by Ross McKitrick

A number of authors, including the IPCC, have argued that climate models have systematically overstated the rate of global warming in recent decades. A recent paper by Millar et al. (2017) presented the same finding in a diagram of temperature change versus cumulative carbon emissions since 1870.

The horizontal axis is correlated with time but by using cumulative CO₂ instead the authors infer a policy conclusion. The line with circles along it represents the CMIP5 ensemble mean path outlined by climate models. The vertical dashed line represents a carbon level where two thirds of the climate models say that much extra CO₂ in the air translates into at least 1.5 ^oC warming. The black cross shows the estimated historical cumulative total CO₂ emissions and the estimated observed warming. Notably it lies below the model line. The models show more warming than observed at lower emissions than have occurred. The vertical distance from the cross to the model line indicates that once the models have caught up with observed emissions they will have projected 0.3 ^oC more warming than has been seen, and will be very close (only seven years away) to the 1.5 ^oC level, which they associate with 615 GtC. With historical CO₂ emissions adding up to 545 GtC that means we can only emit another 70 GtC, the so-called “carbon budget.”

Extrapolating forward based on the observed warming rate suggests that the 1.5 ^oC level would not be reached until cumulative emissions are more than 200 GtC above the current level, and possibly much higher. The gist of the article, therefore, is that because observations do not show the rapid warming shown in the models, this means there is more time to meet policy goals.

As an aside, I dislike the “carbon budget” language because it implies the existence of an arbitrary hard cap on allowable emissions, which rarely emerges as an optimal solution in models of environmental policy, and never in mainstream analyses of the climate issue except under some extreme assumptions about the nature of damages. But that’s a subject for another occasion.

Were Millar et al. authors right to assert that climate models have overstated recent warming? They are certainly not the first to make this claim. Fyfe et al. (2013) compared Hadley Centre temperature series (HadCRUT4) temperatures to the CMIP5 ensemble and showed that most models had higher trends over the 1998-2012 interval than were observed:

Original caption: a, 1993–2012. b, 1998–2012. Histograms of observed trends (red hatching) are from 100 reconstructions of the HadCRUT4 dataset¹. Histograms of model trends (grey bars) are based on 117 simulations of the models, and black curves are smoothed versions of the model trends. The ranges of observed trends reflect observational uncertainty, whereas the ranges of model trends reflect forcing uncertainty, as well as differences in individual model responses to external forcings and uncertainty arising from internal climate variability.

The IPCC’s Fifth Assessment Report also acknowledged model over-estimation of recent warming in their Figure 9.8 and accompanying discussion in Box 9.2. I have updated the IPCC chart as follows. I set the CMIP5 range to gray, and the thin white lines show the (year-by-year) central 66% and 95% of model projections. The chart uses the most recent version of the HadCRUT4 data, which goes to the end of 2016. All data are centered on 1961-1990.

Even with the 2016 EL-Nino event, the HadCRUT4 series does not reach the mean of the CMIP5 ensemble. Prior to 2000 the longest interval without a crossing between the red and black lines was 12 years, but the current one now runs to 18 years.

This would appear to confirm the claim in Millar et al. that climate models display an exaggerated recent warming rate not observed in the data.

Not So Fast

Zeke Hausfather has disputed this in a posting for Carbon Brief. He presents a different-looking graph that seems to show HadCRUT4 and the other major data series lining up reasonably well with the CMIP5 (RCP4.5) runs.

How does he get this result?

Hausfather isn’t using the CMIP5 runs as shown by the IPCC; instead he is using data from a different archive that modifies the outputs in a way that tilts the post-2000 model trends down. Cowtan et al. (2015) argued that, for comparisons such as this, climate model outputs should be sampled in the same way that the HadCRUT4 (and other) surface data are sampled, namely using Surface Air Temperatures (SAT) over land, Sea Surface Temperatures (SST) over water, and with maskings that simulate the treatment of areas with missing data and with ice cover rather than open ocean. Global temperature products like HadCRUT use SST data as a proxy for Marine Air Temperature (MAT) over the oceans since MAT data are much less common than SST. Cowtan et al. note that in the models, SST warms more slowly than MAT but the CMIP5 output files used by the IPCC and others present averages constructed by blending MAT and SAT, rather than SST and SAT. Using the latter blend, and taking into account the fact that when Arctic ice coverage declines, some areas that had been sampled with SAT are replaced with SST, Cowtan et al. found that the discrepancy between models and observations declines somewhat.

