You mean a massive harvesting operation like the lumber industry?
No. Much more massive than that, if you want to really make a dent in CO2.
Historic deforestation hasn't contributed all that much to atmospheric CO2, compared to fossil fuels, so an equivalent reforestation (and subsequent sequestration) wouldn't decrease CO2 much, either. Not unless you forest much larger tracts of land than originally had forests (where?). Well, you can get a boost by harvesting early and replanting, since young stands sequester carbon at a faster rate than average, but this isn't an order-of-magnitude improvement.
Well built buildings can store wood for hundreds of years,
Irrelevant on the scale we're talking about. We're talking far more trees than we have need for building lumber. (Otherwise the existing lumber industry would already be sequestering large amounts of carbon, and it's not.)
and when it comes time to replace them, you convert them to biochar, which lasts hundreds more years.
Yes, biochar would help, but I still don't think you appreciate the magnitude of the effort required. Realistic (actually, in my opinion, optimistic) biochar burial estimates I've seen put the maximum sequestration rate at about 1 gigaton of carbon per year, which is about 0.5 ppm CO2, so -50 ppm/century. But we've already added 100 ppm and will likely add several hundred ppm more this century.
Now, I don't know if this artificial tree idea can do any better, but my point is that real trees aren't going to solve the problem, even if you scale up the industry. And you have to scale up the industry a lot over commercial logging (except without the profit, because we don't need that much more lumber).
Wouldn't it make more sense to plant more trees instead, and spend the rest of your time and money on cleaner and more efficient methods of powering well everything?
It would make more sense not to put the CO2 into the air in the first place. But if you're going to pull it back out again, it remains to be seen whether artificial trees or real trees have the best cost/benefit ratio. It takes really, really huge forests to make a dent in atmospheric CO2 and reforesting the whole planet won't get CO2 back down to pre-industrial levels any time soon. Maybe a large industrial operation could do better, if the tech improves and economies of scale set in.
Besides which, the trees just release all the CO2 back to the atmosphere when they die and decay, so you need a massive harvesting operation to cut them all down and convert their carbon to a more permanently bound form (e.g., biochar). That massive harvest operation also will require large amounts of energy, time, and money.
Trees don't really control the amount of water vapor in the atmosphere; they don't sequester a large amount of water for a long time. To first order, the amount of water vapor in the atmosphere is controlled by temperature.
Hating ice ages doesn't mean liking global warming. If you want to prevent the planet from cooling into an ice age, you don't need to warm it up above present temperatures. You just have to keep it from cooling below present temperatures.
Human civilization has adapted itself to a relatively stable range of climate over the last 10,000 years. Large warming or large cooling pushes us outside of that range. It may be costly to adapt our civilization to a completely different climate, particularly if it happens "fast" (century time scale). Thus, it's possible to hate both global warming and "ice ages".
If you want to use the greenhouse effect to prevent the planet from falling into a glacial period, then you should want to save fossil fuels for when we need them, rather than using them up now, when we don't. That is, dole them out slowly over thousands of years to keep the interglacial climate stable, as the next glacial period gradually deepens, instead of our current course of using them up rapidly and elevating temperatures well above the Holocene climate range.
Besides which, this study is controversial. Everyone agrees that we will see another glacial period someday, barring human intervention. The question is when. This study suggests 1500 years; a number of others have suggested that the next glacial period isn't due for as long as 50,000 years. Which is even less of an argument for global warming.
We must be increasingly on the alert to prevent our enemies from taking over our satellite fuel lines, thus knocking out our military communications. Mr. President, we must not allow a dirty rag gap!
A cluster of stations within a 50 mile radius at about 2/3 warming and 1/3 cooling.
Again, so what? This does not imply there is anything wrong with the data. You are deeply confused about what should happen, meteorologically speaking, on microclimatic scales.
Instead, what they're doing is the same crap science we see so much of these days; gather a bunch of data and look at it for all kinds of things after the fact. There's value to that, because it can tell you what you should look for next time; but it should never be confused with science.
I think that's the stupidest thing I've read on Slashdot.
Where do you think new theories and discoveries come from, anyway? Scientists do experiments, not knowing ahead of time what they're going to find, and find something new. There would be no point in doing experiments that can only confirm what we already expect to find.
Gravity can be explained by these equations but I don't know how it works or why. It's useful, but it's inaccurate and it's not science.
This equally clueless. Gravity is one of the most accurate theories we have, and the fact that you don't understand it has no bearing on its scientific validity.
