Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
This is the Tmin (daily minimum temperature) version of the Canada temperature trend results I posted yesterday, which were for Tmax (daily maximum temperatures). These results are quite different: whereas the high temperatures have seen essentially no warming trends across southern Canada since 1900, the nighttime temperatures have warmed in each one of the 6 provinces. In the next few days I will post just how much these observed Canadian temperature trends depart from the CMIP6 climate model simulations, which are the primary tool being used to change energy policy.
NOTE: This is the Tmin (daily minimum temperature) version of the Canada temperature trend results I posted yesterday, which were for Tmax (daily maximum temperatures). These results are quite different: whereas the high temperatures have seen essentially no warming trends across southern Canada since 1900, the nighttime temperatures have warmed in each one of the 6 provinces. In the next few days, I will post just how much these observed Canadian temperature trends depart from the CMIP6 climate model simulations, which are the primary tool being used to change energy policy.
SUMMARY
Over the period 1900-2023, the average summer (JJA) daily low temperatures across the six southernmost large provinces of Canada (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, and Quebec) show warming trends, averaging +0.14 C/decade.
The strongest warming (+0.18 C/decade) occurred for the coolest summer nights (coolest 3 days per summer month), while the warmest summer nights warmed at +0.10 C/decade.
Whereas 7 of the 10 warmest summer daytime (high) temperatures occurred in the 1930s, 8 of 10 of the warmest nighttime (low) temperatures have occurred since 2003.
Results for the 6 provinces separately are also presented.
Introduction
Below I present analyses of summertime daily low temperature (Tmin) trends from all available stations in the 6 southernmost large provinces, based upon the daily Global Historical Climate Network (GHCNd) dataset. These are the 6 provinces that border the Lower 48 and contain 86% of Canada’s population. (The results for daily high temperatures [Tmax] were posted yesterday.)
I simply averaged together the relevant statistics (monthly average Tmin, average of the warmest 3 days’ Tmin in each month, and average of the coolest 3 days’ Tmin in each month) from all available stations. Each station had to have at least 90% of the days in a month reporting data for that month to be included in the analysis.
Since stations come and go over the years, and since there are some large terrain elevation variations in western Canada, I performed an elevation correction to these Tmin metrics, in all provinces, using the departure of each year’s station-average elevation from the all-year (1900-2023) station average elevation, using a lapse rate of 6.5 deg. C per km. Corrections for average changes in station-average latitude were not done, which might be necessary in the winter since there are large North-South gradients in air temperature then. Such corrections in the summer would likely be small, but I can revisit that nuance at a later time.
Results
I’ll start with the 6-province average Tmin temperature time series, along with the total number of stations available in each year. In all plots that follow, I list the linear temperature trends, but plot a 3rd order polynomial fit to the data to help capture any multi-decadal variations not well reflected in simple linear trends. In all provinces the number of stations increases from 1900 to the 1970s, then decreases substantially in recent years.
As can be seen in the first plot (averages for all 6 major provinces), there has been an average summertime warming trend of +0.14 C/decade.
I have also annotated 2021, which experienced the extreme heatwave in late June in western Canada. That event helped to push the warmest 3-day average Tmin metric (red curve) to the highest average temperature of any year since 1900. (Just to be clear, this is the warmest 3 days in each month in *minimum* daily temperature [Tmin]).
Notably, 8 of the 10 warmest summers in the all-days average Tmin have occurred since 2003. But, as I will show in the next few days, numbers matter: these warming trends are well below what the CMIP6 climate models produce for the same region of Canada.
Individual Provinces
The results for the individual provinces follow. I present them without comment; my Canadian friends can peruse the results for their home province if they wish. These are presented from West to East:
In the cacophony of voices clamouring for a hasty ‘energy transition’ away from fossil fuels, there are several tropes that are regularly employed by devotees of the Church of Climate. Over the past few decades, these tropes have been weaponised to convince lay people to cede all power to climate bureaucrats to ‘save the planet’. One trope that permeates the mass media and writings by ‘climate experts’ like Michael Mann and Bill McKibben is ‘cheap’ solar and wind energy.
In the cacophony of voices clamouring for a hasty ‘energy transition’ away from fossil fuels, there are several tropes that are regularly employed by devotees of the Church of Climate. Over the past few decades, these tropes have been weaponised to convince lay people to cede all power to climate bureaucrats to ‘save the planet’. One trope that permeates the mass media and writings by ‘climate experts’ like Michael Mann and Bill McKibben is ‘cheap’ solar and wind energy. This is despite the debunking of the magical thinking of the ‘new’ energy economy by those who understand and respect the laws of physics and economics.
Another trope that has gained traction among green ideologues is the ‘primary energy fallacy’. Social media commentary is peppered with references to this fallacy, frequently invoked to show that fossil fuels need not be replaced ‘one-to-one’ by ‘efficient’ renewable energy. This notion, peddled by advocates of wind and solar power such as Dr Jan Rosenow, Senior Research Associate at Oxford and Cambridge Universities, argues that traditional metrics of primary energy consumption — measuring the raw energy extracted from nature before conversion — systematically underestimate the contributions of renewables.
Why? Because fossil fuels like coal and gas lose much of their energy as waste heat during electricity generation, while wind turbines and solar panels deliver electricity with near-perfect efficiency. Thus, the argument goes, comparing energy sources on a primary energy basis penalises ‘efficient’ low-carbon technologies and inflates the role of ‘inefficient’ fossil fuels. It’s a clever rhetorical device that paints unreliable, intermittent renewables as the unsung heroes of decarbonisation.
A fallacy which isn’t
But scratch beneath the surface, and this ‘fallacy’ reveals itself as little more than a sleight of hand, a convenient narrative to prop up costly and unreliable energy sources at the expense of economic rationality. Drawing on the incisive analyses of advocates of energy literacy such as Lars Schernikau and Ronald Stein, it’s clear that the real distortion lies in ignoring the full system-level costs and inefficiencies of wind and solar as well as the multiple uses of oil and gas as feedstock for myriad products.
Far from being a progressive insight, the primary energy fallacy critique serves to obscure the harsh realities of intermittency, resource intensity and escalating costs that plague the push for renewables. In an era where energy security and affordability are paramount — especially for developing economies in Asia, Africa and Latin America — this misdirection threatens to impose vast financial costs on societies already straining under fiscal and trade deficits.
