One of the things about "extreme weather attribution" that makes me uncomfortable is the asymmetry of it. To be clear: I'm in no way an expert (or even educated) in the area.
To give an example. Let's say there's a 1% chance every year that there will be a catastrophic wildfire in some particular area. Now let's say climate change increases a chance of drought in that area. Then we get a wildfire one year and the attribution is (say) "20% due to climate change" -- which I guess means there is now a 1.2% annual chance of a catastrophic fire.
But what if climate change causes more rain in the area? And, for the sake of argument, that causes the change of catastrophic fire to go down to say 0.8%. Then, in a given year, there is no fire. No one is going to bother doing an attribution study to say, "Climate change is 20% responsible for the fact that we didn't have a fire this year."
That's why in these discussions it's usually better to talk about specific statistics like 'the chance of a wildfire has gone up by 20%', or perhaps more clearly 'the average time between extreme floods has gone down from once every 100 years to once every 60' (which I find communicates the point a bit more clearly).
That said personally I think that should be as far as the discussion goes, talking about a specific event and asking whether it's because of climate change is silly and leads to counter intuitive results because by nature extreme weather events are mostly caused by bad luck.
Climate changes the frequency of extreme events, but talking about the frequency of a singular specific event is impossible. And talking about just 1 or 2 of them isn't quite enough either, events will occur at a rate far above or below that predicted by the best weather models simply because of (bad) luck.
I sometimes get the feeling that people try to attribute extreme weather to climate change because it improves the narrative, or as proof that climate change has happened. But personally I find this a bit silly since the fact that the global average temperature has increased and the effects this has on weather shouldn't be all that controversial.
As a climatologist by education and a dabbler in politics, I think you are radically misjudging the ability of most people to have rational discussions about unwelcome statistics. If you say severe weather will increase by 20% due to climate change, people will hear that there’s an 80% chance weather will stay the same. And they say I’m just being alarmist.
When I say that this storm may have been enhanced by climate change, but it could also could have happened anyway, statistically: people hear natural variability, and that I don’t know for sure that climate change impacted their daily lives. And if I don’t know for sure, there must not be much of a case for climate change.
Most of the world is not HN, where nuanced discussions work. Most of the world does not give a shit about climate change unless they will personally suffer in some way. So I lie. So that even if this storm or that hurricane didn’t hurt them, they will have empathy for those who did suffer from a climate-enhanced storm. Because if we wait for everyone to feel the impact in earnest, it will be far too late.
In my view, the problem with the noble lie, which you are engaging in, is that it has been an unmitigated disaster with regards to Covid messaging.
As it became obvious some officials were willing to lie, this opened the door to many people having no idea who to trust.
I believe public trust in leaders and institutions is at an incredible low, and I’m not sure how it can be repaired.
Regardless of whether you think people are capable of nuance, most people have people in their circle who they trust and do have that capacity.
Meanwhile, keeping secrets on the internet is very hard.
I think a much better strategy is to explain honestly and with nuance, and hope that this will maintain your credibility in the face of an army of internet researchers who will probably do more to shape average people’s conception than you can ever hope to do directly.
Can you give a specific example of what you are talking about? What noble lies were being told about COVID? I heard a lot of "we don't knows, but here is the best bet" which were not lies, but all of the lies I heard absolutely were not noble in that they were not for the public's best interest, but rather for the best interest of the individual saying them.
Fauci's infamous quote was: “There’s no reason to be walking around with a mask. When you’re in the middle of an outbreak, wearing a mask might make people feel a little bit better and it might even block a droplet, but it’s not providing the perfect protection that people think that it is."
I not sure it can be considered a lie though. Wrong, but at the time the precedent was pandemic flus and SARS-1, and there isn't much presymptomatic transmission of either. Quarantining once you have symptoms will do the job. SARS-2 is freakish in just how much it replicates in the nose for a couple days before symptoms. It was another 3 weeks before enough data coming out of China established there was likely an appreciable degree of presymptomatic transmission occuring and the CDC guideline was changed.
That said, he definitely should have expressed the reason he was making a stand either way, i.e. that healthcare workers needed the supplies more than folks in the street.
A better example of a lie was when he said he was titering up the number for what is required for herd immunity based on what he thinks will be publicly palatable, while that's dodgy imo it is kinda small potatoes.
Fauci later said that the reason they advised against masks was because of fears of a PPE shortage. So either Fauci admitted that his earlier position was a deception, or his later claim was itself deceptive.
He very clearly said that people should not be wearing masks, and the reason was very clearly implied to be that they wouldn't be effective; in fact, in that 60 mins interview, he outright said that wearing a mask could be worse than not wearing one.
That has always been obvious horseshit to anyone who knows anything about biology. His later statement was that the "real reason" they advised people not to wear masks was not actually the stated reason, but a completely different reason. That's literally a deception, and it from Fauci's own words, it appears to have been an intentional deception.
Now you might be ok with public health officials deceiving the public about public health, but I am not. I think it's doubly important that such an official be above reproach in the midst of a pandemic.
> His later statement was that the "real reason" they advised people not to wear masks was not actually the stated reason, but a completely different reason.
Again, he didn't comment on the factual basis of the original mask guidance, only the reason why he made a proclamation one way or another. You are reading between the lines a bit too hard here I reckon.
> That has always been obvious horseshit to anyone who knows anything about biology.
With SARS-2 and hindsight, sure. But had it been like all previous pandemic respiratory viruses including the one most closely related to this virus, where the vast majority of transmission occurs only after a carrier is symptomatic, no, masks wouldn't do much for people in the street as long as anyone with symptoms stayed home.
I think the guidance was muddled and he could have explained the rationale re: healthcare workers. But Fauci didn't later admit deception and what he said at the time wasn't out of line with a reasonable interpretation of available evidence.
Furthermore, if the factual basis of the original recommendations were not false, why were masks mandated soon after shortages were no longer a concern? (and continue to be)
> But had it been like all previous pandemic respiratory viruses including the one most closely related to this virus, where the vast majority of transmission occurs only after a carrier is symptomatic
That's irrelevant to this topic. You don't know who else around you in public might be symptomatic, that's the whole point of wearing masks.
> But Fauci didn't later admit deception and what he said at the time wasn't out of line with a reasonable interpretation of available evidence.
Yes, he literally did. He said people should not be walking around wearing masks. He justified this recommendation by saying that it's because masks are not providing the protection people think they do, and that they might only stop a droplet or two (all clear nonsense). He even said that wearing a mask might be more dangerous than not wearing one. These are all claims he made in that interview.
When asked later why his advice around masks changed, he didn't say the understanding of the facts changed, he said that those original recommendations were made for reasons completely unrelated to effectiveness (fears of PPE shortages).
You can try to spin this however you want to suit your narrative, or try to be as charitable as you want to Fauci, but this was clearly deceptive messaging, and almost certainly intentionally deceptive. At the very least, this would make Fauci among the worst science communicators I've ever seen, at worst he violated the public trust, and is that really who you want communicating to the public in the midst of a public health emergency?
> why were masks mandated soon after shortages were no longer a concern? (and continue to be)
...because that is when the scientific evidence changed. Preprints from China were streaming in in March 2020 that were making a significant degree of presymptomatic transmission look increasingly likely.
That's not what he cited as the rationale though, is it? If he had we wouldn't even be having this conversation.
Why would he even bring up PPE shortages in response to a question about changing mask recommendations? You've invented a charitable narrative in an attempt to explain the changing recommendations, but this narrative doesn't actually explain the available evidence.
Like I've been saying all along, he either very clearly lied, or he's literally the worst science communicator I've ever seen.
Either way he should not be the face of public health.
Maybe you think Fauci changed his mind based on new evidence on mask effectiveness, but he literally says they advised against masks due to a fear of PPE shortages, and not due to changing evidence. So either way he deceived: either he deceived about mask effectiveness thus spreading health misinformation, or he deceived about why he advised against wearing masks thus undermining public trust in his advice.
Fauci admits to moving the goalposts on vaccination rates and explains it's because of his "gut feeling that the country is finally ready to hear what he really thinks":
I think public health officials like Fauci really shot themselves in the foot by undermining their credibility so early on. Public health officials in a public health crisis should be beyond reproach, but that's not how they've been acting, and continued partisan support of these figures only further deteriorates trust in scientific institutions and journalism.