Figure 4 in Cowtan et al. shows that the use of SAT/SST (“blended”) model output data doesn’t actually close the gap by much: the majority of the reconciliation happens by using “updated forcings”, i.e. peeking at the answer post-2000

.Top: effect of applying Cowtan et al. blending method (change from red to green line)

Bottom: effect of applying updated forcings that use post-2000 observations

Hausfather also uses a slightly later 1970-2000 baseline. With the 2016 El Nino at the end of the record a crossing between the observations and the modified CMIP5 mean occurs.

In my version (using the unmodified CMIP5 data) the change to a 1970-2000 baseline would yield a graph like this:

The 2016 HadCRUT4 value still doesn’t match the CMIP5 mean, but they’re close. The Cowtan et al. method compresses the model data above and below so in Zeke’s graph the CMIP5 mean crosses through the HadCRUT4 (and other observed series’) El Nino peak. That creates the visual impression of greater agreement between models and observations, but bear in mind the models are brought down to the data, not the other way around. On a 1970-2000 centering the max value of the CMIP5 ensemble exceeds 1C in 2012, but in Hausfather’s graph that doesn’t happen until 2018.

Apples with Apples

The basic logic of the Cowtan et al. paper is sound: like should be compared with like. The question is whether their approach, as shown in the Hausfather graph, actually reconciles models and observations.

It is interesting to note that their argument relies on the premise that SST trends are lower than nearby MAT trends. This might be true in some places but not in the tropics, at least prior to 2001. The linked paper by Christy et al. shows the opposite pattern to the one invoked by Cowtan et al. Marine buoys in the tropics show that MAT trends were negative even as the SST trended up, and a global data set using MAT would show less warming than one relying on SST, not more. In other words, if instead of apples-to-apples we did an oranges-to-oranges comparison using the customary CMIP5 model output comprised of SAT and MAT, compared against a modified HadCRUT4 series that used MAT rather than SST, it would have an even larger discrepancy since the modified HadCRUT4 series would have an even lower trend.

More generally, if the blending issues proposed by Cowtan et al. explain the model-obs discrepancy, then if we do comparisons using measures where the issues don’t apply, there should be no discrepancy. But, as I will show, the discrepancies show up in other comparisons as well.

Extremes

Swanson (2013) compared the way CMIP3 and CMIP5 models generated extreme cold and warm events in each gridcell over time. In a warming world, towards the end of the sample, each location would be expected to have a less-than-null probability of a record cold event and a greater-than-null probability of a record warm event each month. Since the comparison is done only using frequencies within individual grid cells it doesn’t require any assumptions about blending the data. The expected pattern was found to hold in the observations and in the models, but the models showed a warm bias. The pattern in the models had enough dispersion in CMIP3 to encompass the observed probabilities, but in CMIP5 the model pattern had a smaller spread and no overlap with observations. In other words, the models had become more like each other but less like the observed data.

(Swanson Fig 2 Panels A and B)

The importance here is that this comparison is not affected by the issues raised by Cowtan et al, so the discrepancy shouldn’t be there. But it is.

Lower Troposphere

Comparisons between model outputs for the Lower Troposphere (LT) and observations from weather satellites (using the UAH and RSS products) are not affected by the blending issues raised in Cowtan et al. Yet the LT discrepancy looks exactly like the one in the HadCRUT4/CMIP5 comparison.

The blue line is RSS, the black line is UAH, the red line is the CMIP5 mean and the grey bands show the RCP4.5 range. The thin white lines denote the central 66% and 95% ranges. The data are centered on 1979-2000. Even with the 2016 El Nino the discrepancy is visible and the observations do not cross the CMIP5 mean after 1999.

A good way to assess the discrepancy is to test for common deterministic trends using the HAC-robust Vogelsang-Franses test (see explanation here). Here are the trends and robust 95% confidence intervals for the lines shown in the above graph, including the percentile boundaries.

