Category: Commentary

Global average temperature data show 1.2-1.3C increases since pre-industrial times and continue rising at 0.02-0.03C per year, according to data-sets from NASA, NOAA, the UK Met Office and academic institutions. This note assesses their methodologies and controversies. Uncertainty in the data is likely much higher than admitted. But the strong upward warming trend is robust.

2020 is said to be the joint-hottest year on record, tied with 2016, which experienced a particularly sharp El Nino effect. 2020 temperatures were around 1.2C warmer than 1880-1900, on data reported by NASA’s Goddard Institute for Space Studies (GISS), and 1.3C warmer on data reported by the UK Met Office’s Hadley Center and East Anglia’s Climatic Research Unit (HadCRUT).

2020’s hot temperatures were partly influenced by COVID-19, as “global shutdowns related to the ongoing coronavirus (COVID-19) pandemic reduced particulate air pollution in many areas, allowing more sunlight to reach the surface and producing a small but potentially significant warming effect”, per NASA. But the largest component of the warming is attributed to rising CO2 levels in the Earth’s atmosphere, which reached 414 ppm.

Overall, NASA’s data show temperatures have warmed by 0.02C per year over the past 50-years, 0.023C per year over the past 25-years and 0.03C per year over the past 10-years (chart above). Likewise, HadCRUT shows 0.02C per year over the past 50-years, 0.022C/year over the past 25-years and 0.024C/year over the past 10-years (below). Both data-sets suggest the rate of warming is accelerating.

How accurate are the data-sets? To answer this question, this note delves into global average surface temperature records (GASTs). It is not as simple as shoving a thermometer under the world’s armpit and waiting three minutes…

How are global average surface temperatures measures?

Surface air temperatures (SATs) are measured at weather stations. 32,000 weather stations are currently in operation and feed into various GAST indices.

Surface sea temperatures (SSTs) are measured at the surface of the sea as a proxy for immediately overlying air temperatures. Up until the 1930s, SSTs were most commonly taken by lowering a bucket overboard, and then measuring the temperature of the water in the bucket. This most likely under-estimated water temperatures in the past. From 1930-1990, SSTs were mostly measured from ships’ engine intakes. From 1990 onwards, SSTs were most commonly measured by specialized buoys and supplemented by satellite imagery.

Sea ice is particularly complicated. It takes 80% as much heat to melt 1kg of ice as it takes to boil 1kg of water, which means that the surface temperatures above sea ice can be higher than 0C. But it is difficult to access locations that are iced over with permanent weather stations. So temperatures over sea ice are often modelled not measured.

Temperatures are measured at each of these sites noted above. However GAST indices do not take raw temperature data as their inputs. First, absolute temperatures vary markedly between different weather stations that are scattered over short distances (e.g., due to shade, aspect, elevation, wind exposure), making the readings too site-specific. Moreover, the global average temperature is actually 3.6C higher in July-August than it is in December and January, because land masses experience greater seasonal temperature fluctuation than oceans, while two-thirds of the world’s land is in the Northern hemisphere, experiencing summer conditions in July-August. This would introduce too much noise into the data.

Temperature anomalies are the input to GAST indices. These are calculated by comparing average temperatures throughout each day with a baseline temperature for that site at that particular time of year. These anomalies are highly correlated, site-by-site, across hundreds of kilometers. By convention the 30-year average period from 1951-1980 is used as the baseline by NASA.

Averaging is used to aggregate the temperature anomaly data from different temperature stations across regions. Regional anomalies are then averaged into a global anomaly. Each region is weighted by its proportionate share of the Earth’s surface area. Thus a GAST index is derived.

Controversies: could there be systematic biases in the data?

Very large data-sets over very long timeframes are complicated beasts. They are prone to being revised and adjusted. Some commentators have worried that there could be systematic biases in the revisions and adjustments.

More data. As an example, NOAA digitized and added more observations from the early 20^th century into its methodology in 2016. This caused prior data to be re-stated. But this reason seems fair and relatively uncontroversial.

