In light of the portions I quoted from Armstrong and Green’s paper, I’ll look at Gavin Schmidt’s post:
Principle 1: When moving into a new field, don’t assume you know everything about it because you read a review and none of the primary literature.
Score: −2
G+A appear to have only read one chapter of the IPCC report (Chap 8), and an un-peer reviewed hatchet job on the Stern report. Not a very good start…
The paper does cite many other sources than just the IPCC and the “hatchet job” on the Stern Report, including sources that evaluate climate models and their quality in general. ChrisC notes that the author’s fail to cite the ~788 references for the IPCC Chapter 8. The authors claim to have a bibliography on their website that includes the full list of references given to them by all academics who suggested references. Unfortunately, as I noted in my earlier comment, the link to the bibliography from http://www.forecastingprinciples.com/index.php?option=com_content&view=article&id=78&Itemid=107 is broken. This doesn’t reflect well on the authors (the site on the whole is a mess, with many broken links). Assuming, however, that the authors had put up the bibliography and that it was available as promised in the paper, this critique seems off the mark (though I’d have to see the bibliography to know for sure).
Principle 2: Talk to people who are doing what you are concerned about.
Score: −2
Of the roughly 20 climate modelling groups in the world, and hundreds of associated researchers, G+A appear to have talked to none of them. Strike 2.
This seems patently false given the contents of the paper as I quoted it, and the list of experts that they sought. In fact, it seems like such a major error that I have no idea how Schmidt could have made it if he’d read the paper. (Perhaps he had a more nuanced critique to offer, e.g., that the authors’ survey didn’t ask enough questions, or they should have tried harder, or contacted more people. But the critique as offered here smacks of incompetence or malice). [Unless Schmidt was reading an older version of the paper that didn’t mention the survey at all. But I doubt that even if he was looking at an old version of the paper, it omitted all references to the survey.]
Principle 3: Be humble. If something initially doesn’t make sense, it is more likely that you’ve mis-understood than the entire field is wrong.
Score: −2
For instance, G+A appear to think that climate models are not tested on ‘out of sample’ data (they gave that a ‘-2′). On the contrary, the models are used for many situations that they were not tuned for, paleo-climate changes (mid Holocene, last glacial maximum, 8.2 kyr event) being a good example. Similarly, model projections for the future have been matched with actual data – for instance, forecasting the effects of Pinatubo ahead of time, or Hansen’s early projections. The amount of ‘out of sample’ testing is actually huge, but the confusion stems from G+A not being aware of what the ‘sample’ data actually consists of (mainly present day climatology). Another example is that G+A appear to think that GCMs use the history of temperature changes to make their projections since they suggest leaving some of it out as a validation. But this is just not so, as we discussed more thoroughly in a recent thread.
First off, retrospective “predictions” of things that people already tacitly know, even though those things aren’t explicitly used in tuning the models, are not that reliable.
Secondly, it’s possible (and likely) that Armstrong and Green missed some out-of-model tests and validations that had been performed in the climate science arena. While part of this can be laid at their feet, part of it also reflects poor documentation by climate scientists of exactly how they were going about their testing. I did read that IPCC AR4 chapter that Armstrong and Green did, and I found it quite unclear on the forecasting side of things (compared to other papers I’ve read that judge forecast skill, in weather and short-term climate forecasting, macroeconomic forecasting, and business forecasting). This is similar to the sloppy code problem.
Thirdly, the climate scentists whom Armstrong and Green attempted to engage could have been more engaging (not Gavin Schmidt’s fault; he wasn’t included in the list, and the response rate appears to have been low from mainstream scientists as well as skeptics, so it’s not just a problem of the climate science mainstream).
Overall, I’d like to know more details of the survey responses and Armstrong and Green’s methodology, and it would be good if they combined their proclaimed commitment to openness with actually having working links on their websites. But Schmidt’s critique doesn’t reflect too well on him, even if Armstrong and Green were wrong.
Now, to ChrisC’s comment:
Call me crazy, but in my field of meteorology, we would never head to popular literature, much less the figgin internet, in order to evaluate the state of the art in science. You head to the scientific literature first and foremost. Since meteorology and climatology are not that different, I would struggle to see why it would be any different.
The authors also seem to put a large weight on “forecasting principles” developed in different fields. While there may be some valuable advice, and cross-field cooperation is to be encouraged, one should not assume that techniques developed in say, econometrics, port directly into climate science.
