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.

138: Evidence against temperature adjustments #3 (Scandinavia)

One of the main aims of this blog has been to investigate the extent to which the various datasets in the global temperature record have been adjusted and to ascertain both the impact of these adjustments and their validity. Most of the blog posts for individual countries or territories have sought to quantify the magnitude of these adjustments by calculating two versions of the mean temperature anomaly (MTA) for each region; one based on its raw unadjusted data and a second using Berkeley Earth adjusted data. Then the two are compared and the difference calculated. This difference is often considerable and often shows that the adjustments have increased the amount of reported warming. But I have also investigated the second issue, that of validity. One way to do this is to compare the MTA for neighbouring regions or different data samples from the same region. 

The rationale is as follows. If there are errors in the data that are sufficient to affect the MTA, then comparing MTAs from different samples from the same region, or samples from adjacent regions that would be expected to be almost identical, could highlight the errors. Of course any difference between MTAs from different regions does not prove that the data is wrong; it may be that the regions aren't as similar as one supposed. But if the data is virtually identical then that does suggest both that the temperature trends for the two samples or regions are behaving the same, and that any data errors in the temperature datasets (which are likely to be numerous) are not significant and so are not in need of correction or adjustment.

In Post 57 I used this approach to compare the temperature trends of neighbouring countries in central Europe (Germany, Czechoslovakia, Austria and Hungary). The results showed that if the MTA for a country was determined using data from more than about fifteen different station records then there was little difference between MTAs for different countries, and thus very little error in the MTA of each country. This is because of a property of statistics called regression towards the mean. This basically states that if any dataset contains errors in its measurements (which most data does), and those errors are random in their size and distribution (which they often are), then the errors will tend to cancel each other when you average the data. Moreover, the more data you average, the greater the cancellation of errors and so the more accurate will be the result. If errors don't cancel, then that is because the errors are systematic not random, so the process also helps to identify these as well.

In Post 67 I repeated this process for temperature data from the USA. In this case instead of comparing data from adjacent regions I compared different samples of one hundred stations from the same region: the entire contiguous United States. The result was the same as in Post 57 with each sample exhibiting an identical temperature trend over time with identical fluctuations in the 5-year moving average of the trend.

In this post I will repeat the country comparison of Post 57 but using the 5-year moving average of the temperature trend data from the four neighbouring Scandinavian countries of Norway, Sweden, Finland and Denmark. These trends were determined in Post 135, Post 136, Post 137 and Post 48 respectively. The results are shown in Fig. 139.1 below.

Fig. 138.1: A comparison of the 5-year average temperature trends since 1700 for Norway, Finland and Denmark compared to that of Sweden. The trends for Finland and Norway are offset by ±3°C for clarity.

In Fig. 139.1 I have compared the trends of Norway, Finland and Denmark with that of Sweden. The reasons for choosing Sweden as the comparator were both geographic and practical. It sits between the other three countries and so is a near neighbour for each (Finland and Denmark are not near neighbours so would not be good comparators). But it also has the most stations of the four countries and so should have the most reliable trend.

The data in Fig. 139.1 clearly shows that the trends for all four countries are very similar after 1900 but diverge as one looks further back in time towards 1800. The reason for this is the reduction in station numbers seen in each country as one moves back in time from 1950 (see Fig. 138.2 below). Given that it seems that somewhere between ten and thirty stations are needed in the MTA average in order for the errors to be minimized, we can see from Fig. 138.2 that this condition is satisfied for all four countries after 1890. That is why the MTAs diverge before 1890 but are very similar after that date.

Fig. 138.2: The number of station records included each month in the averaging for the mean temperature trends in Fig. 138.1.

If we just consider the data after 1850 we see that the agreement between trends for the different countries is remarkably good after 1890 (see Fig. 138.3 below). The agreement between Norway and Sweden, and Finland and Sweden are both particularly good to the point of their three trends being almost identical. There is also excellent agreement between Denmark's trend and that of Sweden after 1980 but less so before. This is probably the result of Denmark not only having much fewer stations than the other three countries, but also having fewer than ten stations before 1975.

Fig. 138.3: A comparison of the 5-year average temperature trends since 1850 for Norway, Finland and Denmark compared to that of Sweden. The trends for Finland and Norway are offset by ±3°C for clarity.

Summary

The data in Fig. 138.3 once again demonstrates the futility of temperature adjustments. The fact that the mean temperature anomalies (MTAs) of Norway, Sweden and Finland agree so well for over 120 years from 1890 onwards without data adjustments indicates that the averaging process alone can eliminate most errors.