New weather stations are slightly more controversial. How do you know what the baseline temperature would have been at a site in 1950-1980, if the first weather station was only added there in 2000? Some of the baselines must therefore be derived from models, rather than hard data. Some commentators have criticized that the models used to set these baselines themselves pre-suppose anthropogenic climate change, assuming past temperatures were cooler, thereby placing the cart before the horse. This fear may be counter-balanced by looking at weather stations with longer records. For example, of the 12,000 weather stations surveyed by NASA’s 2020 data, as many as 5,000 may have records going back beyond 1930.

Urban heat islands are somewhat more controversial again. Imagine a weather station situated in the countryside outside of a city. Over the past century, the city has grown. Now the weather station has been engulfed by the city. Cities will tend to be 1-3C warmer than rural lands, due to the urban heat island effect. So for the data to remain comparable, past data must be adjusted upwards. GISS notes that the largest change in its calculation methodology over time has been to adjust for urban heat islands. Although some commentators have questioned whether the adjustment process is at risk of not being extensive enough. This fear may be counter-balanced by the relatively small portion of weather stations experiencing this engulfing effect.

The adjustment of anomalous-looking data is most controversial. Algorithms are used to sift through millions of historical data-points and filter away outliers that run counter to expectations. The algorithms are opaque. One set of algorithms ‘homogenizes’ the data of stations showing divergent patterns to its neighbours, by replacing that station’s data with that of its neighbours. As the general trend has been for a warming climate, this means that some stations showing cooling could be at risk of getting homogenized out of the data-set, causing the overall data to overstate the degree of warming.

The data also do not correlate perfectly with rising CO2 levels: especially in 1880-1920, which appears to get cooler; and around the Second World War, which appears to produce a spike in temperatures then a normalization. On the other hand, no one is arguing that CO2 is the sole modulator of global temperature. El Nino, solar cycles and ‘weather’ also play a role. And despite the annual volatility, the recent and most accurate data from 1970+ rise in lockstep with CO2.

The most vehement critics of GAST indices have therefore argued that past temperature adjustments could be seen to contribute over half of the warming shown in the data. There is most distrust over the revisions to NASA’s early temperature records. One paper states “Each new version of GAST has nearly always exhibited a steeper warming linear trend over its entire history. And, it was nearly always accomplished by systematically removing the previously existing cyclical temperature pattern. This was true for all three entities providing GAST data measurement, NOAA, NASA and Hadley CRU”.

Uncertainties should not detract from the big picture

Our own impression from reviewing the evidence is that the controversies above should not be blown out of proportion. The Earth is most likely experiencing a 0.02-0.03C/year warming trend over the past 10-50-years.

Multiple independent bodies are constructing GAST indices in parallel, and all seem to show a similar warming trend. Pages could be written on the subtle differences in methodologies. For example, NOAA and the Berkeley Earth project use different, more complex methodologies than GISS, but produce similar end results. NOAA, for example, does not infer temperatures in polar regions that lack observations, and thus reports somewhat lower warming, of just 1.0C. This is because Arctic warming exceeds the global average, as minimum sea ice has declined by 13% each decade, allowing more sunlight to be absorbed and in turn, and causing more warming.

No doubt the construction of a global average temperature index, covering the whole planet back to 1880 is fraught with enormous data-challenges that could in principle be subject to uncertainties and biases. But it is nothing short of a conspiracy theory to suggest that multiple independent agencies are wilfully introducing those biases. And then lying about it. The Q&A section of NASA’s website states “Q: Does NASA/GISS skew the global temperature trends to better match climate models? A: No”.

You can also review all of the adjusted and unadjusted data for individual weather stations side-by-side, here. If we take the example below, in New York’s Central Park, the adjusted/homogenized and unadjusted data are not materially different, especially in recent years. Although the uncertainty is visibly higher for the 1880-1940 data.

Criticisms of the NASA data adjustments, cited in the skeptical technical paper above, do not appear particularly well founded either. It is true that NASA’s 1980s estimates of temperatures in 1920-1980 have been progressively lowered by 0.1-0.3C from 1981 to 2017, which could be seen to over-exaggerate the warming that has occurred since that time-period. However this is mostly because of bad data back in the pre-digital world of the 1980s. In fact, NASA’s 1981 data-set did not include any sea surface temperature data and only included data from 1,219 land stations, all in the Northern hemisphere.

Revisions in more recent data-sets are minimal. For example, HadCRUT’s data are shown below.

