It isn’t. Well, it is a little, but not totally. OK, it is, but actually, it is complicated.
First, you are probably asking about the Atlantic hurricane season, not the global issue of hurricanes and typhoons and such. If you are asking world-wide, recent prior years were worse if counted by how many humans killed and how much damage done.
With respect to the Atlantic, this was a bad year and there are special features of this year that were bad in a way that is best accounted for by global warming. But looking at the Atlantic hurricanes from a somewhat different but valid perspective, last year was worse (so far) and this year is ordinary, within the context of global warming. So, let’s talk about the global warming question first.
How Global Warming Makes Hurricane Seasons Worse
The effects of global warming on hurricanes in the Atlantic have two interesting features that must be understood to place this discussion in proper context.
First, we are having a bunch of bad decades in a row probably because of global warming. If we compare pre-1980, for a decade, with post 1980, or pre vs. post 1990, or anything similar, the more recent years have had more hurricanes than the earlier years. Comparing to even earlier time periods is tricky because of differences in available data (Satellites make a difference, probably, even with giant weather features like hurricanes). This is mainly due to increasing sea surface temperatures, but there are other factors as well.
Hurricanes are more likely to form when sea surface temperatures are higher. Higher sea surface temperatures can make a hurricane larger or stronger. Hurricanes will last longer if there is more, higher, hurricane-hot sea to travel over. If sea surface temperatures are high enough to cause hurricanes earlier in the year or later in the year, the hurricane season can be longer. Possibly, storms that in a non-warmed world would not have made it to “named storm” status are moved to that level of strength and organization because of the elevated sea surface temperature.
Sea surface temperature increases of small amounts cause large changes in hurricanes, and large changes in hurricanes cause larger changes in potential damage level. The increase in Atlantic sea surface temperatures over recent decades have probably been sufficient, according to my thumb-suck estimate that I strongly suspect is close to correct, to make about half the hurricanes that would have existed anyway jump up one category. Then, when hurricanes get stronger, the amount of damage they can do goes up exponentially. So the sea surface temperature increases we’ve see with global warming easily explain the fact that we’ve had more hurricanes overall, and stronger ones, over the last twenty or thirty years than during the previous years back to when the data are still pretty good.
Second, the science says this will get worse. There is one 2007 study (by Vecci and Soden, in Geophysical Research Letters) that suggests that maybe in the Atlantic, smaller size hurricanes will be less likely to form because of increased vertical wind shear, but that study does not mean much for larger or stronger hurricanes. This decade old study is constantly cited as evidence that global warming will not increase hurricanes in the Atlantic. Other studies show that the overall amount of hurricane activity, and the potential higher end of hurricane strength, and the size, and the speed at which they form, and the amount of water they can contain, and possibly the likelihood of a hurricane stalling right after landfall, go up. Up. Up. Up. One study says down and that word, “down” it resonates across the land like a sonic boom. The other studies say we can expect, and to varying degrees already see, up, up, up, up, up, and denial makes words like “up” and “more” and “worse” and “exasperated” dangerously quiet. Please don’t fall into that trap. Oh, by the way,the one study that says “down” has not been replicated and though experts feel it has some merit, it is far from proven and there are reasons to suggest it my be problematic.
Comparing the 2017 Atlantic Hurricane Season to Other Years
Funny thing about hurricanes: They exist whether or not they menace you. Every year a certain number of hurricanes (usually) form and wander about in the Atlantic ocean for a while, maybe hitting some boats, but otherwise doing little more than causing some big waves to eventually reach beaches in the Caribbean or the eastern US.
This year, we’ve had four major hurricanes so far. Harvey, which maxed out as a Cat 4, ravaged and flooded Texas and Louisiana. Irma, maxing at Cat 5, ravaged Florida after wiping out islands in the Leewards and doing great damage to Cuba and elsewhere in the Caribbean. Maria, maxing out as a Cat 5, did major damage in the Leewards and notably wiped out Puerto Rico. So, four Major Hurricanes formed in the Atlantic and hit something major.
Meanwhile, Jose, another Major hurricane at Cat 4 status, still spinning about in the North Atlantic, is one of those that hit nothing. And that’s all so far this year.
Last year, there were almost exactly the same number of named storms in total (so far) and just like 2017, 2016 had four major hurricanes.