Primary energy, as defined by institutions like the International Energy Agency (IEA) and BP in their annual statistical reviews, tallies the unprocessed energy from sources such as coal, oil, gas, uranium, wind and sunlight. It relates to the total energy content of natural resources before any conversion processes take place. When converted to electricity, thermal sources like coal-fired plants operate at efficiencies of around 35-40%, with combined-cycle gas turbines reaching up to 60%. The rest dissipates as heat.
In contrast, wind and solar convert their ‘primary’ inputs — kinetic wind energy or solar irradiance — directly into electricity with minimal thermal losses, approaching 100% in accounting terms. A simple example illustrates the point: 100 units of gas primary energy might yield only 40 to 60 units of electricity, while 100 units from wind deliver the same 100 units as usable power. On primary energy charts, gas appears to contribute more, making renewables seem marginal.
Yet this comparison is myopic, confined as it is to the electricity sector, which accounts for just about 20% of global final energy consumption. The bulk of energy use — some 80% — occurs in non-electric forms: industrial heat for steelmaking and cement production, gas for cooking and home heating, petroleum for transportation, and petrochemicals for everything from fertilisers to plastics. Here, fossil fuels often deliver energy services with far higher efficiencies than the pro-renewable narrative admits. Direct combustion of gas for heating, for instance, achieves 80-90% efficiency, dwarfing the losses in electricity generation.
Renewables, by their nature, produce only electricity – intermittently at that — leaving vast swathes of the energy economy untouched without massive, inefficient ‘electrify everything’ efforts. As Dr Schernikau aptly notes in his writings, primary energy remains ‘king’ because it captures the raw inputs needed across the entire energy system, not just the narrow slice of grid power.
The indispensable role of fossil fuels extends far beyond energy provision requisite to modern civilisation. As Ronald Stein emphasises in his work, including the book Clean Energy Exploitations, oil and gas are the foundational feedstocks of over 6,000 products that underpin human progress, from plastics and cosmetics to pharmaceuticals and ammonia for fertilisers.
These materials cannot be replicated by so-called renewables like wind and solar, which generate only electrons and offer no viable pathways to synthesise the complex hydrocarbons essential for everything from medical devices and electronics to agricultural inputs that feed billions. Stein’s energy literacy advocacy highlights how decarbonisation zeal overlooks this reality: without fossil-derived ammonia, global food production would plummet, exacerbating hunger in developing countries already facing population pressures.
Petrochemicals derived from crude oil enable the sterile packaging for vaccines, the durable materials for wind turbine blades (ironically) and the synthetic fibres in clothing. Attempting to ‘electrify everything’ ignores that these products require molecular building blocks from fossils, not just electricity, rendering the transition not just costly but fundamentally impossible without alternatives that don’t exist at scale. This dependency underscores why primary energy metrics are vital — they reflect the total resource base sustaining not only power but the myriad goods that define quality of life.
Moreover, the efficiency boast of renewable energy enthusiasts crumbles when we consider the intermittency of wind and solar energy — sources that generate electricity only when nature cooperates, typically at capacity factors of 15-40% for wind and 10-25% for solar, compared to 80-90% for baseload coal or nuclear. This variability demands a ‘redundancy’ infrastructure of dispatchable power backup plants, largely fossil-fuelled, to fill the gaps. It is tantamount to paying twice for the same thing.
The real costs of ‘cheap’ renewables
In Germany, the poster child for the Energiewende (energy transition), wind and solar, now comprise over 50% of installed capacity, yet the country has had to maintain and even expand coal and gas-fired plants as backups. These backups operate at low utilisation rates, burning fuel inefficiently and inflating primary energy use. Grid-scale battery storage, often touted as the solution to intermittency, remains prohibitively expensive and resource-intensive. Even Tesla’s Megapacks can provide mere minutes or hours of backup for utility-scale needs, and scaling to days or weeks during periods of Dunkelflautewould require astronomical investments in rare earths and minerals, much of it controlled by China. Germany and fellow energy-suicide countries such as the UK now boast among the world’s highest electricity prices.
Schernikau’s system-level analysis exposes the core deception: while individual wind turbines or solar panels may appear efficient, their integration into a reliable grid erodes that advantage. To achieve dispatchable power — electricity available 24/7/365 with stable voltage, frequency and phase — renewables necessitate overbuilding by factors of three to five times or more, plus ancillary systems like short-duration batteries and upgraded transmission lines.
These additions consume vast amounts of primary energy in mining and manufacturing, often unaccounted for in standard metrics. For example, producing one TWh of lifetime electricity from solar requires 340-560 kilotons of steel plus copper and rare earths, compared to just one to two kilotons of steel for coal or gas. Wind fares little better, demanding 30-50 kilotons of steel and three to six kilotons of copper per TWh. Mining these materials is energy-intensive, predominantly powered by trucks and mining equipment powered by fossil fuels, embedding hidden primary energy costs that the IEA’s ‘partial substitution method’ conveniently ignores by assuming near-100% efficiency for renewables.
Selected materials required by electricity generation technology. Source: Schernikau based on Department of Energy, USA. See also here.
This brings us to the energy return on investment (eROI), a metric that measures how much usable energy a source delivers relative to the energy invested in its extraction, processing and deployment. At the system level, wind and solar’s eROI plummets to 5-10 to 1 for solar and 10-20 to 1 for wind (even lower with battery storage), versus 25-30 to 1 for coal and gas and over 75 to 1 for nuclear. Schernikau highlights that renewables’ short operational lifetimes — 10-20 years for wind, 12-15 for solar — mean that they must be replaced two to four times over a 40-60 year fossil plant lifecycle, generating mountains of waste and further primary energy demands. Global primary energy statistics underplay this; in 2024 IEA data, wind and solar’s 4,655 TWh of primary energy yielded 4,623 TWh of electricity, but this excludes the energy for overbuilding and backups.
The analysis provided by renewables cheerleader the International Energy Agency leaves out the fact that as renewables penetration as a percentage of total grid generation increases, the marginal value of each additional renewable kWh diminishes. This leads to higher system costs and, counterintuitively, potentially even greater primary energy use overall.
