are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

 

The map in Fig. 139.1 shows that most of the temperature data for Alaska come from stations that are outside the Arctic Circle. In fact of the one hundred longest stations in Alaska only eight are actually inside the Arctic Circle. And while the remainder are fairly evenly distributed geographically, there are significant clusters of stations around Anchorage, Fairbanks and the panhandle along the coast in the southeast between the the border of Canada and the Alexander Archipelago. As usual for simplicity I will disregard this clustering and assume it makes very little difference to the measured temperature change as it only affects the contribution or weighting of about 15% of stations.

In order to quantify the changes to the climate of Alaska the temperature anomalies for all stations with over 480 months of data before 2014 were determined and averaged. This was done using the usual method as outlined in Post 47 and involved first calculating the temperature anomaly each month for each station relative to its monthly reference temperatures (MRT), and then averaging those anomalies to determine the mean temperature anomaly (MTA) for the country. This MTA is shown as a time series in Fig. 139.2 below with the MRTs for each station calculated using data between 1961 and 1990,  (again using the methodology outlined in Post 47).

Fig. 139.2: The mean temperature change for Alaska since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1921 to 2000 and has a positive gradient of +0.31 ± 0.31 °C per century.

The data in Fig. 139.2 above illustrates the difficulty of determining a definitive temperature trend when the data is subject to significant variability over time. In this case choosing to fit to the data from 1921 to 2000 leads to a small positive gradient of 0.31°C per century, but this is no bigger than the uncertainty and so is not statistically significant. If other fitting intervals are chosen then the gradient can be significantly different. For example, an interval of 1921-1995 results in a gradient of 0.18°C per century while 1926-2005 produces 0.96°C per century. All of which poses the awkward question, which result is correct?

In my opinion there is no obvious answer, but there are two factors that we could consider that may shed some additional light on the problem. The first of these is to choose an appropriate fitting interval based on the cycle of the natural variations (i.e. fitting from peak to peak), while the second is to concentrate on data that is the result of averaging the greatest number of station records. 

In Post 4 I explained how the best fit line to a single period of a sine wave gives a non-zero gradient (see Fig. 4.7) whereas fitting to a cosine wave does not. This is because a cosine wave is symmetric about the y-axis while the sine wave is anti-symmetric. As most temperature data tends to oscillate over time due to natural variations it therefore follows that the gradient of any fit to that data will depend on the interval chosen relative to the peaks of those natural oscillations. 

In order to avoid biasing the gradient due to asymmetry in the fitting range, the range should be symmetric relative to the natural oscillations. These natural oscillations are seen most clearly in the 5-year moving average (see the yellow curve in Fig. 139.2). So the fitting range should be chosen so that it starts and ends on a peak in the 5-year average, or alternatively starts and ends on a trough. The best fit in Fig. 139.2 does not do this. It starts near a trough at 1921 and ends on a plateau in 2000. But if we change the fitting interval from 1914 to 2003 then the interval starts and ends on a peak in the 5-year average. The result is the best fit shown in Fig. 139.3 below.

Fig. 139.3: The mean temperature change for Alaska since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1914 to 2003 and has a positive gradient of +0.71 ± 0.26 °C per century.

The gradient of the best fit in Fig. 139.3 is more than twice that in Fig. 139.2 even though the data hasn't changed. This is simply a result of changing the fitting interval. Of course the underlying reason why a change of fitting interval makes such a big difference in this case is that the natural fluctuations in the 5-year average are so large. These changes in temperature can exceed 2°C in less than five years. So we could ask, is the temperature rise of about 0.7°C indicated by the best fit in Fig. 139.3 really that significant in comparison?

Fig. 139.4: The number of station records included each month in the mean temperature anomaly (MTA) trend for Alaska in Fig. 139.2 and Fig. 139.3.

The second factor in determining any choice of fitting range is the quantity of data available. The graph in Fig. 139.4 above shows the number of stations included in the MTA in Fig. 139.2 and Fig. 139.3. From 1920 onwards there are over twenty stations each month. In the previous post and in Post 57 I argued that at least ten, and possibly over twenty-five stations are needed in order for the MTA to be reliable, so this condition is satisfied for all months after January 1920. The data before 1920 will therefore be much less reliable, but there is still enough data to allow us to calculate an approximate MTA as far back as the 1820s. This is shown in Fig. 139.5 below.