Blows my mind that people still wonder why the US response to the pandemic has been “less than optimal”. They seem to forget that this narcissist has been the primary advisor to both presidents…and has literally got everything he asked for from both administrations.
The US pandemic response hasn’t been a Trump or Biden problem, it’s been a Fauci problem.
He certainly controls the public media and Democratic Party Covid narrative in the US. If you have that much power in your corner, your influence is vast.
Maybe editorialized a bit, though I didn't attribute the quote to Fauci particularly because the fib wasn't coming from only him. A lot of people were saying similar things. And the context, of course, is fear of mask shortages for people who needed them most.
But what really gets my goat, much more than that white lie, is when grown adults feign ignorance and pretend they never heard the lie, that the lie was never told. Two years ago isn't that long ago, we all remember it. I think the feigned ignorance is perhaps another white lie motivated by good intentions; maybe some think they can fight covid by defending the reputations of public health officials who were only telling a well intentioned lie in the first place. But despite those good intentions, I think this denial of history is tantamount to gaslighting.
In a conversation about accuracy of statements and not making misstatements just to improve rhetoric, this seems like a weird choice. "No one actually said this, but it captures the zeitgeist (just trust me)".
I specifically did not quote Fauci, so I did not misquote Fauci. I did not quote Fauci because similar sentiments were being expressed by numerous people, including:
> “Seriously people — STOP BUYING MASKS!” the surgeon general, Jerome M. Adams, said in a tweet on Saturday morning. “They are NOT effective in preventing general public from catching #Coronavirus, but if health care providers can’t get them to care for sick patients, it puts them and our communities at risk!”
If your intent here is to cast aspersions on claims that comments like this were made, then you are gaslighting. Anybody without serious brain damage remembers the medical technocrats and their media lackeys were signaling against masks early in the pandemic. The above article contains quotes from numerous officials, each worded in different ways but getting at the same message that I wrote above, that masks don't work for proles but do for doctors. Trying to make people question their memory of this is gaslighting.
Can you find a source for an actual authority (ideally a health authority) saying that?
There’s a similar meme going around the US, but I’ve yet to find any (except one, noted below) health expert who actually said “masks don’t work” rather than “we don’t know that masks work.”
The one exception I’ve been able to find was the US Surgeon General Jerome Adams.
This seems like a clear example of health authorities sharing a nuanced true-at-the-time message that got stripped of that nuance and rounded out to the nearest (false) simplified interpretation.
From the chief public health officer of Canada who is also a WHO advisor. She's also said other very questionable things (which are all Googleable and which pretty much every Canadian can tell you as it's been a bit of an ongoing scandal, among others...).
I actually can’t watch that video, potentially not available outside of Canada? Was able to find this one which, yes, contains some concerns and ideas that ended up being invalid (like transmission through eyes or fomites/touching), but which were not completely crazy at the time.
Same in France. Then a couple of months later, masks were mandatory outdoors even in low-density areas. I feel that treating people like children overall hurts public trust.
Asian countries already used them at the time, not because of COVID. Unfortunately it appears that no one had researched this phenomenon so there were no good statistics on whether, or not, they were effective.
Operating theatres and streets are radically different environments. The surgeon is leaning over the patient and breathing directly toward an open wound so of course it makes sense for a surgeon to wear a mask. That's not what happens in the street.
“When polls said only about half of all Americans would take a vaccine, I was saying herd immunity would take 70 to 75 percent,” Dr. Fauci said. “Then, when newer surveys said 60 percent or more would take it, I thought, ‘I can nudge this up a bit,’ so I went to 80, 85.”
“We need to have some humility here,” he added. “We really don’t know what the real number is. I think the real range is somewhere between 70 to 90 percent. But, I’m not going to say 90 percent.”
This is another good example of the issue of misinterpretating true statistics by large portions of the population as outlined above about climate change. Many now understand that even 100% vaccination alone might not end the pandemic, so the conclusion is often "vaccination doesn't work" -- which is much further away from the truth, but fits better into binary thinking and "intuition".
Of course, the problem with people who don't believe in vaccination is not only due to their lack of statistical understanding, but it's massively enforced by politicians and "journalist" who spread those lies.
I think the more likely issue is that they are assuming that the vaccine's efficacy is aligned with the strength of the efforts being put into mandating it. When they discover those efforts are excessive they get upset.
A reasonable scenario that doesn't get bought up enough is that average people have a radically different risk tolerance than professionals working in the healthcare industry. 1 in 10,000 incidents mean very little to a person on the street but translate into actual work for a healthcare worker.
It's actually quite common that vaccinations need to be done multiple times (up to 5 times for DPT) and to have boosters for these when traveling to certain countries.
> going trough an inflammatory process every six months
Are you claiming this as a universal truth? Or your specific experience?
My experience and that of two of my three adult children is that it was like a slight bruise at the injection site for about 24 hours. The other child felt slightly under the weather for a couple of days, similar severity to having a cold. We all had Pfizer.
My impression is that my experience is the more common one, but it is not the one that grabs anyone's attention.
The more serious side effects seem to be vanishingly rare.
> My experience and that of two of my three adult children is that it was like a slight bruise at the injection site for about 24 hours. The other child felt slightly under the weather for a couple of days, similar severity to having a cold. We all had Pfizer.
How is that bad? A vaccine is designed to cause a response by the immune system; this is certainly not pleasant, but exactly the trade against the potentially much worse effects of an actual infection.
Feeling “slightly under the weather for a couple of days, similar severity to having a cold” is what Covid looks like in most children.
Covid infection is “potentially” much worse, that’s true, but so is a drive to a vaccination site — the child might die or get seriously injured in a traffic accident. What matters is not the “potential” but rather the actual likelihood, the expected value. Has Covid been dangerous to children? The answer is, rather overwhelmingly, no.
Belief is comforting. Many prefer to focus on established findings and accepted facts as they emerge. We know that the vaccine does not prevent infection or transmission. Its advantage lies in its effect in reducing but not always preventing severe symptoms and death in vulnerable people. https://www.gov.uk/government/publications/covid-19-vaccine-....
Both believers and non-believers tend to be immune - to any findings that run counter to their belief systems. Instead of objectively weighing up pros and cons they get rather heated and adopt binary thinking along pro- and anti-vaccination lines with some indulging in the same trap themselves.
> As it became obvious some officials were willing to lie, this opened the door to many people having no idea who to trust.
> I believe public trust in leaders and institutions is at an incredible low, and I’m not sure how it can be repaired.
I’m not sure you can conclude that the ability or willingness of people to trust anyone is the thing that has weakened when so many people who boastfully distrust traditional mainstream experts also do trust some extremely specific set of claims from some random YouTuber or talk show.
That's fair but the problem with such a deliberate lie is that I must view everything with suspicion when people say something that supports climate change. Pretty much the only way I can know something is mostly accurate is when there's a shiny leaflet in front of it 'for educators' that I can ignore.
And it's not just this problem, it's all problems where people need to be convinced 'for the greater good'. I truly worry that the rightful distrust that those 'white lies' are causing is doing irreparable harm. It's also quite fragile since it opens room for people to point out the misleading or even false information, and in those cases people have a nasty tendency to assume the opposite must therefore be true.
I think part of the problem is that the discussion has already been rigged beforehand, and now you _must_ lie to be heard. Not because you want to, but because those that came efore you did. So if you now simply state the facts as they are, the other side will go "oh, things are actually better then, because the previous experts were pretty alarmist, and now this person is telling me something that sounds normal an reasonable".
You know where else I see this pattern? Digital forensics, my area of work. People have been selling snake oil all around for years, and know lawyers, attorneys and judges (in general, with some god particular exceptions) believe in whatever they've been seeing in CSI, or their "IT guy" convinced them as possible. Who cares that I'm one renowned expert in the field? _Their guy_ said this thing was possible, so I must be lying!
Lies of omission are unavoidable when talking to a general audience about any deep scientific or technical issue.
A physicist actually explaining Magnetism takes years. If a PHD gives you a short answer their simply lying to you. For example, no ferromagnetic materials aren’t simply all atoms arranged in a specific fashion that’s a monumental simplification.
There's a pretty stark difference. The PhD would retain their credibility if someone explained the difference between the short answer and the complete answer.
But that weather dude will lose his credibility, because his audience will feel deceived, not educated.
I have gotten the short answer and a significantly longer answer from a climatologist. The short answer while wrong still seemed like a reasonable summery and quite understated. I can’t say if the full answer would change my opinion, but it seems unlikely.