 

UAHv6.0              0.0156 C/yr       ( 0.0104 , 0.0208 )

RSSv4.0                 0.0186 C/yr       ( 0.0142 , 0.0230 )

GCM_min              0.0252 C/yr       ( 0.0191 , 0.0313 )

GCM_025             0.0265 C/yr       ( 0.0213 , 0.0317 )

GCM_165             0.0264 C/yr       ( 0.0200 , 0.0328 )

GCM_mean           0.0276 C/yr       ( 0.0205 , 0.0347 )

GCM_835             0.0287 C/yr       ( 0.0210 , 0.0364 )

GCM_975             0.0322 C/yr       ( 0.0246 , 0.0398 )

GCM_max             0.0319 C/yr       ( 0.0241 , 0.0397 )

All trends are significantly positive, but the observed trends are lower than the model range. Next I test whether the CMIP5 mean trend is the same as, respectively, that in the mean of UAH and RSS, UAH alone and RSS alone. The test scores are below. All three reject at <1%. Note the critical values for the VF scores are: 90%:20.14, 95%: 41.53, 99%: 83.96.

H0: Trend in CMIP5 mean =

Trend in mean obs          192.302

Trend in UAH                      405.876

Trend in RSS                         86.352

 

The Tropics

In addition to the above comparison, if the treatment of Arctic sea ice is the major problem, there should be no issues when confining attention to the tropics. Also, since models project the strongest response to GHG warming in the tropical LT, this is where models and observations ought best to agree.

Again the blue line is RSS, the black line is UAH, the red line is the CMIP5 mean, the grey bands show the RCP4.5 range and the thin white lines denote the central 66% and 95% ranges. The data are centered on 1979-2000.

Trends:

UAHv6.0              0.0102 C/yr       ( 0.0037 , 0.0167 )

RSSv4.0                 0.0139 C/yr       ( 0.0085 , 0.0193 )

GCM_min             0.0282 C/yr       ( 0.0199 , 0.0365 )

GCM_025             0.0277 C/yr       ( 0.021 , 0.0344 )

GCM_165             0.0281 C/yr       ( 0.0207 , 0.0355 )

GCM_mean           0.0289 C/yr       ( 0.0209 , 0.0369 )

GCM_835             0.0296 C/yr       ( 0.021 , 0.0382 )

GCM_975             0.032 C/yr          ( 0.0239 , 0.0401 )

GCM_max             0.0319 C/yr       ( 0.023 , 0.0408 )

H0: Trend in CMIP5 mean =

Trend in mean obs          229.683

Trend in UAH                      224.190

Trend in RSS                         230.100

 

All trends are significantly positive and the models strongly reject against the observations. Interestingly the UAH and RSS series both reject even against the (year-by-year) lower bound of the CMIP5 outputs (p<1%).

Finally, Tim Vogelsang and I showed a couple of years ago that the tropical LT (and MT) discrepancies are also present between models and the weather balloon series back to 1958.

Summary

Millar et al. attracted controversy for stating that climate models have shown too much warming in recent decades, even though others (including the IPCC) have said the same thing. Zeke Hausfather disputed this using an adjustment to model outputs developed by Cowtan et al. The combination of the adjustment and the recent El Nino creates a visual impression of coherence. But other measures not affected by the issues raised in Cowtan et al. support the existence of a warm bias in models. Gridcell extreme frequencies in CMIP5 models do not overlap with observations. And satellite-measured temperature trends in the lower troposphere run below the CMIP5 rates in the same way that the HadCRUT4 surface data do, including in the tropics. The model-observational discrepancy is real, and needs to be taken into account especially when using models for policy guidance.

Moderation note:  As with all guest posts, please keep your comments civil and relevant.

 

Filed under: climate models

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September 26, 2017 at 12:10PM

By Paul Homewood

 

 

h/t Patsy Lacey

 

 

Will somebody please explain to this silly little girl that we cannot run a grid on renewables and nuclear alone:

Britain’s energy system provided its greenest ever electricity to homes and businesses over the summer due to a surge in wind and solar power which spells trouble for traditional power plant operators.

National Grid said almost 52pc of the country’s power demand was met by low carbon sources, such as renewable energy and nuclear power, compared to around 35pc four years ago.

 

The low-carbon boom was led by renewables which made up almost a quarter of all power from June 21 to September 22 from less than 10pc four years ago, and a fifth last year.

The warmer months were dotted with milestone energy moments including the first working day since the industrial revolution where the UK’s energy system was completely coal-free in April. Later in May a quarter of energy demand was met by the 7GW of solar power that was supplying electricity to the grid.

