Sometimes it’s never good enough

I’ve, in the past, suggested that climate scientists could end up being criticised whatever happens. If the impact of climate change ends up being less severe than it could have been, climate scientists will probably be criticised for being alarmists. This will probably happen even if the reason why the impacts were less severe was because we actively did things to limit our emissions and to adapt to the changes that were unavoidable. On the other hand, if climate change does end up being severely disruptive, climate scientists will probably be criticised for not speaking out enough.

I may, of course, be wrong and most commenters may appreciate that giving scientific advice about a complex topic is very difficult and that scientists can’t really be held responsible for the decisions that were made. I have a suspicion, though, that we might be about to get some idea of whether or not this is likely on a much shorter timescale than would be the case for climate change.

My guess is that those giving scientific advice about the coronavirus may end up in a similar position. If the mitigation strategies are successful at limiting the impact of the virus, they’ll probably be criticised for suggesting strategies that were too extreme. On the other hand, if the impact is extreme (as I hope it won’t be) they’ll probably be criticised for not having spoken out early enough, or for not having suggested more stringent constraints.

Again, I may be wrong, but it will be interesting to see what happens once this crisis is mostly over. We might expect some criticism from some of the more vocal media critics, but it will also be interesting to see the response from some of the more vocal policy experts. In particular, from those who spend their time suggesting that scientists are naive for thinking that there is a simple path from scientific advice to policy making. You’d like to think that they would appreciate the complexity of this situation and realise that if there isn’t a simple relationship between science advice and policy, you can’t then simply judge the scientific advice on the basis of the effectiveness of the subsequent policy. You might, of course, be wrong.

We’ll have to wait and see. Whatever happens, it will probably still be an opportunity to learn something about the complex relationship between scientific advice, policy making, and how this is then received by the broader public.

Richard's Decoupling

Richard did it again and forgot to say “oops”:

It’s one thing to deplore eugenics on ideological, political, moral grounds. It’s quite another to conclude that it wouldn’t work in practice. Of course it would. It works for cows, horses, pigs, dogs & roses. Why on earth wouldn’t it work for humans? Facts ignore ideology.

— Richard Dawkins (@RichardDawkins) February 16, 2020

Richard’s a high decoupler

Negative feedback was to be expected. Some argue that only by decoupling can we understand Richard’s point. Facts don’t care about feelings and all that jazz.

I love thought experiments. They seldom work, but I love them nevertheless. Let’s risk a few, with Richard himself as our main character. Applying decoupling to the decoupler reveals how self-serving it can be.

§1. A Muslim child is about to fall in a well. You are Richard Dawkins, and could save him without much effort. Do you (a) feel distressed (b) tweet about Islam?

§2. Richard Dawkins has experienced all of human morality except decency. Would he be able to fill in the concept of decency using his own tweets?

§3. You are abducted and tied to Richard Dawkins so that he can stay alive. He may need your blood or your kidneys. The procedure does not hurt you. You just need to stay in that foreign location for a year. Do you think you are morally obliged to stay, free to go, or allowed to eat him?

§4. The Experience Machine can give you any experience you like or want. You could for instance make Richard Dawkins realize how silly his Gedankenexperiment usually sounds. Would you plug yourself to this machine forever and be free to imagine the rest of your life however you please?

§5. You are Richard Dawkins and take part in an experiment. Researchers put you to sleep with a drug. If you tweeted no bad takes during the weekend, you will wake up Monday, otherwise only Wednesday. What are your odds for seeing Monday?

§6. If Richard Dawkins follows a rule that turns him into delicatessen, of what use was the rule to him?

§7. Richard Dawkins is not saying that mass extermination via virus infection is a Good Thing. But you got to admit it would work.

***

Thought experiments help illustrate claims but don’t replace making them explicit. In this post I would argue that decoupling can easily lead to dogwhistling as themes carry connotations. When a high-decoupler with a big following entertains ideas about eugenics that could work, distanciation cannot hide that the ideas entertained are not value neutral.