The Denmark data also adds weight to the conjecture that between ten and thirty stations are needed in the average in order to eliminate most of the data errors. As the error size decreases with the square root of the sample size, an average of 25 datasets should decrease the error size by 80% (reducing each error to a fifth of its nominal value). 

Comparing the data of these four countries in this way also gives us more confidence in the determining the true nature of the regional temperature trend. All the data after 1900 pretty much agree so we can conclude that temperatures from 1900 to 1980 rose marginally by less than 0.3°C and then jumped by about 1°C in the 1980s. But this jump is still only comparable to the size of the fluctuations in the 5-year average.

From 1850 to 1900 both Denmark and Norway diverge from Sweden slightly but in different directions. But this is based on a comparison of only one or two stations in each case and so is not unexpected.

137: Finland - temperature trends STABLE to 1980

The climate change seen in Finland over the last 200 years closely resembles that seen in Sweden. Between 1830 and 1920 the mean temperature rose by just over 0.5°C. Unfortunately the trend before 1890 is based on the average of less than three datasets so its reliability is open to question. Then from 1920 to 1980 the temperature appears broadly stable before rising again by about 1°C in the 1980s.

Like Norway and Sweden, Finland has an impressive number of weather stations, but only about twenty of them predate 1960. Overall the country has eleven long stations with over 1200 months of data before 2014 but only two have a significant amount of data before 1890. The country also has an additional 106 medium stations with over 480 months of data. These 117 long and medium stations are distributed across the country with a higher concentration in the southwest than elsewhere, and only eighteen are within the Arctic Circle (see Fig. 137.1 below). For a full list of stations see here.

Fig. 137.1: The (approximate) locations of the 117 longest weather station records in Finland. 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.

In order to quantify the changes to the climate of Finland 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, 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. 137.2 and clearly shows that temperatures were fairly stable up until 1980. However at some point in the 1980s (probably in 1988) the mean temperature appears to increase abruptly by about 1°C. A similar temperature jump is seen in many regions across Europe, but in this case it is merely comparable to other fluctuations seen in the 5-year average such as those seen in the 1930s. It is therefore hard to ascertain if this jump is real, or exactly where in time this temperature rise is occurring and how much of it is permanent.

Fig. 137.2: The mean temperature change for Finland since 1880 relative to the 1971-2000 monthly averages. The best fit is applied to the monthly mean data from 1896 to 1980 and has a positive gradient of +0.23 ± 0.31 °C per century.

The process of determining the MTA in Fig. 137.2 involved first determining the monthly reference temperatures (MRTs) for each station using a common reference period, in this case from 1971 to 2000, 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 calculation of the mean temperature anomaly (MTA). The total number of stations included in the MTA in Fig. 137.2 each month is indicated in Fig. 137.3 below. The peak in the frequency between 1960 and 1990 suggests that the 1971-2000 interval was indeed the most appropriate interval to use for the MRTs.

Fig. 137.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Finland in Fig. 137.2.

The data in Fig. 137.3 indicates that the greatest amount of temperature data available in Finland occurs after 1960 with almost 120 long and medium stations in operation at any one time. This drops to about twenty before 1960 and to about ten for most of the first half of the 20th century. Before 1890 there are only two stations in continuous operation, Helsinki (BE ID: 13544) and Helsinki-Vantaa Airport (BE ID: 175422), neither of which have any data before 1829. All the other data before 1840 comes from fragmented data from three or four other stations. 

All this means that the MTA for Finland before 1960 will be less reliable than its values after 1960 while the MTA before 1890 is based on only two stations, both of which are in Helsinki. As an MTA generally needs to be calculated using data from at least sixteen different stations in order to be accurate (see Post 57 for evidence), this suggests that any MTA before 1890 is unlikely to be representative of the country as a whole while the MTA before 1950 may also be slightly biased. Nevertheless, we can calculate an MTA for Norway back to 1740. If we do so we obtain the trends shown in Fig. 137.4 below. This appears to indicate continuous warming of about 1°C from 1840 to 1930 with a further 1°C of warming occurring since 1930. But if we discount the data before 1890 due to insufficient stations, then we are left with the data in Fig. 137.2 which paints a rather different picture.

Fig. 137.4: The mean temperature change for Finland since 1740 relative to the 1971-2000 monthly averages. The best fit is applied to the monthly mean data from 1891 to 2010 and has a slight positive gradient of +1.00 ± 0.18 °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. 137.5 below. This again was determined by averaging each adjusted monthly anomaly from the 117 longest stations and suggests that the climate has warmed continuously by nearly 2°C since 1830. In fact the 10-year average suggests a warming of nearly 2.5°C.