Fig. 139.5: The mean temperature change for Alaska since 1820 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1911 to 2010 and has a positive gradient of +0.73 ± 0.22 °C per century.

The data in Fig. 139.5 indicates that it is possible to calculate and MTA as far back as 1829, but before 1900 there are gaps in the data and most of the MTA data for this period is based on an average of anomaly data from less than three different stations. So that raises questions over its reliability.

So how should we interpret this data? The station frequency data in Fig. 139.4 suggests only data after 1900 or even 1920 is sufficiently reliable. As for the data after 1900, there are many ways to interpret it. For example, if we just look at data from 1901 to 1975 the best fit (as determined from trough to trough) is strongly negative (see Fig. 139.6 below). But after 1975 the temperature appears to increase abruptly by about 1°C. So is this interpretation of the temperature trend any more believable than those shown in Fig. 139.2 or Fig. 139.3? It is hard to tell, again because of the high level of natural variability in the data which could be varying on multiple timescales. Such multi-frequency variability is potentially indicative of chaotic or fractal behaviour as I discussed in Post 9, Post 17 and Post 42.

Fig. 139.6: The mean temperature change for Alaska since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 1975 and has a negative gradient of -0.52 ± 0.33 °C per century.

If we next consider the change in temperature based on Berkeley Earth (BE) adjusted data we get the MTA data in Fig. 139.7 below. This again was determined by averaging the anomalies for each month from the one hundred longest stations in Alaska and suggests that the climate of Alaska has warmed by over 1°C since 1870, but with large natural variations of up to 1.5°C in the 10-year average.

Fig. 139.7: Temperature trends for Alaska based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1876-2010 and has a positive gradient of +1.00 ± 0.06°C/century.

Comparing the curves in Fig. 139.7 with the published Berkeley Earth (BE) version for Alaska in Fig. 139.8 below we see that there is good agreement between the two sets of data as far back as 1880. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 139.7 using adjusted data is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 139.8. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 139.2 and Fig. 139.5. In other words, any discrepancy between the adjusted data in Fig. 139.7 and the unadjusted data in Fig. 139.5 cannot be due to the averaging process. Any form of weighted averaging would also not affect the results.

Fig. 139.8: The temperature trend for Alaska since 1820 according to Berkeley Earth.

Most of the differences between the MTA in Fig. 139.6 and the BE versions using adjusted data in Fig. 139.7 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. 139.5 and Fig. 139.7.

Fig. 139.9: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 139.7 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 1921-2000 has a positive gradient of +0.262 ± 0.009 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

The magnitudes of these adjustments are shown graphically in Fig. 139.9 above. The blue curve is the difference in MTA values between adjusted (Fig. 139.7) and unadjusted data (Fig. 139.5), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. The overall adjustment from 1920 to 2000 is small, about +0.2°C. Nevertheless, it can be seen in the difference in the 5-year means (see Fig. 139.10 below) for the unadjusted data (blue curve) and the adjusted data (red curve). The difference, though, is about ten times less than the variability in the two MTAs over time. The data in Fig. 139.10 also highlights the difficulty in interpreting the data. If the data between 1940 and 1980 were missing or ignored, then one could postulate that Alaska has seen fairly consistent warming since 1900 amounting to about 1°C in total. But if the 1940-1980 data is included the data all looks very random.

Fig. 139.10: The 5-year mean temperature change for Alaska since 1900 based on the original raw data (in blue) and the Berkeley Earth adjusted data (in red).

Summary

The temperature data for Alaska demonstrates the difficulty in determining an accurate temperature trend for a region when the climate is subject to a high degree of variability.

It is possible that the climate has warmed by almost 1°C since 1900 (see Fig. 139.3), or it might not have warmed at all (see Fig. 139.2).

If the climate has warmed, this warming may have been fairly continuous (see Fig. 139.3), or it could have been fairly recent, occurring mainly after 1980 (see Fig. 139.6).