Another review of the data concludes that the net effect of revisions has been to under-state global temperature increases, by adjusting the temperatures in 1880-1930 upwards, which would under-state warming relative to this baseline (here). This is actually a blend of effects. Temperatures on land have generally been adjusted downwards by 0.1-0.2C in the 1880-1950 timeframe. Temperatures at sea have been adjusted upwards by 0.2-0.3C over 1880-1935. The original article also contains some helpful and transparent charts.

Finally, recent data are increasingly reliable. In total, 32,000 land stations and 1.2M sea surface observations are now taken every year, across multiple data-sets. Hence GISS estimates the uncertainty range in its global annual temperature data is +/- 0.05C, rising to 0.1-0.2C prior to 1960, at a 95% confidence level. From our review above, we think the uncertainty is likely higher than this. But the strong upwards trend is nevertheless robust.

Conclusions: 30-years to get to net zero?

Global temperatures are most likely rising at 0.023-0.03C per year, and 1.2-1.3C of warming has most likely occurred since pre-industrial times. This would suggest 30-years is an appropriate time-frame to get to Net Zero while limiting total warming to 2C, per our recent research note below.

A paradox in our research is that the $3trn per year economic cost of reaching net zero by 2050 seems to outweigh the $1.5trn per year economic cost of unmitigated climate change. One of the most popular solutions to this paradox, per the recent survey on our website below, was to consider re-optimizing and potentially softening climate targets. There may be economic justifications for this position. But the temperature data above show the result could be materially more warming.

Our own view is that the world should also decarbonize for moral reasons and as an insurance policy against tail-risks (arguments 3 and 6, above). And it should favor decarbonization pathways that are most economical and also restore nature (note below).

Our climate model, including all of the temperature data cited in this report are tabulated in the data-file below.

Our lowest cost route to an energy transition was spelled out in December-2020 (note below), looking across 90 prior research reports and 270 data-files. It is fully possible to reach ‘net zero’ by 2050. The economic costs ratchet up to $3trn per annum.

However, based on the latest disclosures from the IPCC, we estimate that the unmitigated costs of climate change are only $1.5trn per year. So paradoxically, the energy transition appears to cost 2x more than climate change itself (note below).

What implications? The note above spells out our own solutions to the paradox. However, we also undertook a survey, to understand broader perspectives among decision makers. The results are tabulated below…

(1) Does energy transition matter? 68% of those who participated in our survey agreed or strongly agreed that the energy transition matters. Conversely, 23% disagreed or strongly disagreed. It is interesting to note that support for targeting an energy transition is strong but not universal.

(2) Do economics matter in the energy transition? 90% agreed that economics should be a material consideration in targeting an energy transition. 77% strongly agreed, the most strongly held view in the entire survey. This would suggest there is great importance in understanding which transition technologies will ultimately be economic. We expected a more polarized view here, although there could be some sampling bias at play.

(3) The paradox identified in our recent research is that the $3trn per year costs of achieving net zero appear to exceed the $1.5trn per year costs of unmitigated climate change. 65% agreed that this paradox might seem to challenge the rationale for targeting an energy transition. 23% disagreed. This is a similar split as on question (1).

(4) Could energy transition therefore be becoming a bubble? 74% agreed with this statement. 48% strongly agreed. Only 9% disagreed or strongly disagreed. This is interesting, albeit frightning support for our own thesis below.

(5) A higher cost of climate change was one solution that we proposed to resolve our paradox above. It is possible, the argument goes, that the costs of climate change are much higher than the $1.5trn per year that we estimated. 42% agreed or strongly agreed that the costs of climate change would likely be higher than we had estimated. 32% disagreed. There is no clear consensus on the unmitigated costs of climate change.

(6) A lower cost of achieving a transition was another solution that we proposed to the paradox. 35% agreed or strongly agreed that the costs of climate change would likely be lower than we had estimated. Again, there is no clear consensus on the cost of delivering the energy transition. Questions (5) and (6) had the highest portion of ‘neutral’ responses of any categories in our survey.

(7) The insurance argument is that it may still be rational to target an energy transition, even if the most likely costs of the transition exceed the most likely costs of unmitigated climate change, because climate change creates tail-risks of very large, catastrophic problems. 48% agreed or strongly agreed with this argument, making it the second most popular resolution to the paradox.