You remember Matthew, which scraped the Atlantic coast and was rather damaging. But do you remember Gaston (Cat 3)? Nicole (Cat 4)? Otto (Cat 3)?
Gaston and Nicole wandered about in the Atlantic and hit nothing. Otto was for real, it hit Central America, but not the US, so from the US perspective, it counts as a non-hitting hurricane. Also, it was only barely cat 3 and weakened quickly.
From 2000 to 2016, inclusively, we have had an average of 15 named storms per year, with a minimum of 8 and a maximum of 28, with most years being between 10 and 16. So far 2017 has had 13 named storms. We may have a couple more. So, likely, we will be right in the middle.
For the same period, the number of hurricanes has ranged from 2 to 15 with an average of about 7. This year, we have had … wait for it … 7. We may or ma not get another one, not very likely two more. In other words, this is an average year for the number of hurricanes.
For the same period, the number of major hurricanes ranges from 0 (though only one year ad zero, it is more typical to have 2 in a low year) to 7, but again, 7 is extreme. It is usually from 2-5. The average is just over 3. This year, we have four. That’s pretty typical.
So, within the context that the last couple of decades has had a somewhat higher than average frequency of hurricanes, and probably more strong ones than previous decades, this we had a typical year this year.
Why does it feel different? Why is it in fact difference, with respect to the horror of it all? Because we had more landfalls, and more serious landfalls.
Keep in mind that Harvey could have hit Houston differently and done more damage. Keep in mind that Cuba beat up Irma, then Irma failed to strike Florida in just the right way to do maximum damage. Keep in mind that after wiping out Puerto Rico, Maria swerved quickly out to sea. In other words, keep in mind that this year could have been much worse than it was.
This is the point that you must understand: Any year can be like this year, or worse. And, with increasing sea surface temperatures and other global warming related factors, worse still.
Three statisticians go hunting for rabbit. They see a rabbit. The first statistician fires and misses, her bullet striking the ground below the beast. The second statistician fires and misses, their bullet striking a branch above the lagomorph. The third statistician, a lazy frequentist, says, “We got it!”
OK, that joke was not 1/5th as funny as any of XKCD’s excellent jabs at the frequentist-bayesian debate, but hopefully this will warm you up for a somewhat technical discussion on how to decide if observations about the weather are at all explainable with reference to climate change.
We are having this discussion here and now for two reasons. One is that Hurricane Harvey was (is) a very serious weather event in Texas and Louisiana that may have been made worse by the effects of anthropogenic global warming, and there may be another really nasty hurricane coming (Irma). The other is that Michael Mann, Elisabeth Lloyd and Naomi Oreskes have just published a paper that examines so-called frequentist vs so-called Bayesian statistical approaches to the question of attributing weather observations to climate change.
First, I’ll give you the abstract of the paper then I’ll give you my version of how these approaches are different, and why I’m sure the authors are correct.
The conventional approach to detecting and attributing climate change impacts on
extreme weather events is generally based on frequentist statistical inference wherein a null hypothesis of no influence is assumed, and the alternative hypothesis of an influence is accepted only when the null hypothesis can be rejected at a sufficiently high (e.g., 95% or Bp = 0.05^) level of confidence. Using a simple conceptual model for the occurrence of extreme weather events, we
show that if the objective is to minimize forecast error, an alternative approach wherein likelihoods
of impact are continually updated as data become available is preferable. Using a simple proof-of-concept, we show that such an approach will, under rather general assumptions, yield more
accurate forecasts. We also argue that such an approach will better serve society, in providing a
more effective means to alert decision-makers to potential and unfolding harms and avoid
opportunity costs. In short, a Bayesian approach is preferable, both empirically and ethically.
Frequentist statistics is what you learned in your statistics class, if you are not an actual statistician. I want to know if using Magic Plant Dust on my tomatoes produces more tomatoes. So, I divide my tomato patch in half, and put a certain amount of Magic Plant Dust on one half. I then keep records of how many tomatoes, and of what mass, the plants yield. I can calculate the number of tomatoes and the mass of the tomatoes for each plant, and use the average and variation I observe for each group to get two sets of numbers. My ‘null hypothesis’ is that adding the magic dust has no effect. Therefore, the resulting tomato yield from the treated plants should be the statistically the same as from the untreated plants. I can pick any of a small number of statistical tools, all of which are doing about the same thing, to come up with a test statistic and a “p-value” that allows me to make some kind of standard statement like “the treated plants produced more tomatoes” and to claim that the result is statistically significant.