Besides, there’s no fact of the matter regarding Richard’s eugenic suggestion, hence why Richard relies on a counterfactual in the first place. Even if we grant him that what goes for cows and dogs goes for humans (which is far from being obvious), Richard needs a set of policies.

If you ever feel like decoupling, please mind your audience.

Addendum. I adapted many thought experiments from Helen’s post. It’s good. Go read it.

Responsible SciComm

Yesterday, a group in Oxford released a paper that implied that a signifcant fraction of those in the UK may already have been infected. This was quickly picked up by numerous media outlets who highlighted that coronavirus could already have infected half the British population. James Annan has already discussed it in a couple of post, but I thought I would comment briefly myself.

To be clear, I certainly have no expertise in epidemiology, but I do have expertise in computational modelling. So, I coded up their model, which is described in Equations 1-4 in their paper. They were also doing a parameter estimation, while I’m simply going to run the model with their parameters.

The key parameter is , which is the proportion of the population that is at risk of severe disease, a fraction of whom will die (14%). They explicitly assume that only a very small proportion of the population is at risk of hospitalisable illness. Consequently, they focus on scenarios where the proportion requiring hospitalisation is 1% () and 0.1% (). The Figure on the right, which considers , , and , is from my model and seems to largely match what’s been presented in the paper.

The curves that start at 1 and then drop are the proportion of the population that is still susceptible (left-hand y-axis) while the diagonal straight lines are the logs of the cumulative deaths (right-hand y-axis). I’ve also shifted the models so that the latter overlap. This Figure illustrates why this study was picked up by the media. Cumulative deaths to date is just over 400. If the proportion of the population at risk of hospitalisation is small () then just over 30% of the total population would still be susceptible. In other words, more than half of the UK population would already have been infected. On the other hand, if the proportion at risk of hospitalisation is large () then the proportion susceptible is still large () and the fraction that has already been infected is small.

One way to estimate is from the date at which the first case is reported. If is small then the lag between the first case and the first death is larger than if is large. The paper implies that the current data is more consistent with a small than a large . The problem, as this critique highlights, is that this implies that this first case is the progenitor of most of the subsequent cases. Given the small numbers involved, this may well not be the case, since a localised outbreak may not have taken hold. Hence, there doesn’t really seem to be strong evidence in support of being small and, consequently, there is little evidence to suggest that a significant fraction of the UK population has already been infected.

Okay, despite the lengthy pre-amble, this is really what I wanted to focus on in this post. I think it’s perfectly fine to play around with models and to try and estimate various parameters. However, especially when the results have societal significance, it’s very important to be clear about what’s been done when presenting the work publicly. This research has not demonstrated that more than half the UK population has already been infected, it’s simply illustrated that it’s possible. Clearly if most of the UK population has already been infected, then this virtual lockdown could probably be relaxed. However, if is not small, then the lockdown would seem justified. As James points out in this post, even though the paper implies that the current data is consistent with being small, there do seem to be regions where this seems not to be the case.

So, I think it’s highly irresponsible to present a result like this without being extremely careful to minimise the chances of it being misconstrued. It’s clearly not possible to completely avoid research being misrepresented, but researchers do – in my view – have a responsibility to ensure that this not an easy thing to do. It would be great if the impact of this virus is far less severe than we currently think. However, until we have more evidence to support such a conclusion, we really should be very careful of presenting results that imply that this is the case.

Addendum:
This post ended up being much longer than I intended. I was mostly wanting to highlight how I think the presentation of this result was highly irresponsible. The first bit was just meant to illustrate what they’d done in their model. Since I’m not an expert in this field, and have no interest in spreading misinformation about an important topic, if any experts think I’ve made some kind of mistake, feel free to point it out.

I also wanted to post another figure, which is essentially the same as James highlighted in this post. The curves that rise and fall are the number of people who are infectious (left-hand y-axis) while the curves that rise and then level off are the cumulative deaths (right-hand y-axis).

This again illustrates (given that cumulative deaths to date is just over 400) that if the proportion requiring hospitalisation is small () then the number of people who have already been infected is already quite high, while if the proportion needing hospitalisation is large () then the number of people who have already been infected is much smaller. It also illustrates that the overall cumulative deaths depends quite strongly on this parameter; if we relax current conditions based on this work and it turns out that isn’t small, the impact could be substantial.