(8) The affordability argument is that it may still be rational to target an energy transition, even if the costs exceed the costs of climate change, because the costs of the former will be borne by wealthier populations in the developed world, while the costs of the latter would be incurred by poorer populations in the emerging world. Only 29% agreed with this argument, making it the least popular argument in the survey.

(9) The optimization argument is that targeting 2C of warming may not be the optimal balance, as the costs appear to outweigh the benefits. But there could be alternative timings, or alternative ceilings on global warming (e.g., 2.5C, 3C, 3.5C ?) where the costs do outweigh the benefits. 58% agreed or strongly agreed with this argument, making it the most popular argument in the survey. It may be interesting to consider how markets would react if a re-optimization of global climate targets ever came onto the political agenda.

(10) The moral argument is that it is morally ‘wrong’ to change the world’s climate, so it does not matter whether the energy transition is economically rational. 35% agreed or strongly agreed with this statement, making it the second least popular argument in the survey.

Notable perspectives and feedback

Notable perspectives were also shared by those taking part in the survey. In order to help understand others’ perspectives, some of the most cogent examples are noted below.

“The climate has always changed and humans have adapted in the past. Low cost adaption plus nature based solutions could go a long way to address the issue. Additionally, we really do not know if the 1.5-2.0 degrees is the tipping point. It was just a number conveniently picked. Reduce carbon and emissions yes, but not to the extent it wrecks the global economy.” — investor

“Difficult to see where rationality will come from, as what began as a worthy goal has become an uber-ideology that will cut any politician who stands in the way to shreds. I don’t think humanity has seen such a pervasive injection of zeal since the Christianization of the Roman Empires.” — investor

“I think the missing piece is the much higher likelihood of war(s) in an adverse climate change scenario. Judging purely by the costs incurred in a (relatively) small conflict like Iraq ($1tn per year), such costs would likely quickly exceed the $1.5tn total estimate. There are further risks to democracies and government stability if climate change results in mass migrations, epidemics, additional wealth inequality, etc. Such risks would also easily “cost” meaningfully more than the $1.5tn estimate implies.” — investor

“Ideally, the energy transition would be guided by the optimal low cost solution. this is unlikely to occur because the politicians will not support it. Furthermore, current politicians who set goals for decades in the future will not be around to enforce the painful restrictions required to achieve the targets”. — energy company strategist

“Paradoxically, we must be as economically rational as possible in execution of something that may be economically irrational! Thus, I find your comparisons of various costs of ton of captured to be extremely powerful” — investor

“Climate change will cost lives while energy transition will save it. Covid has shown that cost should not be the only factor driving policy decisions. So even if the transition is expensive (though I don’t agree with the thesis), it is worth pursuing.” — individual

“There should be a Pareto optimal solution analysis of the costs of mitigation and adaption (the latter of which tends to be ignored) relative to the costs of climate change itself.” — strategy consultant

Energy Transition Paradox Survey

The survey is still open, below, and we welcome additional perspectives…

We published 250 new research notes and data-files on our website in 2020. The purpose of this review is to highlight the ‘top ten’ reports. This includes our economic roadmap to reaching ‘Net Zero’, the greatest risks and opportunities that we have found in the transition, and the analysis that has most shaped our views.

(1) The single most powerful decarbonization option in all of our work is reforestation. Costs are as low as $3-10/ton. There is 15GTpa of carbon-offsetting potential (note here). But this is not an investment. It is an act of charity. It matters because increasing numbers of decision-makers are choosing to restore nature and offset their CO2 at low cost, rather than purchasing higher-cost new energies, which could make them uncompetitive.

(2) Restoring soil carbon is equally powerful, and surprisingly fascinating. Agricultural soil has lost three-quarters of its carbon since pre-industrial times. Restoring it could offset another 3-15GTpa of CO2. With a $30/ton CO2 price, mid-Western farmers could make more money farming carbon than corn. The theme would also disrupt the global fertilizer industry.

(3) Is energy transition becoming a bubble? If you read a single piece of research on energy transition this year, I would recommend this one. We fear an “investment bubble” is forming in the energy transition space. Half of all transition technologies we have evaluated are on the “wrong” side of the cost curve and may be displaced by the nature based solutions we described above.. Deflation and profitability are often antagonistic. And some spaces have seen incredible run-ups despite challenging economics and overlooked technical challenges. The purpose of this note is to suggest pragmatic responses.

