UK Power Generation 2017-18

The balancing of energy supply with demand on the UK National Grid is performed by ELEXON and as a result they provide a live snapshot of the power generated to match that demand  by fuel type. I have been monitored this since December 2016. Peak demand in general occurs around 6pm and I use this value to compare the relative importance of different energy sources to energy security. The values provided by ELEXON  are for centrally ‘metered’ power supply and do not include smaller ‘feed-in’ sources. Feed in sources are mostly household solar panels, solar farms, and small wind farms. The University of Sheffield began estimating Solar power around the end of 2017 based on their regional insolation/capacity model. I have been monitored this solar value since the beginning of 2018. In addition unmetered ‘feed in’ wind farms are estimated to add ~46% to the larger metered wind farms. This correction is applied to the overall results below.

UK Power Generation at 6pm. The upper curve is the Peak Electricity Demand. The blue section combines French & NL imports with pumped storage and Hydro. (Click for full size version)

Peak demand in winter still exceeds 50GW despite energy saving measures. Nuclear Power provides a stable baseline of about 8GW. Coal generation remains essential to meet demand during winter months, however most of the bulk generation balancing is now met by Gas. Wind power is extremely variable, but remained strong this winter and contributed  an average 10.5% of peak demand or about 4.5GW. Maximum output at 6pm was ~12.5GW, while a record 14GW was recorded in the morning of March 17. This was also due to upgraded power transmission from Scotland to England. Solar Energy contributes essentially nothing during winter, and only becomes a significant factor after April and during daylight. Bio fuel has grown since 2016, but this growth is dominated by the DRAX turbines converting from burning coal to burning wood chips.

After November 2017 the demand curve is matched by the sum of all the fuel components, whereas before then there is an apparent small shortfall. I don’t have an answer as to why this is the case, but can only guess that the fuel figures were a little too low before November 2017. The Solar component apparently carries the supply of power beyond real time demand. That is because the effect of solar is to reduce the national demand curve through localised feed-in. However this ‘hidden’ solar is plotted here for comparison to the metered contributions from other fuels.

Erratic Wind

The week 31 May-5 June 2018 saw almost no wind across the UK, but instead a lot of sunshine. Here are the results for that week.

Power output from different fuels for the week 31 May – 5 June. From bottom to top Nuclear- Orange, Imports-purple, Hydro-cyan, Bio-brown,Red-coal, Gas-Pink, Wind-Green,Solar-Yellow

Total net Wind output fell as low as 0.05GW on Friday 1st June. Such lulls are not only restricted to Summer months. During the 2013/14 winter wind output fell below 0.2GW at 6pm on three separate occasions. For this reason the UK will need to keep in reserve an equal Gas capacity to that of all installed Wind farms simply to cover such lulls. Nor is it really feasible to store such huge amounts of energy. To cover one day without any wind (5GW) would need store 120GWh (430 TJ) of energy. This is 5 times larger than the bomb that destroyed Hiroshima. The largest Battery storage so far is the one that Elon Musk’s Tesla built in Southern Australia which can store 130MWh of Energy. Unfortunately this is a factor 1000 to small. The cost to the SA government for its installation was around  AUD 100 million. Nuclear power looks cheap in comparison!

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19th Century Volcanic Eruptions

There is clear evidence of a cooling effect in the GHCN-Daily data resulting from large volcanic eruptions during the 19th and 18th century.  If an eruption of a similar size to Tambora were to occur today it would (temporarily) cancel out all CO2 warming.

The severn largest volcanic eruptions in the last 270 years compared to Global Land Temperatures. The 3 largest eruptions all occurred before 1850. A comparison of GHCN-Daily with CRUTEM4 is shown in Green.

Note that early ‘Global’ temperatures are dominated by European stations. This is probably why Laki appears to be as strong as Tambora, which had a much larger effect in Asia.

Laki, Iceland – 1793 

Globally, those 95 Mt of sulphuric dioxide reacted with atmospheric water to form 200 Mt of sulphuric acid aerosols. Almost 90% of that sulphuric acid was removed in the form of acid rain or fogs, while 10% stayed aloft for over a year. This might explain why northern hemisphere temperatures were 1.3ºC below normal for 2-3 years after the eruption.  (Wired)

Tambora, Indonesia – 1815

Many volcanologists regard the Mount Tambora eruption as the largest and most-destructive volcanic event in recorded history, expelling as much as 150 cubic km (roughly 36 cubic miles) of ash, pumice and other rock, and aerosols—including an estimated 60 megatons of sulphur—into the atmosphere. As that material mixed with atmospheric gases, it prevented substantial amounts of sunlight from reaching Earth’s surface, eventually reducing the average global temperature by as much as 3 °C (5.4 °F). (Britanica)

Consiguina, Nicaragua, 1835

The January 1835 eruption of Cosigüina volcano, Nicaragua, ranks among the Americas’ largest and most explosive historical eruptions, but whether it had effects on global climate remains ambiguous. New petrologic analyses of the Cosigüina deposits reveal that the eruption released enough suphur to explain a prominent circa A.D. 1835 sulphate anomaly in ice cores from both the Arctic and Antarctic. A compilation of temperature?sensitive tree ring chronologies indicates appreciable cooling of the Earth’s surface in response to the eruption, consistent with instrumental temperature records. We conclude that this eruption represents one of the most important sulphur?producing events of the last few centuries and had a sizable climate impact rivaling that of the 1991 eruption of Mount Pinatubo. (Longpré et al.)