Of course it's going up, NASA has confirmed that with satellite information as well as several other sources all showing quite clearly that the temperature is rising.
News flash: the satellite and surface station temperature records closely agree.
Basing models on data that is at least 1/3 bogus is fucking stupid
News flash: "data that shows cooling" != "bogus data". Parts of the Earth do cool from time to time, you know (and are expected to, even with the enhanced greenhouse effect). The satellite data shows this as well.
NOAA puts a LOT of weight on the land-based temperature data in their models.
Climate models usually don't use temperature data at all (i.e., it's not an input to the model). They're run freely using only the forcing data (greenhouse gases, solar varations, aerosol loadings, etc.) and allowed to predict their own temperatures, without reference to any temperature observations.
When they are initialized with temperature data (in "data assimilation" mode), land gets exactly the weight it should: about 30% of the Earth's surface.
Temperature observations are used for testing the predictions of climate models, but the above remains true: land data gets exactly as much weight as its area average. And models are compared to a variety of temperature records (surface and satellite), not that it matters much, since (as noted above), they all agree pretty closely.
I would like nothing more than accurate climate models but we'll never get them until people admit that the data we have is shit.
As amply demonstrated above, you have no idea what you're talking about.
Virginia Tech developed the data acquisition hardware that the CICAS-V project used to collect the dataset. That probably influenced the choice of location. The MIT paper apparently used the data because it was convenient, and did not mention any interaction with Virginia Tech researchers.
This basically makes 3rd-party software - like you get from Fink, for example - non-existent, as far as a Mac user is concerned, because all software for Macs will have to be retrieved from this "app store".
You're spreading FUD.
Software for Macs will NOT have to be retrieved from the app store only. This does not kill 3rd-party software or Fink. This announcement ONLY applies to applications that are voluntarily listed in the app store by their developers. Developers do not have to use the app store to distribute their apps.
It is possible that Apple may someday require all apps go through the app store, as you suggest, but that's not what this announcement is about.
The key is that the further into the future they look, the more uncertain the results of modeling chaotic system become, whether you average them or not. All averaging does is abstract away the underlying behavior, and in the absence of any additional information (variance for instance) is essentially useless for drawing conclusions from the model.
The previous poster is correct: chaos limits your ability to predict the exact state of the system (e.g., the weather over Sydney in 2093), but it doesn't necessarily limit your ability to predict statistical averages (such as the global surface temperature). Certainly there is wide uncertainty in predicting global average quantities, but this is generally not related to chaos. It's more a function of model structural errors and input parameter uncertainties.
Now I'm sure climate scientists are publishing a little more complete statistical analysis of the results of their modeling experiments. However, when they communicate their findings to policy makers and the general public, they seem to have some difficulty expressing the full scope of such an analysis, and instead point to the average or possibly a most likely outcome without the benefit of the additional information which is necessary to properly contextualize the single number.
A full uncertainty analysis of climate models has been difficult because of their complexity. (You see the same problems in many other fields that rely on large computer models.) But there are statistical uncertainty analyses of climate models, and the IPCC has been continually adding more discussion of uncertainties, error bars, etc. to its reports including summaries for policymakers.
The only way to validate them is to continue to tune them against the historical record.
Climate models are generally not tuned to the historical record, in the sense of fitting them to a historical temperature time series or something. They are tuned to data, however. This is a bit subtle, so let me elaborate:
Typically, climate modelers don't try to tune the entire model at once. They isolate subcomponents of the model, such as the cloud parameterization, and tune that. And they usually don't tune it to time series data or trends. Rather, they try to tune the submodel to the mean climate state over some period of time (e.g., to reproduce the average cloud cover in the 1990s).
This does have potential for overfitting, but by tuning subcomponents individually, they reduce the potential for compensating errors between components, and by tuning to base climate instead of climate trends, they try to keep the tuning independent of the human changes or "forcings" which occur over longer periods of time. It also allows for "independent" validation on later periods of time beyond the period of baseline climatology.
In addition, climate models can be "validated" against completely different periods of time that were not used in any tuning exercise, such as to reproduce the climate of the Last Glacial Maximum, although this is only approximately a validation due to data and input uncertainties.
In short, no, you can't truly validate a climate model's predictions for the next century without just waiting a century. (You also can't avoid tuning the model, which will always have unknown effective parameters that can't be calculated from first principles.) But you can build some confidence in the model physics through weak tuning, separation of concerns, and testing of subcomponents on independent data.