Critics of primary energy metrics acknowledge efficiency gains in end-use technologies like electric vehicles (EVs), which are three to four times more efficient than internal combustion engines. But they too warn of over-optimism, noting that electrification amplifies resource demands: EVs require six times more critical minerals than conventional cars, and scaling renewables for an all-electric world would demand 12-16 times more minerals overall, plus over 100 times more land.
Land use is no trivial matter: solar farms at 5-7 MW per km2 and wind at 1-2 MW per km2 sprawl across vast areas, sparking opposition across rural communities in the US, Europe and the UK. In the US, the work of Robert Bryce on a renewables rejection database has tracked this phenomenon in detail. The damage to ecosystems and flora and fauna caused by wind and solar farms has sparked opposition by rural communities and conservationists around the world. It should be noted, however, that often rentier solar and wind energy companies with business models which harvest subsidies and reap guaranteed profits can buy farmers out in the agricultural sector.
Habitat destruction in areas set aside for solar and wind farms – which disrupts traditional livelihoods, hurts property values, wrecks vital croplands, ruins scenic vistas and kills birds, bats and other wildlife – has been amply documented over the years across rural communities around the world. We have yet to consider supply chain vulnerabilities of renewable energy systems and geopolitical risks — China dominates 80% of rare earth processing as well as dominating the global production of wind and solar energy components.
The full cost of electricity (FCOE), which includes intermittency and grid integration, reveals renewables as far costlier than the misleading ‘levelised cost of electricity’ (LCOE) metric suggests. In Europe, household electricity prices have doubled since the early 2000s, largely due to renewable subsidies and grid upgrades. Germany’s Energiewende has cost over €500 billion, yet emissions reductions stall as coal lingers for reliability. Developing nations, where energy demand is booming, cannot afford such experiments; Asia’s coal fleet, the world’s largest, continues to expand rapidly because it delivers affordable, dispatchable power.
What primary energy fallacy?
In truth, the ‘primary energy fallacy’ is a fallacy in itself, a distraction from the unpopular truths of energy physics and economics. By fixating on narrow efficiency gains, ideologues overlook how wind and solar, at scale, regress humanity toward low net-energy systems reminiscent of pre-industrial eras. Primary energy metrics, far from obsolete, illuminate the total inputs required for industrial societies that promise prosperity for the vast majority of people in the Global South. Until breakthroughs in storage render intermittent energy sources viable without mandates and massive subsidies, fossil fuels — and yes, even “beautiful, clean coal” with pollution-limiting filters and equipment — remain indispensable.
Decades of delusion and fanaticism over man-made climate crises predicted by junk science models collided with reality at the conclusion of the UN’s chaotic climate jamboree this year at Belem, Brazil. The concluding global outcome document of COP30 – which was not attended by the leaders of the world’s most prolific emitters of greenhouse gases, China, the US and India – had stripped out any mention of eliminating fossil fuels. This though did not stop UN bureaucrats from telling its members that they should triple their spending on the ‘climate crisis’ over the next decade.
Policymakers would do well to heed energy experts like Schernikau and Stein. Chasing luxury beliefs do not cost well-heeled climate bureaucrats and renewables ideologues much, but the burdens of irrational energy policies will be borne by the world’s poorest. The real path forward lies in pragmatic, technology-neutral approaches that prioritise energy abundance over austerity.
Dr Tilak K. Doshi is the Daily Sceptic‘s Energy Editor. He is an economist, a member of the CO2 Coalition and a former contributor to Forbes. Follow him on Substack and X.
CBS News posted an article titled, “Southeast Asia floods and landslides kill more than 1,000 as climate change turbocharges monsoon season,” claiming that extreme rainfall associated with the Southeast Asian monsoon is worse now because of climate change. This is false. There is no clear trend of increasing monsoon rainfall patterns or worsening monsoon seasons. In fact, ample research indicates monsoons may have become less severe in recent decades.
CBS News posted an article titled, “Southeast Asia floods and landslides kill more than 1,000 as climate change turbocharges monsoon season,” claiming that extreme rainfall associated with the Southeast Asian monsoon is worse now because of climate change. This is false. There is no clear trend of increasing monsoon rainfall patterns or worsening monsoon seasons. In fact, ample research indicates monsoons may have become less severe in recent decades.
Already, CBS is off to a bad start. The first link they provided regarding extreme rain and “turbocharged” storms does not say that climate change HAS done those things, but only that it could in the future, according to projections from climate models. The models being relied on are famously inaccurate at predicting the future, which does not bode well for the claim.
The second link discusses only attribution studies that presupposes storms are worse because of human-caused warming – they do not show this is true with real world measured data. Climate Realism has discussed the grave error of relying on attribution studies in past articles. What was true in those is still true for the case of CBS’ recent post: attribution studies are not evidence, and they aren’t even good science because they begin with the conclusion that human activities are related to a particular storm’s severity, and then set out to prove how bad global warming made them. They are more like propaganda constructed with complex computer models.
It’s true that data suggest average rainfall is higher in the mid-latitudes of the northern hemisphere, but this does not translate to severe monsoons and flooding. The Intergovernmental Panel on Climate Change even admits that they have only “low confidence” that climate change is impacting flooding globally.
Past studies on the monsoon in South Asia have shown no clear trend across the region experiencing annual monsoon seasons. For instance, studies looking at India have supported claims that both the monsoon is getting more severe and making dangerous floods more likely, and also weakening, threatening South Asia with eternal drought. Yet another study from 2013 concluded that global warming has had no impact on monsoon rainfall at all.
Historically, Southeast Asia has seen severe and weak monsoon seasons, which are partially tied to natural fluctuations of warmth in the Pacific, called the El Niño Southern Oscillation. Paleo data from rivers across Southeast Asia shows a strong correlation between El Niño and rainfall pattern across the region. When the Pacific enters a cooler to neutral phase, rainfall increases, and during the warm period, the region gets more drought. This year saw the end of the warm phase, and has begun to lean more towards the cool La Niña, so more rain should be expected.
According to the World Bank Group’s climate portal, Sri Lanka has seen no established long-term trend in annual precipitation. The past few years have been rainier than the 1980s and 1990s, but not more than the 1960s.
According to the same data group, Malaysia has also experienced a recent recovery from low precipitation in the 1980s/1990s, but no clear sustained long-term trend.