The one thing we can say is that the difference between the temperature rise based on Berkeley Earth adjusted data (see Fig. 139.7) and that based on the raw unadjusted data (see Fig. 139.5) is small (less than 0.3°C) and much less that the 5-year natural variability of the data (about 2°C).


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

List of all stations in Alaska with links to their raw data files.

44. Europe - temperature trends since 1700 - STABLE to 1980

The longest temperature records that we have are almost all found in Europe. In fact Europe has over 30 records that predate 1800, and three that go back beyond 1750. One of those three is the De Bilt record from the Netherlands (Berkeley Earth ID: 175554) that I discussed in both Post 41 and Post 42 and which dates back to 1706. The second is Uppsala in Sweden (Berkeley Earth ID: 175676) which dates back to 1722, and the third is Berlin-Tempelhof in Germany (Berkeley Earth ID: 155194) which has data as far back as 1701. Overall, there are nearly 120 temperature records with over 1200 months of data that also have data that predates 1860 (see here for a list). If we average the anomalies from these records, we get the temperature trend shown in Fig. 44.1 below.

 

Fig. 44.1: The temperature trend for Europe since 1700. The best fit is applied to the interval 1731-1980 and has a positive gradient of +0.10 ± 0.04 °C per century. The monthly temperature changes are defined relative to the 1951-1980 monthly averages.

 

To construct the trend in Fig. 44.1 above the raw temperature data from each of 109 records was first converted to monthly anomaly data by subtracting the monthly reference temperatures (MRTs). The MRTs were in turn calculated for the time interval 1951-1980 by averaging the data in that record over all months in that period. This is the same time frame that was used by climate scientists in the 1980s to analyse temperature data, but is significantly earlier than the time intervals normally used today which tend to be 1961-1990 or 1981-2010. The reasons for the differences in time frame I intend to discuss in a later post.

The temperature trend in Fig. 44.1 has two features of note. The first is the very slight upward trend from 1730 to 1980 of approximately 0.10 °C per century. This amounts to a total temperature increase over that time period of about 0.25 °C which is significantly less than the standard deviation of the 10-year moving average of the same data. This suggests that this trend is insignificant when compared to natural variations in temperature.

The second feature is the sudden temperature rise of almost 0.8 °C seen in 1988. This looks unnatural. So much so that, if it were to occur in just one temperature record, then it could be ascribed to a random fluctuation, or a sudden change in the local environment or undocumented location change. But this is not seen in just one record; it is seen in the average of over 100 temperature records, as the data in Fig. 44.2 below shows.

 

Fig. 44.2: The number of sets of station data included each month in the temperature trend for Europe.

 

Nor can we claim that this is just a local effect. The map below in Fig. 44.3 shows the approximate location of all 109 stations whose data was used to construct the trend in Fig. 44.1 above. While it is clear that the greatest concentration of stations is in central Europe between France and Poland, it is also evident that there are significant numbers of stations with very long records located on the edges of Europe such as in the UK, Scandinavia and eastern Europe. This suggests that the sudden rise in temperature seen in 1988 is real and widespread.

 

 Fig. 44.3: The locations of long stations in Europe with more than 1800 months of data, or more than 1200 months of data but with significant data from before 1860. Those stations with a high warming trend from 1700-1980 are marked in red.

 

For comparison, I have performed the same averaging process on the adjusted data for each station created by Berkeley Earth. This adjusted data for each station incorporates two adjustments to the data. Firstly, the monthly reference temperatures (MRTs) are constructed from homogenized data for the region rather than from the raw station data. Secondly, the trend of each temperature record is spliced into segments using breakpoints, and each segment is adjusted up or down relative to its original position. These breakpoint adjustments are supposed to remove local measurement errors (such as those due to changes in instrumentation or location) and thus make the data more reliable, but as I pointed out in my previous post, reliability in temperature data is very hard to measure due to the amount of natural variability that it contains.

 

Fig. 44.4: Temperature trends for all long and medium stations in Europe since 1750 derived by aggregating and averaging the Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1801-1980 and has a gradient of +0.33 ± 0.03 °C/century.

