152: Belarus - temperature trends STABLE before 1980

When it comes to analysing the temperature data of Belarus the biggest problem is the low quantity of data. There is no data before 1880 and only three stations have data before 1950, two of which are long stations with over 1200 months of data (for a full list of stations see here). On a more positive note, there are seventeen medium stations with over 480 months of data and these are quite evenly distributed across the country. That means it should be possible to construct a reliable measure of the overall mean temperature change for Belarus, at least for the last sixty years. What this data then shows is that the climate of Belarus appears to have been fairly stable over the hundred years prior to 1980, but then in 1988, like much of Europe, the temperature suddenly increased by about 1.1°C (see Fig. 152.1 below).

Fig. 152.1: The mean temperature change for Belarus since 1880 relative to the 1981-2010 monthly averages. The best fit is applied to the monthly mean data from 1881 to 1980 and has a statistically insignificant positive gradient of +0.23 ± 0.23 °C per century. After 1980 there is an abrupt warming of 1.1°C.

In order to quantify the changes to the climate of Belarus 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 of the nineteen valid stations relative to its own monthly reference temperature (MRT). Then those anomalies were averaged to determine the mean temperature anomaly (MTA) for the whole country for each month. The MRTs for each station in Belarus were calculated using the same 30-year period, namely from 1981 to 2010. The resulting MTA for Belarus is shown as a time series in Fig. 152.1 above and clearly shows that temperatures were rising slowly (at about 0.23°C per century) for about 100 years up until 1980 but that this rise was only comparable to the uncertainty in the trend and so is not significant. After 1980, however, the MTA suddenly increases by about 1.1°C. Such behaviour is seen in the MTA for many other European countries and for Europe as a whole (see Post 44).

The total number of stations included in the MTA in Fig. 152.1 each month is shown in Fig. 152.2 below. The peak in the frequency after 1970 indicates why the 1981-2010 interval was determined to be the most appropriate to use for calculating the MRTs in this case.

Fig. 152.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Belarus in Fig. 152.1.

The locations of the nineteen stations used to calculate the MTA in Fig. 152.1 are indicated on the map in Fig. 152.3 below. These stations appear to be distributed very evenly across the country with no significant clusters. This means that a simple average of their temperature anomalies should be just as accurate as any of the gridding or homogenization processes that are used by the main climate science groups in their analyses.

Fig. 152.3: The (approximate) locations of the 19 longest weather station records in Belarus. Those stations denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

If we next consider the change in temperature based on Berkeley Earth (BE) adjusted data we get the MTA data in Fig. 152.4 below. This again was determined by averaging the anomalies each month from the nineteen longest stations and also suggests that the climate was warming very slightly before 1980 and then more rapidly thereafter. In this case, however, the post-1980 warming is more continuous and gradual in nature than that seen in the raw data in Fig. 152.1.

Fig. 152.4: Temperature trends for Belarus based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the 12-month moving average data over the period 1881-1980 and has a positive gradient of +0.25 ± 0.08°C/century.

If we compare the curves in Fig. 152.4 with those from the published Berkeley Earth (BE) version for Belarus shown in Fig. 152.5 below, we see that there is excellent agreement between the two sets of data at least as far back as 1880. This indicates that the simple averaging of adjusted anomalies used to generate the BE MTA in Fig. 152.4 is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 152.5. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 152.1. What is more difficult to explain is how Berkeley Earth was able to determine the mean temperature of Belarus as far back as 1750 when there appears to be no reliable data before 1880.

Fig. 152.5: The temperature trend for Belarus since 1750 according to Berkeley Earth.

But if we next compare the adjusted data in Fig. 152.4 with the raw data shown in Fig. 152.1 we see that there is excellent agreement between these two sets of data as well (see Fig. 152.6 below).

Fig. 152.6: A comparison of the 5-year mean temperature change for Belarus since 1880 between the original raw data from Fig. 152.1 (in blue) and the Berkeley Earth adjusted data from Fig. 152.4 (in red).

The small differences between the MTA from the raw data in Fig. 152.1 and that from the BE adjusted data in Fig. 152.4  are 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. 152.1 and Fig. 152.4. The magnitudes of these adjustments are shown graphically in Fig. 152.7 below.

Fig. 152.7: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 152.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1911-2010 has a negative gradient of -0.242 ± 0.016 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

The blue curve in Fig. 152.7 is the difference in MTA values between adjusted (Fig. 152.4) and unadjusted data (Fig. 152.1), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. Both are relatively small with the former adding a slight cooling to the data since 1911 of about 0.24°C. The large offset between the blue curve and the orange curve in Fig. 152.7 is due to the different MRT intervals used for calculating the anomalies in Fig. 152.1 (1981-2010) and in Fig. 152.4 (1961-1990).

Fig. 152.8: A comparison of the 5-year mean temperature change for Belarus (blue curve) with that of the Baltic States in Post 51 (red curve).