This exactly. After a year of high school physics and a year of mechanics and electrodynamics, they finally get around to telling you: that was kind of all lies, here’s relativity. And then a month later they do it again for quantum, and finally admit they don’t know what is really going on. But Newtonian mechanics is useful!
They should state the truth in first year: We have absolutely no final answers for you, and these years will soon be forgotten. You'll earn more earlier taking any job now. Education is overrated in business anyway.
I think we can reasonably treat collage students as adults capable of making informed decisions. Most people failing to finish the physics PHD end up in reasonable places, it’s hardly the trap most people getting a 200,000+$ history degree would be in.
An omission really only becomes a lie of omission if somebody would feel deceived upon learning of the omission. Nobody would reasonably feel deceived by your omission of the 0.000000001% cosmic ray hypothesis.
One of the big arguments against climate change is the suspicion they "the scientists are lying". You're taking away the credibility of the entire field.
1 - Making predictions about the future with certainty is not something that is generally considered in the realm of mortals.
2 - People can distrust models as being inaccurate and bad at predicting the future.
Yes, they are. Every election winter, or so, some performative artist shows up to Congress with a bag of ice, and announces in front of cameras something along the lines of "Checkmate, scientists, if global warming is real, why is it snowing in my congressional riding?"
I really don’t think that’s true. Tens (hundreds?) of thousands of additional Americans are dead from Covid because they believed easily disprovable lies about the safety and efficacy of vaccines by people who have reputations for lying. Most people believe anyone they consider to be an authority. It’s too exhausting otherwise.
More accurately, people are really good at sniffing out lies when it benefits their worldview somehow (and terrible when it doesn't). Why people think conspiracy theories benefit their worldview is a different and stickier question, but somehow they do, so they're hypersensitive to any lies deployed against the lies they prefer.
I didn't say people are good at sniffing out lies. I said that people are better at sniffing out lies than they are at sniffing out good intentions between those lies.
I'll phrase it another way: When you tell a lie, some people will see through it and some people will believe it. Of those who see through it, some will believe you had good intentions for lying, and some won't. These proportions all vary depending on the exact nature of the lie and the audience receiving it, but it's virtually always the case that [see through the lie] > [see through the lie AND believes it well intentioned]
Or put another way: Every time you tell a white lie, you burn your own credibility because some people will see through your lie and won't be inclined to excuse you for it.
> Most of the world does not give a shit about climate change unless they will personally suffer in some way.
That's uncharitable and untrue.
> So I lie. So that even if this storm or that hurricane didn’t hurt them, they will have empathy for those who did suffer from a climate-enhanced storm.
People distrust science after decades of bad science journalism and bad science communication, and you think the solution is to outright lie and undermine all of your credibility.
I'm sure that'll work just great, and it won't at all simply reinforce the beliefs of those who already agree with you while arming those you were actually trying to reach with reasons to distrust you. Not like we didn't see exactly this sort of lie backfire during the pandemic.
This is a strange misconception about climate change, even among many of what should be the better educated and informed section of the population, at least on the issues of the science. The misconception is that more people need to be convinced about climate change and the need to take action.
Many climate change topics that come up even on here get well received comments about problem of the greedy selfish uneducated rednecks who are preventing climate change action, and the subsequent hand wringing about how to educate or scare or convince them into changing their minds.
The facts just don't support this outlandish idea though. Even in the country with the most climate deniers in the world, Indonesia, they number just about 21% of the population. In the USA, deniers are under 20% and a staggering two thirds of people and more than half of Republicans think the government should take more action on climate change. This is an overwhelming political mandate, it's not even a question.
The ruling class enacts far less popular policies and legislation all the time and doesn't bat an eye. You're telling me they'll go on expeditionary wars on flimsy pretexts that last decades and cost trillions of dollars and kill thousands of Americans, but they won't implement overhwelmingly popular policy that has bipartisan support of voters to address what they keep telling us they believe is the biggest and most important problem facing humanity? This is clearly utter bullshit.
And that's the way they like it. Their divisive propaganda (which includes seeding distrust in science) has worked extremely well. The facts show that they never had any intention to more about climate change, that they routinely lie about the political reasons for not doing more, and they're happy that the commoners are blaming one another for it instead of the robber barons who own them.
Exactly. In the political sphere, "excuses" are not "reasons". Well meaning people get these confused all of the time, accepting a politician's excuses at face value as the reason they don't support some policy.
Ah, scientists lying for the benefit of stupid masses. Worked great for public health experts over past 2 years, I'm sure it'll work great for climatologists too.
Are you saying there were no white lies involved? I recollect a series of events that involved people happily spewing Nobel lies especially at the start of the pandemic.
In my country they said medical masks should be prioritized for hospitals. Experts doubted they would work well for untrained people, but later research changed that bias. Very few died of Covid as a result of how leadership acted fast, but they admitted it could've been wrong.
I address this mostly to this comment's siblings: the fact that you're upset about the lying bit only reinforces labster's point. I say this based on the comments thinking that the lie is actually meant to deceive, getting distracted with the word "lie" and its connotations, and missing the broader issue. The issue is that the general population can't be reasonably expected to understand how climate change works, and how statistics play out in climate change modeling. Unless the message is dead simple, the message will be lost.
What the "lie" "really" says is subtextural/etc: to those that need to hear it, it says what it needs to say to get them to understand what needs to be done (and, importantly, isn't wrong, just isn't the whole picture either); to those that already know the answer, you didn't need to hear the lie in the first place. There is no deceit.
Climate change needs participation from everyone, and, for better or worse, everyone includes a majority of, eh-hem, less-than brilliant people. We need them to understand regardless of our own righteousness.
> to those that need to hear it, it says what it needs to say to get them to understand what needs to be done
This only works if truth is used. The labster seems to be assuming they have credibility; but if they're lying, or if they're associated with liars, they'll lose it (if they haven't already).
That’s not really true. Anyone who has ever taught a freshman physics class is a bald-faced liar. We are absolutely certain Newtonian mechanics does not represent our universe. Yet for some reason we do not assume physicists lack credibility, even though we know for a fact that thousands upon thousands of physicists are teaching an entire semester of lies.
The lies are useful for making good decisions, though. I offer the same. A simplification that does not quite represent reality, but you should 100% consider it anyway if you build a bridge, because it will save you a world of hurt in the future.
"Newtonian physics is an accurate model of atomic scale physics, and that is what we will be teaching in this class" is not a lie, well within the understanding of a freshman, and very accurate. It might be worth discussing briefly when you need non-newtonian physics!
Sure, but if you have a semester to talk statistics and climate modelling, you will be able to caveat the information with plenty of 'this has been simplified' warnings.
"it's not that we have to change models every five years as none of the old are tracking the present, it's everyone else that's stupid"
- yeah sounds about like something a climatologist would say
When you say you lie, your replies are from people who think that you’re being deceptive. I don’t think that’s true and I don’t think lie is an accurate descriptor of what you’re doing. You’re lowering the fidelity of a model you’re communicating. What you’re doing is tailoring your message to a wider audience. That’s not as dramatic of a description as claiming you lie to people, but I think it’s more accurate.
If you want to communicate reality, you need to understand your intended audience. Finding what to emphasize and what to simplify will determine whether people understand you.
On BBC.com there was an article which quoted 5 scientists about climate change and one said that 40 years ago we had an extreme weather event every 3 months, in 2021 it was every 19 days and that we will run out of the capacity to pay for it.
I can't find the article atm, but its a pretty good way to visualize it.
> Most of the world is not HN, where nuanced discussions work. Most of the world does not give a shit about climate change unless they will personally suffer in some way. So I lie. So that even if this storm or that hurricane didn’t hurt them, they will have empathy for those who did suffer from a climate-enhanced storm. Because if we wait for everyone to feel the impact in earnest, it will be far too late.
This is fascinating. I would normally have written this off as a troll like a climate denier trying to discredit one side, but you seem to be well established here and quite possibly identifiable from your about page (not that I tried or am interested in trying to troll through it, just had a quick glance).
Don't get me wrong I absolutely accept that some scientists may take a bit of license with the truth when they say a particular weather event was caused or made more likely by climate change without having gone through a full analysis of that particular event and situation. And I even accept there is corruption and dishonesty all the way from a bit of unconscious bias to much worse.