 

The results of averaging the Berkeley Earth adjusted data are shown in Fig. 44.4 above. Three things are noticeable in this data. Firstly, the trend in the data before 1980 has increased by a factor of three. There are two main reasons for this. One reason is that the adjustments made to the data have increased the trend slightly and smoothed out some of peaks before 1830 (see Fig. 44.6 below). The other is that the interval used for the fitting of the linear regression is shorter. This in turn reduces the gradient of the trend.

The second feature of the data in Fig. 44.4 above is that the jump in temperature after 1988 is still present, and is just as large as that seen in Fig. 44.1.

The third feature of the data in Fig. 44.4 is that it closely resembles that data shown for the 12-month and 10-year trends that has been published by Berkeley Earth (see Fig. 44.5 below). This suggests that the averaging process I have used is sufficiently accurate without the need to apply different weightings to the data from different stations as Berkeley Earth does. The weightings that Berkeley Earth use are supposedly to correct for any clustering of stations, but the map in Fig. 44.3 suggests these weightings are not likely to vary significantly for most stations, and so are not likely to be of primary importance. The agreement between the data in Fig. 44.4 and that in Fig. 44.5 appears to confirm that hypothesis.

 

Fig. 44.5: The temperature trend for Europe since 1750 according to Berkeley Earth.

 

It can be seen from these results that the differences between the trends I have constructed using the original data and the trends derived using Berkeley Earth's adjusted data are not as large as has been seen in previous regional analyses, such as those for South Africa (Post 37), South America (Post 35), the South Pacific (Post 33 and Post 34), Papua New Guinea (Post 32), Indonesia (Post 31), Australia (Post 26) and New Zealand (Post 8). These differences for Europe are shown in Fig. 44.6 below.

 

Fig. 44.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 44.4 after smoothing with a 12-month moving average. The linear best fit (red line) to the breakpoint adjustment data (shown in orange) is for the period 1841-2010 and has a gradient of 0.057 ± 0.001 °C per century. The blue curve represents the total BE adjustments including those from homogenization.

 

Overall, the adjustments made by Berkeley Earth to their data have probably only added about 0.2 °C to the warming. More significant are the adjustments made to data before 1830 which appear to be designed to flatten the curve. Such adjustments, though, assume that the mean temperature before 1830 was stable. Yet data from 1830 to 1980 suggests that the temperature trend for Europe was anything but stable, even though the trend shown in Fig. 44.1 was constructed from between 50 and 109 different datasets over that period. The full extent of that instability for the 5-year average temperature can be seen in Fig. 44.7 below.

 

Fig. 44.7: The 5-year moving average of the temperature trend for Europe since 1700. The best fit is applied to the monthly anomaly data for the interval 1731-1980 and has a positive gradient of +0.10 ± 0.04 °C per century.

Conclusions

In 1981 James Hansen and co-workers at NASA's Goddard Institute for Space Studies (GISS) published a paper in the pre-eminent journal Science (which incidentally, has an impact factor of 41.8, where impact factors over 1.0 are considered good) that was one of the first to warn of the impact that increased levels of carbon dioxide in the atmosphere could have on global warming and climate change. But here is the problem: the data shown here appears to indicated that there was no significant warming in Europe before 1981. As the data shown in Fig. 44.1 indicates, the total warming in Europe for the 250 years before 1981 was so small (less than 0.25 °C) that it was less than the natural variation in the mean decadal temperatures over the same period.

Then, in 1988 the mean temperatures in Europe suddenly jumped by over 0.8 °C (see Fig. 44.1), just in time for the IPCC's  first assessment report on climate change in 1990 (PDF). A similar abrupt jump was seen at about the same time in Botswana and, to a lesser extent, in South Africa. Convenient, certainly. But is this just coincidence or 20:20 foresight by the IPCC?

As I have shown throughout the course of this blog, before 1981 there does not appear to have been any exceptional warming in most of the Southern Hemisphere either. So the above analysis raises important concerns regarding the reported extent of climate change in Europe and beyond. The most important question is: is the temperature rise seen after 1988 in Fig. 44.1 real? And if so, what is causing it? 