The authors also make much of a wild goose chase on google for sites matching their specific phrases, such as “global warming” AND “forecast principles”. I’m not sure what a lack of web sites would prove. They also seem to have skiped most of the literature cited in AR4 ch. 8 on model validation and climatology predictions.
Part of the authors’ criticism was that the climate science mainstream hadn’t paid enough attention to forecasting, or to formal evaluations of forecasting. So it’s natural that they didn’t find enough mainstream stuff to cite that was directly relevant to the questions at hand for them.
As for the Google search and Google Scholar search, these are standard tools for initiating an inquiry. I know, I’ve done it, and so has everybody else. It would be damning if the authors had relied only on such searches. But they surveyed climate scientists and worked their way through the IPCC Working Group Report. This may have been far short of full due diligence, but it isn’t anywhere near as sloppy as Gavin Schmidt and ChrisC make it sound.
Thanks for a comprehensive summary—that was helpful.
It seems that A&G contacted the working scientists to identify papers which (in the scientists’ view) contained the most credible climate forecasts. Not many responded, but 30 referred to the recent (at the time) IPCC WP1 report, which in turn referenced and attempted to summarize over 700 primary papers. There also appear to have been a bunch of other papers cited by the surveyed scientists, but the site has lost them. So we’re somewhat at a loss to decide which primary sources climate scientists find most credible/authoritative. (Which is a pity, because those would be worth rating, surely?)
However, A&G did their rating/scoring on the IPCC WP1, Chapter 8. But they didn’t contact the climate scientists to help with this rating (or they did, but none of them answered?) They didn’t attempt to dig into the 700 or so underlying primary papers, identify which of them contained climate forecasts, and/or had been identified by the scientists as containing the most credible forecasts and then rate those. Or even pick a random sample, and rate those? All that does sound just a tad superficial.
What I find really bizarre is their site’s conclusion that because IPCC got a low score by their preferred rating principles, then a “no change” forecast is superior, and more credible! That’s really strange, since “no change” has historically done much worse as a predictor than any of the IPCC models.
We sent out general calls for experts to use the Forecasting Audit Software to conduct their own audits and we also asked a few individuals to do so. At the time of writing, none have done so.
It’s not clear how much effort they put into this step, and whether e.g. they offered the Forecasting Audit Software for free to people they asked (if they were trying to sell the software, which they themselves created, that might have seemed bad).
My guess is that most of the climate scientists they contacted just labeled them mentally along with the numerous “cranks” they usually have to deal with, and didn’t bother engaging.
I also am skeptical of some aspects of Armstrong and Green’s exercise. But a first outside-view analysis that doesn’t receive much useful engagement from insiders can only go so far. What would have been interesting was if, after Armstrong and Green published their analysis and it was somewhat clear that their critique would receive attention, climate scientists had offered a clearer and more direct response to the specific criticisms, and perhaps even read up more about the forecasting principles and the evidence cited for them. I don’t think all climate scientists should have done so, I just think at least a few should have been interested enough to do it. Even something similar to Nate Silver’s response would have been nice. And maybe that did happen—if so, I’d like to see links. Schmidt’s response, on the other hand, seems downright careless and bad.
My focus here is the critique of insularity, not so much the effect it had on the factual conclusions. Basically, did climate scientists carefully consider forecasting principles (or statistical methods, or software engineering principles) then reject them? Had they never heard of the relevant principles? Did they hear about the principles, but dismiss them as unworthy of investigation? Armstrong and Green’s audit may have been sloppy (though perhaps a first pass shouldn’t be expected to be better than sloppy) but even if the audit itself wasn’t much use, did it raise questions or general directions of inquiry worthy of investigation (or a simple response pointing to past investigation)? Schmidt’s reaction seems evidence in favor of the dismissal hypothesis. And in the particular instance, maybe he was right, but it does seem to fit the general idea of insularity.
(Your quote is mangled, you probably have four spaces at the beginning which makes the rendering engine interpret it as a needing to be formatted like code, i.e. No linebreaks)
Actually, it’s somewhat unclear whether the IPCC scenarios did better than a “no change” model—it is certainly true over the short time period, but perhaps not over a longer time period where temperatures had moved in other directions.
Co-author Green wrote a paper later claiming that the IPCC models did not do better than the no change model when tested over a broader time period:
But it’s just a draft paper and I don’t know if the author ever plans to clean it up or have it published.