It's quite amazing to see a scientist out and admit they lie about the science in order to try to influence public opinion in the hope of effecting the social or political change they want. I really admire and appreciate your honesty here, if nothing else. Can I ask whether you act in any professional or academic capacity as a climatologist? And what are the nature of the lies? Who are they to, how are they communicated?
I’m just a software dev now. It’s mostly just people I meet, though I’ve done some lobbying too. Lobbying is a fascinating thing, because all the staff really wants to know is if this issue could help/hurt them in a campaign. I remember once when Zoe Lofgren told me that while she agreed with my issue, there was zero chance Congress would take it up without handling a bunch of barely related issues.
The lie mainly has to do with people wanting to see the direct cause. But there is never a direct cause to weather. There’s a probability of enhanced storm intensity, because we observe storms getting more intense. Was it that last storm? Maybe. But the answer I give is yes. Because in all likelihood, there is some influence.
But climate change is in fact real, has in fact caused damages. If you are concerned about immigration in Europe, consider that a drought led to a famine led to a civil war led to mass migration. Are there a lot more causes here? Sure. What was the cause? Probably a lot of things. But the weather was surely one of them, and models tell us extreme events become more common under a warming Earth.
Climate change has already affected your life. Statistically, it must be so! Just because I cannot say how exactly it has done so, leads people to discount me. So let’s just pretend I know how, so I have a bare chance of being taken seriously. Else my hedging be mistaken for lack of certainty, by confirmation bias of people who yearn for my uncertainty.
I see it as akin to novels. The whole point of fiction is to create empathy for another point of view, though the method is a thicket of lies. The truth can very often not be looked at directly. Tell a teenager not to worry about what other people think — see if they get it. Tell a person that being racist is dumb, and see no behavior change. But a story might work, as a magick spell to get one to feel empathy, a lie that reveals truth.
You've articulated a very real problem that exists across all public policy and communication.
As other people have stated, its not as simple as lie/don't lie.
Many people simply don't want to acknowledge certain realities:
There is an overwhelming majority of people who lack the capacity/ability/attention span to look clearly and rationally at a complex topic and evaluate the available evidence in anything remotely close to a nuanced, well educated fashion.
You then have to add to this that these same people are being mass manipulated by bad actors with nefarious agendas and large budgets and resources. And then there's the fact that the internet has allowed people to live with the illusion that their understanding parallels that of people with infinitely greater education and experience.
Any public communication has to take this in to account. Believing that the rubes will somehow find their way to a reliable conclusion if you simply tell them 'the truth', is a recipe for playing into the hands of the nefarious (who are more skilled at portraying their self-serving bullshit).
We are well and truly fucked! There's just no way people are going to accept exponential and non-linear worsening conditions, before after step 99 of 100.
Lung cancer risk was established with control group. We don't have control group for a planet so we use climate models. The article is about remembering that we use models because of lack of alternatives, not because they are as good as control group.
That isn’t controversial. The issue is your first point, there is no control group for the Earth as a whole system. So demanding a control group as a barrier to causal reasoning about the relationship between weather and the climate is an impossible bar.
Weather is real. Weather has causes. We can and do reason about those causes all the time.
No I stand by it, talking about cause when the effect is probabilistic is an endless quagmire that people should avoid. Trying to attribute an individual case of cancer to smoking is nigh impossible, which is part of the reason tobacco companies got away with it for so long.
Plenty of companies have gotten away with bad practices by simply demanding proof for each individual case, the earlier we recognize this as a meaningless question the better.
Smoking causes cancer is true. But not everyone who smokes will get cancer and some people who don’t smoke will get cancer. Causation is hard. But we also know that particular cancers are caused by particular acts of smoking. The causal generalization is built on the observation of particulars. It might be hard but I don’t see why we wouldn’t, a priori, be able to say ‘this event was made worse by climate change because it likely affected input x’.
I generally agree with you that this isn’t easy to do, the value isn’t clear, and whether we can say such things with the evidence we have for any particular event isn’t obvious.
Absolutely agree with all this. I'd also want to add that another thing to think about is not just how unlikely any particular event is, but the probability of multiple unlikely events occurring across the globe in a relatively short time. After all, Earth is big enough that rare events will happen somewhere. Unusual weather patterns in multiple locations start to point to a systematic cause.
This year, the pacific north west got hit with the four Fs. Fires, floods, and fucking freezing weather.
In the summer, we had an unprecedented heat wave which killed thousands of people. Seattle hit 108°F/42°C. The town of Lytton, BC, hit 118°F/48°C[0], and then completely burnt down in a 30 minute firestorm. More rain would have been great.
We didn't get it.
We got it in the winter, and it was absolutely devastating. [1] To cap the year off, we ended with a record-setting freeze (With, thankfully, comparatively moderate snowfalls).
So, yes, more rain, less rain, more heat, less heat, we got all that. Unfortunately, weather is not a bank account, and having your town burn down to the ground in the summer, followed by a flood in the winter doesn't tend to cancel things out.
Extreme weather can occur in both directions. A year with more rain may be less prone to fire but more prone to flooding. It also does not take natural disaster to notice extremes. People who have lived in the same area awhile will know if there is an unusually wet, dry, hot, or cold year.
Actually it even gets more complicated. California is a good example. Dry weather makes more fires yes. More fires means less vegetation with deep roots. Not only do these shrubs burn more easily, but they neither hold back water. This makes heavy rainfall more dangerous as flash floods become more common. It's crazy how interconnected these things are and frustrating because it becomes difficult to talk about. The whole picture is so complicated but focusing on a small aspect over simplifies the problem too much.
The wetter weather enjoyed by the past few generations of Californians was an aberration. California is usually even dryer than it is presently.
> Paleoclimatological studies indicate that the last 150 years of California's history have been unusually wet compared to the previous 2000 years. Tree stumps found at the bottom of lakes and rivers in California indicate that many water features dried up during historical dry periods, allowing trees to grow there while the water was absent. These dry periods were associated with warm periods in Earth's history. During the Medieval Warm Period, there were at least two century-long megadroughts with only 60-70% of modern precipitation levels. Paleoclimatologists believe that higher temperatures due to global warming may cause California to enter another dry period, with significantly lower precipitation and snowpack levels than observed over the last 150 years.[7]
Might be wrong, but I think unseasonably wet weather can also increase the fuel load for a fire. i.e., boisterous undergrowth due to wet weather during Spring, which then dries out and is available for fire season.
Of course that's true. What I guess I was trying to say is that a change in climate doesn't necessarily mean more extreme weather (in every instance, I mean). A change in climate could cause something to happen more or happen less. But no one is going to study a given year and a given place that doesn't have an extreme weather event.
Discrete events are a human construct but the underlying data is continuous. I'm not sure the attribution of individual events is that meaningful and the article suggests for a variety of reasons the models aren't really built for that purpose anyway. So I think in a sense you are right, but this doesn't necessarily imply any problem for the predictive value of the models or research going into them.
one thing to note is that, generally, any comparatively calmer system with fewer extreme events is in a lower energy state. and that is exactly what we are moving away from.
Washington has also had unusually wet years that encourage more growth on the forest floor so there is more fuel by the time the 3 month dry and hot season rolls over
Extreme weather generally involves intersecting weather systems. For example, 'The Perfect Storm' of movie fame was the intersection of the remnants of a hurricane coming up from Gulf of Mexico with a polar cold front coming across Canada and as I recall a low pressure system out around Nova Scotia. The timing of these events was basically random; if the events had been staggered a few days apart instead of hitting at the same time, it wouldn't have been a perfect storm.
So, where models predict an increase in such events comes down to increasing probabilities of the individual events, kind of like throwing the dice twice as often as before, then you're going to get double sixes (i.e. extreme events) more frequently.
As far as the attribution of specific events, well, you can look back in history and say, historically we've had these events every 100 years or so, so we shouldn't expect three such events over a twenty-year period. This is similar to how a casino might suspect a player is using a loaded die.
However, for one specific event, is it possible? Plausibly you can examine the details of a single event and ask, okay, are there physical characteristics here directly related to climate change, in the same way one could examine a die to see if it was really symmetrical or not? One could perhaps use expectation results, i.e. "we expected this kind of extreme weather event to occur with greater frequency outside the regions where they normally take place, according to frequency results in models forced with extra CO2" etc.
Really though, it's when you get 500-year floods every 5-10 years in Europe, then you know you're 'testing the historical limits'.