If it is being driven by CO₂, then why does it not correlate with increases in CO₂ levels in the atmosphere? If it is a natural phenomenon, why are there no other jumps of a similar magnitude in the previous 250 years? Could it be another example of chaotic behaviour similar to the self-similarity I explored in Post 42? And if so, is it just random, or is it the consequence of a complex system being driven between meta-stable states by, for example, greenhouse gases? What I don't see so far is conclusive evidence either way.

41. Netherlands - temperature trends - VARIABLE

The Netherlands has one of the longest instrumental temperature records in the world, and probably the most complete record covering the last 300 years. The record from De Bilt (Berkeley Earth ID: 175554) had nearly 3700 months of data in 2013 that stretched back to 1706 (see Fig. 41.1 below). Only Berlin Tempelhof (Berkeley Earth ID: 155194) has earlier data that extends to 1701, but it has fewer months overall and significant gaps in its record before 1756.

Fig. 41.1: The temperature trend for De Bilt since 1706. The best fit is applied to the interval 1731-2005 and has a positive gradient of +0.29 ± 0.04 °C per century. The monthly temperature changes are defined relative to the 1976-2005 monthly averages.

As I showed in the last post, Belgium also has one long record that stretches back to the 18th century, but it has virtually no other data before 1973. The Netherlands is much better in this respect. There is one other dataset with some sporadic 19th century data, and overall there are five long station records with more than 1200 months of data. In addition, there are another 25 medium records with more than 480 months of data. Details of all these 30 stations (and other shorter records) are listed here, while their geographical locations are shown on the map in Fig. 41.2 below.

 

Fig. 41.2: The locations of long stations (large squares) and medium stations (small diamonds) in the Netherlands. Those stations with a high warming trend are marked in red.

It can be seen from the map above that the stations in the Netherlands are fairly randomly distributed across the country, but that their number appears to be significantly less than the 30 stations stated previously. This is because in nearly a dozen cases two or more stations are located within 10 km of each other. I intend to look at this is more detail in a later post, where I will look at what this says about data reliability. 

The other impact of this clustering is the effect it could have on the station weightings in the regional average. Normally if a cluster of records is found the weighting of each record should probably be reduced as they will tend to repeat each other's data and geographical coverage. However, as most of the station records appear in effect to be paired up, they will almost all have the same reduced weighting, so the weighting reduction should largely cancel. This is largely confirmed by the results I will show later in this post. The other point to note, is that the clustering really only impacts the medium stations, most of which have data after 1970 only. So the weighting problem will only have a slight effect on the overall trends after 1970.

Fig. 41.3: The temperature trend for the Netherlands since 1706. The best fit is applied to the interval 1731-2005 and has a positive gradient of +0.31 ± 0.04 °C per century. The monthly temperature changes are defined relative to the 1976-2005 monthly averages.

If we average the anomaly data for all the long and medium stations we get the trends shown above in Fig. 41.3. The overall trend indicates that the region has warmed by about 0.31 °C per century since 1700. This equates to an overall warming of about 0.97 °C. But as I explained in Post 14, the current human industrial and domestic energy consumption in the country suggests that the region should have warmed by at least 1.0 °C over the same period simply as a consequence of all the heat that is produced each year by human activity. So, just as for Belgium, we see little need to call on the effects of carbon dioxide emissions and the Greenhouse Effect to explain the observed temperature rise.

The other interesting feature of the data in Fig. 41.3 is the shape of the temperature trend between 1800 and 1950. There is clearly a peak around 1860 that is seen not just in the De Bilt record in Fig. 41.1, but also in the Zuid-Limburg station data. This suggests that temperatures in the mid-19th century in the Netherlands were actually higher than they are today. This is a phenomenon that I have identified and highlighted previously in other countries and regions such as New Zealand (see Post 8), Australia (see Post 26) and South America (see Post 35). In fact it appears to occur over most of the Southern Hemisphere, or at least in those parts that have sufficient data before 1900.