Nothing about this data indicates some kind of a climate change driven trend towards a “turbocharged” monsoon, nor does it show drought becoming more extreme or severe. Interannual and decadal fluctuations are normal for regional climate.
CBS is taking advantage of the suffering of people in Southeast Asia in order to promote the human-driven catastrophe narrative, even when the data fail to support such a connection. If one’s goal is to motivate demands for government action on climate, as CBS’s seems to be, it is easier to convince people using emotional stories sans historical context, especially when trying to convince people to be afraid of climate change. Presenting the facts in a nuanced and accurate way would be more honest, if CBS were concerned about honest reporting, but it would be less likely to motivate demands for government action since the data suggest there are no alarming trends to be worried about.
Google unveiled Project Suncatcher earlier this month.
It aims to reduce AI’s environmental impact by relocating data centers in space, powered by the sun.
Google CEO Sundar Pichai said the company plans to begin sending ‘machines’ to space next year.
The great AI space race has begun.
Google has been quietly working on a long-term research initiative, internally known as Project Suncatcher, to “one day scale machine learning in space.”
Google CEO Sundar Pichai told Shannon Bream on Fox News Sunday that Google’s goal is to start putting data centers in space, powered by the sun, as soon as 2027.
“We are taking our first step in ’27,” he said. “We’ll send tiny, tiny racks of machines, and have them in satellites, test them out, and then start scaling from there.”
In a decade, Pichai said that it’ll be normal to build extraterrestrial data centers.
…
Google’s cosmic pivot comes amid growing global scrutiny over the power demands of data centers.
Obviously there is the launch cost. Even with the insane cost of data center components, the cost of launching the whole assembly into space adds significantly to the overall cost, along with the cost of sending technicians into orbit to service the equipment.
But the biggest issue with orbital data centers might be heat dissipation. Data centers which consume hundreds of megawatts have to dissipate all of that energy as heat.
Most people think of space as cold, but for heat emitting systems like machines or humans in spacesuits, space functions more like a giant vacuum flask. The kind of vacuum which keeps your Thermos of soup hot all day on Earth completely surrounds anything you put into orbit.
On Earth, data center heat dissipation is a barely manageable problem. Data centers on Earth pump out so much heat, a sizeable portion of the cost of a constructing a data center is building the cooling system.
In space the heat dissipation problem will be far worse – you can’t just dump all the heat into a convenient large body of water located next to your data center; all the heat has to be radiated into the vacuum of space without any help from air convection.
I read the Google press release on their new orbital data center idea, and not once did they mention heat dissipation or cooling.
Why is Google advocating such unlikely sustainability ideas? First claiming data centers will be powered by nuclear fusion in the next decade, now claiming they’ll put all the data centers in space and power them with solar energy?
These grandiose plans might just be a manifestation of late-stage investment bubble fever. But I wonder how bad Google’s internal divisions are, whether Google management is catching a lot of internal heat over their skyrocketing data center CO2 footprint? Perhaps these wild sustainability plans are a desperate attempt to pacify Google’s in-house climate worriers – at least untilskynet suncatcher is ready to take over all the programming jobs. But that’s just my own wild theory.
A few years ago Dr. Koutsoyiannis and colleagues used equations associated with the chemistry of temperature-driven organic respiration to demonstrate that, since the late 1950s, temperature-induced increases in plant and soil emissions (31.6 Gt-C/yr) account for a 3.4 times greater ratio of the >100 ppm rise in atmospheric CO2 than the contribution from the increase in fossil fuel emissions (9.4 Gt-C/yr).
“[T]he fraction of [fossil] fuel-related emissions still remaining in the air (about 23 ppm out of 425 ppm at the end of 2024) cannot have any climatic effect.” – Veyres et al., 2025
A few years ago Dr. Koutsoyiannis and colleagues used equations associated with the chemistry of temperature-driven organic respiration to demonstrate that, since the late 1950s, temperature-induced increases in plant and soil emissions (31.6 Gt-C/yr) account for a 3.4 times greater ratio of the >100 ppm rise in atmospheric CO2 than the contribution from the increase in fossil fuel emissions (9.4 Gt-C/yr).
This conclusion is rooted in the observation that, since 1959, the causality direction has consistently been T→CO2, and not CO2→T (Koutsoyiannis et al., 2022), when observing annual changes in atmospheric CO2 concentrations. In other words, respiration analyses indicate the rise in CO2 has been the consequence, not the cause, of temperature.
And now, in a new study, scientists have used the time-integrated effect of past sea surface temperatures and time-series modeling to establish that temperature-driven oceanic CO2 outgassing can also explain the bulk of the rise in atmospheric CO2 since the late 1950s. In contrast, there is “no correlation (R² = 0.01) between the detrended 12-month CO2 increments and fossil-fuel emissions.”
Notably, fossil fuel emissions rates can be shown to have grown from 2.4 Gt-C/yr in 1959 to 10.3 Gt-C/yr in 2025, a net +7.9 Gt-C/yr change. In contrast, natural emissions from oceanic outgassing grew from 133.2 Gt-C/yr in 1959 to 175.2 Gt-C/yr in 2025 (a net +42 Gt-C/yr change). Significantly:
“The +42 Gt-C/yr increase in temperature-driven natural inflow explains 84% of the total inflow rise since 1959…”
Other ratios detailed in the study also identify oceanic temperature-driven natural emissions as the predominant contributor to the rise in atmospheric CO2 concentration.
“[I]n 1960, oceanic degassing was 32 times the flux from ‘fossil fuels’; since 2010, it has been 11 times greater.”
“[SST anomalies] increased from 0.12°C in 1959 to 0.97°C in 2024 and accounts for 83% (+89 ppm) of the total increase (+107 ppm) in atmospheric CO2 over that period.”
“The resulting growth of [fossil fuel emissions] is 5 x 0.12 = +0.6 Gt-C/yr, or +0.28 ppm/yr – i.e. eight times smaller than the observed increase of [natural CO2 emissions] = +5 Gt-C/yr or +2.4 ppm/yr over the past decade.”