Chaos isn't the problem being discussed in the article, nor is it a problem for long-term climate predictions. The problem with both the geophysical models discussed in the article, and climate models, is that historical data aren't sufficient to eliminate the uncertainty in model parameters.
In extreme cases (non-identifiable models), you lose all predictive skill. In milder cases, you simply get wide uncertainties. For example, in climate models the parameter identifiability problems mean that the climate sensitivity (predicted response to doubling atmospheric CO2) is uncertain by a factor of 2 (the "canonical" range of this parameter has been 3 +/- 1.5 degrees C). This uncertainty will go down over time, as more observations are made, but to some extent it is an unavoidable limitation of finite data.
The point of the article is not that economic models are flawed (don't represent reality), but that models can give wrong predictions even if they're perfect (accurately represent reality), due to unavoidable uncertainty in their inputs. I go into more detail in this comment.
I really don't see anything insightful in the article; it looks a lot like circular reasoning - that models built to fit events X Y and Z will fit X Y and Z well. This is fine and dandy till A B and C come along.
The point of the article is commonly known, but slightly subtler than your interpretation: it's that events X Y and Z may be uninformative about A B and C even if the model is perfect and is capable of making perfect predictions (if the inputs are known, which is the problem, because X Y and Z don't let you infer the inputs).
So small changes in inputs can produce big, unpredictable changes in the output of complex systems?
The article is actually about the exact opposite: when big changes in inputs produce similar outputs (and therefore you can't use the output to infer what the inputs were).
Yes, situation (ii) is the one I tend to encounter in practice. It may also be the situation that TFA is describing, which could be this paper. In that study, they find a very multimodal objective function (analogous to the log-likelihood function). But whether a likelihood is multimodal is a function of the data. It can happen that if you accumulate enough data, the likelihood concentrates about one of its former modes. But in practice, that could require far more data than are available for training and validation.
Again, you have clearly no idea what you're talking about. I'd advise you to stop blustering and get more than a Masters level education in statistics.
Nowhere does the article claim that calibration is "twisting the data" or "changing the data". It quite clearly says that calibration is changing the values of variables used in the model: "Calibrating a complex model for which parameters can't be directly measured usually involves taking historical data, and, enlisting various computational techniques, adjusting the parameters so that the model would have 'predicted' that historical data."
What the article is describing is fitting a model: finding parameter values that cause it to fit the data.
"Calibration" is a commonly used statistics term in some sub-disciplines. (Others call it "fitting", "tuning", or "parameter estimation".) It literally means "fitting the model", or in a Bayesian context, computing a posterior distribution over the model parameters (e.g., this discussion).
It is simply false that a model which validates well on out-of-sample data will necessarily predict well. The article is in fact about circumstances under which this assumption does NOT hold, such as in statistically non-identifiable models.
One way in which this can happen is if your likelihood surface (or, more generally, objective function or error metric) is multimodal. It can easily happen that both your training data and validation data identify the same mode, but the true value ends up being a different mode, and you can only find that out farther into the future.
To understand better what the article is talking about, you may want to read this paper, which I suspect is by the same guy cited in TFA.
And yes, I do build statistical models for a living. Pretty much everything I do on a daily basis is model calibration. I am not a card-carrying statistician myself (i.e. my Ph.D. is not in statistics), but I'm trained in the field, all of my research is in statistics, I collaborate regularly with statisticians, and publish research in statistics journals.
The problem can show up with an "uninformative" prior as well. Usually this happens when the "true" values of the prior are on the edges of the prior range, e.g., you have a uniform prior on [x1,x2] and the true value happens to be near x1. If the range [x1,x2] is very wide, the prior mean (x1+x2)/2 will be far from x1, and it will drag the posterior there. Sometimes this is ok, if the predictive distribution near (x1+x2)/2 is similar to the predictive distribution near x1. But if x1 and (x1+x2)/2 have very different predictions, it's a problem.
Slashdot Mirror
There are far more scientists who say there is no such thing as man-made global warming than there are who say there is.
False, if you're talking about scientists who actually study the climate.
The indisputable fact remains: Earth has warmed in the past, it has also cooled.
True but fairly irrelevant to what humans are doing to the climate now and what its impacts will be.
Climates change. It's normal and there's nothing we can do one way or the other to affect it.
False.
You mean a massive harvesting operation like the lumber industry?
