http://landshape.org/enm/csiro-data-policy-go-pound-sand/ The Power of Numeracy Tue, 07 Feb 2012 17:57:00 +0000 hourly 1 http://wordpress.org/?v=3.3.1 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-13206 CSIRO adopts Phil Jones’ Stonewall Tactic « Climate Audit Wed, 12 Jan 2011 15:56:29 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-13206 [...] the most recent episode, CSIRO stated: I’m not able to hand over the data from the 13 models, due to restrictions on Intellectual [...] [...] the most recent episode, CSIRO stated: I’m not able to hand over the data from the 13 models, due to restrictions on Intellectual [...]

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4334 Mangled Thoughts » Scoop: Can the CSIRO explain an explosive contradiction? What is going on? Wed, 17 Feb 2010 09:08:33 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4334 [...] Stockwell-CSIRO exchange: Stockwell, ‘CSIRO Wars’. Mr. Peter Gallagher, summation, to which one of his readers wrties of the same type of mendacious [...] [...] Stockwell-CSIRO exchange: Stockwell, ‘CSIRO Wars’. Mr. Peter Gallagher, summation, to which one of his readers wrties of the same type of mendacious [...]

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4333 admin Mon, 21 Jul 2008 03:28:55 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4333 I agree with you. The spread of the models is a reflection mostly of uncertainty in the models themselves, knowledge of the system, and not of inherent physical variability. that said, there are results that claim to show that averaging or mixing models leads to improved prediction, but the field of the distributional properties of statistics composed of mixtures of models is an active area of statistical research, see http://arxiv.org/abs/math/0702781. I agree with you. The spread of the models is a reflection mostly of uncertainty in the models themselves, knowledge of the system, and not of inherent physical variability. that said, there are results that claim to show that averaging or mixing models leads to improved prediction, but the field of the distributional properties of statistics composed of mixtures of models is an active area of statistical research, see http://arxiv.org/abs/math/0702781.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7332 admin Mon, 21 Jul 2008 03:28:00 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7332 I agree with you. The spread of the models is a reflection mostly of uncertainty in the models themselves, knowledge of the system, and not of inherent physical variability. that said, there are results that claim to show that averaging or mixing models leads to improved prediction, but the field of the distributional properties of statistics composed of mixtures of models is an active area of statistical research, see http://arxiv.org/abs/math/0702781. I agree with you. The spread of the models is a reflection mostly of uncertainty in the models themselves, knowledge of the system, and not of inherent physical variability. that said, there are results that claim to show that averaging or mixing models leads to improved prediction, but the field of the distributional properties of statistics composed of mixtures of models is an active area of statistical research, see http://arxiv.org/abs/math/0702781.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4332 david Sun, 20 Jul 2008 22:56:31 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4332 No, models are fine by me in principle. It's a question of how they are validated. It's obvious to me (so obvious that I keep thinking I'm missing the point, so please tell me if I am) that each model must be validated independently. If model A predicts temperatures that are far too high and model B predicts temperatures that are far too low, we have too failed models. I don't think it means anything at all if the average of the two happens to agree reasonably well with observations. To think it does implies some kind of mechanistic understanding about the overestimation of one model being "corrected" by the other. But if that's the case, surely you would abandon models A and B and incorporate the "correction" to give model C, which is then tested against the observations. I think that doing a significance test is just an extension of that mistake. No, models are fine by me in principle. It’s a question of how they are validated. It’s obvious to me (so obvious that I keep thinking I’m missing the point, so please tell me if I am) that each model must be validated independently. If model A predicts temperatures that are far too high and model B predicts temperatures that are far too low, we have too failed models. I don’t think it means anything at all if the average of the two happens to agree reasonably well with observations. To think it does implies some kind of mechanistic understanding about the overestimation of one model being “corrected” by the other. But if that’s the case, surely you would abandon models A and B and incorporate the “correction” to give model C, which is then tested against the observations. I think that doing a significance test is just an extension of that mistake.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7331 david Sun, 20 Jul 2008 22:56:00 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7331 No, models are fine by me in principle. It's a question of how they are validated. It's obvious to me (so obvious that I keep thinking I'm missing the point, so please tell me if I am) that each model must be validated independently. If model A predicts temperatures that are far too high and model B predicts temperatures that are far too low, we have too failed models. I don't think it means anything at all if the average of the two happens to agree reasonably well with observations. To think it does implies some kind of mechanistic understanding about the overestimation of one model being "corrected" by the other. But if that's the case, surely you would abandon models A and B and incorporate the "correction" to give model C, which is then tested against the observations. I think that doing a significance test is just an extension of that mistake. No, models are fine by me in principle. It’s a question of how they are validated. It’s obvious to me (so obvious that I keep thinking I’m missing the point, so please tell me if I am) that each model must be validated independently. If model A predicts temperatures that are far too high and model B predicts temperatures that are far too low, we have too failed models. I don’t think it means anything at all if the average of the two happens to agree reasonably well with observations. To think it does implies some kind of mechanistic understanding about the overestimation of one model being “corrected” by the other. But if that’s the case, surely you would abandon models A and B and incorporate the “correction” to give model C, which is then tested against the observations. I think that doing a significance test is just an extension of that mistake.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4331 admin Sun, 20 Jul 2008 21:20:32 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4331 It is risky to trust models too much, unless they have been throughly validated be and based in solid physical knowledge. Wouldn't you be throwing out all models in your view. It is risky to trust models too much, unless they have been throughly validated be and based in solid physical knowledge. Wouldn’t you be throwing out all models in your view.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7330 admin Sun, 20 Jul 2008 21:20:00 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7330 It is risky to trust models too much, unless they have been throughly validated be and based in solid physical knowledge. Wouldn't you be throwing out all models in your view. It is risky to trust models too much, unless they have been throughly validated be and based in solid physical knowledge. Wouldn’t you be throwing out all models in your view.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4330 david Fri, 18 Jul 2008 08:07:31 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-4330 But isn't it risky (if the aim is to keep these guys honest about testing their models) to allow that such a test is even in principle valid? The way I see it is that it would be possible to pick one of the model parameters, say p, and run simulations such that a selected output (say, predicted temperature in 2050) is some single-valued function f(p) of p. Now, if I find out what statistical test you intend to do then I as the modeller can produce a set of "observations" f(p(i))(model outputs) that can either pass or fail it, at will. In doing that I would obviously be open to major criticisms on other grounds (why those p(i)?) but given the current climate of debate I would easily get away with playing the card: you attacked me on statistical grounds and I won, and now you are trying a completely different tack on dynamical grounds. So when I win that, what next? Personal grounds? Typical Big Oil. But isn’t it risky (if the aim is to keep these guys honest about testing their models) to allow that such a test is even in principle valid? The way I see it is that it would be possible to pick one of the model parameters, say p, and run simulations such that a selected output (say, predicted temperature in 2050) is some single-valued function f(p) of p. Now, if I find out what statistical test you intend to do then I as the modeller can produce a set of “observations” f(p(i))(model outputs) that can either pass or fail it, at will. In doing that I would obviously be open to major criticisms on other grounds (why those p(i)?) but given the current climate of debate I would easily get away with playing the card: you attacked me on statistical grounds and I won, and now you are trying a completely different tack on dynamical grounds. So when I win that, what next? Personal grounds? Typical Big Oil.