In the first week of June renewable power met over 50pc of the nation’s electricity supply and days later a surge of wind, solar, and nuclear power pushed the energy grid’s carbon intensity to record lows.

Renewables made up almost a quarter of all power from June 21 to September 22 from less than 10pc four years ago, and a fifth last year Credit: Getty

“It’s been an exciting year managing the many ‘network firsts’ – from a day where we operated the system with zero coal power, to one where over half of Great Britain’s energy demand was met by renewable generation,” said National Grid’s systems boss Duncan Burt.

But the green energy bonanza is likely to put greater pressure on the operators of traditional power plants, including nuclear reactors, which make weaker returns when subsidised renewables flood the market and reduce the wholesale market price.

Roshan Patel, an analyst at Investec, told The Daily Telegraph: “Higher renewables output has meant thermal power plants are operated over ever fewer hours. In addition, generation with zero marginal cost, such as renewables, also puts downwards pressure on average wholesale prices, affecting all but subsidised renewables.”

The fall in market prices also poses a dilemma for EDF Energy which operates the country’s fleet of low-carbon nuclear plants.

Gareth Redmond-King, from WWF, said the success of the renewables industry must be matched by further commitment from the Government, which is expected to publish its long-awaited Clean Growth Plan within weeks.

“It’s time for the UK Government to step up and deliver a strong and ambitious clean growth plan, continuing to support renewables, cleaning up our transport and making our homes more energy efficient,” he said.

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Take today as an example. Wind power has been nearly invisible, and the grid has been reliant on coal and gas for the bulk of demand, with nuclear supplying most of the rest.

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Still, I must not be too harsh on Jillian. She does at last seem to have grasped that the subsidies shelled out for renewables are making conventional plants, both thermal and nuclear, uncompetitive. Not least because they are only able to operate at reduced hours.

It is a strange world when the National Grid’s boss finds this situation “exciting”!

Meanwhile you can always rely on the marxists at the WWF for a good laugh. Their solution to the problems caused by subsidies for reneweables? Let’s have some more!

via NOT A LOT OF PEOPLE KNOW THAT

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September 26, 2017 at 11:39AM

 

With five days left in the month, we can project the likely 2017 September results and compare with years of the previous decade.  2017 is provisional depending on the next five days, but MASIE is averaging 4.8M km2 and the daily extents are over that amount.  SII is 60k km2 lower, but just went over 4.9M, so has a chance to also reach 4.8M.

In August, 4.5M km2 was the median estimate of the September monthly average extent from the SIPN (Sea Ice Prediction Network) who use the reports from SII (Sea Ice Index), the NASA team satellite product from passive microwave sensors.

The graph below shows September comparisons through day 268.Note that as of day 268, 2016 had begun its remarkable recovery, now matching the 10 year average, nearly 200k km2 below 2017. Meanwhile 2007 is 800k km2 behind and the Great Arctic Cyclone year of 2012 is 1.3M km2 less than 2017.  Note also that SII is currently showing slightly more ice than MASIE.

The narrative from activist ice watchers is along these lines:  2017 minimum is not especially low, but it is very thin.  “The Arctic is on thin ice.”  They are basing that notion on PIOMAS, a model-based estimate of ice volumes, combining extents with estimated thickness.  That technology is not mature, and in any case refers to the satellite era baseline, which began in 1979.

The formation of ice this year does not appear thin, since it is concentrated in the central Arctic.  Consider how CAA (Canadian Arctic Archipelago added 100k km2 in the last two weeks:

The table shows ice extents in the regions for 2017, 10 year averages and 2007 for day 268. Decadal averages refer to 2007 through 2016 inclusive.

Region	2017268	Day 268 Average	2017-Ave.	2007268	2017-2007
(0) Northern_Hemisphere	4824033	4648420	175613	4025906	798128
(1) Beaufort_Sea	358982	488920	-129937	466599	-107617
(2) Chukchi_Sea	71545	180769	-109224	3054	68491
(3) East_Siberian_Sea	259179	276825	-17646	311	258868
(4) Laptev_Sea	275826	138290	137536	222968	52858
(5) Kara_Sea	42802	22613	20189	18246	24556
(6) Barents_Sea	6112	20560	-14448	4851	1261
(7) Greenland_Sea	111111	229228	-118116	335161	-224050
(8) Baffin_Bay_Gulf_of_St._Lawrence	74169	36672	37497	41385	32784
(9) Canadian_Archipelago	472601	293992	178610	274334	198267
(10) Hudson_Bay	1276	3154	-1878	1936	-661
(11) Central_Arctic	3149271	2956302	192969	2655784	493487

Note the strong surpluses in Canadian Archipelago and the Central Arctic, which is already at 95% of its March maximum.  On the Russian side, Laptev and Kara are surplus to average, while East Siberian has grown to approach average.