The authors identify the remaining anthropogenic contribution to the current (2024) 425 ppm atmospheric CO2 concentration as amounting to just 23 ppm, or 49 Gt-C. This means approximately ninety-five percent of today’s CO2 levels are derived from natural processes. Thus, even if the costly (€ 800 billion per year) EU decarbonization policies intended to dramatically reduce human CO2 emissions were to be fully implemented today, it would “lower atmospheric CO2 by only about 0.5 ppm by 2035.”
MasterResource has chronicled the rise and fall of major U.S. solar firms this year. The rooftop solar business is in shambles, with leaders such as Sunnova becoming worthless and defaulting on their long-term projects. And more recently, utility-scale solar developers are in trouble despite the last-minute deal to continue major tax subsidies until next year.
“What about the triple-digit number of creditors who banked on a loser? What about taxpayers who funded around 30 percent of the solar investment? … Ben Catt is just another rent-seeker, a political crony, like Sunnova’s John Berger, benefitting at the expense of just about everyone else.”
MasterResource has chronicled the rise and fall of major U.S. solar firms this year. The rooftop solar business is in shambles, with leaders such as Sunnova becoming worthless and defaulting on their long-term projects. And more recently, utility-scale solar developers are in trouble despite the last-minute deal to continue major tax subsidies until next year.
Add this to the list. As reported in Solar Power World, (November 7, 2025):
National solar developer Pine Gate Renewables is filing for Chapter 11 bankruptcy and selling its solar and energy storage project portfolio and independent power producer business. This will include completed and under-development projects, of which the company stated there is 10 GWDC in progress.
Founded nine years ago, Pine Gate has commercial and utility solar business in 32 states. With 500 MW of development in 2022, the company made the 2023 Top Solar Contractors List.
“Since our founding almost 10 years ago, Pine Gate has grown tremendously, deploying innovative solar and energy storage projects at scale that enable us to deliver renewable, reliable, and affordable energy. To ensure that our projects continue generating renewable energy, we made the strategic decision to commence this court-supervised sales process. With significant financial support from certain of our current lenders, we’re confident that we will successfully conduct a competitive sales process that reflects the inherent value of our nationwide portfolio of solar and energy storage projects.
Catt continues:
I’m grateful for the hard work and dedication of the Pine Gate team who has been key to helping us drive the renewable energy transition. As we move through this process, we remain committed to supporting our valued project partners across our more than 100 operational solar facilities and forging ahead with our projects in development and under construction.
This is winner talk, not loser talk. What about the triple-digit number of creditors who banked on a loser? What about taxpayers who funded around 30 percent of the solar investment? What if these projects had been sustainable from consumer demand (such as natural-gas-fired plants)? Ben Catt is just another rent-seeker, a political crony, like Sunnova’s John Berger, benefitting at the expense of just about everyone else.
The individual Tidal Gauges will be processed first. They cover a wide range of total periods, and some have missing data, generally small gaps but occasionally substantial periods that impinge on the calculations and need to be allowed for. The Aberdeen working will be presented in full whilst the other Tidal Gauges will be shown in a standard, abbreviated layout.
Note: This is Part 2 of a series — you might want to read Part 1 first if you haven’t already done so.
Curve Fitting
The individual Tidal Gauges will be processed first. They cover a wide range of total periods, and some have missing data, generally small gaps but occasionally substantial periods that impinge on the calculations and need to be allowed for. The Aberdeen working will be presented in full whilst the other Tidal Gauges will be shown in a standard, abbreviated layout.
Aberdeen
The Aberdeen data covers 1862 to 2022 a period of 161 years. The spectral analysis gave 3 curves with a period above11 years (P1, P2 and P3) , namely (84.9, 43.0 and 18.1) years. Associated with these are 3 values of amplitude (amp1,amp2 and amp3) at (32.7, 15.1 and 10.3) respectively. Each of these will have an associated sinusoidal curve given by Equation 1. The 3 values of Amplitude of the curves (AMP1, AMP2 and AMP3) are related to the values (amp1,amp2 and amp3) by Equation 2 which results in the relative values of (a1, a2 and a3) being in the ratios (5.7, 3.9 and 3.2) these being the square root values of (amp1, amp2 and amp3).
The last 3 values (a1,a2 and a3) being relative must be multiplied by a factor, F, to obtain actual Amplitudes of the sinusoidal curves. F can be guesstimated or if there is a sufficiently number of cycles the extreme range would be close to the range of the individual maximum and minimum amplitudes (Amaxand Amin) of the data resulting in a value of F as shown in Equation 3.
F = (Amax – Amin) / 2 x (a1 + a2 + a3) Equation 3
For the Aberdeen data Amax = 35.7 and Amin = -42.2 giving a range of 77.9
And with (a1 + a2 + a3) = (5.7 + 3.9 + 3.2) = 12.8
F = 77.9 / 25.6 = 3.04
resulting in Amplitudes (AMP1, AMP2 and AMP3) of (17.3, 11.9 and 9.7).
These are probably underestimates and the slightly higher values 18, 12.5 and 10 will be tried.
Figure 14 shows the Aberdeen data (an approximately 8-year moving average) on which the three periods are shown.
Figure 15 shows the Aberdeen data with the combined values of the 3 sinusoidal curves. It shows a reasonable fit but will not be pursued any further as the averaged actual data is affected in places by gaps in the initial full data set. Also, the sinusoidal curve phase shifts have been eyeballed in, and no constant term (CONST) has been included, that is the sinusoidal curves oscillate about the zero residual value. The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-30, -20 and -90).
Figure 14 Figure 15
The main deviations appear at approximately Time = 110 (2010). From 2016 to 2021 there is a lot of missing data as can be seen in the NOAA data shown in the section on Aberdeen above. Portions of the NOAA data are presented below in Figure 16.
Figure 16
Bergen
The Bergen data covers 1915 to 2022 a period of 108 years with 2 medium gaps in the 1940’s and 1970’s.
(P1, P2 and P3) = (66.9, 28.9 and 19.2) years
(amp1,amp2 and amp3) = (13.3, 10.2 and 10.1)
(a1, a2 and a3) = (3.6, 3.2 and 3.2)
(Amax and Amin) = (32.3 and -40.9)
Applying Equation 3 results in
F = 73.2 / 20.0= 3.66
giving (AMP1, AMP2 and AMP3) 13.2, 11.7 and 11.7 respectively.