As mentioned at the start of this post, the main weakness of the data for Belarus is the lack of good data before 1950 which obviously raises questions over its accuracy and reliabilty. One way to test the accuracy is to compare the Belarus data with that from neighbouring states to see what the level of similarity is. This has been done in Fig. 152.8 above where the comparator set is data from the Baltic States in Post 51. As can clearly be seen, the level of agreement between the two datasets is very good, particularly after 1950. However, even before 1950 there is good agreement even though the Belarus temperature trend is based on data from three stations at most. This suggests that the MTA for Belarus in Fig. 152.1 is reliable and likely to reflect the true climate of Belarus as far back as 1880.

Summary

The raw temperature data for Belarus clearly shows that the climate was stable up until 1980. Any warming in the trend over this period was significantly less than the natural variation in the mean temperature anomaly (MTA) that was seen for all timescales up to ten years in duration (see Fig. 152.1 and Fig. 152.4).

After 1980 the climate has warmed sharply by about 1.1°C. This behaviour is similar to patterns seen across Europe. The reason for this abrupt temperature increase is still unknown as it does not correlate with increases in carbon dioxide levels in the atmosphere.

There appears to be a very good level of correlation between the MTA trend for Belarus and that for the Baltic States reported in Post 51. This allows each to in effect corroborate the other and therefore strengthen the validity of each.



Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Long station = a station with over 1200 months (100 years) of data before 2014.

Medium station = a station with over 480 months (40 years) of data before 2014.

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

151: Lateral thoughts #7 - the problems with wind power

Fig. 151.1: Wind turbines.

There are many claims that are made about wind power, not least that it is cheap. It isn't. In fact it costs almost the same as nuclear.

A nuclear power plant costs about £10bn and delivers 1GW of power almost constantly over a lifetime of up to fifty years. So that is £10 of capital cost per watt of output.

A 1MW wind turbine costs about £1.25m (offshore turbines cost even more). So that is only £1.25 of capital cost per watt of nominal output, much less than nuclear. But wind turbines rarely deliver their maximum or nominal output because they cannot operate in high winds for safety reasons, and at normal wind speeds (v) the output varies as v³. So a drop in wind speed of 50% results in the output power dropping to an eighth of its previous value (see Fig. 151.2 below). 

 

Fig. 151.2: The observed power output of a 1.5MW wind turbine.

 

But there is another problem, and that is that wind speeds are weighted in their frequency of occurrence towards lower values (see Fig. 151.3 below). The result is the power output is both highly variable and weighted towards low values, and so most turbines struggle to deliver more than 33% on average over time of their nominal output. So the true capital cost of a wind turbine is about £3.75 per watt of output. But if we also factor in the 25 year lifetime of wind turbines (i.e. half that of nuclear), then the true capital cost relative to nuclear is going to be about £7.50 per watt. So wind is only marginally cheaper, and remember, offshore wind is even more expensive.

 

Fig. 151.3: The observed frequency of wind speed.

But this is not the biggest problem with wind power. That is the energy storage or backup dilemma. What do we do when the wind doesn't blow?

This was the problem in December of last year. The UK experienced a cold snap with temperatures dropping below -10°C. This is not unusual: it happens every year and is caused by an area of high pressure sitting over the UK. So, just as the UK needed more power for extra heating in the cold weather in mid-December, there was no power coming from the UK's main renewable source: wind power. But this is not just a winter problem. A similar phenomenon is seen during heatwaves in summer. In both cases the wind across most of the UK drops to almost zero for days, or sometimes even weeks on end. So how do we compensate for this?

Well, there are two options. We can either build extra wind turbines to generate surplus electricity in times of plenty and store the excess energy, or we can build backup generators using different and more reliable energy sources.

The problem with the energy storage route is the sheer amount of storage required. The cold snap described above lasted about a week but could have lasted up to twenty days. According to Worldometers, the UK generated 318,157 GWh of electricity in 2016, or about 870 GWh per day. That is 3132 TJ per day, or the energy equivalent of exploding fifty Hiroshima-sized atomic bombs every day. So twenty days of storage would require the equivalent energy storage of over one thousand atomic bombs. And if we want to completely de-carbonize our energy and transport systems that number could easily double. That would require an awful lot of batteries and so is totally unrealistic. It cannot be done.

So what about backup alternatives? Well the issues here are cost and reliability. Because wind is unreliable the backup source needs to be very reliable and immediately accessible. But it also needs to be green. So the obvious candidate is nuclear. But nuclear is more expensive than wind power, so using it as a backup means adding its capital cost to that of wind power when it is rarely going to be used. That makes no sense economically. If we are going to build enough nuclear power stations to satisfy all our electricity needs when the wind isn't blowing, then we may as well use them continuously all the time rather than keeping them idle as backups. If a backup is only going to be used sporadically then its capital cost needs to be much smaller than that of the primary generator it is backing up otherwise it is just an unnecessary additional cost. That leaves only two viable options for backup energy sources: coal and natural gas.