(4) The green hydrogen economy may be the largest bubble. Our work this year has assessed the theme in detail, both in power markets and as a transportation fuel. Costs are immutably high. This is due to the laws of physics and thermodynamics. Transporting green hydrogen will also be more challenge than any other commodity in history (note here). The note below is the best overview of our work. Many expect c80% deflation in the total costs of electrolysers. Our data suggest this is impossible. We welcome challenges to these numbers, but so far, have not received any from our contacts in the hydrogen industry.

(5) The green hydrogen bubble will give way to blue. Blue hydrogen is not just a low-carbon fuel. More importantly, it is the most economic and practical route to establishing large-scale carbon capture and storage. Economics are 80-90% superior to green hydrogen. Risks are materially lower. Our research note ends by identifying projects that should reach FID in 2021, and a public company with a clear ‘moat’ in the space.

(6) Non-obvious opportunities. Other novel technologies have a vast role in the route to Net Zero. The non-obvious opportunities are best, and are not at risk of becoming bubbles. Our research has covered many examples in 2020: deep geothermal, supercapacitors eclipsing batteries, industrial efficiency initiatives such as fully subsea offshore or next-generation refining; and backing up renewables with CHPs, PCMs and smart energy systems. If you read one research note into a non-obvious opportunity, we recommend this deep-dive below into additive manufacturing, which will re-shape every industry globally.

(7) Patent analysis can give you an edge identifying opportunities in the energy transition and, and avoiding hidden risks, particularly as bubbles build. Our note below lays out six themes, including worked examples, based on reviewing over 1M patents.

(8) Our most economic roadmap to Net Zero ties together all of our work. We find it is possible to decarbonize global energy by 2050, even as global energy demand rises by 65%. The total cost of decarbonization is $50trn, which is almost halved versus last year’s estimate in December-2019. The fully decarbonized energy system still contains 85Mbpd of oil and 375TCF per year of natural gas.

(9) Oil and gas are heading for devastating under-supply if our analysis is correct. This is historically precedented during technology transitions. Below we have evaluated supply-demand and pricing in the whale oil industry from 1805-1905, as it was disrupted by rock oil and later by electric lighting. Whale oil prices outperformed over this timeframe, as supply peaked before demand. Our latest our oil market outlook is here, and our gas market outlook is here.

(10) The optimal strategy for incumbent energy companies is thus suggested in our research note below. We argue that Energy Majors embracing these principles can uplift their valuations by c50% (assuming flat commodity prices).

A fully decarbonized energy system may still require 85Mbpd of oil and 375TCF of gas. Hence a focus of our research is to find improved technologies that can improve the efficiency and lower the CO2 intensity of oil and gas production. This note profiles the exciting new prospect of ‘wet sand’ for hydraulic fracturing in shale plays. It can reduce breakeven costs up to $1/bbl and CO2 intensity up to 0.6kg/bbl.

Wet sand is defined as having a moisture content between 1% and 10% by weight. This is opposed to the 43MTpa of dry sand supplied in the Permian in 2018, where all the moisture has been burned away in a kiln, shortly after the washing process.

Wet sand has reached technical maturity. In a December-2020 technical paper, PropX described a patent pending process to “screen, transport, deliver and meter wet sand from local mines to the frac blender, bypassing the drying process altogether”.

A full trial of wet sand has also been undertaken at a 10-well pad in Oklahoma, implied to have been operated by Ovintiv. Over 4Mlbs of sand per day was delivered and pumped downhole. The system was reliable and consistently flowed wet sand with 4-8% moisture content.

The advantages: cost and CO2 savings?

Cost savings from pumping wet sand are estimated at $2-10/ton. The largest capital component is in potential capex savings, as the kiln at a sand mine usually comprises $20-50M (including associated drying, storage and conveyance) out of a $180M total budget at a 2.5MTpa mine. A second saving is in opex, as labor costs average $5/ton across 15 surveyed sand mines (chart below), and the drying unit requires one-third of the labor force. Finally, there are fuel savings, likely around $1/ton.

After modelling the economics below, our base case estimate is that a greenfield sand mine can lower its total production costs by $5/ton, with a shift from dry to wet sand.