Values of Amplitude 14, 12 and 12 will be tried.
Figure 17 shows the Bergen data (approximately 8-year moving average) on which the three periods are shown.
Figure 18 shows the Bergen data with the combined values of the 3 sinusoidal curves. The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-50, -40 and -90).
Figure 17 Figure 18
The only main deviations appear at approximately Time = 110 (2010) when there is a lack of a peak in the data. No explanation can be found but Figure 19 is inserted to show the full NOAA data at this time.
Figure 19
Pause for thought
At this stage 2 examples of fitting sets of sinusoidal curves have resulted in a satisfactory result.
Is it a Eureka Moment or am I fooling myself?
The process boils down to using the Spectral Analysis to indicate the main modes considered as being restricted to periods over about 11 years. Based on these modes a set of sinusoidal curves are formed that use the periods and calculated amplitudes together with the knowledge of the peak minimum and maximum residual values obtained from a roughly 8 year moving average of the residuals. Phase shifts for each sinusoidal curve are eyeballed in, and a combined curve obtained. Is this process a bit cyclic or biased so that it produces good looking fits?
The process will be applied to the other 7 shorter period cases and then judgement on the methodology revisited. The reasons for this are that it may help to judge the suitability of the method and present a fuller picture of 9 Tidal Gauge data sets.
Narvik
With this dataset the remaining records span less than 100 years and analyses may be problematic. Narvik has a large gap in the data early on as well as strange values for the first year or so. The data file was therefore truncated and the data used starts at 1947.
The partial Narvik data covers 1947 to 2022 a period of 76 years.
(P1, P2 and P3) = (26.0, 17.6 and 13.4) years
(amp1,amp2 and amp3) = (2.6, 5.0 and 1.9)
(a1, a2 and a3) = (1.6, 2.2 and 1.4)
(Amax and Amin) = (28.9 and -30.4)
Applying Equation 3 results in
F = 59.3 / 10.4= 5.7
giving (AMP1, AMP2 and AMP3) = (9.1, 12.6 and 8.0)
Values of Amplitude 9.0, 13.0 and 8.0 could be tried.
Figure 20 shows the Narvik data (approximately 8-year moving average) on which the three periods are shown.
Figure 21 shows the Narvik data with the combined values of the 3 sinusoidal curves. The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-30, -30 and -90).
Figure 20 Figure 21
The result is quite satisfying.
Reykjavik
The Reykjavik data covers 1956 to 2022 a period of 67 years.
(P1, P2 and P3) = (37.0, 18.7 and 14.0) years
(amp1,amp2 and amp3) = (12.5, 9.2 and 9.0)
(a1, a2 and a3) = (3.5, 3.0 and 3.0)
(Amax and Amin) = (52.8 and -39.4)
Applying Equation 3 results in
F = 92.2 / 19.0= 4.85
giving (AMP1, AMP2 and AMP3) = (17.0, 14.6 and 14.6)
Values of Amplitude 18.0, 15.0 and 15.0 could be tried.
Figure 22 shows the Reykjavik data (approximately 8-year moving average) on which the three periods are shown.
Figure 23 shows the Reykjavik data with the combined values of the 3 sinusoidal curves. The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-80, -16 and -4).
Figure 22 Figure 23
All fits quite well.
Murmansk
The data covers 1952 to 2022 a period of 71 years but has a strange divergence between 1990 and 2010 where there seems to be an increase in sea levels of about 100mm over the general trend (figure 24). This will affect the spectral analysis, but the process will be completed with the full data.
Figure 24
(P1, P2 and P3) = (65.2, 17.6 and 13.1) years
(amp1,amp2 and amp3) = (143.5, 9.8 and 5.0)
(a1, a2 and a3) = (12.0, 3.1 and 2.2)
(Amax and Amin) = (112.5 and -79.7)
Applying Equation 3 results in
F = 192.2 / 34.6= 5.55
giving (AMP1, AMP2 and AMP3) = (66.6, 17.2and 12.2)
Values of Amplitude 18.0, 15.0 and 15.0 could be tried.
Values of amplitude 67, 17.5 and 12.5 could be tried. These are a bit higher because with a smaller total period there is less chance of the peaks and dips of the 3 curves coinciding.
Figure 25 shows the Murmansk data (approximately 8-year moving average) on which only the longest period is shown. The 2 short periods are not shown as the strange data between 1990 and 2010 greatly affects the plot. The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-45, -20 and -100).
Figure 26 shows the Murmansk data with the combined values of the 3 sinusoidal curves.
Figure 25 Figure 26
The effect of the strange data between 1990 and 2010 shows up with an untypical plot in which the smaller oscillations are dominated by a saw tooth form of the main mode.
Barentsburg
The Barentsburg data covers 1948 to 2022 a period of 75 years.
(P1, P2 and P3) = (41.6, 17.8 and 13.4) years
(amp1,amp2 and amp3) = (15.2, 16.3 and 14.0)
(a1, a2 and a3) = (3.9, 4.0 and 3.7)
(Amax and Amin) = (26.5 and -56.7)
Applying Equation 3 results in
F = 83.2 / 23.2= 3.59
giving (AMP1, AMP2 and AMP3) = (14.0, 14.3 and 13.3)
Values of Amplitude 15, 15 and 14 could be tried.
Figure 27 shows the Barentsburg data (approximately 8-year moving average) on which the three periods are shown.
Figure 28 shows the Barentsburg data with the combined values of the 3 sinusoidal curves. The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-50, -30 and -97).
Figure 27 Figure 28
The two curves in figure 27 generally have the same form. In this case there were 3 similar peaks which made eyeballing in the phase shifts more difficult.
Lerwick
The Lerwick data covers 1957 to 2022 a period of 66 years with a medium gap at about 2002 and later smaller gaps.
(P1 and P2) = (21.4 and 12.8) years
(amp1 and amp2) = (5.5 and 3.05)
(a1 and a2)= (2.35 and 1.75)
(Amax and Amin) = (17.1 and -20.0)
Applying Equation 3 results in
F = 37.1 / 8.20 = 4.52
giving (AMP1 and AMP2)= (10.6 and 7.9)
Values of Amplitude 11 and 8 could be tried.
Figure 29 shows the Lerwick data (approximately 8-year moving average) on which the two periods are shown.