If the difference, though, is very small, I might not get a good statistical result. So, maybe I do the same thing for ten years in a row. Then, I have repeated the experiment ten times, so my statistics will be more powerful and I can be more certain of an inference. Over time, I get sufficient sample sizes. Eventually I conclude that Magic Plant Dust might have a small effect on the plants, but not every year, maybe because other factors are more important, like how much water they get or the effects of tomato moth caterpillars.
In an alternative Bayesian universe, prior to collecting any data on plant growth, I do something very non-statistical. I read the product label. The label says, “This product contains no active ingredients. Will not affect tomato plants. This product is only for use as a party favor and has no purpose.”
Now, I have what a Bayesian statistician would call a “prior.” I have information that could be used, if I am clever, to produce a statistical model of the likely outcome of the planned experiments. In this case, the likely outcome is that there won’t be a change.
Part of the Bayesian approach is to employ a statistical technique based on Bayes Theorem to incorporate a priori assumptions or belief and new observations to reach towards a conclusion.
In my view, the Bayesian approach is very useful in situations where we have well understood and hopefully multiple links between one or more systems and the system we are interested in. We may not know all the details that relate observed variation in one system and observed variation in another, but we know that there is a link, that it should be observable, and perhaps we know the directionality or magnitude of the effect.
The relationship between climate change and floods serves as an example. Anthropogenic climate change has resulted in warmer sea surface temperatures and warmer air. It would be very hard to make an argument from the physics of the atmosphere that this does not mean that more water vapor will be carried by the air. If there is more water vapor in the air, there is likely to be more rain. Taken as a Bayesian prior, the heating of the Earth’s surface means more of the conditions that would result in floods, even if the details of when, how much, and where are vague at this level.
A less certain but increasingly appreciated effect of climate change is the way trade winds and the jet stream move around the planet. Without going into details, climate change over the last decade or two has probably made it more likely that large storm systems stall. Storms that may have moved quickly through an area are now observed to slow down. If a storm will normally drop one inch of rain on the landscape over which it passes, but now slows down but rains at the same rate, perhaps 3 inches of rain will be dropped (over a shorter distance). What would have been a good watering of all the lawns is now a localized flood.
That is also potentially a Bayesian prior. Of special importance is that these two Bayesian priors imply change in the same direction. Since in this thought experiment we are thinking about floods, we can see that these two prior assumptions together suggest that a post-climate change weather would include more rain falling from the sky in specific areas.
There are other climate change related factors that suggest increased activity of storms. The atmosphere should have more energy, thus more energetic storms. In some places there should more of the kind of wind patterns that spin up certain kinds of storms. It is possible that the relationship between temperature of the air at different altitudes, up through the troposphere and into the lower stratosphere, has changed so that large storms are likely to get larger than they otherwise might.
There is very little about climate change that implies the reverse; Though there may be a few subsets of storm related weather that would be reduced with global warming, most changes are expected to result in more storminess, more storms, more severe storms, or something.
So now we have the question, has climate change caused any kind of increase in storminess?
I’d like to stipulate that there was a kind of turning point in our climate around 1979, before which we had a couple of decades of storminess being at a certain level, and after which, we have a potentially different level. This is also a turning point in measured surface heat. In, say, 1970 plus or minus a decade, it was possible to argue that global warming is likely but given the observations and data at the time, it was hard to point to much change (though we now know, looking back with better data for the previous centuries, that is was actually observable). But, in 2008, plus or minus a decade, it was possible to point to widespread if anecdotal evidence of changes in storm frequency, patterns, effects, as well as other climate change effects, not the least of which was simply heat.