I think you're mostly correct. And this is a problem with the idea of trying to model for this given all the variables and outcomes. Climate change should be as simple as: 1) we have pretty decent models and data that show that man is having a considerable impact on weather/climate. 2) we live in a fragile system and are fragile creatures, between us and mother nature we lose every single time, and we lose big, 3) do you really want to find out where this ends?
I think most people just look around and see that things are changing. The attribution studies are sort of a moot point. It's the actual change that is going to make us act.
Already is, a little.
Nobody is going to understand all the weaseling around this could have occurred by chance but we think it's more likely to occur because we modeled it but our models can't predict those low frequency really severe events so we've modeled less severe events and are guessing. If we do start seeing more and more serious weather events (like we seem to be) there's going to be some drastic action (too late?) and if we won't then I guess we're ok by some luck.
> No one is going to bother doing an attribution study to say, "Climate change is 20% responsible for the fact that we didn't have a fire this year."
This is not true. In fact, I'm aware of several papers that have made an argument that climate change will lead to more rain in California. They don't appear to be winning the argument but for many years this was a contentious discussion among atmospheric scientists. Academics absolutely do the research you are describing.
Would those papers make their way to mass media or social media discussions? Probably less so. But they are there amongst the communities that matter most in the conversation.
"a fire this year" is a really vague and imprecise way to measure things, and leads to trying to conclude weird things like whether or not a quiet fire season was caused by climate change. that's why scientists don't measure things that way.
the more precise way to measure would be to increase the sample size by counting a precise metric like total acres burned per season, and measuring it over a longer period of time to determine whether or not forest fires are generally getting worse or not. and if they're getting worse, how much are they getting worse by, and how much of that trend can be attributed to climate change.
This comment reads more like dismissive pedantry. What happens on the whole statistically matters more than hoping to get lucky with perhaps some local effects cancelling each other out.
The point here is that "local effects cancelling each outer out" sum up to what happens statistically on the whole. You need to account for all of these.
FWIW, I think it's quickly becoming an academic question. Last year had an extreme heat wave in Canada (worst in record) and a cold wave in Texas. Sure, last year might have been a freak year, but the thing with unmitigated climate change is that a "freak year" quickly becomes a "normal year" which then becomes a thing of the past.
For example, see the graph in [1], and imagine we only have data up to 1999. The year 1998 sticks out like a sore thumb - it was a freak hot year. Except that it became the new normal temperature in the 2000s, and then the most recent six or seven years have handily beat it. It will be an unusually cool year if it happened now.
We are transitioning into a world where "worst since weather records began" is a regular phrase at evening news. When a town burns down, will its residents even care if the particular fire was linked to climate change or not?
The point is that warning about extreme weather events is the only narrative that got people to consider global warming a problem at all.
A 2 degree change in average temperature doesn’t change how people live. A heat wave does.
Now I don’t know if it’s actually true or not but it feels like the frequency of extreme weather event has increased. It’s difficult to say because that’s something our grandparents already said 50 years ago.
But then there are 2 possibilities:
1. Either the frequency of extreme events has actually increased and it would be hard not to pin it down on climate change (despite our inability to do it scientifically).
2. Or it hasn’t changed significantly (statistically speaking), and it’s all a bit of mass hysteria.
Either way doesn’t actually matter. I personally don’t care if people are scared about the acidification of oceans or about rain for their next barbecue. As long as they vote for people who want to do something about it.
I personally have been criticized by friends for saying that we don’t know what the impact of climate change is on the weather. I don’t care anymore. It’s all about narrative. That one works, let’s not poke holes in it. It only benefits people running polluting industries.
> It only benefits people running polluting industries.
That's not true. It benefits anyone who can blame anything on climate change. For instance, decimation of fish stocks in the Pacific Northwest. Mostly due to dams and fish farming in the mouths of important rivers, and over fishing, but wild attributed to climate change. Massive forest fires in BC. Mostly due to forestry management practices where forests aren't allowed to burn, and any forests that do are quickly sprayed with Glyphosate so that Aspen don't grow (a natural firebreak species, but the lumber industry doesn't want it) and replanted with GMO pine. Monoculture forests have exacerbated Pine Beetle issues as well. Also largely attributed to climate change.
You take a locally solvable problem, and make it so that you can do nothing about it and the money continues to flow right in.
This attitude exemplifies the problem with the public discourse. The public discourse isn’t about finding out truth and forming actionable democratic consensus, it is about duping “stupid people” into supporting “good things”™.
By the time Machiavelli wrote The Prince, the very act of writing the book was a tracer round to rampant political cynicism. Once an attitude or ideology is the target of satire, it has already bcome interwoven into a society. I presume that to be even moreso in eras where publishing had high cost.
There's also this naive commitment to truth. Yes, we want our mental models to be more accurate and predictive. What if a model results in an increase of net suffering? What is the role of a democratic government, to provide services to a society to lessen individual offering, or to pursue truth and align legislation at all costs?
The point of a democratic government is being ruled by demos, the people. You are describing a government run by benevolent elites. In such case of course the government may lie and deceive people, and even pretend that it is democratic, but it won’t be a democratic government.
The naive commitment to truth is the most fundamental requirement for the most basic freedom, the freedom to reason. To lie, to deceive someone means to take away that freedom.
That makes sense. I'm not sure why the right to reason didn't cross my mind. Maybe I presumed it in the scenario I pictured. I'm not convinced that governments should have mandates to pursue truth at all costs.
I think the true danger is when there is little tension between government and the fourth estate. Government actions need interpretation and presentation, and when those functions lapse or are neglected, is when individuals begin losing the right to reason. To be able to make decisions presupposes that non-elites are presented with the decision in the first place. This is the context within which the naive commitment for truth is a virtue.
> I personally have been criticized by friends for saying that we don’t know what the impact of climate change is on the weather. I don’t care anymore. It’s all about narrative. That one works, let’s not poke holes in it. It only benefits people running polluting industries.
I get it. it's exhausting and usually pointless to make these sorts of arguments. I think it's fine to give up after a while, at least with certain people. every single issue can't be our hill to die on.
but this goes further than acceptance; we shouldn't go so far as to embrace obviously wrong narratives, however effective they may be in the moment. it really damages the trust we have in each other, which is already in short supply.
Narrative might win you an election or two but climate change is a long term issue with long term consequences. In the long term, telling the truth is the correct strategy.
Another great video and article from Sabine. So it seems like yes they do, and they can give us useful indicative estimates like lower bounds for the increased likelihood of extreme events, but there are still huge unknowns.
Honestly I’m amazed we can get the estimates we do, given that this particular field of analysis is so young. I’m not as pessimistic about the usefulness of this data though. Knowing that these events are becoming much more likely and having an understanding of why is useful work. There are always unknowns, and now there are fewer of them. That’s good.
Take the estimate that bush fires in Australia are now at least twice as likely than they were. Sure it would be better to know if they are twice, three times or 10x as likely, but knowing they are at least twice as likely and why that is the case is still useful data.
Statistics are tricky to think about, even for people with technical educations.
Something that was a lightbulb moment when someone was discussing professional baseball players. I forget the exact point the blog was making, but it came down to this: many things in life, like the ability to throw or hit a baseball, falls on a distribution that is close enough to a gaussian distribution. But when talking about professional baseball players, they represent just the very right hand tip of that distribution. Whatever intuitions you might have about gaussian distributions needs to be thought about in light of that.
The same applies with extreme weather events. It is easy to look at articles and headlines describing an exponential increase in the frequency of extreme weather events. But think for a second about the equation of a gaussian distribution. When you shift the offset by a small, fixed amount, the area under that right hand tip goes up exponentially.
No, one cannot point to climate change as the cause of any individual weather event. But it is a measured certainty that the warming is happening; the number of record high temperatures are a multiple higher than the number of record low temperatures every year for the past couple of decades. It would be weird if the temperature rose and the number of extreme events went up, say, linearly.
If this logic applies here then the increase in “extreme events” almost seems like a non statement. The distribution has shifted (as everyone agrees, though some debate cause) and consequently we will get more extreme events.
The point is it isn't just an increase -- it is an exponential increase. I myself have seen people claim that an author of an article citing an exponential increase in weather disasters is just hyperbolic, when (if the math of the distribution is to be believed) is literally true.
