Comments on: Effects of Global Warming http://landshape.org/enm/effects-of-global-warming/ The Power of Numeracy Tue, 07 Feb 2012 17:57:00 +0000 hourly 1 http://wordpress.org/?v=3.3.1 By: Temperature Index Drought http://landshape.org/enm/effects-of-global-warming/#comment-4534 Temperature Index Drought Sat, 31 Jan 2009 01:23:27 +0000 http://landshape.org/enm/?p=422#comment-4534 [...] of temperature, and the 5th percentile of rainfall data provided for 13 models and 7 regions in the Drought Exceptional Circumstances report. These data show the area affected by exceptional high temperatures or low rainfalls, and typically [...] [...] of temperature, and the 5th percentile of rainfall data provided for 13 models and 7 regions in the Drought Exceptional Circumstances report. These data show the area affected by exceptional high temperatures or low rainfalls, and typically [...]

]]> By: WikiChecks » Blog Archive » Tests of Regional Climate Model Validity in the Drought Exceptional Circumstances Report http://landshape.org/enm/effects-of-global-warming/#comment-4533 WikiChecks » Blog Archive » Tests of Regional Climate Model Validity in the Drought Exceptional Circumstances Report Thu, 11 Dec 2008 03:50:14 +0000 http://landshape.org/enm/?p=422#comment-4533 [...] R code used in this analysis is available at http://landshape.org/enm/effects-of-global-warming. Verification of the accuracy of my code, and further analysis of the data would be [...] [...] R code used in this analysis is available at http://landshape.org/enm/effects-of-global-warming. Verification of the accuracy of my code, and further analysis of the data would be [...]

]]> By: Western Australia Future Rainfall http://landshape.org/enm/effects-of-global-warming/#comment-4532 Western Australia Future Rainfall Thu, 11 Dec 2008 03:40:50 +0000 http://landshape.org/enm/?p=422#comment-4532 [...] reference to the Drought Exceptional Circumstances report, there is evidence the predictions of extremely low precipitation are particularly sensitive to the [...] [...] reference to the Drought Exceptional Circumstances report, there is evidence the predictions of extremely low precipitation are particularly sensitive to the [...]

]]> By: admin http://landshape.org/enm/effects-of-global-warming/#comment-4531 admin Sat, 09 Aug 2008 09:25:42 +0000 http://landshape.org/enm/?p=422#comment-4531 Something like this I would think would be appropriate. http://www.agu.org/pubs/crossref/2002/2001WR000575.shtml "The statistical procedures, already fully established in the statistical analysis of survival data, convert the problem into one in which a generalized linear model is fitted to a power-transformed variable having Poisson distribution and calculates the trend coefficients (as well as the parameter in the power transform) by maximum likelihood." Something like this I would think would be appropriate.

http://www.agu.org/pubs/crossref/2002/2001WR000575.shtml

“The statistical procedures, already fully established in the statistical analysis of survival data, convert the problem into one in which a generalized linear model is fitted to a power-transformed variable having Poisson distribution and calculates the trend coefficients (as well as the parameter in the power transform) by maximum likelihood.”

]]> By: admin http://landshape.org/enm/effects-of-global-warming/#comment-7500 admin Sat, 09 Aug 2008 09:25:00 +0000 http://landshape.org/enm/?p=422#comment-7500 Something like this I would think would be appropriate. http://www.agu.org/pubs/crossref/2002/2001WR000575.shtml "The statistical procedures, already fully established in the statistical analysis of survival data, convert the problem into one in which a generalized linear model is fitted to a power-transformed variable having Poisson distribution and calculates the trend coefficients (as well as the parameter in the power transform) by maximum likelihood." Something like this I would think would be appropriate.

http://www.agu.org/pubs/crossref/2002/2001WR000575.shtml

“The statistical procedures, already fully established in the statistical analysis of survival data, convert the problem into one in which a generalized linear model is fitted to a power-transformed variable having Poisson distribution and calculates the trend coefficients (as well as the parameter in the power transform) by maximum likelihood.”