Fig. 137.5: Temperature trends for Finland based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1891-2010 and has a positive gradient of +1.03 ± 0.08°C/century.

Comparing the curves in Fig. 137.5 with the published Berkeley Earth (BE) version for Finland in Fig. 137.6 below we see that there is good agreement between the two sets of data. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 137.5 using adjusted data is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 137.6. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 137.4 and Fig. 137.2. In other words, any discrepancy between the adjusted data in Fig. 137.5 and the unadjusted data in Fig. 137.4 cannot be due to the averaging process. And any form of weighted averaging would also not affect the results.

Fig. 137.6: The temperature trend for Finland since 1750 according to Berkeley Earth.

Most of the differences between the MTA in Fig. 137.4 and the BE versions using adjusted data in Fig. 137.6 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. 137.4 and Fig. 137.5.

The magnitudes of these adjustments are shown graphically in Fig. 137.7 below. The blue curve is the difference in MTA values between adjusted (Fig. 137.5) and unadjusted data (Fig. 137.4), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. The overall adjustment from 1840 to 2013 is small, less than ±0.1°C (see orange curve). The main impact is an offset in the MTA values of about 0.5°C (see blue curve). This is solely due to a use of different MRT intervals for the data in Fig. 137.4 and that in Fig. 137.5 and so can be ignored.

Fig. 137.7: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 137.5 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 +0.021 ± 0.005 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Summary

The temperature data for Finland illustrates the difficulty of determining the true extent of climate change when the data is partial or inadequate. Has the climate of Finland warmed by almost 2.5°C since 1840 as the Berkeley Earth adjusted data in Fig. 137.5 suggests, or has the climate been stable for most of the 20th century as the data in Fig. 137.2 appears to indicate? Is the warming seen before 1900 (when carbon dioxide levels barely increased) due to climate change, or is it due to an urban heat island (UHI) in Helsinki (which accounts for all the data)? Without more data it is hard to tell, but one way we could might be to compare Finland with its neighbours. This I will do in the next post.


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

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

136: Sweden - temperature trends WARMING mostly after 1980

The climate change seen in Sweden over the last 200 years is similar to that seen in Norway over the same period. This is perhaps not surprising given that they are nearest neighbours. The only major difference is the temperature change before 1980. In the case of Norway this was negligible (see Fig. 135.4 in Post 135) and was less than a tenth of the natural variation in the 5-year average. For Sweden there is a modest but distinct upward warming trend from 1860 to 1980 that amounts to about 0.6°C in total. That said this warming is still only comparable with the natural variation in the 5-year average of the mean temperature anomaly (MTA). Then after 1980 the MTA jumps abruptly by about 1°C, just as it did in Norway. The net result is that the temperature trend of Sweden over the last 150 years is one of the few regional trends that actually resembles the global trends published by NOAA, NASA-GISS, Hadley-CRU and Berkeley Earth, but this may not be quite what it seems.

As Sweden has a larger area and larger population than Norway it is not surprising that it has more weather stations. In total Sweden has 25 long stations with over 1200 months of data before 2014 and another 126 medium stations with over 480 months of data (for a full list of stations see here). Their approximate locations are shown on the map in Fig. 136.1 below. The stations are fairly evenly distributed across most of the country, but there does appear to be a greater concentration in the south and only twelve stations (i.e. 8%) are within the Arctic Circle. It does have two of the longest and earliest temperature records though: Uppsala and Stockholm. Both have over 3000 months of more or less continuous data that extend back to before 1760.

 

Fig. 136.1: The (approximate) locations of the 151 longest weather station records in Sweden. 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.

 

In order to quantify the changes to the climate of Sweden 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, 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. 136.2 and clearly shows that temperatures were rising slowly up until 1980. Then at some point in the 1980s (probably in 1988) the mean temperature appears to increase abruptly by about 1°C.

 

Fig. 136.2: The mean temperature change for Sweden since 1880 relative to the 1971-2000 monthly averages. The best fit is applied to the monthly mean data from 1881 to 1980 and has a positive gradient of +0.46 ± 0.19 °C per century.

 

The process of determining the MTA in Fig. 136.2 involved first determining the monthly reference temperatures (MRTs) for each station using a common reference period, in this case from 1971 to 2000, 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 calculation of the mean temperature anomaly (MTA). The total number of stations included in the MTA in Fig. 136.2 each month is indicated in Fig. 136.3 below. The peak in the frequency between 1960 and 2010 suggests that the 1971-2000 interval was indeed the most appropriate to use for the MRTs.

