5 ms·

While I'm not a "denier" per se, the IPCC has made some monumental errors (hockey stick, anyone?) in the past that have really hampered their ability to be a so

While I'm not a "denier" per se, the IPCC has made some monumental errors (hockey stick, anyone?) in the past that have really hampered their ability to be a source of non-political "good science".

snowwrestler 14y ago
> The test in science is whether findings can be replicated using different data and methods. More than two dozen scientific papers, using various statistical methods and combinations of proxy records, have produced reconstructions supporting the broad consensus shown in the original 1998 hockey-stick graph, with variations in how flat the pre-20th century "shaft" appears.[12][13] The 2007 IPCC Fourth Assessment Report cited 14 reconstructions, 10 of which covered 1,000 years or longer, to support its strengthened conclusion that it was likely that Northern Hemisphere temperatures during the 20th century were the highest in at least the past 1,300 years.[14] Ten or more subsequent reconstructions, including Mann et al. 2008, have supported these general conclusions. http://en.wikipedia.org/wiki/Hockey_stick_controversy http://en.wikipedia.org/wiki/Hockey_stick_controversy
- schoper 14y ago
  The collected historical temperature data series are messy. You plot them and it looks like noise. A simple average will tell you nothing. The regression analysis that goes on in these reconstructions tends to follow something like this algorithm: 1) Compare your collected data series to each other to determine a "reliability" index for each data series. 2) Construct a series as a weighted average on this reliability index. 3) Iterate, using your new series to calculate the reliability index. There are substantial issues with the above approach, having to do with sensitivity to particular kinds of signals. For a while, the Climate Audit folks got a lot of mileage over a data set that was put in "upside down" in one of the reconstructions. Rather than claiming "data error," the author defended it based on the idea that the above process would just give it a negative weighting! Regardless, you should see why the above could easily be susceptible to issues. Further, the disagreement between these reconstructions and what our limited historical sources tell us about the MWP, etc., makes them further suspect. This is a problem for the global warming model because it means that we don't know what the historical standard deviation for temperature is. So we can't say whether the last 3 decades warming is "within normal deviations" or "unprecedented."
  - snowwrestler 14y ago
    Your critique sounds interesting but I don't know enough to evaluate it--I'm not a scientist myself. I would just point out that scientific debate occurs through published papers and conference presentations, not programming message boards. I hope that you are publishing in those channels if you feel you have substantive contributions to this kind of research.
    - schoper 14y ago
      Nope, that's not actually how science works. Rather, that is "formally" how science work. Actual practice is far more messy. Personalities matter, personal bureaucratic clout matters. And happily, informal communication and message boards matter more than you would think! Also, the published papers reporting these studies are all easily available. I highly advise reading them for yourself.
      - snowwrestler 14y ago
        No offense, but those are the sorts of things that people say when their own research cannot pass muster for publication. edit: misspelling
        vacri 14y ago
        You just claimed not to be a scientist, yet you claim the high ground in knowing how science is conducted?
        snowwrestler 14y ago
        I made a jerk comment above. I stand by the importance of published work in scientific discourse, but I apologize to schoper for taking a personal shot that was not called for.
      - scarmig 14y ago
        snowwrestler, it may vary by field, but my (non-climate) field relied almost entirely on informal communications, presentations, write ups being shared but not published. I don't have direct experience with climatology, but from what I understand it's somewhat similar: conferences like AGU are really important to climatologists for that reason.
scarmig 14y ago
What's your critique of the hockey stick graph? In what sense was it an error, let alone a "monumental" one?
- run4yourlives 14y ago
  The main issue with any "Hockey Stick" as presented is the length of the shaft. The stick all but vanishes when you look at a long term climate projection, rather than the last 500 years or so.
  - moultano 14y ago
    [citation needed]
    - run4yourlives 14y ago
      http://www.intellicast.com/Community/Content.aspx?a=194 http://www.intellicast.com/Community/Content.aspx?a=194
- gd1 14y ago
  The flaws are numerous, but the easiest to explain is that they employ a post-hoc screening bias. Imagine that you gather a few dozen time-series together. They go back two thousand years. Then you regress them against the (very short) instrumental temperature record in the period where they overlap. You keep the ones that pass and throw the rest away. You form some sort of linear weighted average of the selected proxies (i.e. PCA for example). The thing about this process is, if you feed in completely random red-noise time series you will get a hockey stick. The reason is simple, the selection period will mirror the instrumental temperature (mental check: it has to, after all we only selected the proxies that matched the instrumental temperature). You have your blade. But none of those red noise proxies actually reflect past temperature do they? They just happened to be selected by random chance. So in the time before the selection period, the noise cancels out and you have a flat line. There is your stick. This is the core of how these "proxy reconstructions" work, and it is nonsense. Outside of that, it gets even better. The stories are comical and numerous. Mann famously fed a contaminated Finnish lake sediment series into his algo, which happily regressed it against temperature and found that it passed significance if it was used upside down (in the opposite orientation to that suggested by the author in the original source paper). So in it went. GIGO. Or how about the question of which temperature series to regress against? You'd think you'd regress against local temperature right? Or maybe at least the same continent? Oh ye optimistic fool, go and google "teleconnection" and "non-local statistical relationship". This is what passes as "climate science".