]]> By: admin http://landshape.org/enm/effects-of-global-warming/#comment-4530 admin Sat, 09 Aug 2008 08:40:25 +0000 http://landshape.org/enm/?p=422#comment-4530 Nick, Your points are taken, I don't intend to defend regression fits in this case, as I said the statistics (droughts) is an extreme value over threshold type of thing, as the distribution suggests. Given that, I doubt Cochrane-Orcutt would be strictly appropriate either, I don't know enough about it, but the extreme values are not like temperature (lots of zeros). You really need to get an extreme value expert onto it. The reason for the basic fit was to do the first of a number of basic tests, and running trends through is probably the most basic. I think the return period is the best of the group as it differences the values, and so should reduce any autocorrelation. Only the return period does not give trend information, only an average frequency of each series, so a trend would be good too. That said, when the message is strong in a number of tests, it doesn't make much difference what tests you use. Enough people have shown that GCMs are not reliable for precipitation at the regional scale, that it shouldn't need to be proven to the n-th degree over again. Nick, Your points are taken, I don’t intend to defend regression fits in this case, as I said the statistics (droughts) is an extreme value over threshold type of thing, as the distribution suggests. Given that, I doubt Cochrane-Orcutt would be strictly appropriate either, I don’t know enough about it, but the extreme values are not like temperature (lots of zeros). You really need to get an extreme value expert onto it. The reason for the basic fit was to do the first of a number of basic tests, and running trends through is probably the most basic.

I think the return period is the best of the group as it differences the values, and so should reduce any autocorrelation. Only the return period does not give trend information, only an average frequency of each series, so a trend would be good too.

That said, when the message is strong in a number of tests, it doesn’t make much difference what tests you use. Enough people have shown that GCMs are not reliable for precipitation at the regional scale, that it shouldn’t need to be proven to the n-th degree over again.

]]> By: admin http://landshape.org/enm/effects-of-global-warming/#comment-7499 admin Sat, 09 Aug 2008 08:40:00 +0000 http://landshape.org/enm/?p=422#comment-7499 Nick, Your points are taken, I don't intend to defend regression fits in this case, as I said the statistics (droughts) is an extreme value over threshold type of thing, as the distribution suggests. Given that, I doubt Cochrane-Orcutt would be strictly appropriate either, I don't know enough about it, but the extreme values are not like temperature (lots of zeros). You really need to get an extreme value expert onto it. The reason for the basic fit was to do the first of a number of basic tests, and running trends through is probably the most basic. I think the return period is the best of the group as it differences the values, and so should reduce any autocorrelation. Only the return period does not give trend information, only an average frequency of each series, so a trend would be good too. That said, when the message is strong in a number of tests, it doesn't make much difference what tests you use. Enough people have shown that GCMs are not reliable for precipitation at the regional scale, that it shouldn't need to be proven to the n-th degree over again. Nick, Your points are taken, I don’t intend to defend regression fits in this case, as I said the statistics (droughts) is an extreme value over threshold type of thing, as the distribution suggests. Given that, I doubt Cochrane-Orcutt would be strictly appropriate either, I don’t know enough about it, but the extreme values are not like temperature (lots of zeros). You really need to get an extreme value expert onto it. The reason for the basic fit was to do the first of a number of basic tests, and running trends through is probably the most basic.

I think the return period is the best of the group as it differences the values, and so should reduce any autocorrelation. Only the return period does not give trend information, only an average frequency of each series, so a trend would be good too.

That said, when the message is strong in a number of tests, it doesn’t make much difference what tests you use. Enough people have shown that GCMs are not reliable for precipitation at the regional scale, that it shouldn’t need to be proven to the n-th degree over again.

