As a mathematician myself, I can confirm this would be a major flaw. Time Series Analysis 101 would tell you the importance of checking the order of integration of your data. For data following an additive random walk (i.e. tomorrow = today + some change), you have to difference the data.
Dinosaurs would not have survived even one single WINTER, so the world has been cooling drastically for, uh, 65 million years or so...
As a mathematician myself, I can confirm that following your advice would be a major flaw.
If the sky isn't falling, then we don't need to pay as much money for climate research, and the climate "scientists" may see their pay drop. They seem to have chosen profits over integrity, which could be a terrible error if they're actually right despite their sloppiness [I'd put in a "boy who cried wolf" picture, but I have a one fairy tale per post limit].
That is a startling omission, one with consequences for how the IPCC's recommendations should be interpreted. A fairly elementary alternative assumption that some researchers and I have tested fits the actual temperature data better than the IPCC's AR1 assumption—so much better that we can conclude that the IPCC's assumption has no support. Under the alternative assumption, the data do not show a significant increase in global temperatures. We don't know whether the alternative assumption itself is reasonable—other assumptions might be even better—but the improved fit does tell us that until more research is done on the best assumptions to apply to global average temperature series, the IPCC's conclusions about the significance of the temperature changes are unfounded.
None of this is opinion. This is factual and indisputable. It applies to any warming—whether attributable to humans or to nature. This assumption problem is not unique to the IPCC, either. The U.S. Climate Change Science Program, which advises Congress, published its report on temperature increases in 2006, and relied on the same insupportable assumption.
As a mathematician myself, I can confirm this would be a major flaw. Time Series Analysis 101 would tell you the importance of checking the order of integration of your data. For data following an additive random walk (i.e. tomorrow = today + some change), you have to difference the data.Nowhere in the IPCC report is any testing done on the changes in global temperatures; only the temperatures themselves are considered.
Although not a mathematician but an engineer, I have been accused of being a mathematician by engineers. And I have to say, from my experience of random data from tests, that just from the waveforms of the sunlight intensity and the ice quantity you can see that taking the derivative of the ice quantity will phase shift the data toward alignment with the inverse light intensity data. Seeing the actual result of the "derivative" operation is a great big DUH! Of course.And when you think about it, why assume that the quantity of ice is what is determined by the intensity level, when we know that they vary with time? That is the assumption that the ice has no thermal inertia. It is the rate of change of the ice quantity which is driven by the intensity. Makes perfect sense.