Krakatoa, Indonesia, 1883

In May 1883, the captain of the Elizabeth, a German warship, reported seeing clouds of ash above Krakatau. He estimated them to be more than 6 miles (9.6 km) high. For the next two months, commercial vessels and chartered sightseeing boats frequented the strait and reported thundering noises and incandescent clouds….On the morning of the 27th August, four tremendous explosions, heard as far away as Perth, Australia, some 2,800 miles (4,500 km) distant…… The explosions hurled an estimated 11 cubic miles (45 cubic km) of debris into the atmosphere, darkening skies up to 275 miles (442 km) from the Volcano…Within 13 days, a layer of sulphur dioxide and other gases began to filter the amount of sunlight able to reach Earth. The atmospheric effects made for spectacular sunsets all over Europe and the United States. Average global temperatures were as much as 1.2 degrees cooler for the next five years. (LiveScience)

By comparison the 20th century and the 21st century so far has seen far less Volcanic activity. Mount Pinatubu caused a temporary short term drop in global temperatures of about 0.4C for 2 years. Another Tambura type event would be far more serious with longer lasting effects. After-all we know how cold it can get at night!

Posted in Climate Change, climate science, UK Met Office | 9 Comments

GHCN-Daily Temperature Anomaly Results

Shown below are my new Global land temperature anomalies calculated directly from daily  temperature measurements of over 35300 weather stations (NCDC-Daily) compared to Berkeley Earth. The NCDC-Daily archive extends back to 1762, although coverage then is mostly restricted to central Europe. The new methodology is described.

Comparison of the new Icosahedral result for GHCN-Daily versus Berkeley Earth. Berkeley Annual is essentially a rolling 12 monthly average whereas ICOS is the yearly average. Also shown in Orange are the new GHCN Daily anomalies presented by Robert Rhode at EGU.

GHCN Daily contains the raw measurement data from 106283 weather stations. However of these ~ 35300 stations contain temperature data,  the rest are mainly precipitation only data. Each of these stations record daily values of the maximum temperature (TMAX) and the minimum temperature (TMIN) over a 24 hour period. This is then complicated by each station having different time coverage spans, and often containing gaps within this data.

To derive a global temperature estimate when spatial distributions are continuously changing means you must calculate monthly temperature ‘anomalies’, ideally for each station relative to a common baseline. For this I always use the CRU standard 30 year period of 1961-1990. The normals are simply the 30-year temperature averages for each station and for each month. The temperature anomalies are then deviations from these averages. For GHCN-DAILY you actually need to calculate two sets of normals, one for TMAX and one for TMIN. The daily average temperature TAV is then simply (TMAX+TMIN)/2.

It turns out that 22645 stations have sufficient coverage within the 30 year normalisation period in  order to calculate individual station temperature anomalies. The remaining 12655 stations must be treated in a separate way, by comparing them to nearby stations which overlap with their time coverage.

Of course in the end what we really want to calculate is the global temperature ‘anomaly’ on Land. This involves a spatial integration of all temperature anomalies over the earth’s Land surface. It turns out that the optimum equal area binning over the spherical surface of the Earth is to use so-called ‘Icosahedral’ grids as has been described previously. The first attempt I made (see last post) had coverage bias problems because the normalisation in early years used bins that were too large. As a result I have now increased the number of bins by a factor 4 to 10242. This means that each bin now covers an area of about 5000 square km or roughly 70km by 70km. This reduction in scale is important because I must also calculate the average monthly temperatures within each bin by averaging together all station normals within the standard 1961-1990. This is because the reference normalisation used to derive temperature anomalies for those 12655 stations outside the  1961-1990 range are instead those of near neighbours which do.

Partial view of the 10242 element Icosahedral grid

Actual Temperature Distribution for March 2018

The full algorithm used is as follows:

  1. Generate a level 5 Icosahedral grid with 10242 bins. Loop over all stations and geolocate each station based on Lat, Lon to a bin index number.
  2. Loop over the 30 year period 1961-1990 and calculate both all individual station normals and the average bin normals for each month.
  3. Process all stations over their full time coverage. Use station normals where available or bin anomalies where not available to calculate time series for  TMAX and TMIN anomalies. Derive the average temperature anomaly for each month and for each occupied bin from 1762 to 2018. This gives the spatial distribution of temperature (anomaly)  with time.
  4. Integrate all bins to form a global average Land temperature anomaly for each month and an annual average global temperature anomaly.

The monthly results look like this.

GHCN-Daily monthly anomalies. In red are sown the TMAX-TMIN (Diurnal) anomalies. This shows that 20th century warming has occurred predominantly at night.

It is interesting that  minimum temperature anomalies have risen faster than maximum anomalies since 1950, yet the inverse was true in the 19th century. That implies that “Global Warming” has mostly occurred at night. The new Berkeley Daily temperature anomalies show exactly the same effect.



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