Figure 30 shows the Lerwick data with the combined values of the 3 sinusoidal curves. The shifts (SHIFT1 and SHIFT2)) used are (-54 and -25)
Figure 29 Figure 30
The fit is very good.
Tiksi
The Tiksi data covers 1949 to 2010 a period of 62 years.
(P1 and P2) = (47.4 and 18.1) years
(amp1 and amp2) = (0.835 and 4.28)
(a1 and a2)= (0.91 and 2.07)
(Amax and Amin) = (35.6 and -28.6)
Applying Equation 3 results in
F = 64.2 / 5.96 = 10.77
giving (AMP1 and AMP2)= (9.8 and 22.3)
Values of Amplitude 10 and 24 could be tried.
Figure 31 shows the Tiksi data (approximately 8-year moving average) on which the two periods are shown.
Figure 32 shows the Tiksi data with the combined values of the 3 sinusoidal curves. The shifts (SHIFT1 and SHIFT2)) used are (-54 and -20).
Figure 31 Figure 32
Torshavn
The Torshavn data covers 1957 to 2006 a period of 50 years.
(P1, P2 and P3) = (38.1, 18.7 and 13.1) years
(amp1,amp2 and amp3) = (1.27, 6.72 and 4.19)
(a1, a2 and a3) = (1.13, 2.59 and 2.05)
(Amax and Amin) = (26.5 and -17.7)
Applying Equation 3 results in
F = 44.2/ 11.54 = 3.83
giving (AMP1, AMP2 and AMP3) = (4.33, 9.92 and 7.85)
Values of Amplitude 4.5, 10 and 8 could be tried.
Figure 33 shows the Torshavn data with the combined values of the 3 sinusoidal curves.
The shifts (SHIFT1, SHIFT2 and SHIFT3) used are (-10, -8 and -82).
Figure 33
The fit is not very good due to the short period of the dataset and that there are several breaks in the data. The plot showing the periods is not shown.
Before a general discussion it is opportune to apply the methodology developed to the Brest data.
Brest
The Brest data covers 1807 to 2022 a period of 216 years. Figure 10 gave the Spectral Analysis peaks for the Residuals.
(P1, P2, P3 and P4) = (92.8, 36.1, 28.2 and 19.0) years
(amp1,amp2, amp3 and amp4) = (16.6, 8.8, 15.6 and 9.0)
(a1, a2, a3 and a4) = (4.1, 3.0, 3.9 and 3.0)
(Amax and Amin) = (50.0 and -38.4)
Applying Equation 3 results in
F = 88.4 / 28.0= 3.16
giving (AMP1, AMP2, AMP3 and AMP4) = (12.9, 9.5, 12.3 and 9.5)
Values of Amplitude 14.0, 10.0 13.0 and 10.0 could be tried.
Figure 34 shows the Brest data (approximately 8-year moving average) on which the four periods are shown.
The shifts (SHIFT1, SHIFT2, SHIFT3 and SHIFT4) used are (-55, -70, 101 and -120).
Figure 34
All fits quite well except at the times of missing data, i.e. around 1850 and1950.
Discussion
The main finding is that every Tidal Gauge exhibits 2 or more oscillations in the residual sea levels between 11 and 100 years in addition to the many below 11 years that occur due to irradiance variation or El Niño/La Niña cycles. Above a period of 20 years these oscillations vary from gauge to gauge in periods and the combined effect is difficult to ascertain but not measuring them over the northly seas would impinge on the satellite “global” readings.
A query still exists into the validity of the methodology used.
A plot is shown in figure 35 that displays all the Tidal Gauge combined sinusoidal curves from 1993 to 2025 except for Murmansk due to its possible bad data and Brest as this was an extra study. There is a similarity between the curves.
Figure 35
The 8 curves are averaged and plotted in Figure 36 at the same scale. It shows an approximate 18-year cycle.
Figure 36
The significance of the 18-year cycle can be appreciated by plotting the Amplitudes and Periods of each set of sinusoidal curves as shown in figure 23 (plus Murmansk). There are 2 clusters. One at a Period of 13.5 years with 6 of the 9 curves involved and one at 18 years with 8 of the 9 curves involved with the 9th value being 21.4 years. The second cluster matches the 18-year cycle in figure 36.
Figure 37
The commonality of periods may be an indication of one or more shortish decadal oscillations. Values in the range of about 25 to 30 years were being searched for based on the satellite analyses. The period of satellite coverage was only 32 years, and the area not covered, whilst being mainly the upper Atlantic regions, also covered other parts of the Arctic Sea and smaller sea areas surrounding the Antarctic region. It is not unreasonable that the areas unmonitored could induce sinusoidal variations in the “global” results.
The instigation for this work was the sinusoidal variation in the range of 26 to 29 mm/year2 found whilst studying NASA Satellite Data. This oscillation was determined for the residual values calculated from the linear best fit of the data. The Tidal Gauge results are for the residuals calculated from a quadratic curve over, in most cases, the entire data set. Over the short (33 years) period of the satellite data the deviation of the best fit from a straight line was assumed to be minimal but as the satellite period increases it may become more important to consider it.
The latest NASA data was first looked at. Unfortunately, there has been a hiatus, due to political reasons, and before that a reduction in the amount of processed data issued. Therefore, the April 2025 data will be used as that is the last set with a fuller form of data I have analysed and reported on since starting in 2018. Figure 38 shows the April 2025 data on which best fit linear and quadratic curves have been fitted.
Figure 38
Fitting quadratic and sinusoidal curves to the residuals (actual values – straight line values) resulted in figures 39 and 40. Note statistically the sinusoidal is more accurate than the quadratic when judged on the errors.
Figure 39 Figure 40
Four spectral analyses were performed
Full data (As shown on Figure 38 – blue line)
Residuals (As shown on figures 39 or 40 – blue dots)
Differences of Residuals to Quadratic Curve (Figure 41)
Differences of Residuals to Sinusoidal Curve(Figure 42)
Figure 41 Figure 42
Results for the 4 Spectral Analyses are shown in figures 43 to 46.
Figure 43 Figure 44Figure 45 Figure 46
What do figures 43 to 46 say? All 4 figures have several peaks below approximately a 10-year period, but it is the rest that are of interest in this study.