I believe in climate change, but if you look into how the climate change computer models are written, it really makes me feel like it is contrived. Here's the problem with climate change modeling, you are modeling a complex chaotic system, you just cannot do it accurately. You have to be honest about it.
It’s complex and chaotic, sure. But as you increase time horizons, and area of simulation larger and larger, that complexity and chaos starts to average out. So you can’t take a singular datapoint from a climate model, and make any concrete statements, but you can take thousands to millions of them and start making probabilistic statements.
Additionally it’s not like these models are untested. Their predictions are continually compared to actual reality, including by taking know data, and asking the model to predict more known data. Pretty basic stuff for testing the capability of any model, and guess what, that data shows these models are pretty darn good.
Everything in this world is complex and chaotic, how cars behave in collisions is complex and chaotic. But no one doubts our ability to use computer models to design safer cars. Equally most people generally trust the one week weather forecast, despite the fact that it’s substantially more difficult to predict weather than it is to predict climate.
Generally the defining feature of non linear mechanisms is that the long term behaviour is non-intuitive. I'd expect that to include not having a predictable mean
Non intuitive doesn't mean not predictable. Nor does chaos. Chaos just means that a small perturbation in input parameters can result in a large change in the output. So to have good predictive power you need to pick good inputs with good accuracy. But chaos is also a spectrum so not all chaotic systems are as sensitive as others.
Chaos is also different at different scales. At certain point chaos can be simulated as noise with a known distribution. You can’t make predictions within the noise, but you can describe the shape of the noise and it’s impact on macro scale systems.
Perfect example of this is quantum effects. Zoom in far enough and quantum effects make the world entirely unpredictable, and yet I can still accurately predict where I throw is going to land with no knowledge of quantum effects. That doesn’t mean those effects don’t influence the ball, it just that noisy chaos of those influences is tightly bound enough that it doesn’t matter. Newtonian physics is enough to make a prediction, despite being an incomplete description of the physical system at play.
But different noises do different things and have different predictive powers. I think your statements about these systems being impossible to calculate are too strong. We use gaussian noise all the time. In fact there's plenty of machine learning models that use noise and change of variables (we learn the transforms) to represent different distributions. Ones that do actually have good clustering. We can be predictive in these domains. Chaos and randomness don't make inference impossible. This is the 21st century, we're not beholden to Newton nor to deterministic processes.
I think we in perfect agreement here. Like I said in my comment, we can accurately describe the shape of noise, and that’s incredibly useful for making predictions. But we can’t make absolute deterministic predictions within the noise.
I.e. we can say thing X has a 90% probability of happening. But we can’t say thing X will happen.
And of course a strong probabilistic prediction is incredibly powerful and useful. I don’t want to suggest that it isn't, but it’s not an absolute prediction. And once again usefulness of a probabilistic prediction is almost always as useful as an absolute prediction (I would also argue that absolute prediction don’t really exist in the real world, they’re just extremely high confidence probabilistic predictions, but you haven’t bothered to model the actual noise).
Sure. Pick a spot offshore and measure the water depth, to the centimeter. Moment to moment, the precise value varies chaotically due to countless transient phenomena, from the direction and strength of the wind to the heading of the last boat to come anywhere near it. However the tides can be forecast years out.
Just because weather is chaotic and unpredictable doesn't mean that climate is.
>Sure. Pick a spot offshore and measure the water depth, to the centimeter. Moment to moment, the precise value varies chaotically due to countless transient phenomena, from the direction and strength of the wind to the heading of the last boat to come anywhere near it.
That's a good analogy of my skepticism for AGW. We are saying temps went up 1c over 100 years, taking daily measurements, and comparing them to ice cores which are our measurements of historic temps... Only those ice cores aren't daily- they're thousands of years apart. So the ice cores data is irreparably smoothed in a way the recent data is not.
This is trivially falsifiable by a simple google search - "ice core resolution". You may want to reassess the general trustworthiness of your information sources.
Incidentally, the evidence for AGW, and indeed geologically sudden climate change without precedent, goes far beyond ice cores. Very little in science in predicated on lone observations that can't be cross checked. We have a wide variety of information sources that all paint a coherent picture:
Measuring historical temperatures is not unlike measuring distance. We know, for instance, that the Andromeda galaxy is 2.537 million light years away. How can we possibly make such a precise assertion so confidently? We started by measuring small, earthly distances, and used those to triangulate larger ones. We worked our way up the distance scale by a series of inferences. Thus, we built up a model of how things look at various distances. It's more or less the same type of methodology - yet mysteriously, the anti-AGW pundits don't seem to have a problem with that.
Climate scientists, believe it or not, know what they're doing. It is the height of arrogance to flippantly dismiss their efforts simply because you personally don't understand them, because you can't be bothered to research even basic things like the resolution of ice core data.
Ice core measurements give you a value for each year. You’re looking for changes in the ice that are driven by yearly seasonal changes, notably the periods of summer where the ice received 24hour of sun for weeks.
It’s perfectly reasonable to take daily measurements, average them into yearly measurements. Then compare them to yearly averages taken from ice cores.
The only reason we can even use ice cores for temperature measurements is because they’re have high enough resolution that we’ve been able to observe the impact of global temperature on the most recent layers of ice. It’s not like someone drilled an ice core one day and discovered a bunch of temperatures sharpied down the side. They drilled many ice cores, then spent months correlating observed patterns to temperature records so they could build a model that allows them to understand the link between temperature and observed patterns in the ice.
Don’t mistake a record measuring thousands of years of history for a record that only records every thousand years.
Thes resolution is yearly going back only about 2000 years. Everything beyond that, the resolution is much lower. Where is that data from and don't you see how much "smoother" it is?
Your link has a citation leading to the source article. I would recommend you read it if you’re interested (use Scihub if you need to).
The thrust of the underlying article is looking at the exact question you’re asking, can ancient ice records that have a more limited resolution of 20-500 years be compared to centennial or millennial scale data, and can you draw useful conclusions about time periods of that range.
Thank you for pointing that out, it was a fascinating read. I did observe that they said "essen-tially no variability preserved at periods shorter
than 300 years" which seems to imply that drawing a conclusion between their older data and the recent data could be perilous when looking at periods shorter than that (e.g. last 100 years)
Calculation of the future locations of astronomical bodies?
The difference is, Astronomical models can be simplified significantly, whereas climate models must be high fidelity to produce accurate results.
To me the critical question is at what fidelity are the models useful for our (humanities) purposes?
If our purpose is to predict climate and its impact we will need at least +/- 0.1C at a resolution of 100km at a timestep of 1 month. It must be possible to do mesh refinements to determine how accuracy changes with changing resolution, and then combine that with the prior resolution conditions to choose an appropriate mesh.
As far as I know most modern climate models are accurate to within a degree for at least a 30 year range. I haven't looked at the mesh sizes for a while but I'm sure someone has done something similar.
Planetary orbits. Stability is important there. But 3 and greater body problem in Newtonian mechanics is inherently chaotic. Add in spin-orbit coupling, relativity and other effects, and its a real mess, with no exact closed form solutions.
That said, we can predict, with some level of accuracy the stability of of orbits in a number of cases. Approximate solutions can be managed with various levels of theory, perturbations, and numerical simulation.
Just about any basic physical system. If you zoom into our universe enough, down to the quantum level, you’ll find completely unpredictable chaos and complexity. But just because the quantum world is pretty much unpredictable doesn’t mean the macro world is unpredictable.
A climate model can’t you whether or not is going to rain in exactly 300 years time, but it can tell you how much rain will fall during an entire year, 300 years from now.
Climate modelling is macro scale modelling to weathers quantum scale modelling. You’re operating at entirely different scales, and further you zoom out, the more you can ignore the minutia and still produce useful predictions.
Additionally taking about absolute time periods is the wrong approach. The real question is now many time steps can your model take before it starts to significantly diverge from reality. A car collision simulation will be operating with time steps measured in milliseconds to nanoseconds, a climate model in time steps measured in days to weeks. Both of these models will probably be predicting a similar number of time steps into the future, and both will have a similar number of quantised simulation areas. For the car you’re simplifying cubes measured in millimetres, for climate, zones measured in km.
Again, climate models have been measured, tested and repeatedly proven their predictive power. If empirical results aren’t enough to convince you, then what is?
This sentiment is so frustrating to read. Fundamental science is hard. Working with extremely complex systems is hard. Putting them together is extremely hard.
That does not mean you cannot do it accurately. It means you have to continually refine and improve and throw out and restart and refine and....