No. Much more massive than that, if you want to really make a dent in CO2.
Historic deforestation hasn't contributed all that much to atmospheric CO2, compared to fossil fuels, so an equivalent reforestation (and subsequent sequestration) wouldn't decrease CO2 much, either. Not unless you forest much larger tracts of land than originally had forests (where?). Well, you can get a boost by harvesting early and replanting, since young stands sequester carbon at a faster rate than average, but this isn't an order-of-magnitude improvement.
Well built buildings can store wood for hundreds of years,
Irrelevant on the scale we're talking about. We're talking far more trees than we have need for building lumber. (Otherwise the existing lumber industry would already be sequestering large amounts of carbon, and it's not.)
and when it comes time to replace them, you convert them to biochar, which lasts hundreds more years.
Yes, biochar would help, but I still don't think you appreciate the magnitude of the effort required. Realistic (actually, in my opinion, optimistic) biochar burial estimates I've seen put the maximum sequestration rate at about 1 gigaton of carbon per year, which is about 0.5 ppm CO2, so -50 ppm/century. But we've already added 100 ppm and will likely add several hundred ppm more this century.
Now, I don't know if this artificial tree idea can do any better, but my point is that real trees aren't going to solve the problem, even if you scale up the industry. And you have to scale up the industry a lot over commercial logging (except without the profit, because we don't need that much more lumber).
That doesn't actually change the net water vapor content of the atmosphere.
Wouldn't it make more sense to plant more trees instead, and spend the rest of your time and money on cleaner and more efficient methods of powering well everything?
It would make more sense not to put the CO2 into the air in the first place. But if you're going to pull it back out again, it remains to be seen whether artificial trees or real trees have the best cost/benefit ratio. It takes really, really huge forests to make a dent in atmospheric CO2 and reforesting the whole planet won't get CO2 back down to pre-industrial levels any time soon. Maybe a large industrial operation could do better, if the tech improves and economies of scale set in.
Besides which, the trees just release all the CO2 back to the atmosphere when they die and decay, so you need a massive harvesting operation to cut them all down and convert their carbon to a more permanently bound form (e.g., biochar). That massive harvest operation also will require large amounts of energy, time, and money.
Yesterday, wasn't the general consensus from the scientific community that we were 1500 years off from the next ice age
No. It's a brand new paper. Time will tell whether a consensus forms around it.
Trees don't really control the amount of water vapor in the atmosphere; they don't sequester a large amount of water for a long time. To first order, the amount of water vapor in the atmosphere is controlled by temperature.
Hating ice ages doesn't mean liking global warming. If you want to prevent the planet from cooling into an ice age, you don't need to warm it up above present temperatures. You just have to keep it from cooling below present temperatures.
Human civilization has adapted itself to a relatively stable range of climate over the last 10,000 years. Large warming or large cooling pushes us outside of that range. It may be costly to adapt our civilization to a completely different climate, particularly if it happens "fast" (century time scale). Thus, it's possible to hate both global warming and "ice ages".
If you want to use the greenhouse effect to prevent the planet from falling into a glacial period, then you should want to save fossil fuels for when we need them, rather than using them up now, when we don't. That is, dole them out slowly over thousands of years to keep the interglacial climate stable, as the next glacial period gradually deepens, instead of our current course of using them up rapidly and elevating temperatures well above the Holocene climate range.
Besides which, this study is controversial. Everyone agrees that we will see another glacial period someday, barring human intervention. The question is when. This study suggests 1500 years; a number of others have suggested that the next glacial period isn't due for as long as 50,000 years. Which is even less of an argument for global warming.
We must be increasingly on the alert to prevent our enemies from taking over our satellite fuel lines, thus knocking out our military communications. Mr. President, we must not allow a dirty rag gap!
A cluster of stations within a 50 mile radius at about 2/3 warming and 1/3 cooling.
Again, so what? This does not imply there is anything wrong with the data. You are deeply confused about what should happen, meteorologically speaking, on microclimatic scales.
Instead, what they're doing is the same crap science we see so much of these days; gather a bunch of data and look at it for all kinds of things after the fact. There's value to that, because it can tell you what you should look for next time; but it should never be confused with science.
I think that's the stupidest thing I've read on Slashdot.
Where do you think new theories and discoveries come from, anyway? Scientists do experiments, not knowing ahead of time what they're going to find, and find something new. There would be no point in doing experiments that can only confirm what we already expect to find.