I would really like to see more calibrations and scorings of the models from a pure outside view approach over longer time periods.
Armstrong was (perhaps wrongly) confident enough of his views that he decided to make a public bet claiming that the No Change scenario would beat out the other scenario. The bet is described at:
Overall, I have high confidence in the view that models of climate informed by some knowledge of climate should beat the No Change model, though a lot depends on the details of how the competition is framed (Armstrong’s climate bet may have been rigged in favor of No Change). That said, it’s not clear how well climate models can do relative to simple time series forecasting approaches or simple (linear trend from radiative forcing + cyclic trend from ocean currents) type approaches. The number of independent out-of-sample validations does not seem to be enough and the predictive power of complex models relative to simple curve-fitting models seems to be low (probably negative). So, I think that arguments that say “our most complex, sophisticated models show X” should be treated with suspicion and should not necessarily be given more credence than arguments that rely on simple models and historical observations.
Actually, it’s somewhat unclear whether the IPCC scenarios did better than a “no change” model—it is certainly true over the short time period, but perhaps not over a longer time period where temperatures had moved in other directions.
There are certainly periods when temperatures moved in a negative direction (1940s-1970s), but then the radiative forcings over those periods (combination of natural and anthropogenic) were also negative. So climate models would also predict declining temperatures, which indeed is what they do “retrodict”. A no-change model would be wrong for those periods as well.
Your most substantive point is that the complex models don’t seem to be much more accurate than a simple forcing model (e.g. calculate net forcings from solar and various pollutant types, multiply by best estimate of climate sensitivity, and add a bit of lag since the system takes time to reach equilibrium; set sensitivity and lags empirically). I think that’s true on the “broadest brush” level, but not for regional and temporal details e.g. warming at different latitudes, different seasons, land versus sea, northern versus southern hemisphere, day versus night, changes in maximum versus minimum temperatures, changes in temperature at different levels of the atmosphere etc. It’s hard to get those details right without a good physical model of the climate system and associated general circulation model (which is where the complexity arises). My understanding is that the GCMs do largely get these things right, and make predictions in line with observations; much better than simple trend-fitting.
P.S. If I draw one supportive conclusion from this discussion, it is that long-range climate forecasts are very likely to be wrong, simply because the inputs (radiative forcings) are impossible to forecast with any degree of accuracy.
Even if we’d had perfect GCMs in 1900, forecasts for the 20th century would likely have been very wrong: no one could have predicted the relative balance of CO2, other greenhouse gases and sulfates/aerosols (e.g. no-one could have guessed the pattern of sudden sulfates growth after the 1940s, followed by levelling off after the 1970s). And natural factors like solar cycles, volcanoes and El Niño/La Nina wouldn’t have been predictable either.
Similarly, changes in the 21st century could be very unexpected. Perhaps some new industrial process creates brand new pollutants with negative radiative forcing in the 2030s; but then the Amazon dies off in the 2040s, followed by a massive methane belch from the Arctic in the 2050s; then emergency geo-engineering goes into fashion in the 2070s (and out again in the 2080s); then in the 2090s there is a resurgence in coal, because the latest generation of solar panels has been discovered to be causing a weird new plague. Temperatures could be up and down like a yo-yo all century.
In light of the portions I quoted from Armstrong and Green’s paper, I’ll look at Gavin Schmidt’s post:
The paper does cite many other sources than just the IPCC and the “hatchet job” on the Stern Report, including sources that evaluate climate models and their quality in general. ChrisC notes that the author’s fail to cite the ~788 references for the IPCC Chapter 8. The authors claim to have a bibliography on their website that includes the full list of references given to them by all academics who suggested references. Unfortunately, as I noted in my earlier comment, the link to the bibliography from http://www.forecastingprinciples.com/index.php?option=com_content&view=article&id=78&Itemid=107 is broken. This doesn’t reflect well on the authors (the site on the whole is a mess, with many broken links). Assuming, however, that the authors had put up the bibliography and that it was available as promised in the paper, this critique seems off the mark (though I’d have to see the bibliography to know for sure).
