During the cold snap that affected much of the central part of the country, U.S. dry natural gas production fell to as low as 69.7 billion cubic feet per day (Bcf/d) on February 17, a decline of 21%, or down nearly 18.9 Bcf/d from the week ending February 13. Natural gas production in Texas fell almost 45% from 21.3 Bcf/d during the week ending February 13 to a daily low of 11.8 Bcf/d on Wednesday, February 17, according to estimates from IHS Markit. Temperatures in Texas averaged nearly 30 degrees Fahrenheit lower than normal during the week of February 14.
The decline in natural gas production was mostly a result of freeze-offs, which occur when water and other liquids in the raw natural gas stream freeze at the wellhead or in natural gas gathering lines near production activities. Unlike the relatively winterized natural gas production infrastructure in northern areas of the country, natural gas production infrastructure, such as wellheads, gathering lines, and processing facilities, in Texas are more susceptible to the effects of extremely cold weather. [Emphasis added]
After reaching a daily low on February 17, natural gas production in Texas began increasing as temperatures started to rise. Daily production reached an estimated 20.9 Bcf/d on February 24, only about 0.3 Bcf/d lower than the average in the week ending February 13.
That explains the evaporation of dispatched power.
Following a similar weather event in 2011, plant operators in Texas created a set of best practices for winterizing equipment, but these were voluntary recommendations, according to Dan Woodfin, senior director of system operations at Ercot.
Another major source of Texas’ electricity also susceptible to the cold contributed in part to the outages. Wind turbines installed in warm weather climates can generally operate above -4 degrees Fahrenheit but will shut down in colder temperatures. Ice buildup on the blades can also lead to slower rotations and even shut down a turbine. In northern climates such as Canada and Scandinavia, winterization measures are typically built into the turbines during manufacturing, but can be retrofitted to turbines already in operation.
IPCC AR5 (2014) presents 4 emission pathways which are at the heart of the climate change debate. There are a range of other topics that are also debated – notably, the atmospheric and ocean physics effects from GHG concentrations assumed. Those effects are further attenuated by factors that modeled by IPCC such as a more robust coupled atmospheric-oceanic physics, astrophysical and geophysical dynamics.
The emission scenario dominates the first order effect produced by the IPCC climate models. That first order effect is amplified by the transient climate sensitivity (TCS – the feedback effect from higher concentrations.
So, depending on which RCP scenario you assume, you can get a wide range of effects.
Oh, and not to go off-point too far, TCS estimates are now coming in at 1/2 – 1/3 below early nightmare assumptions
Hazard component (relates to the frequency and intensity of events)
Engineering component (relates to physical assets at risk)
Financial component (relates to monetary losses)
The AIR Report purports to “capture the full range ofplausible events that could impact an area.”
My critique focuses solely on the hazard component. A summary of my analysis is provided below:
The driver for AIR’s assessment is warming associated with the emissions/concentration scenario RCP8.5, which AIR refers to as a ‘business as usual scenario.’ In fact, RCP8.5 is increasingly being judged as implausible by energy economists, and is not recommended for use in policy planning.
The hurricane risk from climate change focuses on the number and intensity of U.S. landfalls in a changing climate. Their scenario of the number of major hurricanes striking the U.S. by 2050 is judged to be implausible for medium emissions scenarios such as RCP4.5.
The sea level scenarios used in the AIR Report are higher than the recent IPCC consensus assessments and are arguably implausible for medium emissions scenarios.
The AIR Report ignores the ‘elephant in the room’ that is of relevance to their target period to 2050: the Atlantic Multi-decadal Oscillation (AMO). A shift to the cool phase of the AMO would arguably portend fewer major hurricanes striking the U.S.
Whereupon Dr. Curry painfully dissects AIR through a thousand cuts.
Too bad for AIR – a respected company that any physicist will likely observe in this instance their consultants got a bit ahead of themselves.
What SLV needs to do is break above 26.3, then we’re probably off to the races. The long-term chart of silver is very encouraging. This is all augmented by the fact that our friends in Gainesville think gold and silver are totally doomed.
ARKK USD-volume today was un-fucking-believable! That’s a fifth of the ETF’s market cap!
Cryptos were crushed.
Bitcoin back below $50k…
Ether ended back below $1500…
And while stocks were clubbed like a baby seal at the cash market open...
…Powell’s promise of moar of whatever it takes (which is exactly what he has now said consistently for a year) sparked the standard buying panic, lifting the Dow (orange), S&P (green), and Nasdaq (blue) back to unchanged, Small Caps (red) ended red…
[COMMENT: mo’ dough for Greenwich, mo’ inflation for the rest of us]
NOTE: Europe’s open also saw major selling pressure
“Did you not hear a single word of what I just said? Buy the f**king dip!”
Nasdaq found support at its 50DMA…
[COMMENT: 50DMA saves the day]
“Most Shorted” Stocks puked by the most since March 2020 at the open, only to be squeezed back higher…
Energy stocks ended the day best with Cons Disc worst but everything levitated after the initial puke…
[COMMENT: can you say Super-Cycle?]
TSLA tumbled but dip-buyers also screamed in there too…
Could be worse – could be Workhorse…
Small Cap Biotechs had a bad day…
Bonds were mixed with the belly outperforming the tails (30Y +3bps, 5Y -2bps, 2Y unch)…
Despite all the chaos in stocks, 10Y Yields traded in a very narrow range on the day…
The dollar ended lower are tumbling along with stocks at the equity open…
Gold was lower on the day, but held above $1800… just…
Oil prices continued higher with WTI back above $62 ahead of tonight’s API inventory data….
