Similar to Australia in 2005-06 – large grid box in southern Africa shows huge warming departure in UAH lower troposphere satellite temperature anomalies compared to RSS
6 thoughts on “Similar to Australia in 2005-06 – large grid box in southern Africa shows huge warming departure in UAH lower troposphere satellite temperature anomalies compared to RSS”
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Glad to see it work again
Liking the simple layout … and a useful blogroll
Again, very interesting stuff.
Not only does UAH show a large warming departure relative to RSS around 2005-6, the differences between the two series are more or less the same over the whole period in both Australia and South Africa. In both places, UAH is warmest relative to RSS in 1980-81, 1988 and 2006-12, and is coolest relative to RSS in 1984, 1992-95 and 2002-05.
I wonder if the anomalies are the opposite way round over water?
But what it all means baffles me – I hope RSS and UAH know, and maybe they can explain some time.
Mind you, they probably have other jobs to do too. Why are more researchers not delving into these data and helping improve them by pointing out issues like this? Hundreds of papers pick over the surface temperature records, but they are lousy data to start with anyway, with inhomogeneities all over the place, many of which can never be fixed properly. But getting the satellite record right – while far from easy I am sure – is a much more tractable problem and will lead to much more useful data in the end.
Hah. I just finished a project in which I tried to figure out how much water (and oil) was produced out of a number of connected oil pools and the amount of water that was disposed of (i.e. injected down other wells in the same group of oil pool wells). I plotted up produced volume of water minus injected volume of water. The difference between the two plotted the same as above: one pattern until a certain date, then the reverse pattern.
I concluded that a systemic problem had been created in an early time. First, there was more disposed water than produced:some one was stealing oil by not reporting the total production, but didn’t control the person recording disposing of the “excess” water (there was no profit in that person falsifying the records). But the ratio of oil and water GOING INTO the separation facilities were still the same as the samples at the producing wells, so nobody would notice.Then the scam changed. There was more produced water than injected: now the oil to water ratio coming OUT OF the plant was okay, but there was still oil (and water) missing. The control was now at the disposal side.
The only way to notice the problem was to “square the circle”, but the process was too messy for most people.
The analogy here: first, the data going in to either RSS or UAH is wrong. Then a “change” happens, but the fundamental error style exists, but on the other side. So the disconnect continues, but it is the reverse.
With my oil problem, those who lumped many years together told me that there was “nothing” wrong, just variations in the natural errors, that I needed to look at the longer time period to understand that “it” all was fine. In fact, within my data, it looked as though someone had tried every now and then to fix the long-term situation by suddenly over- or under-reporting production; the year end seemed normal. But of course the accounting was all messed up, but, again, it was too messy and too much work and the profit margin was still okay, so who really cares?
Not saying a scam is going on with the temp data, just relating a story. And pointing out that there looks to be a systemic problem that is misunderstood (probably at least two problems, only one of which has been recognized), and so the “fix” is too much.
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Primarily exposing faulty methodologies behind global temperature trend compilations
Test
test
Glad to see it work again
Liking the simple layout … and a useful blogroll
Again, very interesting stuff.
Not only does UAH show a large warming departure relative to RSS around 2005-6, the differences between the two series are more or less the same over the whole period in both Australia and South Africa. In both places, UAH is warmest relative to RSS in 1980-81, 1988 and 2006-12, and is coolest relative to RSS in 1984, 1992-95 and 2002-05.
I wonder if the anomalies are the opposite way round over water?
But what it all means baffles me – I hope RSS and UAH know, and maybe they can explain some time.
Mind you, they probably have other jobs to do too. Why are more researchers not delving into these data and helping improve them by pointing out issues like this? Hundreds of papers pick over the surface temperature records, but they are lousy data to start with anyway, with inhomogeneities all over the place, many of which can never be fixed properly. But getting the satellite record right – while far from easy I am sure – is a much more tractable problem and will lead to much more useful data in the end.
Hah. I just finished a project in which I tried to figure out how much water (and oil) was produced out of a number of connected oil pools and the amount of water that was disposed of (i.e. injected down other wells in the same group of oil pool wells). I plotted up produced volume of water minus injected volume of water. The difference between the two plotted the same as above: one pattern until a certain date, then the reverse pattern.
I concluded that a systemic problem had been created in an early time. First, there was more disposed water than produced:some one was stealing oil by not reporting the total production, but didn’t control the person recording disposing of the “excess” water (there was no profit in that person falsifying the records). But the ratio of oil and water GOING INTO the separation facilities were still the same as the samples at the producing wells, so nobody would notice.Then the scam changed. There was more produced water than injected: now the oil to water ratio coming OUT OF the plant was okay, but there was still oil (and water) missing. The control was now at the disposal side.
The only way to notice the problem was to “square the circle”, but the process was too messy for most people.
The analogy here: first, the data going in to either RSS or UAH is wrong. Then a “change” happens, but the fundamental error style exists, but on the other side. So the disconnect continues, but it is the reverse.
With my oil problem, those who lumped many years together told me that there was “nothing” wrong, just variations in the natural errors, that I needed to look at the longer time period to understand that “it” all was fine. In fact, within my data, it looked as though someone had tried every now and then to fix the long-term situation by suddenly over- or under-reporting production; the year end seemed normal. But of course the accounting was all messed up, but, again, it was too messy and too much work and the profit margin was still okay, so who really cares?
Not saying a scam is going on with the temp data, just relating a story. And pointing out that there looks to be a systemic problem that is misunderstood (probably at least two problems, only one of which has been recognized), and so the “fix” is too much.