]]> By: Nick Stokes http://landshape.org/enm/effects-of-global-warming/#comment-4529 Nick Stokes Fri, 08 Aug 2008 14:20:59 +0000 http://landshape.org/enm/?p=422#comment-4529 I should back up this claim of residual test need with an <a href="http://landshape.org/enm/surface-temperatures-estimating-the-sd-of-the-trends" rel="nofollow">authoritative quote</a> "On the contrary, temperature measurements are serially correlated to a degree that affects statistics such as measures of variability, particularly the standard deviation (SD). In a serial correlation the new temperature is determined in part by the previous one. Stated another way, there appears to be more information in the series than there really is. One of consequences is that the calculated SD is lower than it should be, making the confidence limits smaller than they should be." I think this drought data is even more correlated. I should back up this claim of residual test need with an authoritative quote
“On the contrary, temperature measurements are serially correlated to a degree that affects statistics such as measures of variability, particularly the standard deviation (SD). In a serial correlation the new temperature is determined in part by the previous one. Stated another way, there appears to be more information in the series than there really is. One of consequences is that the calculated SD is lower than it should be, making the confidence limits smaller than they should be.”
I think this drought data is even more correlated.

]]> By: Nick Stokes http://landshape.org/enm/effects-of-global-warming/#comment-7498 Nick Stokes Fri, 08 Aug 2008 14:20:00 +0000 http://landshape.org/enm/?p=422#comment-7498 I should back up this claim of residual test need with an <a href="http://landshape.org/enm/surface-temperatures-estimating-the-sd-of-the-trends" rel="nofollow">authoritative quote</a> "On the contrary, temperature measurements are serially correlated to a degree that affects statistics such as measures of variability, particularly the standard deviation (SD). In a serial correlation the new temperature is determined in part by the previous one. Stated another way, there appears to be more information in the series than there really is. One of consequences is that the calculated SD is lower than it should be, making the confidence limits smaller than they should be." I think this drought data is even more correlated. I should back up this claim of residual test need with an authoritative quote
“On the contrary, temperature measurements are serially correlated to a degree that affects statistics such as measures of variability, particularly the standard deviation (SD). In a serial correlation the new temperature is determined in part by the previous one. Stated another way, there appears to be more information in the series than there really is. One of consequences is that the calculated SD is lower than it should be, making the confidence limits smaller than they should be.”
I think this drought data is even more correlated.

]]> By: Nick Stokes http://landshape.org/enm/effects-of-global-warming/#comment-4528 Nick Stokes Fri, 08 Aug 2008 09:59:00 +0000 http://landshape.org/enm/?p=422#comment-4528 David, I didn't mean the distribution of the points, but of the residuals. Whenever you do a statistical test for significance, you have a model where there is an underlying iid (independent and identically distributed) random variable. From its distribution, you decide wqhether the results could have arisen by chance. For regression, it is the residuals that are assumed to be iid, and usually, for the test, assumed normal. Now I was surprised when you chose to compare regression fits, because neither the model nor observed results seem to be good candidates. Because the drought statistic is close to zero in many years, and large positive in others, the notion of random variations about a mean line seems unlikely to work, and then will not give a good test. To be convincing, you really need to do test the residuals. Testing for autocorrelation is easy and so is the simplest remedy (eg <a>Cochrane-Orcutt</a>). To test normality, you can use a <a href="http://en.wikipedia.org/wiki/Jarque-Bera_test" rel="nofollow"> Jarque-Bera</a>, although the remedy if that fails is not so clear. David,
I didn’t mean the distribution of the points, but of the residuals. Whenever you do a statistical test for significance, you have a model where there is an underlying iid (independent and identically distributed) random variable. From its distribution, you decide wqhether the results could have arisen by chance. For regression, it is the residuals that are assumed to be iid, and usually, for the test, assumed normal.

Now I was surprised when you chose to compare regression fits, because neither the model nor observed results seem to be good candidates. Because the drought statistic is close to zero in many years, and large positive in others, the notion of random variations about a mean line seems unlikely to work, and then will not give a good test.

To be convincing, you really need to do test the residuals. Testing for autocorrelation is easy and so is the simplest remedy (eg Cochrane-Orcutt). To test normality, you can use a Jarque-Bera, although the remedy if that fails is not so clear.

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