To test the economic impacts of a $5/ton reduction in sand costs, we turn to our economic model below. At a large, sand-intensive well, we estimate $0.1M of potential savings. This flows through to a $0.5/bbl reduction in the well’s NPV10 breakeven. However, the savings will be lower at industry average wells, which only consume 10-20M lbs of proppant.

CO2 savings are realized by cutting out fuel demand in the drying kilns at sand mines, typically at 0.3-0.4mmbtu per ton of dried sand, requiring 0.4mcf of gas, whose combustion would release 20kg of CO2. Again, we can run the savings through our models (below) and estimate CO2 could be reduced by up to 0.6kg/boe, which is not bad against a baseline of 26kg/boe of total upstream Scope 1 and Scope 2.

HSE advantages are also noted in the technical paper. Fugitive silica dust is materially reduced, as wet sand particles adhere to one-another. This helps meet OSHA’s 2016 silica exposure limits, below 50mg/m3 of air, averaged over an 8-hour shift.

The challenges: is wet sand more difficult to pump?

The challenge of wet sand is that wet san grains cohere to one-another, which impedes their smooth flow and can cause sand to “clump together”. In cold climates (but probably not Texas!) the water molecules can also freeze. Hence, PropX notes three areas where it has needed to innovate the sand supply chain.

Last-mile. It is recommended to use containers (rather than trailers) to transport sand to well-sites. These can be lifted from trucks onto the wellsite with “the fewest touch points and the least modification”.  A typical container system carries 23,000-27,000lbs of sand, with a capacity of 28,500 lbs. These volumes have been emulated by PropX, by enlarging the container opening (from 20” diameter to 6’x6’, and covering it with a tarp, as is widely used in transportation of agricultural products.

Emptying containers is easy with dry sand as it flows naturally, emptying in c60-seconds. Wet sand containers need to be emptied into a blender hopper. This likely takes 120-seconds, via a 100bpm slurry rate carrying 2.7ppg of sand. PropX has undertaken successful trials placing the sand containers on a vibration table, with a sloped discharge cone, silicon-inserts to lower friction and a larger discharge exit gate.

Sand delivery to the well must occur at a rate of 16,800 lbs of sand per minute, for example, comprising 80-100bpm of slurry carrying 0.5-4.0ppg of sand. A unique belt has been designed which can carry up to 7.9ppg at up to 100bpm. It includes a metering system and screwless surge hopper, also on vibrating tables, to enable accurate, reliable and continuous pumping of wet sand without the risk of bridging.

Sand has undergone huge changes in the past, which suggests this supply chain is not ossified. For example, back in 2017, 90% of sand was shipped by rail to the Permian from Minnesota, Illinois and Wisconsin. Today there are 50 “local” sand mines in the Permian basin, with 107MTpa of capacity. This has reduced transload cost, and allowed sand pricing to run as low as $20/ton recently. Further deflation may lie ahead.

Why it matters: deflation and CO2 reduction?

A fully decarbonized energy system may still require 85Mbpd of oil and 375TCF of gas, as per the conclusion of our research to date. Hence a focus of our research is to find improved technologies that can improve the efficieny and lower the CO2 intensity of oil and gas production.

We still see great productivity enhancements ahead for the shale industry, after reviewing over 1,000 technical papers. A 5% CAGR is possible from 2019’s baseline.

There is also great potential for shale to lower its CO2 intensity, potentially towards zero (Scope 1 and 2 basis), as argued in our recent research (below). The potential is further enhanced by using waste water to cultivate nature based solutions (also below).

Shale thus sets the marginal cost in oil markets, as our numbers require of 2.5Mbpd of shale growth each year from 2022-2025 (models below).

But nearer-term we see risks, that sentiment will sour around shale capex, while productivity could temporarily disappoint during the COVID recovery.

Unprecedented high-grading is now occurring in the US shale industry, amidst challenging industry conditions. This means 2020-21 production surprising to the upside, and we raise our forecasts +0.7 and +0.9Mbpd respectively. Conversely, when shale activity recovers, productivity may disappoint, and we lower our 2022+ forecasts by 0.2-0.9 Mbpd. This 7-page note explores shale productivity data, and the causes and consequences of this whipsaw effect.