Scientists as a group are by far the most honest group of people you can point at. No other profession comes even close. Alluding to some dishonesty in the work behind climate change is straight up denialism. Yes some of the science has not panned out. That's how science works. It's part of the bones.
If you drive your car off the road in a mountainous region of the US, there is a very good chance that you will hurt yourself and/or damage your car. Scientists can predict this statistically. What scientists want to be able to do is predict exactly which rock will kill you or break your axle. They can’t really do that yet except to make statistical guesses about how much more likely the crash was on the road vs. off it. But we’re heading into increasingly rough country, so please let’s all try to stay on the road.
> you are modeling a complex chaotic system, you just cannot do it accurately
You can't really model anything "accurately", but you can assign a subjective (probability is subjective) probability to certain outcomes based off an analysis of your model, and what in your best judgement are the correct parameters.
This is why the papers containing uncertainty analysis of varying quality, the press releases will have some handwaving, and the articles in the newspapers will contain nothing of the sort.
>Here's the problem with climate change modeling, you are modeling a complex chaotic system, you just cannot do it accurately
Its more than that. Large models have hundreds of adjustable parameters, which each may be tweaked within reasonable ranges to simultaneously backfit historic data and potentially get any kind of future result. It's very easy to get biased outputs which look totally reasonable, which is especially dangerous if the discipline is subject to a rigid orthodoxy.
The orthodoxy's stance has been to make the most mild predictions possible for decades. And now, we're seeing worse-than-predicted extreme weather events. Go figure.
Mild perhaps, but still leaning in one direction. I don't know that there's been any evidence of more severe weather on average, other than record breaking heat which typically has some x% attribution to climate change (i.e. it would have happened regardless, e.g. recent warm weather in Texas).
The tricky part of this argument is the use of the word "accurate". Since you are talking about a statistical model, it's worth keeping in mind that this word has a mathematical definition in that context. The output of the said models have an accuracy attribute presented for them. It's not a binary value of accurate/not-accurate, and what matters is how accurate and about what specific prediction. You can argue that the modeling process for designing the breaks of your car is also not 100% accurate, and it really doesn't need to be! It needs to be accurate enough about the break's relevant properties (stopping power, wear and tear, performance in wet wheather, etc.) This is not a scientific analogy to climate forecasting, I just used it to explain the core idea in more familiar terms.
The issue is that it's hard to get these across correctly to the general audience. It requires analytical thinking and patience and attention to details, which none are compatible with how today's media and social networks are incentivized and structured (short, catchy, flashy, clickbaity, infuriating, etc.)
Climate models have shown to be very accurate predicting the climate change that we've been able to observe so far - what aspect of climate change model are you referring to?
It makes no sense to talk about the 'chaotic' nature of a system without relating to the scale and scope of the model. Weather is a chaotic system but this doesn't mean we can't predict it at all what it'll be like an hour later or tomorrow. As we scale it out further, the accuracy decreases but we can predict with high certainty that it's not going to snow in Los Angeles six months out from today.
Climate researchers don’t just make up models and then assume they are right. They actually compare them against the reach world.
For example, we have fairly detailed climate related data sets going back many thousands of years, coming from many different kinds of sources. You input these as starting conditions to a model, run the model forward, and then see how accurate its predictions are against later historical data.
Despite your feelings about is or is not possible, there is very strong research that shows that climate modeling works.
That you described there is not modeling, it's curve fitting. Run enough model, and one will track. It's survival bias tho, and offer very little guarantees to track the future.
We can look at the predictions (inference) from older models and check how accurate they were at predicting the future. No back testing required. Those models have been very accurate. Current models have only improved and reduced error.
We can test the accuracy, you have to be honest about it.
Your point that we can check prediction against reality is well taken, but honestly I think we got lucky. Knowing what we know, 50-year-old model shouldn't have been accurate. It didn't model aerosol, which we now know to be important. It's just that errors happened to cancel out.
I think you're looking to have an answer and seeking to justify that answer. There's not just one model. There's dozens of competing ones. So we have to draw one of two concussions.
If climate is only model able by chance, we got lucky a lot of times. Someone should be buying a lot of lottery tickets because the science community is way luckier than the expected value. That dozens of independent research teams were about to exactly cancel out errors in precisely the right way (but different from one another) to have good predictive power. Certainty too lucky for a purely random process. So what's the bound? Clearly this process is repeatable.
The models are accurate and science can deal with probabilities. Or rather that scientists (experts) know what they're doing.
The odds on the first conclusion are too high. The second is easier to justify. I think it's easier to justify that you're not as informed on the subject and that there's nuance you're missing that the experts know (I'm confident they know more than me).
I am saying while 40-year-old model is likely accurate by skill, it was luck 50-year-old model produced similar results to 40-year-old model. The reason is aerosol. Agreement of multiple models doesn't invalidate this because all 70s models didn't include aerosol.
Just because they didn’t include aerosols doesn’t mean they’re useless. Aerosols might be important, but they’re not the single determining factor for climate change, there are many factors.
If your model includes most of determining factors for the system your modelling, then it’s going to produce pretty good results. There will be places where the model drifts, but you still expect the model to be pointing in pretty much the right direction. As you introduce more factors, your model starts to model details with increasing accuracy, but again you don’t expect it to substantially change the macro result.
I’m short we’re perfectly capable of knowing that the warming will happen without knowing about aerosols, the direction of travel is perfectly predictable, aerosols will only change the amount of travel, and not by an order of magnitude.
Just simple extrapolating logic. Climate is chaotic. Chaotic systems cannot be forecasted years out. Therefor a computer model for climate forecasting is not going to be accurate.
This is a pretty extreme claim to make without references. Have you looked at climate change computer models? Which ones what are your credentials in making this sort of "code review"? Etc.
I don't really understand the conflict that seems to exist between the title of the article ("Do Climate Models predict Extreme Weather? ") and the body of the article, which argues that event attribution can't be done with much accuracy.
So ... "Do Climate Models predict Extreme Weather? ": yes, yes they do.
But .... "how much impact does climate change have on extreme weather?": hard to say with much accuracy, but the factor isn't negative!
How useful is it to note that the factor might 2 or 200 or 2 million?
There is a difference between asking “is it a good idea to play in the casino with the loaded dice” and “can that one particular loss be attributed to the loaded dice.” I think the second question is interesting, but it’s not really that important compared to the first question. Naturally the second question is the one that’s getting all the attention from people who want to keep playing in this particular casino. I guess that’s better than just outright climate denial, but aim not sure how much better it is.
It bothers me that the A/B in these models is between "pre-industrial" and now, seems like a better comparison over the timescales people are expecting to match up with their "perceived experiences of increased climate disasters" should be "20 years ago vs now" e.g.
We are taking a system that was in equilibrium and we introduce a huge disturbance (+ multiple degrees C). Hell yes it will be unstable until it reaches the new equilibrium (whatever that equilibrium will look like - catastrophic for life or not).
From what I know about climate on Earth, it has literally never been in equilibrium — see periodic ice ages with glaciers up to 3—4 km thick, or periods of warmer climates when plants grew in huge amounts at latitudes where we have now vast oil reserves in Siberia.
Looks to me Sabine made a fairly elementary mistake towards the end. She mentions 3 probability distribution curves, 2 model produced (blue and red) and one empirical (green). She forgot to mention a fourth one, the empirical-preindustrial. In any case, you'd like to be able to measure the ratio of empirical distributions, but let's say that's difficult because of data retention.
So, instead of having the actual ratio x/y you have a ratio of proxies, x'/y'. This ratio could be both higher and lower than x/y. She claimed it can only be lower, and that's not quite true.
I also thought that. But on second thought I agree with her analysis. The reason that y and y' are different is the same that x and x' are different. It is a problem of lacking resolution, so it will always underestimate more on the higher value, at least if you do enough simulations to get enough samples.
It's not just the frequency of extreme events that is going up, but the intensity of the most extreme events. That is a function of the shape of the tail of the distribution. A very small change in the mean or median, or even just the standard deviation, can result in a huge increase in the expected value of the extremes.
Combine this with human psychology and you have a recipe for catastrophe -- literally. You get an extreme event, and then everything reverts to the mean and things seem normal for a while, and people assume that the extreme event was a fluke. It wasn't. It's the new normal, and the next extreme event will be even worse.