The anomaly data used to construct the trend in Fig. 41.3 was derived by first calculating the monthly reference temperatures (MRT) for the period 1976-2005 for each record, and then subtracting these from the raw data. These were then averaged. Temperature records were only included in the trend in Fig. 41.3 if they had at least 480 months of data, and at least 320 months of this data was within the MRT interval of 1976-2005. This was to ensure that all temperature anomaly records were measured relative to identical reference points. The result was that three medium stations were excluded because they had insufficient data after 1975. These were the stations at Den Helder, Maastricht and Groningen. 

Fig. 41.4: The number of sets of station data included each month in the temperature trend for the Netherlands.

The actual number of stations used to construct each monthly point in the trend in Fig. 41.3 is illustrated above in Fig. 41.4. This shows that the trend before 1900 is almost entirely due to the data from De Bilt in Fig. 41.1, while the data from 1900 to 1950 comes from the five long stations. After 1950 as many as 27 station records were used for each monthly average.

Fig. 41.5: Temperature trends for all long and medium stations in the Netherlands since 1750 derived by aggregating and averaging the Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1801-1980 and has a gradient of +0.29 ± 0.03 °C/century.

So the question is, how significant are these results? And also how reliable are they?

Well, one way to test this is to compare these results against those produced by climate science groups like Berkeley Earth. The first thing to remember, though, is that the Berkeley Earth anomaly data for each station record is different from that which I have calculated here because it uses homogenization and breakpoint alignment to adjust the data, techniques that I have profound misgivings about because they could introduce warming to the overall trend that is not actually there. That is why I restrict my analysis to the raw data with all its imperfections.

However, if we apply the same averaging process to the Berkeley Earth adjusted data as I have employed to the raw data, we see that the trends we get (as illustrated in Fig. 41.5 above) agree very well with those published by Berkeley Earth and shown in Fig. 41.6 below. In fact the size and positions of most of the peaks in the two figures are virtually identical. This suggests that the two processes (mine and Berkeley Earth's) are broadly consistent, even if the anomaly data for each station that is used in the averaging is different. What it also shows, though, is that the Berkeley Earth trend that incorporates homogenization and breakpoint adjustments is somewhat different from the trend I have presented in Fig. 41.3 that avoids using such controversial techniques. For example, according to Berkeley Earth, the warming in the Netherlands since 1900 is at least 1.5 °C, and there was no warm period in the mid-19th century. It is these disagreements over data and methodology, and the effects they have on the resulting temperature trends, that partly fuels the climate scepticism debate.

Fig. 41.6: The temperature trend for the Netherlands since 1750 according to Berkeley Earth.

If we try to quantify the difference between the Berkeley Earth temperature trend and the raw trend I have constructed in Fig. 41.3 we find that the adjustments made by Berkeley Earth  have two main effects. The first is to flatten the trend before 1900. The second is to exaggerate the temperature rise after 1900 by about 0.3 °C. These adjustments are illustrated in Fig. 41.7 below.

Fig. 41.7: The contribution of Berkeley Earth (BE) adjustments to the anomaly data after smoothing with a 12-month moving average. The linear best fit to the data is for the period 1901-2010 (red line) and the gradient is 0.266 ± 0.009 °C per century. The orange curve represents the contribution made to the BE adjustment curve by breakpoint adjustments only.

Conclusions

It is clear from Fig. 41.3 that there has been a large degree of warming in the Netherlands over the last 300 years, but that this is probably less than than the 1.5 °C we are being led to expect for anthropogenic global warming (AGW) in the Northern Hemisphere as claimed by the IPCC and the HadCRUT4 data. 

The magnitude of this warming is probably only about 1 °C. However, this temperature rise is only what one would expect from the growth of industrial energy use over this period (for the Netherlands I have previously calculated that it should be about 1.0 °C) as I explained in Post 14. 

However, there is also evidence of significant natural variation in the temperature record (such as the warming in the mid-19th century) that is inconsistent with current IPCC claims.

Consequently, the data presented here does not really add support to the theory that carbon dioxide is the primary driver of warming, otherwise the warming seen in the Netherlands should be much larger, and there would be no anomalous fluctuations in temperature before 1900.

Finally, there is the issue of historical perspective. If temperatures in the recent past were both higher than now and at times lower than now, why are we worried about current temperatures when they appear to be fluctuating between normal bounds?