 

Fig. 136.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Sweden in Fig. 136.2.

 

The data in Fig. 136.3 indicates that the period of greatest coverage of the country for temperature data is after 1960 with up to 150 long and medium stations in operation at any one time. This drops to about 25 between 1890 and 1960 and to four or less before 1860. This means that the MTA for Sweden before 1860 will be less reliable than its values after 1890. Note that a reliable MTA generally needs data from at least sixteen stations (see Post 57 for evidence). 

Nevertheless, we can calculate an MTA for Sweden back to 1720 and if we do so we obtain the trend shown in Fig. 136.4 below. This appears to show that the MTA exhibited a slow and gentle warming for much of the two hundred years before 1980 before the abrupt jump around 1988, but it can be interpreted differently.

 

Fig. 136.4: The mean temperature change for Sweden since 1720 relative to the 1971-2000 monthly averages. The best fit is applied to the monthly mean data from 1731 to 1980 and has a slight positive gradient of +0.30 ± 0.05 °C per century.

 

If the best fit is performed for the interval 1761-1860 then the gradient is -0.06 ± 0.22°C while the best fit to data from 1861 to 2010 yields a strong positive gradient of +0.99 ± 0.11°C (see Fig. 136.5 below). So changing the fitting interval can change the result; but remember, as Fig. 136.3 shows, the MTA data before 1860 is based on only four station records. Similarly, changing the fitting interval to 1861-2010 also changes the interpretation of the data after 1980. The abrupt jump around 1988 now looks more like a temporary plateauing of the MTA data between 1940 and 1980. So which is correct? The problem is it is difficult to know due to the large natural fluctuations in the MTA data: both could be true or neither could be true. What we do know is that the 1988 jump is seen in the MTA for other countries and for Europe as a whole (see Post 44).

 

Fig. 136.5: The mean temperature change for Sweden since 1820 relative to the 1971-2000 monthly averages. The best fit is applied to the monthly mean data from 1861 to 2010 and has a positive gradient of +0.99 ± 0.11 °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. 136.6 below. This again was determined by averaging each monthly anomaly from the 151 longest stations and it suggests that the climate was fairly stable before 1870 but then warmed by over 1.5°C thereafter. In fact the 10-year average suggests a warming of almost 2°C. Moreover the data in Fig. 136.6 is in good agreement with the MTA based on unadjusted data in Fig. 136.4.

 

Fig. 136.6: Temperature trends for Sweden 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. 136.6 with the published Berkeley Earth (BE) version for Sweden in Fig. 136.7 below we see that there is good agreement between the two sets of data. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 136.6 using adjusted data is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 136.7. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 136.4. In other words, any discrepancy between the adjusted data in Fig. 136.6 and the unadjusted data in Fig. 136.4 cannot be due to the averaging process.

Fig. 136.7: The temperature trend for Sweden since 1750 according to Berkeley Earth.

Most of the (slight) differences between the MTA in Fig. 136.4 and the BE versions using adjusted data in Fig. 136.7 are instead mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These can lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values seen in Fig. 136.4 and Fig. 136.6.

The magnitudes of these adjustments are shown graphically in Fig. 136.8 below. The blue curve is the difference in MTA values between adjusted (Fig. 136.6) and unadjusted data (Fig. 136.4), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. The change in the overall adjustment from 1876 to 2010 is small, less than +0.06°C while the much larger 0.4°C offset is due to the difference in MRT intervals used to determine the anomalies in Fig. 136.4 and Fig. 136.6 and can be ignored. What this shows is that the BE adjustments in this case are small which is why the data in Fig. 136.4 and Fig. 136.6 agree so well.

Fig. 136.8: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 136.6 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 1876-1980 has a positive gradient of +0.046 ± 0.002 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Summary

According to the raw unadjusted temperature data, the climate of Sweden has warmed gradually for over 150 years by about 1.5°C in total (see Fig. 136.4). There is some evidence of an abrupt jump of 1°C around 1988 but this depends on how one interprets the data and the fitting process. The temperature before 1860 appear to have been stable for over one hundred years.

In this instance the adjusted temperature data from Berkeley Earth closely follows that of the unadjusted data (see Fig. 136.6).


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

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

135: Norway - temperature trends STABLE before 1980

Over the next four posts I will look at the temperature data of Scandinavia starting with Norway. Like the rest of Scandinavia, Norway has some of the best temperature data in Western Europe despite having large areas with low population densities and harsh Arctic climates. Some of the temperature data extends back to the mid-eighteenth century. What this data shows is that for two hundred years up to 1980 the climate was more or less stable and exhibited no significant temperature increase. Then around 1988 the mean temperature appears to jump abruptly by about 1°C. This pattern is also seen in much of the rest of Europe as I first demonstrated in Post 44.