Gravity can be explained by these equations but I don't know how it works or why. It's useful, but it's inaccurate and it's not science.
This equally clueless. Gravity is one of the most accurate theories we have, and the fact that you don't understand it has no bearing on its scientific validity.
Of course it's going up, NASA has confirmed that with satellite information as well as several other sources all showing quite clearly that the temperature is rising.
News flash: the satellite and surface station temperature records closely agree.
Basing models on data that is at least 1/3 bogus is fucking stupid
News flash: "data that shows cooling" != "bogus data". Parts of the Earth do cool from time to time, you know (and are expected to, even with the enhanced greenhouse effect). The satellite data shows this as well.
NOAA puts a LOT of weight on the land-based temperature data in their models.
Climate models usually don't use temperature data at all (i.e., it's not an input to the model). They're run freely using only the forcing data (greenhouse gases, solar varations, aerosol loadings, etc.) and allowed to predict their own temperatures, without reference to any temperature observations.
When they are initialized with temperature data (in "data assimilation" mode), land gets exactly the weight it should: about 30% of the Earth's surface.
Temperature observations are used for testing the predictions of climate models, but the above remains true: land data gets exactly as much weight as its area average. And models are compared to a variety of temperature records (surface and satellite), not that it matters much, since (as noted above), they all agree pretty closely.
I would like nothing more than accurate climate models but we'll never get them until people admit that the data we have is shit.
As amply demonstrated above, you have no idea what you're talking about.
The paper is here, and it gives ROC curves. They used two approaches, a hidden Markov model and a support vector machine Bayesian filter.
Link
Virginia Tech developed the data acquisition hardware that the CICAS-V project used to collect the dataset. That probably influenced the choice of location. The MIT paper apparently used the data because it was convenient, and did not mention any interaction with Virginia Tech researchers.
The manuscript is freely available here.
Interview here. It gives some perspective on the claims people are making and discusses the strengths and weaknesses of the study.
This basically makes 3rd-party software - like you get from Fink, for example - non-existent, as far as a Mac user is concerned, because all software for Macs will have to be retrieved from this "app store".
You're spreading FUD.
Software for Macs will NOT have to be retrieved from the app store only. This does not kill 3rd-party software or Fink. This announcement ONLY applies to applications that are voluntarily listed in the app store by their developers. Developers do not have to use the app store to distribute their apps.
It is possible that Apple may someday require all apps go through the app store, as you suggest, but that's not what this announcement is about.
The key is that the further into the future they look, the more uncertain the results of modeling chaotic system become, whether you average them or not. All averaging does is abstract away the underlying behavior, and in the absence of any additional information (variance for instance) is essentially useless for drawing conclusions from the model.
The previous poster is correct: chaos limits your ability to predict the exact state of the system (e.g., the weather over Sydney in 2093), but it doesn't necessarily limit your ability to predict statistical averages (such as the global surface temperature). Certainly there is wide uncertainty in predicting global average quantities, but this is generally not related to chaos. It's more a function of model structural errors and input parameter uncertainties.
Now I'm sure climate scientists are publishing a little more complete statistical analysis of the results of their modeling experiments. However, when they communicate their findings to policy makers and the general public, they seem to have some difficulty expressing the full scope of such an analysis, and instead point to the average or possibly a most likely outcome without the benefit of the additional information which is necessary to properly contextualize the single number.
A full uncertainty analysis of climate models has been difficult because of their complexity. (You see the same problems in many other fields that rely on large computer models.) But there are statistical uncertainty analyses of climate models, and the IPCC has been continually adding more discussion of uncertainties, error bars, etc. to its reports including summaries for policymakers.
The only way to validate them is to continue to tune them against the historical record.
Climate models are generally not tuned to the historical record, in the sense of fitting them to a historical temperature time series or something. They are tuned to data, however. This is a bit subtle, so let me elaborate:
Typically, climate modelers don't try to tune the entire model at once. They isolate subcomponents of the model, such as the cloud parameterization, and tune that. And they usually don't tune it to time series data or trends. Rather, they try to tune the submodel to the mean climate state over some period of time (e.g., to reproduce the average cloud cover in the 1990s).
This does have potential for overfitting, but by tuning subcomponents individually, they reduce the potential for compensating errors between components, and by tuning to base climate instead of climate trends, they try to keep the tuning independent of the human changes or "forcings" which occur over longer periods of time. It also allows for "independent" validation on later periods of time beyond the period of baseline climatology.