You see this playing out in California right now. Everyone thinks the drought is over because it rained last month. It's not over. It's just getting started.
To borrow a phrase, one temperature reading is weather, a million is climate. While it seems difficult to attribute any given extreme or uncharacteristic weather event to climate change, the fact that average temperature is increasing means a larger average kinetic energy in the atmosphere, which in the aggregate should lead to more extremes. Of course there is more to it than that, since the Earth's albedo and atmospheric composition is also changing. Even more worrisome the ph of the ocean is changing, which could have profound consequences for oxygen producing phytoplankton, which again affects all of the above and us.
Not a mathematician but I wonder can these discrete perturbed and repeated simulations approximate some well defined probability measure over the actual system?
the climate models stink, and everyone in the science understands this but has difficulty communicating this.
The real world is more extreme than the models. But we have no way to extend the models to cover the seen extremes, nor do we know how to cap the possible extremities in any model.
We’ve terraformed the crap out of the world in ways that are guaranteed to have massive and significant effects, and climate scientists are racing to try to predict where the worst impacts will fall. This is an incredibly difficult thing to do because while in the aggregate the damage we’ve done is visible, weather is chaotic and the problem is near impossible. Instead of seeing modelers as our best, if highly imperfect, guides to weathering this storm we are (predictably) using the imperfection of their models as an excuse to do nothing about the problem, even though the overall effect of our actions is extremely obvious.
There are things we can reasonably do something about in a reasonable time period.
There are things we cannot (as it sits right now) without killing large numbers of people and possibly ending civilization as we know it.
What might happen isn't as important as what will happen. This isn't necessarily evading the problem but it's also inexcusable to not make the changes we can. Wasting non renewable fossil fuels and the amount of litter and pollution we are producing are horrible no matter the ultimate reality of climate change (which I maintain is poorly understood and over-hyped right now).
There are real problems that are well understood we aren't doing anything about while wringing our hands about that which we practically can't at this juncture.
So in this case the modeler just needs to tell the model to (1) accurately calculate the degree to which the Earth's climate will change in the next few decades and (2) give a comprehensive global forecast of the accompanying extreme weather events.
I am unable to find any 1982 Exxon prediction that would meander like the line in that graph. The only plot I was able to find is this [1] from this 1982 Exxon report [2], which is just a slightly curved upward rising sketch, not nearly as detailed as in the xkcd graph.
> the Exxon model from 1982
I also failed to find any signs of there having been any "1982 Exxon model". What [2] is, is a 39-page report, a synthesis and review of publicly available climate research. The list of references is quite extensive: 10 pages.
Oh right, I misinterpreted the xkcd plot. It's only the single (x)-marked dot in the top right of the graph, that is the Exxon prediction. Not the graph itself.
It's the very definition of how computer models work. The modeler writes the program, determines all the inputs, determines how the logic of the program will respond to the inputs, etc. The modeler is in complete control of the model, tells it how to behave, and thus the model says only what the modeler tells it to.
> Are you implying that climate change isn't happening?
Upthread there’s a climatologist openly admitting to lying to make their point seem more dire. The whole field has a credibility problem. I don’t blame laypeople for their skepticism.
You add more physical parameters. Climate models are not parameter fitting from the past data, they are physical models. Of course you test them with old data too, but they are mainly for verification and controlling for variables that are still missing.
They test these models with other planets too. Set in parameters for Mars or Venus and they precinct pretty well how those climates behave.
One way is to verify them. Run multiple variations on data from the 1950's and see how they compare to measured values up to today. Throw away the models that don't fit the measured values. Then you have some confidence that the remaining variations may be representative when run into the future.
This is also done in weather forecasting, though maybe they run the model in reverse, I can't recall. Same idea though, verify with existing data before predicting.
Yes this happens, but it is very controversial. To quote from the article I linked above:
> The question of whether the twentieth-century warming should be considered a target of model development or an emergent property is polarizing the climate modeling community, with 35% of modelers stating that twentieth-century warming was rated very important to decisive, whereas 30% would not consider it at all during development. Some view the temperature record as an independent evaluation dataset not to be used, while others view it as a valuable observational constraint on the model development. Likewise, opinions diverge as to which measures, either forcing or ECS, are legitimate means for improving the model match to observed warming. The question of developing toward the twentieth-century warming therefore is an area of vigorous debate within the community.
Not going to lie but it sounds like there is significant disagreement among scientists on evaluating climate models?
And do I read it right that 35% of scientists deem it important that a model indicates future warming? Science doesn’t work that way, you don’t get to choose a conclusion then model for it.
Yes, there is (and continues to be) significant disagreement among scientists on evaluating climate models. This is normal and has always been the case from the very beginning of climate models.
No, you read it wrong. 35% thinks it appropriate to fit to past warming. Of course nobody is suggesting to fit to future warming! But past warming is observed.
If you are familiar with training/test split in machine learning, some thinks past warming is in training set (and test with things like reproducing ENSO), and some thinks past warming is in test set. I think it boils down to disagreement over how determined was twentieth-century warming.
I believe the climate it changing. I believe human activity has contributed to these changes. However, I do not believe _every_ "extreme event" belongs in that category.
For example, the recent fires in the Denver / Boulder. Have you been there? It's effectively a desert. Dry, very dry. Open. There are few natural barriers once a fire starts. Based on what I know of the area, those homes - God bless those families - were built in harm's way. That disaster - regardless of climate change - was inevitable. To suggest otherwise is simply irresponsible.
Against? The point is, the state of that state in that area is naturally a fire hazard. Toss in some wind - which is as old as the earth itself - and POOF! disaster happens.
Humans love to build homes and structures where they probably should not. We have this false ideal that we're bigger, stronger, and faster than Mother Nature. And then when it goes sideways we blame MN. That's not working out well.
Obviously no, because the factors that would cause extreme weather events are not yet there.
In general, any abstract model has the same relationships to actual reality as cartoons - they look sort of plausible and even convincing, even when a cat or a mouse speaks perfect English.
There should be a clear warning statement, like with GMO labeling, that models are astrology with numbers instead of planets and stars.
And no, no matter how sophisticated, a model will never be more than a cartoon, in the same way that a map would never be a territory.
Climate is a ~30yr moving average of weather. Climate models cannot attempt to model weather events. Such events can rarely be predicted more than a week or two out.
The above should tell you that using a climate model to predict anything about weather events is a pointless exercise. Particularly trying to derive the statistics of weather events. The stats are built in to the models!
This can be easily shown to be false. I can't tell you if it's going to rain next week in most cases, but I can easily tell you about how many days in a year it's going to rain over a large area. Same applies to how many floods we're going to have and bushfires. We've been doing that for a very long time now.
Telling me about averages deduced from past behavior? Great, you've read the statistic that's built into a model. Telling me what way that statistic will behave in future? Especially at extremes? Not a chance.
We're commenting on an article pretty much saying we have models which estimate in the right directing, but don't go far enough. Now we can learn from the changes and improve the models.
If you're discounting a whole field of research, can you provide more reasoning for why it can't be improved from the current results?
Basically she's saying the current models would be poor tools to use for deciding what to do about climate change and moreover the media is too simplistic and confident when they make extreme weather attribution. But that is nothing new --hence Gell-Mann amnesia effect.
>"And that is what I think is the biggest problem. The way that extreme event attributions are presented in the media conveys a false sense of accuracy. The probabilities that they quote could be orders of magnitude too small. The current climate models just aren’t good enough to give accurate estimates."
I think Sabine's point is more limited than "current models would be poor tools to use for deciding what to do about climate change", and more like "current models would be poor tools for extreme weather attribution".
Of course, if your threshold for action on climate change is strong extreme weather attribution, then ok. But I think it's reasonable to also have lower or different thresholds for planning nad action.
I do feel queasy about the current phrasing of extreme weather attribution, especially as portrayed in the media.
To give an example. Let's say there's a 1% chance every year that there will be a catastrophic wildfire in some particular area. Now let's say climate change increases a chance of drought in that area. Then we get a wildfire one year and the attribution is (say) "20% due to climate change" -- which I guess means there is now a 1.2% annual chance of a catastrophic fire.
But what if climate change causes more rain in the area? And, for the sake of argument, that causes the change of catastrophic fire to go down to say 0.8%. Then, in a given year, there is no fire. No one is going to bother doing an attribution study to say, "Climate change is 20% responsible for the fact that we didn't have a fire this year."