I recently watched the documentary, “An Inconvenient Sequel.” This is a fairly misunderstood film. It is not really part two of Al Gore’s original “An Inconvenient Truth.” The latter was really Al Gore’s argument about climate change, essentially presented by him. “An Inconvenient Sequel” was made by independent film makers with no direct input by Gore with respect to contents and production, though it is mostly about him, him talking, him making his point, etc. But I digress. Here is the salient fact associated with these two movies.An Inconvenient Truth came out in May 2006, so it is based mainly on information available in 2005 and before. In it, there are examples of major climate change effects, including Katrina, but it seems like the total range of effects is more or less explicated almost completely. When An Inconvenient Sequell came out a few weeks ago, a solid 10+ years had passed and the list of actual climate effects noted in the movie was a sampling, not anything close to a full explication, of the things that had happened over recent years. Dozens of major flooding, storming, drying, and deadly heat events had occurred of which only a few of each were mentioned, because there was just so much stuff.
My point is that there is a reasonable hypothesis based on anecdotal observation (at least) that many aspects of weather in the current decade, or the last 20 years, or since 1979 as I prefer, are different in frequency and/or severity than before, because of climate change.
A frequentist approach does not care why I think a certain hypothesis is workable. I could say “I hypothesize that flies can spontaneously vanish with a half life of 29 minutes” and I could say “I hypothesis that if a fly lays eggs on a strawberry there will later be an average of 112 maggots.” The same statistical tests will be usable, the same philosophy of statistics will be applied.
A Bayesian approach doesn’t technically care what I think either, but what I think a priori is actually relevant to the analysis. I might for example know that the average fly lays 11 percent of her body mass in one laying of eggs, and that is enough egg mass to produce about 90-130 maggots (I am totally making this up) so that observational results that are really small (like five maggots) or really large (like 1 million maggots) are very unlikely a priori, and, results between 90 and 130 are a priori very likely.
So, technically, a Bayesian approach is different because it includes something that might be called common sense, but really, is an observationally derived statistical parameter that is taken very seriously by the statistic itself. But, philosophically, it is a little like the pitcher of beer test.
I’ve mentioned this before but I’ll refresh your memory. Consider an observation that makes total sense based on reasonable prior thinking, but the standard frequentist approach fails to reject the null hypothesis. The null hypothesis is that there are more tornadoes from, say, 1970 to the present than there were between 1950 and 1970. This graph suggests this is true…
… but because the techniques of observation and measuring tornado frequency have changed over time, nobody believes the graph to be good data. But, it may not be bad data. In other words, the questions about the graph do not inform us of the hypothesis, but the graph is suggestive.
So, I take a half dozen meteorologists who are over 55 years old (so they’ve seen things, done things) out for a beer. The server is about to take our order, and I interrupt. I ask all the meteorologists to answer the question … using this graph and whatever else you know, are there more tornadoes in the later time interval or not? Write your answer down on this piece of paper, I say, and don’t share your results. But, when we tally them up, if and only if you all have the same exact answer (all “yes” or all “no”) then this pitcher of beer is on me.
Those are quasi-Bayesian conditions (given that these potential beer drinkers have priors in their heads already, and that the graph is suggestive if not conclusive), but more importantly, there is free beer at stake.
They will all say “yes” and there will be free beer.
OK, back to the paper.
Following the basic contrast between frequentist and Bayesian approaches, the authors produce competing models, one based on the former, the other on the latter. “In the conventional, frequentist approach to detection and attribution, we adopt a null hypothesis of an equal probability of active and inactive years … We reject it in favor of the alternative hypothesis of a bias toward more active years … only when we are able to achieve rejection of H0 at a high… level of confidence”
In the bayesian version, a probability distribution that assumes a positive (one directional) effect on the weather is incorporated, as noted above, using Bayes theorem.
Both methods work to show that there is a link between climate change and effect, in this modeled scenario, eventually, but the frequentist approach is very much more conservative and thus, until the process is loaded up with a lot of data, more likely to be wrong, while the bayesian approach correctly identifies the relationship and does so more efficiently.
The authors argue that the bayesian method is more likely to accurately detect the link between cause and effect, and this is almost certainly correct.
This is what this looks like: Frank Frequency, weather commenter on CNN says, “We can’t attribute Hurricane Harvey, or really, any hurricane, to climate change until we have much more data and that may take 100 years because the average number of Atlantic hurricanes to make landfall is only about two per year.”