]]> http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7329 david Fri, 18 Jul 2008 08:07:00 +0000 http://landshape.org/enm/csiro-data-policy-go-pound-sand/#comment-7329 But isn't it risky (if the aim is to keep these guys honest about testing their models) to allow that such a test is even in principle valid? The way I see it is that it would be possible to pick one of the model parameters, say p, and run simulations such that a selected output (say, predicted temperature in 2050) is some single-valued function f(p) of p. Now, if I find out what statistical test you intend to do then I as the modeller can produce a set of "observations" f(p(i))(model outputs) that can either pass or fail it, at will. In doing that I would obviously be open to major criticisms on other grounds (why those p(i)?) but given the current climate of debate I would easily get away with playing the card: you attacked me on statistical grounds and I won, and now you are trying a completely different tack on dynamical grounds. So when I win that, what next? Personal grounds? Typical Big Oil. But isn’t it risky (if the aim is to keep these guys honest about testing their models) to allow that such a test is even in principle valid? The way I see it is that it would be possible to pick one of the model parameters, say p, and run simulations such that a selected output (say, predicted temperature in 2050) is some single-valued function f(p) of p. Now, if I find out what statistical test you intend to do then I as the modeller can produce a set of “observations” f(p(i))(model outputs) that can either pass or fail it, at will. In doing that I would obviously be open to major criticisms on other grounds (why those p(i)?) but given the current climate of debate I would easily get away with playing the card: you attacked me on statistical grounds and I won, and now you are trying a completely different tack on dynamical grounds. So when I win that, what next? Personal grounds? Typical Big Oil.

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