[COMMENT: again, can you say Super-Cycle?]
Finally, the most-crowded longs held by hedge funds has erased all its losses relative to the massive WSB short-squeeze (of the “most-shorted” stocks)…
And if Biden and his pals want $1.9 trillion stimulus, they better get it done soon before this pandemic is over…
In a bioRxiv preprint, Chen et al. (2021) report the results of the first genomic sequencing of the virus.
Chen traces CCP back to RaTG13 and RmYN02 – strains reported by Chinese researchers as detected in Yunnan province of China, but yet to be found elsewhere.
The study considered over 4,000 full-length viral sequences made available by the Global Initiative for Sharing All Influenza Data (GISAID) EpiFlu database. Another 11 came from a Chinese database. Lastly, Chen analyzed ~2,61,000 genomes collected globally since the pandemic began. This comprises all the genomes in the database.
The researchers were able to identify distinct genotypes based on how commonplace certain mutations were. This helped trace superspreaders since they shaped the pandemic to a large extent. These individuals passed on specific genotypes with certain highly prevalent mutations. A single introduction of such genotypes led to an outbreak of infection, increasing evolution with spread.
Six genotypes appear to have descended from the original strain with a single sequence – the M type variant originating in Wuhan – and responsible for more than 80% of the sequences in the study. Chen considers this as a true founder, and evident in spread to other regions of China before the Wuhan lockdown.
The six descendant genotypes are directly derived from the ancestral strain by characteristic mutations. The most prevalent genotype among these was the WE1 type, defined by four mutations. Three of the four defining mutations of the WE1 strain were found in three early samples collected in January 2020. Among WE1 genomes, 70% came from Western Europe (the UK, Iceland, Belgium, France, and the Netherlands, perhaps by traffic across the borders. It also made up ~35% of cases in the US.
The SEA type is the most common in the USA, however, but was isolated from three other countries, namely, Australia, Canada, and Iceland, indicating that cases from the USA had been imported there. This is also called the Washington State outbreak clade. The other four descendant genotypes were confined regionally.
The 34 sequences from early Wuhan cases showed two clusters, 30 belonging to the M type, but with extensive diversity. The remaining four formed another co-circulating cluster. Thus, at this early stage, there were 18 different genotypes among the 34 sequences.
In the USA, the prevalent strains belonged to the non-M types, probably from 12 cases imported from the Hubei province. These, in fact, were the earliest cases reported in the USA, with each showing a distinct genotype.
Half the US cases were SEA type, while ~35% were WE1. It indicates that the USA “endured the first wave of case importation from China and the second wave from Europe, which is consistent with the recent COVID-19 study of Washington State.” Among the 32 patients on the two cruise ships, the Grand Princess and the Diamond Princess, there were 25 different genotypes. This indicates that the virus mutates rapidly and extensively during person-to-person transmission.
The researchers developed a Strain of Origin (SOO) algorithm to match each genotype to its genome by mutational profile. When compared with mutation clustering, this approach showed a 90% agreement.
Using the same approach, they found that three of the top four GISAID clades were descendants of WE1. They estimated that one of three nucleotides in the viral RNA had undergone mutation over the 12 months of the pandemic.
They analyzed the top 100 mutations and generated a lineage-based pedigree chart. This story begins with a putative first case, supposed to be a patient with an ancestral SARS-CoV-2 genotype, and postulated to be present on November 17, 2019. This led to more infections. By January 1, 2020, the Huanan market was locked down, and 19 M type genome samples were documented.
However, the M type had already been incubating in the market for weeks, which accounts for the vast majority of genomes belonging to the M type at this time. With the expansion of the outbreak into Wuhan city at large, the city was locked down on January 23, 2020, with 80% of the viral genomes being of the M type. However, the Spring Festival had already prompted extensive travel to and from Wuhan, leading to the Chinese and then the global outbreak of COVID-19.
By April 7, 2020, more than 80% of cases worldwide were M type, but in September, 70% belonged to WE1, in three clades, namely, GR, G, and GH. The rise in M type continued, making up ~98% of cases by December 25, with almost 90% being caused by WE1 strains.
The researchers conclude that beginning with a single superspreader incident, the M type exploded over the world, following a few initial weeks when it passed unrecognized and uncontrolled. The M type acquired two concurrent mutations first, with another four defining mutations that led to the emergence of WE1 strains, and finally, another three that led to the WE1.1 strain. The rate of viral evolution, at ~27 substitutions per year, is not unusual, but the mechanism is still unclear.
Of the two new mutant strains attracting much attention, namely, the D614G point mutation and the N501Y mutation in the receptor-binding domain, both in the spike protein, are thought to be highly transmissible compared to the ancestral strain. The former was first documented in Western Europe in February 2020 and now makes up ~90% of strains, while the latter was first found in New York City on April 21, 2020, and makes up only 0.02% of cases.
Chen, Y., Li, S., Wu, W., Geng, S., & Mao, M. (2021). Distinct mutations and lineages of SARS-CoV-2 virus in the early phase of COVID-19 pandemic and subsequent global expansion. doi:10.1101/2021.01.05.425339
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