On figure 43 there are 3 peaks indicated. First a very long-term peak for which the actual period cannot be determined due to the short span of the readings. Then a 16.9-year period in line with the periods found with all the Tidal Gauges. And lastly a 11-year period possibly due to irradiance variation (solar cycle).
Figure 44 points to the 29-year cycle that is being investigated and again a peak around 10 years. As stated above this is obtained when working relative to a straight line and arose when making judgement on the use of quadratic curve fitting and the associated extrapolation. Sinusoidal curve fitting was studied as an alternative with all this being reported in a number of papers, the last being https://wattsupwiththat.com/2025/06/06/measuring-and-analysing-sea-levels-using-satellites-during-2024/ that covers up to the end of 2024.
Figure 45 shows possibly 2 combined closely positioned peaks around 14 to 15 years.
Figure 46 has a single, very sharp, peak at 10.9 years again possibly due to irradiance variation. Figures 41 and 42 at first glance look very similar but the sinusoidal curve, being a slightly better fit, produces a clearer spectrum.
The overall conclusion is that the 29-year variation is to do with the work involving the linear fitting but there is a general variation of about 18 years in all the tidal graphs which shows up in the full satellite data.
The Guardian’s recent article, “‘My husband and daughter went down to the garage in case it flooded. Then I heard a strange noise’ – This is climate breakdown,” by Ajit Niranjan, presents the tragic story of a family caught in the Valencia floods of October 2024, framing the disaster as evidence of “climate breakdown.” This is false. History shows such storms and floods have repeatedly occurred in that region of Spain before, and the article ignores that important context along with other factors, like rebuilding in known flood zones, which made the recent flooding worse than it otherwise might have been.
The Guardian’s recent article, “‘My husband and daughter went down to the garage in case it flooded. Then I heard a strange noise’ – This is climate breakdown,” by Ajit Niranjan, presents the tragic story of a family caught in the Valencia floods of October 2024, framing the disaster as evidence of “climate breakdown.” This is false. History shows such storms and floods have repeatedly occurred in that region of Spain before, and the article ignores that important context along with other factors, like rebuilding in known flood zones, which made the recent flooding worse than it otherwise might have been.
The article asserts, “devastating storms hit the Iberian Peninsula, bringing the heaviest rain so far this century . . . scientists say the explosive downpours were linked to climate change.” The human tragedy is real, and the suffering undeniable. But The Guardian uses this story as a vehicle for a sweeping climate narrative that the observational record undermines.
An example is a drawing from 1864, by Frederico Ruiz, which showed the scale and violence of flooding in Valencia Province, in Alzira, a city on the left bank of the Xúquer River, 45 kilometers to the south of Valencia in 1864.
Distribution of historic floods in Spain during different periods (according to Benito et al. 1996).
The Guardian ignores this important context entirely, promoting the false narrative that climate change is responsible for or makes worse every present day natural disaster, notwithstanding history.
Observational climate data paint a more complicated picture than “climate breakdown.” The Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report (AR6), on Page 90, table 12.12 (seen below) states that it has low confidence in any global trend in flood frequency or magnitude.
Similarly, rainfall intensity varies strongly across decades due to natural climate oscillations such as the North Atlantic Oscillation (NAO) and Mediterranean teleconnections. None of this appears in the article, which instead wrongly implies a direct, linear link between fossil-fuel emissions and a single storm event.
If readers want data-driven assessments rather than emotional narratives, they can consult the Climate at a Glance summary “Floods,” which explains that global flood trends show no sustained upward pattern. Likewise, “Extreme Weather” summarizes that most categories of extreme weather—hurricanes, tornadoes, droughts, heatwaves, rainfall—show mixed or declining long-term trends in the observational record. Climate Realism has documented many media misrepresentations of rainfall and flooding events, including their recent takedown of a similar BBC claim, “Flooding Facts Drowned by Climate Hysteria: The BBC Ignores Spain’s Weather History.”
The Guardian also fails to acknowledge the decisive role that local factors played in magnifying the Valencia disaster. The flood images in the article, showing submerged vehicles piled together and narrow streets acting as channeled torrents, make clear that urban design, impermeable surfaces, insufficient drainage systems, and inadequate warnings all contributed to the severity of impacts. The increased urbanization in a known flood plain was an invitation to the disaster that occurred. The woman at the center of the article concludes that the regional government’s failure to warn residents or provide preventive measures was the primary cause of loss, not the climate. Yet The Guardian still insists that the true antagonist is “climate breakdown.”
Natural climate variability, infrastructure decisions, and emergency preparedness determine disaster outcomes far more than marginal changes in global average temperature, a fact The Guardian ignores to the detriment of the truth and its readers. The Guardian’s assertion that the recent flood was unprecedented is flat out false, as similar or worse flooding events in the region documented in the historical record attest – floods that occurred long before CO₂ levels rose significantly .
By implying that a heartbreaking family tragedy is proof of global climate collapse, The Guardian irresponsibly misleads its readers, substituting emotional persuasion and advocacy with scientific rigor. Readers deserve factual reporting complete with historical context and data, rather than a false narrative that treats every intense weather event as evidence of a “climate breakdown.”
Households face paying billions more in energy bills to fund green subsidy costs that were not outlined in Rachel Reeves’s Budget last week.
Costs of £1bn a year to fund renewables projects were not outlined in the Chancellor’s Budget
Households face paying billions more in energy bills to fund green subsidy costs that were not outlined in Rachel Reeves’s Budget last week. The Telegraph has the story.
The Office for Budget Responsibility (OBR) revealed in its latest economic assessment that £1bn a year will be added to household energy bills to fund Ed Miliband’s next auction for renewables projects, known as “allocation round 7” (AR7).
Costs of the scheme were not outlined in the Chancellor’s Budget. Instead, they were revealed in a footnote to the OBR’s Fiscal Outlook report released on the same day.
The OBR’s disclosures relate to contracts for difference (CfD), the system under which levies are added to household bills to finance subsidies for green energy projects, including wind, solar and nuclear.
The OBR forecasts that CfD costs are set to rise from £2.3bn in 2024-25 to £4.6bn by 2030-31. However, it warned that the Treasury’s estimates excluded an additional £5bn for AR7 and subsequent subsidy rounds.
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