Coal and gas powered generators are up to ten times cheaper than the equivalent-sized nuclear station or wind farm, so their capital costs are negligible in comparison. They are also reliable, but they are not green. That said, they would only be used intermittently so their carbon emissions would be low.

So here is the dilemma. If we stick with wind power then we will need to compromise and allow some fossil fuels to be used as backup supplies in times of need. This will still massively reduce our CO₂ emissions but it will not make us carbon neutral. The only alternative is to abandon wind power and go nuclear.

150: Malta - temperature trends STABLE before 1980

The difficulty in determining the extent of climate change in Malta is the lack of data. According to Berkeley Earth (BE) there are only two stations with over 480 months of data and one of those (A. M. D.) has virtually no data after 1934. The other station is Luqa (BE ID:156721) which is situated near the main airport and actually has over 1800 months of data, so it could be a good indicator of the climate of Malta as a whole. Its monthly mean temperature anomaly (MTA) relative to the period 1981-2010 is shown in Fig. 150.1 below.

 

Fig. 150.1: The mean temperature change for Luqa since 1840 relative to the 1981-2010 monthly averages. The best fit is applied to the monthly mean data from 1881 to 1980 and has a slight positive gradient of +0.05 ± 0.11 °C per century.

 

The data in Fig. 150.1 indicates that there was virtually no climate change in Malta before 1980. Then after 1980 the local temperature appears to have risen by about 0.6°C, although this depends on how one interprets the data. If one uses the 5-year average (yellow line) as a guide the temperature appears to increase by over 1°C from 1880 to 2010. However, average temperatures in 2010 are only about 0.6°C above the 1881-1980 best fit line (in red) which is virtually horizontal. So this would indicate that temperatures in 2010 are only about 0.6°C above average.

 

Fig. 150.2: Temperature trends for Luqa based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1886-2010 and has a positive gradient of +0.56 ± 0.03°C/century.

 

A similar temperature trend for Luqa is seen in its Berkeley Earth (BE) adjusted data (see Fig. 150.2 above) which appears to have zero adjustments after 1870. However, the actual published BE trends for Malta shown in Fig. 150.3 below appear to exhibit more warming over the period from 1890 to 2000 (~1.6°C) than is seen in the Luqa data in Fig. 150.2 (~1.4°C). It is also interesting that the temperature trend in Fig. 150.3 extends back to 1750 even though there appears to be no temperature data for Malta before 1850 (for a list of stations in Malta see here).

 

 Fig. 150.3: The temperature trend for Malta since 1750 according to Berkeley Earth.

 

If we compare the raw data for Luqa with the BE adjusted version we find that there is virtually no difference between the two (see Fig. 150.4 below). It is actually very unusual for such a long dataset not to undergo at least one breakpoint adjustment so this result is quite surprising.

 

Fig. 150.4: The 5-year average of the monthly mean temperature change for Luqa since 1820 based on the original raw data from Fig. 150.1 (in blue) and the Berkeley Earth adjusted data from Fig. 150.2 (in red).

 

As there is no data from Malta to compare the Luqa data against we could instead compare it with temperature data from the nearby Italian islands of Lampedusa (BE ID: 155869) and Pantelleria (BE ID: 175525 ). The locations of these islands relative to Malta and Sicily is shown in the map in Fig. 150.5 below. Lampedusa is about 150 km from both Malta and Pantelleria.

Fig. 150.5: The (approximate) locations of the weather stations in Malta (Luqa), Lampedusa and Pantelleria. Those stations denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

 

Unfortunately the temperature data from Lampedusa and Pantelleria only start around 1960 but both sets show temperature rises after 1980 that are similar to that seen in the Luqa data. There is also good correlation in the high frequency (less than 12 months) fluctuations, but the medium timescale fluctuations with peak widths of more than 12 months are not well correlated.

 

Fig. 150.6: A comparison of the 5-year average temperature change for the islands of Malta, Lampedusa and Pantelleria since 1960.

 

The other data that we could compare Luqa with is that of Italy. This is shown in Fig. 150.7 below and here the medium timescale fluctuations in the two datasets correlate much better.

 

Fig. 150.7: A comparison of the 5-year average temperature change for Malta and Italy.

 

 

Summary

The temperature data for Malta clearly shows that the climate warmed by over 0.6°C after 1980 (see Fig. 150.1).

Before 1980 there is a large amount of natural variation in the temperature data but no overall upward trend.

The temperature trend for Malta can only be determined using one set of station data: Luqa (BE ID:156721). This makes it very unreliable. Comparing it with the neighbouring islands of Lampedusa and Pantelleria only confirms the temperature trend after 1960 (see Fig. 150.6).

There does appear to be a good correlation between the temperature trend of Luqa in Malta and that of Italy in Fig. 148.2 of Post 148 extending back as far as 1890 (see Fig. 150.7). This would suggest that the temperature trends of Malta and Italy have been very similar over at least the last 150 years.



Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Long station = a station with over 1200 months (100 years) of data before 2014.

Medium station = a station with over 480 months (40 years) of data before 2014.

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