While I'm not a "denier" per se, the IPCC has made some monumental errors (hockey stick, anyone?) in the past that have really hampered their ability to be a source of non-political "good science".

snowwrestler 14y ago

> The test in science is whether findings can be replicated using different data and methods. More than two dozen scientific papers, using various statistical methods and combinations of proxy records, have produced reconstructions supporting the broad consensus shown in the original 1998 hockey-stick graph, with variations in how flat the pre-20th century "shaft" appears.[12][13] The 2007 IPCC Fourth Assessment Report cited 14 reconstructions, 10 of which covered 1,000 years or longer, to support its strengthened conclusion that it was likely that Northern Hemisphere temperatures during the 20th century were the highest in at least the past 1,300 years.[14] Ten or more subsequent reconstructions, including Mann et al. 2008, have supported these general conclusions. http://en.wikipedia.org/wiki/Hockey_stick_controversy http://en.wikipedia.org/wiki/Hockey_stick_controversy

schoper 14y ago
The collected historical temperature data series are messy. You plot them and it looks like noise. A simple average will tell you nothing. The regression analysis that goes on in these reconstructions tends to follow something like this algorithm: 1) Compare your collected data series to each other to determine a "reliability" index for each data series. 2) Construct a series as a weighted average on this reliability index. 3) Iterate, using your new series to calculate the reliability index. There are substantial issues with the above approach, having to do with sensitivity to particular kinds of signals. For a while, the Climate Audit folks got a lot of mileage over a data set that was put in "upside down" in one of the reconstructions. Rather than claiming "data error," the author defended it based on the idea that the above process would just give it a negative weighting! Regardless, you should see why the above could easily be susceptible to issues. Further, the disagreement between these reconstructions and what our limited historical sources tell us about the MWP, etc., makes them further suspect. This is a problem for the global warming model because it means that we don't know what the historical standard deviation for temperature is. So we can't say whether the last 3 decades warming is "within normal deviations" or "unprecedented."
- snowwrestler 14y ago
  Your critique sounds interesting but I don't know enough to evaluate it--I'm not a scientist myself. I would just point out that scientific debate occurs through published papers and conference presentations, not programming message boards. I hope that you are publishing in those channels if you feel you have substantive contributions to this kind of research.
  - schoper 14y ago
    Nope, that's not actually how science works. Rather, that is "formally" how science work. Actual practice is far more messy. Personalities matter, personal bureaucratic clout matters. And happily, informal communication and message boards matter more than you would think! Also, the published papers reporting these studies are all easily available. I highly advise reading them for yourself.
    - snowwrestler 14y ago
      No offense, but those are the sorts of things that people say when their own research cannot pass muster for publication. edit: misspelling
      - vacri 14y ago
        You just claimed not to be a scientist, yet you claim the high ground in knowing how science is conducted?
        snowwrestler 14y ago
        I made a jerk comment above. I stand by the importance of published work in scientific discourse, but I apologize to schoper for taking a personal shot that was not called for.
    - scarmig 14y ago
      snowwrestler, it may vary by field, but my (non-climate) field relied almost entirely on informal communications, presentations, write ups being shared but not published. I don't have direct experience with climatology, but from what I understand it's somewhat similar: conferences like AGU are really important to climatologists for that reason.

scarmig 14y ago

What's your critique of the hockey stick graph? In what sense was it an error, let alone a "monumental" one?

run4yourlives 14y ago
The main issue with any "Hockey Stick" as presented is the length of the shaft. The stick all but vanishes when you look at a long term climate projection, rather than the last 500 years or so.
- moultano 14y ago
  [citation needed]
  - run4yourlives 14y ago
    http://www.intellicast.com/Community/Content.aspx?a=194 http://www.intellicast.com/Community/Content.aspx?a=194
gd1 14y ago
The flaws are numerous, but the easiest to explain is that they employ a post-hoc screening bias. Imagine that you gather a few dozen time-series together. They go back two thousand years. Then you regress them against the (very short) instrumental temperature record in the period where they overlap. You keep the ones that pass and throw the rest away. You form some sort of linear weighted average of the selected proxies (i.e. PCA for example). The thing about this process is, if you feed in completely random red-noise time series you will get a hockey stick. The reason is simple, the selection period will mirror the instrumental temperature (mental check: it has to, after all we only selected the proxies that matched the instrumental temperature). You have your blade. But none of those red noise proxies actually reflect past temperature do they? They just happened to be selected by random chance. So in the time before the selection period, the noise cancels out and you have a flat line. There is your stick. This is the core of how these "proxy reconstructions" work, and it is nonsense. Outside of that, it gets even better. The stories are comical and numerous. Mann famously fed a contaminated Finnish lake sediment series into his algo, which happily regressed it against temperature and found that it passed significance if it was used upside down (in the opposite orientation to that suggested by the author in the original source paper). So in it went. GIGO. Or how about the question of which temperature series to regress against? You'd think you'd regress against local temperature right? Or maybe at least the same continent? Oh ye optimistic fool, go and google "teleconnection" and "non-local statistical relationship". This is what passes as "climate science".