This seems patently false given the contents of the paper as I quoted it, and the list of experts that they sought. In fact, it seems like such a major error that I have no idea how Schmidt could have made it if he’d read the paper. (Perhaps he had a more nuanced critique to offer, e.g., that the authors’ survey didn’t ask enough questions, or they should have tried harder, or contacted more people. But the critique as offered here smacks of incompetence or malice). [Unless Schmidt was reading an older version of the paper that didn’t mention the survey at all. But I doubt that even if he was looking at an old version of the paper, it omitted all references to the survey.]
First off, retrospective “predictions” of things that people already tacitly know, even though those things aren’t explicitly used in tuning the models, are not that reliable.
Secondly, it’s possible (and likely) that Armstrong and Green missed some out-of-model tests and validations that had been performed in the climate science arena. While part of this can be laid at their feet, part of it also reflects poor documentation by climate scientists of exactly how they were going about their testing. I did read that IPCC AR4 chapter that Armstrong and Green did, and I found it quite unclear on the forecasting side of things (compared to other papers I’ve read that judge forecast skill, in weather and short-term climate forecasting, macroeconomic forecasting, and business forecasting). This is similar to the sloppy code problem.
Thirdly, the climate scentists whom Armstrong and Green attempted to engage could have been more engaging (not Gavin Schmidt’s fault; he wasn’t included in the list, and the response rate appears to have been low from mainstream scientists as well as skeptics, so it’s not just a problem of the climate science mainstream).
Overall, I’d like to know more details of the survey responses and Armstrong and Green’s methodology, and it would be good if they combined their proclaimed commitment to openness with actually having working links on their websites. But Schmidt’s critique doesn’t reflect too well on him, even if Armstrong and Green were wrong.
Now, to ChrisC’s comment:
Part of the authors’ criticism was that the climate science mainstream hadn’t paid enough attention to forecasting, or to formal evaluations of forecasting. So it’s natural that they didn’t find enough mainstream stuff to cite that was directly relevant to the questions at hand for them.
As for the Google search and Google Scholar search, these are standard tools for initiating an inquiry. I know, I’ve done it, and so has everybody else. It would be damning if the authors had relied only on such searches. But they surveyed climate scientists and worked their way through the IPCC Working Group Report. This may have been far short of full due diligence, but it isn’t anywhere near as sloppy as Gavin Schmidt and ChrisC make it sound.
Thanks for a comprehensive summary—that was helpful.
It seems that A&G contacted the working scientists to identify papers which (in the scientists’ view) contained the most credible climate forecasts. Not many responded, but 30 referred to the recent (at the time) IPCC WP1 report, which in turn referenced and attempted to summarize over 700 primary papers. There also appear to have been a bunch of other papers cited by the surveyed scientists, but the site has lost them. So we’re somewhat at a loss to decide which primary sources climate scientists find most credible/authoritative. (Which is a pity, because those would be worth rating, surely?)
However, A&G did their rating/scoring on the IPCC WP1, Chapter 8. But they didn’t contact the climate scientists to help with this rating (or they did, but none of them answered?) They didn’t attempt to dig into the 700 or so underlying primary papers, identify which of them contained climate forecasts, and/or had been identified by the scientists as containing the most credible forecasts and then rate those. Or even pick a random sample, and rate those? All that does sound just a tad superficial.
What I find really bizarre is their site’s conclusion that because IPCC got a low score by their preferred rating principles, then a “no change” forecast is superior, and more credible! That’s really strange, since “no change” has historically done much worse as a predictor than any of the IPCC models.
See the last sentence in my longer quote:
It’s not clear how much effort they put into this step, and whether e.g. they offered the Forecasting Audit Software for free to people they asked (if they were trying to sell the software, which they themselves created, that might have seemed bad).
My guess is that most of the climate scientists they contacted just labeled them mentally along with the numerous “cranks” they usually have to deal with, and didn’t bother engaging.
I also am skeptical of some aspects of Armstrong and Green’s exercise. But a first outside-view analysis that doesn’t receive much useful engagement from insiders can only go so far. What would have been interesting was if, after Armstrong and Green published their analysis and it was somewhat clear that their critique would receive attention, climate scientists had offered a clearer and more direct response to the specific criticisms, and perhaps even read up more about the forecasting principles and the evidence cited for them. I don’t think all climate scientists should have done so, I just think at least a few should have been interested enough to do it. Even something similar to Nate Silver’s response would have been nice. And maybe that did happen—if so, I’d like to see links. Schmidt’s response, on the other hand, seems downright careless and bad.
