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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 p

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.

joe_the_user 5y ago
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.
- masteranza 5y ago
  It is not extreme at all. Sabina is using Luboš Motl wisdom as her own for some time now anyway.
- deleted 5y ago
  [deleted]
WoahNoun 5y ago
Examples? This is a pretty dubious claim.
- fasteddie31003 5y ago
  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.
  - yed 5y ago
    Climate is not chaotic, weather is chaotic. Without any models at all we can predict that the Sahara desert will be hot and dry many many years out.
twofornone 5y ago
>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.
- klyrs 5y ago
  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.
  - twofornone 5y ago
    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).
    - klyrs 5y ago
      > I don't know that there's been any evidence of more severe weather on average... Have you looked? Because the data is available. https://e360.yale.edu/digest/extreme-weather-events-have-increased-significantly-in-the-last-20-years https://e360.yale.edu/digest/extreme-weather-events-have-inc...
avianlyric 5y ago
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.
- fasteddie31003 5y ago
  Can you give me another example of a chaotic system that can be correctly forecasted years out?
  - dTal 5y ago
    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.
    - hammock 5y ago
      >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.
mhh__ 5y ago
> 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.
deleted 5y ago
[deleted]
godelski 5y ago
This is verifiably false. 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. https://www.science.org/content/article/even-50-year-old-climate-models-correctly-predicted-global-warming https://www.science.org/content/article/even-50-year-old-cli...
- sanxiyn 5y ago
  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.
  - godelski 5y ago
    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).
    - sanxiyn 5y ago
      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.
a_square_peg 5y ago
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.
Misdicorl 5y ago
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.
geraneum 5y ago
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.)
matthewdgreen 5y ago
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.
jounker 5y ago
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.
- avereveard 5y ago
  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.

joe_the_user 5y ago

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.

masteranza 5y ago
It is not extreme at all. Sabina is using Luboš Motl wisdom as her own for some time now anyway.
deleted 5y ago
[deleted]

WoahNoun 5y ago

Examples? This is a pretty dubious claim.

fasteddie31003 5y ago
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.
- yed 5y ago
  Climate is not chaotic, weather is chaotic. Without any models at all we can predict that the Sahara desert will be hot and dry many many years out.

twofornone 5y ago

>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.

klyrs 5y ago
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.
- twofornone 5y ago
  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).
  - klyrs 5y ago
    > I don't know that there's been any evidence of more severe weather on average... Have you looked? Because the data is available. https://e360.yale.edu/digest/extreme-weather-events-have-increased-significantly-in-the-last-20-years https://e360.yale.edu/digest/extreme-weather-events-have-inc...

avianlyric 5y ago

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.

fasteddie31003 5y ago
Can you give me another example of a chaotic system that can be correctly forecasted years out?
- dTal 5y ago
  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.
  - hammock 5y ago
    >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.

mhh__ 5y ago

> 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.

deleted 5y ago

[deleted]

godelski 5y ago

This is verifiably false. 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. https://www.science.org/content/article/even-50-year-old-climate-models-correctly-predicted-global-warming https://www.science.org/content/article/even-50-year-old-cli...

sanxiyn 5y ago
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.
- godelski 5y ago
  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).
  - sanxiyn 5y ago
    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.

a_square_peg 5y ago

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.

Misdicorl 5y ago

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.

geraneum 5y ago

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.)

matthewdgreen 5y ago

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.

jounker 5y ago

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.

avereveard 5y ago
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.