In addition, climate models can be "validated" against completely different periods of time that were not used in any tuning exercise, such as to reproduce the climate of the Last Glacial Maximum, although this is only approximately a validation due to data and input uncertainties.
In short, no, you can't truly validate a climate model's predictions for the next century without just waiting a century. (You also can't avoid tuning the model, which will always have unknown effective parameters that can't be calculated from first principles.) But you can build some confidence in the model physics through weak tuning, separation of concerns, and testing of subcomponents on independent data.
Chaos isn't the problem being discussed in the article, nor is it a problem for long-term climate predictions. The problem with both the geophysical models discussed in the article, and climate models, is that historical data aren't sufficient to eliminate the uncertainty in model parameters.
In extreme cases (non-identifiable models), you lose all predictive skill. In milder cases, you simply get wide uncertainties. For example, in climate models the parameter identifiability problems mean that the climate sensitivity (predicted response to doubling atmospheric CO2) is uncertain by a factor of 2 (the "canonical" range of this parameter has been 3 +/- 1.5 degrees C). This uncertainty will go down over time, as more observations are made, but to some extent it is an unavoidable limitation of finite data.
The point of the article is not that economic models are flawed (don't represent reality), but that models can give wrong predictions even if they're perfect (accurately represent reality), due to unavoidable uncertainty in their inputs. I go into more detail in this comment.
I really don't see anything insightful in the article; it looks a lot like circular reasoning - that models built to fit events X Y and Z will fit X Y and Z well. This is fine and dandy till A B and C come along.
The point of the article is commonly known, but slightly subtler than your interpretation: it's that events X Y and Z may be uninformative about A B and C even if the model is perfect and is capable of making perfect predictions (if the inputs are known, which is the problem, because X Y and Z don't let you infer the inputs).
So small changes in inputs can produce big, unpredictable changes in the output of complex systems?
The article is actually about the exact opposite: when big changes in inputs produce similar outputs (and therefore you can't use the output to infer what the inputs were).
Yes, situation (ii) is the one I tend to encounter in practice. It may also be the situation that TFA is describing, which could be this paper. In that study, they find a very multimodal objective function (analogous to the log-likelihood function). But whether a likelihood is multimodal is a function of the data. It can happen that if you accumulate enough data, the likelihood concentrates about one of its former modes. But in practice, that could require far more data than are available for training and validation.
Again, you have clearly no idea what you're talking about. I'd advise you to stop blustering and get more than a Masters level education in statistics.
Nowhere does the article claim that calibration is "twisting the data" or "changing the data". It quite clearly says that calibration is changing the values of variables used in the model: "Calibrating a complex model for which parameters can't be directly measured usually involves taking historical data, and, enlisting various computational techniques, adjusting the parameters so that the model would have 'predicted' that historical data."
What the article is describing is fitting a model: finding parameter values that cause it to fit the data.
"Calibration" is a commonly used statistics term in some sub-disciplines. (Others call it "fitting", "tuning", or "parameter estimation".) It literally means "fitting the model", or in a Bayesian context, computing a posterior distribution over the model parameters (e.g., this discussion).
It is simply false that a model which validates well on out-of-sample data will necessarily predict well. The article is in fact about circumstances under which this assumption does NOT hold, such as in statistically non-identifiable models.
One way in which this can happen is if your likelihood surface (or, more generally, objective function or error metric) is multimodal. It can easily happen that both your training data and validation data identify the same mode, but the true value ends up being a different mode, and you can only find that out farther into the future.
To understand better what the article is talking about, you may want to read this paper, which I suspect is by the same guy cited in TFA.
And yes, I do build statistical models for a living. Pretty much everything I do on a daily basis is model calibration. I am not a card-carrying statistician myself (i.e. my Ph.D. is not in statistics), but I'm trained in the field, all of my research is in statistics, I collaborate regularly with statisticians, and publish research in statistics journals.
The problem can show up with an "uninformative" prior as well. Usually this happens when the "true" values of the prior are on the edges of the prior range, e.g., you have a uniform prior on [x1,x2] and the true value happens to be near x1. If the range [x1,x2] is very wide, the prior mean (x1+x2)/2 will be far from x1, and it will drag the posterior there. Sometimes this is ok, if the predictive distribution near (x1+x2)/2 is similar to the predictive distribution near x1. But if x1 and (x1+x2)/2 have very different predictions, it's a problem.