Barbara Bayes, weather commenter on MSNBC, says, “What we know about the physics of the atmosphere tells us to expect increased rainfall, and increased energy in storms, because of global warming, so when we see a hurricane like Harvey it is really impossible to separate out this prior knowledge when we are explaining the storms heavy rainfall and rapid strengthening. The fact that everywhere we can measure possible climate change effects on storms, the storms seem to be acting as expected under climate change, makes this link very likely.”
I hasten to add that this paper is not about hurricanes, or severe weather per se, but rather, on what statistical philosophy is better for investigating claims linking climate change and weather. I asked the paper’s lead author, Michael Mann (author of The Madhouse Effect: How Climate Change Denial Is Threatening Our Planet, Destroying Our Politics, and Driving Us Crazy, The Hockey Stick and the Climate Wars: Dispatches from the Front Lines, and Dire Predictions, 2nd Edition: Understanding Climate Change), about Hurricane Harvey specifically. He told me, “As I’ve pointed out elsewhere, I’m not particularly fond of the standard detection & attribution approach for an event like Hurricane Harvey for a number of reasons. First of all, the question isn’t whether or not climate change made Harvey happen, but how it modified the impacts of Harvey. For one thing, climate change-related Sea Level Rise was an important factor here, increasing the storm surge by at least half a foot.” Mann recalls the approach taken by climate scientist Kevin Trenberth, who “talks about how warmer sea surface temperatures mean more moisture in the atmosphere (about 7% per degree C) and more rainfall. That’s basic physics and thermodynamics we can be quite certain of.”
The authors go a step farther, in that they argue that there is an ethical consideration at hand. In a sense, an observer or commenter can decide to become a frequentist, and even one with a penchant for very low p-values, with the purpose of writing off the effects of climate change. (They don’t say that but this is a clear implication, to me.) We see this all the time, and it is in fact a common theme in the nefarious politicization of the climate change crisis.
Or, an observer can chose to pay attention to the rather well developed priors, the science that provides several pathways linking climate change and severe weather or other effects, and then, using an appropriate statistical approach … the one you use when you know stuff … be more likely to make a reasonable and intelligent evaluation, and to get on to the business of finding out in more detail how, when, where, and how much each of these effects has taken hold or will take hold.
The authors state that one “… might therefore argue that scientists should err on the side of caution and take steps to ensure that we are not underestimating climate risk and/or underestimating the human component of observed changes. Yet, as several workers have shown …the opposite is the case in prevailing practice. Available evidence shows a tendency among climate scientists to underestimate key parameters of anthropogenic climate change, and thus, implicitly, to understate the risks related to that change”
While I was in contact with Dr. Mann, I asked him another question. His group at Penn State makes an annual prediction of the Atlantic Hurricane Season, and of the several different such annual stabs at this problem, the PSU group tends to do pretty well. So, I asked him how this season seemed to be going, which partly requires reference to the Pacific weather pattern ENSO (El Nino etc). He told me
We are ENSO neutral but have very warm conditions in the main development region of the Tropcs (which is a major reason that Irma is currently intensifying so rapidly). Based on those attributes, we predicted before the start of the season (in May) that there would be between 11 and 20 storms with a best estimate of 15 named storms. We are currently near the half-way point of the Atlantic hurricane season, and with Irma have reached 9 named storms, with another potentially to form in the Gulf over the next several days. So I suspect when
all is said and done, the total will be toward the upper end of our predicted range.
I should point out that Bayesian statistics are not new, just not as standard as one might expect, partly because, historically, this method has been hard to compute. So, frequency based methods have decades of a head start, and statistical methodology tends to evolve slowly.
This is a picture of some men.
Since they are men, they have some abilities. They can, for example, knock each other over, and they can play with balls. This is what men do, and this is what these men can do.
This is a picture of some professional NFL foodball players.
They are also men. They can also knock each other over, and they can also play with balls. But the NFL football players are much better at knocking each other over, and you wouldn’t believe how great they are at playing with balls.
They are NFL enhanced. They are trained, embiggened with special diets, and they are clad with armor and vibrant, often scary, colors.
This is a picture of a hurricane from 1938.
It was a big one; It did lots of damage when it slammed into New England and New York.
A hurricane is a large storm that forms in the tropics, and sometimes hits land. The energy from a hurricane comes from a combination of the earth’s spin, trade winds, and so on, but mainly, from the heat on the surface of the sea. The rain that falls from the hurricane also comes mainly from the sea surface indirectly, and any water that evaporates into the atmosphere.