In total Norway has nineteen long stations with over 1200 months of data before 2014 and 78 medium stations with over 480 months of data (for a full list of stations see here). Their approximate locations are shown on the map in Fig. 135.1 below. The stations are fairly evenly distributed across the country with thirty of the stations lying within the Arctic Circle, but there does appear to be a greater concentration in the south and a relatively low number of stations between Trondheim and Tromsø in the middle of the country. It is also apparent that over 60% of stations are located on or near the coast.

 

Fig. 135.1: The (approximate) locations of the 97 longest weather station records in Norway. 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.

 

In order to quantify the changes to the climate of Norway 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, 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. 135.2 and clearly shows that temperatures were fairly stable up until 1980. However at some point in the 1980s (probably in 1988) the mean temperature appears to increase abruptly by about 1°C.

 

Fig. 135.2: The mean temperature change for Norway since 1880 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1881 to 1980 and has a positive gradient of +0.23 ± 0.16 °C per century.

 

The process of determining the MTA in Fig. 135.2 involved first determining 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 calculation of the mean temperature anomaly (MTA). The total number of stations included in the MTA in Fig. 135.2 each month is indicated in Fig. 135.3 below. The peak in the frequency between 1960 and 1990 suggests that the 1961-1990 interval was indeed the most appropriate to use for the MRTs.

 

Fig. 135.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Norway in Fig. 135.2.

 

The data in Fig. 135.3 indicates that the greatest coverage of the country for temperature data is after 1950 with up to 93 long and medium stations in operation at any one time. This drops to about twenty in 1930 and to less than five before 1850. This means that the MTA for Norway before 1890 will be less reliable than its values after 1950. Note that a reliable MTA generally needs data from at least sixteen stations (see Post 57 for evidence). However if we calculate the MTA for Norway back to 1760 we obtain the trends shown in Fig. 135.4 below. These show that the MTA remains stable for much of the two hundred years before 1980 but the noise level increases before 1800 when the MTA is dependent on only one station: Trondheim.

 

Fig. 135.4: The mean temperature change for Norway since 1760 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1781 to 1980 and has a slight positive gradient of +0.06 ± 0.06 °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. 135.5 below. This again was determined by averaging each monthly anomaly from the 97 longest stations and suggests that the climate was fairly stable before 1880 but then warmed by over 1°C thereafter. In fact the 10-year average suggests a warming of over 1.5°C. Not only that but the warming is more continuous in nature than the raw data in Fig. 135.4 actually shows.

 

Fig. 135.5: Temperature trends for Norway 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 +0.86 ± 0.04°C/century.

 

Comparing the curves in Fig. 135.5 with the published Berkeley Earth (BE) version for Norway in Fig. 135.6 below we see that there is good agreement between the two sets of data. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 135.5 using adjusted data is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 135.6. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 135.4 and Fig. 135.2. In other words, the discrepancy between the adjusted data in Fig. 135.5 and the unadjusted data in Fig. 135.4 cannot be due to the averaging process. Any form of weighted averaging would also not affect the results.

 

Fig. 135.6: The temperature trend for Norway since 1750 according to Berkeley Earth.

 

Most of the differences between the MTA in Fig. 135.4 and the BE versions using adjusted data in Fig. 135.6 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. 135.4 and Fig. 135.5.

The magnitudes of these adjustments are shown graphically in Fig. 135.7 below. The blue curve is the difference in MTA values between adjusted (Fig. 135.5) and unadjusted data (Fig. 135.4), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. The overall adjustment from 1880 to 2013 is small, less than +0.2°C. The main impact is to change the shape of the long term trend from a step-like jump in Fig. 135.4 to a more continuous increase in Fig. 135.5. This involves raising temperatures between 1920 and 1980 by 0.2°C while lowering slightly temperatures before 1920.

Fig. 135.7: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 135.5 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 1876-2010 has a positive gradient of +0.143 ± 0.005 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

Summary

According to the raw unadjusted temperature data, the climate of Norway remained stable for over two hundred years up until the 1980s (see Fig. 135.4). Then it suddenly increased in temperature by 1°C. Why?

In contrast, adjusted temperature data from Berkeley Earth claims to show that the climate of Norway has warmed more or less continuously since 1860 by over 1.5°C (see Fig. 135.4 and Fig. 135.5).


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

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