Summary

Earlier observations showed that Arctic ice extents were low in the 1940s, grew thereafter up to a peak in 1977, before declining.  That decline was gentle until 1994 which started a decade of multi-year ice loss through the Fram Strait.  There was also a major earthquake under the north pole in that period.  In any case, the effects and the decline ceased in 2007, 30 years after the previous peak.  Now we have a plateau in ice extents, which could be the precursor of a growing phase of the quasi-60 year Arctic ice oscillation.

For context, note that the average maximum has been 15M, so on average the extent shrinks to 30% of the March high before growing back the following winter.

Background from Sept. 20

Dave Burton asked a great question in his comment below, and triggered this response:

Dave, thanks for asking a great question. All queries are good, but a great one forces me to dig and learn something new, in this case a more detailed knowledge of what goes into MASIE reports. My answer above refers only to a sub-product which combines MASIE with JAXA.

You asked, where do they get their data? The answer is primarily from NIC’s Interactive Multisensor Snow and Ice Mapping System (IMS). From the documentation, the multiple sources feeding IMS are:

Platform(s) AQUA, DMSP, DMSP 5D-3/F17, GOES-10, GOES-11, GOES-13, GOES-9, METEOSAT, MSG, MTSAT-1R, MTSAT-2, NOAA-14, NOAA-15, NOAA-16, NOAA-17, NOAA-18, NOAA-N, RADARSAT-2, SUOMI-NPP, TERRA

Sensor(s): AMSU-A, ATMS, AVHRR, GOES I-M IMAGER, MODIS, MTSAT 1R Imager, MTSAT 2 Imager, MVIRI, SAR, SEVIRI, SSM/I, SSMIS, VIIRS

Summary: IMS Daily Northern Hemisphere Snow and Ice Analysis

The National Oceanic and Atmospheric Administration / National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has an extensive history of monitoring snow and ice coverage.Accurate monitoring of global snow/ice cover is a key component in the study of climate and global change as well as daily weather forecasting.

The Polar and Geostationary Operational Environmental Satellite programs (POES/GOES) operated by NESDIS provide invaluable visible and infrared spectral data in support of these efforts. Clear-sky imagery from both the POES and the GOES sensors show snow/ice boundaries very well; however, the visible and infrared techniques may suffer from persistent cloud cover near the snowline, making observations difficult (Ramsay, 1995). The microwave products (DMSP and AMSR-E) are unobstructed by clouds and thus can be used as another observational platform in most regions. Synthetic Aperture Radar (SAR) imagery also provides all-weather, near daily capacities to discriminate sea and lake ice. With several other derived snow/ice products of varying accuracy, such as those from NCEP and the NWS NOHRSC, it is highly desirable for analysts to be able to interactively compare and contrast the products so that a more accurate composite map can be produced.

The Satellite Analysis Branch (SAB) of NESDIS first began generating Northern Hemisphere Weekly Snow and Ice Cover analysis charts derived from the visible satellite imagery in November, 1966. The spatial and temporal resolutions of the analysis (190 km and 7 days, respectively) remained unchanged for the product’s 33-year lifespan.

As a result of increasing customer needs and expectations, it was decided that an efficient, interactive workstation application should be constructed which would enable SAB to produce snow/ice analyses at a higher resolution and on a daily basis (~25 km / 1024 x 1024 grid and once per day) using a consolidated array of new as well as existing satellite and surface imagery products. The Daily Northern Hemisphere Snow and Ice Cover chart has been produced since February, 1997 by SAB meteorologists on the IMS.

Another large resolution improvement began in early 2004, when improved technology allowed the SAB to begin creation of a daily ~4 km (6144×6144) grid. At this time, both the ~4 km and ~24 km products are available from NSIDC with a slight delay. Near real-time gridded data is available in ASCII format by request.