This is a picture of Harvey the Hurricane, the remnants of which are still circulating around in Texas.
Harvey is a lot like the 1938 hurricane, in that it formed in the tropics, in the Atlantic, and was a big spinny thing. It got its energy in the same way, and formed in the same way, and both slammed into land and scared the crap out of everybody.
But they are different, the 1938 Hurricane and Harvey the Hurricane. How are they different? Have a look at this map:
The pairs of photos above show “then” and “now” for two different things (men and hurricanes). This map shows both then and now in the same graphic. This map represents the current sea surface temperature anomalies, meaning, how much warmer or cooler the current sea temperatures are compared to the same time of year but at some time in the past, averaged over a long period, in this case, from 1971-2000. Global warming was well underway during that period, so present sea surface temperature readings that are above that baseline are not only high but are actually very high, because the baseline is high.
In this map, red is more, blue is less. Look at all the nearly ubiquitous more-ness in sea surface temperatures around the world. That causes the atmosphere across the entire globe to potentially contain much more water vapor than it could have contained during that that baseline period. Look at the sea surface temperature anomalies for the gulf of Mexico, where Harvey formed. They are high. This means that any hurricane that formed over that extra warm water will be stronger, and any tropical storm system that occurs pretty much anywhere on this map (or round the other side of the Earth as well, for that matter) will contain more water, than it would if it existed and all else was equal several decades ago.
This is a picture of a Unicorn.
A unicorn poops rainbows and pees mimosas. Or so I’m told. This is another view of Harvey the Hurricane.
What is the difference between the unicorn and Harvey? Harvey is real, and the unicorn is not.
I won’t quote you or give you links. Why? Because I find this whole thing a bit too embarrassing. But here is the thing. Otherwise intelligent and well informed individuals have stated in various outlets, including major media, and including twitter, that it is simply inappropriate to claim that Harvey the Hurricane is in any way global warming enhanced.
This is wrong. There is no such thing as a storm of any kind that is not a function of the current climatology. The current climatology has widespread and persistent, and in many cases alarmingly high, sea surface temperature anomalies. There will not be a tropical storm, including hurricanes, that escape the physics and poop out rainbows and pee mimosas. They will all be real. They will all have greater power and more moisture than they otherwise would have, had they formed decades ago before the extreme global warming we have experience so far.
There was a time when Harvey was a rabbit, an invisible rabbit only seen by a delusional character in a movie, played by Jimmy Stewart. Today, we have Harvey the Unenhanced Storm, playing that role. It is a fiction, something seen by a few but that is no more real than the above depicted unicorn.
As I was writing this post, Michael Mann posted an item in the Guardian that makes this case.
Sea level rise attributable to climate change – some of which is due to coastal subsidence caused by human disturbance such as oil drilling – is more than half a foot (15cm) over the past few decades … That means the storm surge was half a foot higher than it would have been just decades ago, meaning far more flooding and destruction.
… sea surface temperatures in the region have risen about 0.5C (close to 1F) over the past few decades from roughly 30C (86F) to 30.5C (87F), which contributed to the very warm sea surface temperatures (30.5-31C, or 87-88F).
… there is a roughly 3% increase in average atmospheric moisture content for each 0.5C of warming. Sea surface temperatures in the area where Harvey intensified were 0.5-1C warmer than current-day average … That means 3-5% more moisture in the atmosphere.
That large amount of moisture creates the potential for much greater rainfalls and greater flooding. The combination of coastal flooding and heavy rainfall is responsible for the devastating flooding that Houston is experiencing.
… there is a deep layer of warm water that Harvey was able to feed upon when it intensified at near record pace as it neared the coast….
Harvey was almost certainly more intense than it would have been in the absence of human-caused warming, which means stronger winds, more wind damage and a larger storm surge…
Mann mentions other effects as well, but I’ll let you go read them.
The extra heat at depth Mann mentions is now recognized as responsible for the extra bigness and badness of some other famous hurricanes as well, such as Katrina and Haiyan. Harvey might be a member of a small but growing class of hurricanes, deep-heat hurricanes I’ll call them for now, that simply did not exist prior to global warming of recent decades. Further research is needed on this, but that’s the direction we are heading.