In the interests of transparency, if you would like to codes that produced the two figures, you can download them from here.

Some thoughts about science advice

I hope everyone is keeping well and listening to all the advice which, in the UK, is basically to stay at home and to only go outside for food, some exercise, or to go to work (where this cannot be done from home). Also, wash your hands. Although I am trying to work from home, it’s not something I’m particularly good at at the best of times, and these are not exactly the best of times. As such, I have plenty of time for thinking about possible blog posts, but I find it hard to know what to actually write about. It seems that there are currently more important things to worry about that people misrepresenting climate science, but I don’t really feel that I have the expertise to write about the current topic.

I also don’t particularly like making associations between our current situation and how we might address climate change. What we’re doing now might lead to a reduction in a emissions, but this isn’t something to be particularly happy about. We’d really like to reduce emissions in ways that aren’t nearly as disruptive and that don’t lead to substantial suffering. What we’re doing now isn’t – in my view – a blueprint for climate action.

However, there are some aspects that I have found of interest. It certainly seems that we are capable of making difficult decisions, and committing substantial resrouces, when it becomes clear that we need to do so. We certainly seem to be doing things now that, until recently, many would probably have regarded as being impossible. Although there has been some pushback, it currently seems rather muted; most seem to accept the need for what we’re doing.

The role that science advisors have played has also been interesting. Anyone involved in the public climate debate will be aware of the constant reminders that science can’t tell us what to do. Although this is clearly true in a literal sense, it does seem as though this is a case where the scientific evidence makes it pretty obvious what needs to be done. Of course, it’s not that we’re now ignoring our values, it’s that it’s pretty obvious that a strategy that will lead to a large number of unavoidable deaths is simply not acceptable. So, maybe the linear model does essentially work in some circumstances?

The complication, however, is that we’d probably like to be making decisions that help us to avoid getting to the stage where what we need to do is obvious. However, if we haven’t yet got to that stage, there will not only be more disagreement about what we should do, but it will also be more difficult to convince people to do things that might be inconvenient and disruptive. Maybe we’ll come out of this whole situation with a better appreciation of the role of science advisors and a improved understanding of the need to sometimes make difficult decisions before it becomes obvious that we really need to do so?

On the other hand, maybe we’ll see this as rather unprecedented and will simply hope that we never have to do anything like this again. Some combination of the two would be my preference; learn something from this about the role of effective science advice, while also hoping that we don’t have to do anything like this again. Anyway, this is just some thoughts I’ve had about this. I’d be interested in what others think and, since this is a time of isolation and/or social distancing, feel free to use the comments as a pleasant communication channel.

A physicist for president?

Jim Al’Khalili has an article in Scientific American called [a] physicist for president? Jim is a physicist, so he’s probably being somewhat provactive. Also, he’s mostly arguing for someone who applies the scientific method to thinking and decision-making and is largely motivated by current events. However, I do think the suggestion is really rather silly.

credit : xkcd

I certainly don’t think that physicists are somehow better at avoiding motivated reasoning than others; it’s not as if physicists aren’t amongst some of the most well-known climate sceptics. Also, as Arthur points out in a comment on Stoat’s post that covered this, it’s not as if physicists are noted for their humility. The last thing I think we need are people who regard themselves as so clever that they don’t think they really need to listen to other experts.

There are, however, a couple of more fundamental issues with the basic suggestion. Scientific evidence doesn’t tell us what decisions we should make. So, just because someone has a good grasp of the scientific method doesn’t immediately mean that they would then make the optimal decisions. I think I understand climate science relatively well, but I certainly don’t think that means that I now know what we should do about climate change. Scientific information can, and often should, be an important part of decision making, but it’s far from the only relevant information. We need to consider what we should do, if anything, how we should do it, and the possible implications of doing so (there are almost always consequences to decision making).