In March 2008, the product was migrated from SAB to the National Ice Center (NIC) of NESDIS. The production system and methodology was preserved during the migration. Improved access to DMSP, SAR, and modeled data sources is expected as a short-term from the migration, with longer term plans of twice daily production, GRIB2 output format, a Southern Hemisphere analysis, and an expanded suite of integrated snow and ice variable on horizon.

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Footnote

Some people unhappy with the higher amounts of ice extent shown by MASIE continue to claim that Sea Ice Index is the only dataset that can be used. This is false in fact and in logic. Why should anyone accept that the highest quality picture of ice day to day has no shelf life, that one year’s charts can not be compared with another year? Researchers do this, including Walt Meier in charge of Sea Ice Index. That said, I understand his interest in directing people to use his product rather than one he does not control. As I have said before:

MASIE is rigorous, reliable, serves as calibration for satellite products, and continues the long and honorable tradition of naval ice charting using modern technologies. More on this at my post Support MASIE Arctic Ice Dataset

 

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September 26, 2017 at 11:18AM

By Paul Homewood

 

 

h/t Patsy Lacey

 

 

Another reason not to have a smart meter!

 

 

National Grid will be able to tell people the cheapest time to turn on a washing machine up to two days in advance.

 

New software, developed with conservation charity WWF, breaks the day down into two-hour segments, warning users when energy is at peak demand and informing them when demand is low.

It combines historical data from the grid with weather information from the Met Office to predict times of high and low demand.

The National Grid said it expected energy companies to use the information to produce their own apps encouraging customers to use energy when demand was at its lowest and turn appliances off when there was pressure on the system.

 

The data, verified by experts from Oxford University, also shows when low-carbon energy sources are active, allowing environmentally-conscious households to use energy at the best times for solar and wind energy.

Duncan Burt, director of the system operator at National Grid, said: “We’re providing our forecast data in a format that allows technology companies to build innovative apps and software that could make a real difference to how and when people use energy.

“Clear and concise information that can tell you in advance when’s best to turn on the washing machine, load the dishwasher or charge your car for example, is a step in the right direction towards a low carbon future. 

“This technology puts people at the heart of it, helping everyone to use power when it’s greenest, and likely, more cost efficient.”

In the future the system is likely to be most useful to electric-car owners, who can choose to charge their vehicle at the cheapest and most efficient time.

Some suppliers have started to offer “time-of-use” tariffs, enabled by smart meters, which reward householders for using energy when demand is low.

 

In January Green Energy UK launched its TIDE tariff which charges less per kWh at low-usage times, such as overnight.

British Gas’s long-standing Economy 7 and Economy 10 tariffs also charge users less for energy used at night.

The National Grid also said that 2017 had been the “greenest summer ever”. Between June 21 and September 22, 52 per cent of electricity demand was met by “low-carbon” sources, compared to 35 per cent four years ago.

Gareth Redmond-King, head of climate and energy at WWF, said the development was a “great leap forward” towards more renewable energy use.

“Green energy forecasting could be a game changer – making the connection between the weather and energy and helping people use electricity when it’s greenest.

“This is not just good news for reducing the effects of climate change but could also help us cut our home energy bills and it’s vital the UK Government bring in time of use tariffs quickly to maximise these opportunities,” he said.

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At the end of the day (or night!), the total cost of electricity will still be the same. Interestingly, at the time, the Telegraph reported this on the TIDE tariff mentioned above:

 

 

The cheapest electricity will be available between 11pm and 6am every night, at 4.99p per unit.

The most expensive period will be between 4pm and 9pm on weeknights, when electricity will cost 24.99p per unit. This is the period when demand for energy spikes.

The average price for energy is around 14p per unit, according to consultants the Energy Saving Trust.

On the Green Energy UK plan, a customer who uses 30pc of their electricity at the cheapest time and only 2pc at the most expensive time would spend £891.25 on electricity each year.

But if the same customer used 2pc of their electricity between  at the cheapest time and 30pc at the most expensive time, their bill would be £1,063.13.

In both cases the rest of the usage would be 24pc used during the day, and 16pc used later in the evening.

According to price comparison site UK Power, the cheapest available annual bill for a medium-sized house is £871.

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So, even if you use 30% of your power at night, you’re still no better off. And if you don’t, you’re shafted!

via NOT A LOT OF PEOPLE KNOW THAT

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September 26, 2017 at 11:09AM