Climate scientist Kevin Trenberth recently noted that “The human contribution can be up to 30 percent or so up to the total rainfall coming out of the storm,”
Aside from Michael Mann’s Guardian article, he has this facebook post making the same argument.
Harvey the Hurricane is real, and so was the 1938 Hurricane. Climate change enhancement of Harvey is real, but unicorns are not. Sadly.
I really thought we had stopped hearing this meme, that “you can never attribute a given weather event to climate change.” But, apparently not. That is a statement that is technically true in the same way that we can’t really attribute an Alberta Clipper (a kind of snow storm) to the spin of the Earth. Yet, somehow, the spin of the Earth is why Alberta Clippers come from Alberta. In other words, the statement is a falsehood that can never be evaluated because it is framed incorrectly. Here is the correct framing:
Climate is weather long term, and weather is climate here and now. The climate has changed. Ergo … you fill in the blank. Hit: Unicorns are not involved.
The following information is cribbed (with permission) from a FACTBOX produced by S&P Global Platts. Petroleum companies in the Gulf, especially around Houston, are are responding to likely shutdowns or possible damage due to the strengthening Hurricane, which is expected to have its largest impacts over the next 36 hours or so (longer for some flooding).
Before giving you these details, I also saw this: A map being circulated around energy industry folks showing the amount of land in Houston that has been made impermeable (by construction of things and surfaces) since the last big Hurricane. It is a HUGE amount. It seems that over time, Houston has made the prospect of bad flooding given a certain amount of rain worse rather than better (individual cities can make that choice, they may have failed to choose widely).
OK, her is a selection of facts form the FACTBOX:
* In the afternoon, the NYMEX RBOB crack spread against WTI was $1.91
higher at $17.67/b, boosted by supply concerns. NYMEX September RBOB settled
up 4.52 cents at $1.6641/gal. Physical gasoline prices were higher as well.
S&P Global Platts assessed Gulf Coast conventional gasoline at NYMEX October
RBOB plus 12 cents/gal, a 5.89-cent/gal climb and its highest assessment since
August 13, 2015.
* Platts assessed benchmark Gulf Coast jet fuel on the first day of
trading for Colonial Pipeline’s prompt 50th cycle at the NYMEX October ULSD
futures contract minus 3 cents/gal, after it traded at that level in the
Market on Close assessment process. That was up 4 cents from Wednesday, and
its highest level since October 1, 2014.
* In natural gas, TGP Zone 0 was the largest mover in the region, with
prices jumping almost 6 cents to $2.816/MMBtu. There was a force majeure
issued on Tennessee Gas at 11:30 am CDT time that will impact flows involving
Station 1 and Station 9 near Agua Dulce, Texas. NGPL was evacuating personnel
from Compressor Station 300, and TGP had evacuations at Stations 1 and 9 near
* In shipping, Aframax freight rates rallied with charterers seen working
narrow fixing windows. The east coast Mexico-USGC route climbed 20 Worldscale
points from Wednesday after Chevron took the Bonita for an east coast
Mexico-US Gulf Coast run at w112.5 loading a 70,000 mt cargo with August 27-29
dates. An expectation of potential delays after the hurricane fizzles out and
shipowners heard to be looking for a “hurricane premium” on bookings kept the
Do you know what all that means? Good, let me know in the comments below. I suppose that where pries may be going up, there will be less of an up-going in the event of disaster.
Oil refineries are making some adjustments right now:
* Flint Hills Resources is shutting both the East and West plants of its
296,470 b/d Corpus Christi, Texas, refining complex ahead of Hurricane Harvey,
the company said.
* Other area refiners, such as Valero, Marathon, Phillips 66 and Shell,
said they were monitoring the storm. “We will continue to monitor the storm
and make decisions about refinery operations, especially for our Corpus
Christi and Three Rivers locations where the storm is currently projected to
make landfall,” Valero spokeswoman Lillian Riojas said.
* The Texas Gulf Coast is home to 4.944 million b/d of refining capacity,
while the Louisiana Gulf Coast is home to 3.696 million b/d of capacity,
according to the US Energy Information Administration.