My focus here is the critique of insularity, not so much the effect it had on the factual conclusions. Basically, did climate scientists carefully consider forecasting principles (or statistical methods, or software engineering principles) then reject them? Had they never heard of the relevant principles? Did they hear about the principles, but dismiss them as unworthy of investigation? Armstrong and Green’s audit may have been sloppy (though perhaps a first pass shouldn’t be expected to be better than sloppy) but even if the audit itself wasn’t much use, did it raise questions or general directions of inquiry worthy of investigation (or a simple response pointing to past investigation)? Schmidt’s reaction seems evidence in favor of the dismissal hypothesis. And in the particular instance, maybe he was right, but it does seem to fit the general idea of insularity.
(Your quote is mangled, you probably have four spaces at the beginning which makes the rendering engine interpret it as a needing to be formatted like code, i.e. No linebreaks)
Thanks, fixed!
Actually, it’s somewhat unclear whether the IPCC scenarios did better than a “no change” model—it is certainly true over the short time period, but perhaps not over a longer time period where temperatures had moved in other directions.
Co-author Green wrote a paper later claiming that the IPCC models did not do better than the no change model when tested over a broader time period:
http://www.kestencgreen.com/gas-improvements.pdf
But it’s just a draft paper and I don’t know if the author ever plans to clean it up or have it published.
I would really like to see more calibrations and scorings of the models from a pure outside view approach over longer time periods.
Armstrong was (perhaps wrongly) confident enough of his views that he decided to make a public bet claiming that the No Change scenario would beat out the other scenario. The bet is described at:
http://www.theclimatebet.com/
Overall, I have high confidence in the view that models of climate informed by some knowledge of climate should beat the No Change model, though a lot depends on the details of how the competition is framed (Armstrong’s climate bet may have been rigged in favor of No Change). That said, it’s not clear how well climate models can do relative to simple time series forecasting approaches or simple (linear trend from radiative forcing + cyclic trend from ocean currents) type approaches. The number of independent out-of-sample validations does not seem to be enough and the predictive power of complex models relative to simple curve-fitting models seems to be low (probably negative). So, I think that arguments that say “our most complex, sophisticated models show X” should be treated with suspicion and should not necessarily be given more credence than arguments that rely on simple models and historical observations.
There are certainly periods when temperatures moved in a negative direction (1940s-1970s), but then the radiative forcings over those periods (combination of natural and anthropogenic) were also negative. So climate models would also predict declining temperatures, which indeed is what they do “retrodict”. A no-change model would be wrong for those periods as well.
Your most substantive point is that the complex models don’t seem to be much more accurate than a simple forcing model (e.g. calculate net forcings from solar and various pollutant types, multiply by best estimate of climate sensitivity, and add a bit of lag since the system takes time to reach equilibrium; set sensitivity and lags empirically). I think that’s true on the “broadest brush” level, but not for regional and temporal details e.g. warming at different latitudes, different seasons, land versus sea, northern versus southern hemisphere, day versus night, changes in maximum versus minimum temperatures, changes in temperature at different levels of the atmosphere etc. It’s hard to get those details right without a good physical model of the climate system and associated general circulation model (which is where the complexity arises). My understanding is that the GCMs do largely get these things right, and make predictions in line with observations; much better than simple trend-fitting.
P.S. If I draw one supportive conclusion from this discussion, it is that long-range climate forecasts are very likely to be wrong, simply because the inputs (radiative forcings) are impossible to forecast with any degree of accuracy.
Even if we’d had perfect GCMs in 1900, forecasts for the 20th century would likely have been very wrong: no one could have predicted the relative balance of CO2, other greenhouse gases and sulfates/aerosols (e.g. no-one could have guessed the pattern of sudden sulfates growth after the 1940s, followed by levelling off after the 1970s). And natural factors like solar cycles, volcanoes and El Niño/La Nina wouldn’t have been predictable either.
Similarly, changes in the 21st century could be very unexpected. Perhaps some new industrial process creates brand new pollutants with negative radiative forcing in the 2030s; but then the Amazon dies off in the 2040s, followed by a massive methane belch from the Arctic in the 2050s; then emergency geo-engineering goes into fashion in the 2070s (and out again in the 2080s); then in the 2090s there is a resurgence in coal, because the latest generation of solar panels has been discovered to be causing a weird new plague. Temperatures could be up and down like a yo-yo all century.