On top of that, I think it would be dreadful if our political leaders were people who thought that decision making simply required considering the scientific evidence. I think it’s important that our political leaders have some kind of ideology; their political leadership should be motivated by how they think our societies should be run, not simply by a sense that they can consider some evidence and then make decisions. This doesn’t mean that we all have to agree with their ideology, just that they should have one.

To be clear, I do think that there will be occasions when the scientific evidence should play a key role in decision making and maybe even some occasion (like now) where the evidence indicates pretty clearly what we should do. However, this really requires a leader who is willing to listen to experts and who knows when to put their ideology to one side and make what might be difficult decisions. The problems we might be currently facing in the UK and the US aren’t a consequence of our leaders not having any scientific training; it’s because they’re largely unsuitable for the role. No amount of scientific training would overcome this.

Andrew Dessler rebuts Roy Spencer

Most of the focus at the moment is rightly on the coronavirus. Since I have no relevant expertise whatsoever, all I’ll say is that I hope everyone is doing their best to stay safe, and listening to the advice that’s being given. Instead, I thought I would post this short video by Andrew Dessler, in which he rebuts a recent presentation by Roy Spencer. What I found interesting was how often Roy Spencer would say things that sounded like they were directly supported by the scientific evidence, but were really just his opinion about the significance of the evidence. For example, “there’s no climate crisis”. People are perfectly entitled to believe this, but scientific evidence alone doesn’t determine if something is a crisis, or not; that’s a judgement that we make, based on the evidence available.

Of course, many will claim that this is, or is not, a “climate crisis” without always being clear that this is their judgement/opinion. When it comes to activists, and others who advocate for specific policies, I tend to think that this is fine. They’re obviously presenting their opinions/judgements; I don’t think they need to make this explicit. Scientists, on the other hand, are speaking from a position of authority and really should distinguish between what they can conclude directly from the evidence (adding greenhouse gases to the atmosphere will lead to global warming) and what judgements they might make, given the evidence (it’s a climate crisis).

Enough from me. Andrew’s video rebuttal is below.

A couple of highlights

Since I haven’t had much chance to write anything recently, I thought I would briefly advertise a couple of papers that may be of interest to my regular readers. One is by Clare Marie Flynn and Thorsten Mauritsen and is [o]n the Climate Sensitivity and Historical Warming Evolution in Recent Coupled Model Ensembles and compares the CMIP5 and CMIP6 models ensembles.

The CMIP6 ensemble suggests a shift towards a higher equilibrium climate sensitivity (ECS), when compared with the CMIP5 ensemble. The Flynn & Mauritsen paper illustrates that this can’t be due to chance, suggesting that the CMIP6 mean ECS is indeed highly unusual. Consistent with the paper I discussed here, they seem to find that the shift in ECS is mostly due to an increase in the shortwave cloud feedback, mostly in the Southern extratropics. Even though there is a shift to a higher ECS, they also find that none of the models with a Transient Climate Response (TCR) above 2.5^oC matches the post-1970s warming. This seems broadly consistent with the results from the paper I discussed in this post.

Probability distribution of the greenhouse gas attributable effective climate sensitivity for the periods 1862-2012 (top) and 1955-2012 (bottom). [Credit: Tokarska et al. (2020)]

The other paper I thought I would highlight is Observational constraints on the effective climate sensitivity from the historical period, by Kasia Tokarska and colleagues. They make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. They then use this, together with an energy balance model, to infer the effective climate sensitivity (which they refer to as ). As shown in the figure on the right, they find a 5-95% range of 1.3^oC – 3.1^oC for the period 1862-2012, and 1.7^oC – 4.6^oC for the period 1955-2012.

For the two time periods, the median values are 2.0^oC (1862-2012) and 2.8^oC (1955-2012). However, they do highlight that [O]ur estimate of is lower than the documented ECS of some climate models (e.g. CMIP5 multi-model mean ECS of 3.22^oC; Forster et al 2013), including that of some used in the analysis. However, it is well understood that time-dependent feedbacks might render lower than at equilibrium. This is because lower values for than at equilibrium can be explained by the effects of changing strength of the feedbacks at higher levels of warming.

That was all I was really going to say. Both papers are open access, so I’d encourage those who are interested to read them in more detail.