I suppose that is the part where our entire economy is affected by a hurricane in Houston. Since the hurricane is steering south of Houston this may not be as big a deal, with the direct effects of flooding being the real problem. I assume these plants are all designed to handle pretty much any amount of flooding because they were built in a Hurricane zone by non-idiots. Right?
Meanwhile, the actual production of Texas T is being affected already:
* Some 9.56%, or 167,231 b/d, of US Gulf of Mexico oil output was shut-in
due to Hurricane Harvey as of 11:30 am CDT (1630 GMT) Thursday, the US Bureau
of Safety and Environmental Enforcement said. In addition, some 14.66%, or 472
MMcf/d, of Gulf of Mexico natural gas production was shut-in, BSEE said.
Personnel have been evacuated from 39 production platforms, or 5.29%, of the
737 manned platforms in the Gulf of Mexico, the agency said. Personnel have
been evacuated from one of the 10 non-dynamically positioned rigs currently
operating in the Gulf.
* Shell shut operations at its Perdido facility in the Gulf of Mexico
late Wednesday. Shell’s Perdido is one of the world’s deepest floating oil
production platforms, moored at 8,000 feet of water. It is a production hub
for three fields in which Shell has a stake: the Great White, Tobago and
Silvertip fields. Production is about 100,000 b/d.
* ExxonMobil has begun to curtail oil and natural gas production from the
Galveston 209 platform and is preparing the facility for evacuation, a company
spokeswoman said Thursday.
* Anadarko Petroleum has shut production at four fields offshore Texas.
The company said late Wednesday that it had not only removed
all personnel but temporarily shut production at its operated Boomvang,
Nansen, Gunnison and Lucius facilities. Boomvang and Nansen are sited in the
East Breaks area of the Gulf, nearer the Texas coast than the other two
fields, while Gunnison is located in Garden Banks further west and Lucius is
in southeast Keathley Canyon, sited south of Garden Banks.
* ConocoPhillips has evacuated non-essential personnel from its Magnolia
offshore US Gulf of Mexico producing platform, the company said. Magnolia’s
gross production in 2016 was 4,000 boe/d, of which 3,000 boe/d was net to
* Statoil, which operates two rigs in the Eagle Ford play of South
Texas, said it was securing its rigs and wells and evacuating rig personnel as
well as suspending all non-essential activities.
* ConocoPhillips has suspended drilling and completion activities in the
Eagle Ford Shale and moved non-essential equipment off the six drilling rigs
it is running in the South Texas play.
Seaports and transport terminals are going to shut down or are starting to shut down, and this of course will affect things other than bublin’ crude.
* NuStar Energy is preparing to shut its Corpus Christi crude oil and
refined products terminals in Texas ahead of the storm, spokesman Chris Cho
said Thursday. He did not give a specific timeline for completing the
shut-down process, but said the company has activated its emergency response
plans and will continue to monitor the storm to determine its next course of
action. NuStar’s North Beach Terminal at Corpus Christi in southern Texas
includes a 1.6 million-barrel crude facility, and 10 storage tanks with a
combined capacity of 327,000 barrels for gasoline, distillates, xylene and
* Magellan suspended operations early Thursday at its crude terminal and
condensate splitter in Corpus Christi, Texas, in response to the incoming
storm, said spokesman Bruce Heine. The midstream player operates a 3.5
million-barrel crude and condensate storage storage and a 50,000-b/d
condensate splitter at the facility, Heine said in an email. However, the
company’s refined products and crude oil pipelines in the Houston Ship Channel
area are operating normally at this time.
* Port condition Yankee was set for the Texas ports of Houston, Texas
City, Galveston, Freeport and Corpus Christi. Port condition Yankee is when
hurricane force winds are possible within 24 hours, closing inbound traffic.
PIRA Energy Group estimates Texas’ total crude export capacity to be 2.5
million b/d. PIRA, which is part of S&P Global Platts, has arrived at that
data using available public data.
* A source with a shipowner engaged in the US Gulf Coast oil lightering
market confirmed ship-to-ship operations were suspended through the end of the
weekend. “I can confirm that lightering, everywhere from Corpus to Southwest
Pass, is suspended as of today until at least Sunday.”
Thanks very much to the staff at the SPG Global newsdesk, and editor Lisa Miller.