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That analysis is called "a regression analysis" (for those who have never published such data in the literature). I am familiar with this analysis, as one of my papers evaluating the relationship between serum osmolality and cererbal edema in a tumor model used this very same analysis. The "r" value that more closely approximates .99 suggests a strong correlation.
The latter portion of the graph with the regression analysis is clearly fudged.
As per usual, I would not expect the AGW cult members to understand any analysis of data, as they have no training and would be expected not to know such things (despite the fact that they claim superlative expertise in all things scientific).
As per usual you prove you don't know what you're talking about. (despite the fact that you claim superlative expertise in all things scientific including many different fields of science completely outside your own supposed field). The authors weren't using simple linear regression like your supposed 'paper'. That's basic high school level stats and is enough for the simple 'study' you claim you did.
Clearly you didn't bother to read the paper, just eyeballed the chart posted by the OP, and made some stupid assumptions based on your own lack of knowledge and lack of expertise in more complex statistical analysis methods. They used methods like continuous segmented regression (CSR) among others:
Read the Methods section of the paper (because it's damned obvious you didn't):
We used segmented regression to test for abrupt changes in the trend of ice dates in Torne. Specifically, we wanted to test when a shift in the temporal trend of ice date may have occurred. To estimate the timing and magnitude of a change in the slope of ice dates, we used continuous segmented regression (CSR) models. In CSR, trend lines on either side of the estimated breakpoint intersect (hence making them “continuous”), but are allowed to have different slopes. In general, a CSR takes the form
is a latent variable representing ice dates, xi are the years of the time series, β0 is the intercept of the regression (ice date on year 0), β1 is the trend in ice date prior to any breakpoint (ice date per year), the ak are the breakpoints (k was either 1 or 2 for this study. Because the number of parameters increases with k, we limited k to avoid over-fitting the model), the βk+1 are the changes in the temporal trend at each of the k breakpoints compared to the trend prior to the breakpoint, and the εi are the errors. Note that the βk+1 parameters indicate the effect on ice date of years elapsed since the previous breakpoint once the breakpoint has passed.
I'm not going to read through all of the responses, but has anyone actually read the article?
It REPEATEDLY insists that the Industrial Revolution is responsible for warmer temperatures, as evidenced by its impact on lake and river ice seasonality.
the study found that, from 1443 to 1683, Lake Suwa's annual freeze date was moving almost imperceptibly to later in the year -- at a rate of 0.19 days per decade. From the start of the Industrial Revolution, however, that trend in a later freeze date grew 24 times faster, pushing the lake's "ice on" date back 4.6 days per decade. On the Torne River, there was a corresponding trend for earlier ice break-up in the spring, as the speed with which the river moved toward earlier thaw dates doubled. These findings strongly indicate more rapid climate change during the last two centuries, the researchers report.
So why exactly are all the deniers patting themselves on the back and talking about the 'AGW cult' right now?
My favorite part, from the author of the paper:
Quote:
Our findings not only bolster what scientists have been saying for decades,
Wow... we alarmist warmist cultist liberals sure do look like idiots now.
Not that anyone will actually read it, but this article explains why the Industrial Revolution is responsible for elevated CO2:
From your link....
The temperature trends during most of the post-ice-age period match those expected from natural factors such as the long-term variation in the tilt of Earth’s axis, says Marcott. But in the past century and a half, industrial emissions of the greenhouse gas carbon dioxide have increased — which helps to explain why global temperatures have risen so quickly in recent decades, he suggests.
By the end of this century, regardless of future carbon dioxide emissions, temperatures will be at their highest since the end of the most recent ice age, the researchers say.
The question of climate that paleoclimate studies are most useful is what might happen when we bust through the CO2 levels we have now and reach the heights not seen n this world since the Eocene Warming of 50 million years ago and this will occur when we double the current CO2 levels of 450 ppm and we will reach this level within the life spans of some people who were just born today. Then we might bust through this level and with 10 billion of us at the opening of the 22 nd century all wanting affluence we can only imagine push it up to levels not seen since Jurassic by burning the fossil fuels to achieve it.
The ice sheets in Antarctica which today lock up 5 million cubic miles of water all above todays mean sea level will have melted and all that water will have to find a place in our Oceans raising the Ocean levels by some 150 feet.
Somebody read the methodology. And no one has brought up why that merhodolgy was chosen.
Again, for scientists, comical. Good thing they don't make a living off trend forecasting reliable data.
I read the methodology. It's very clearly explained in detail. I also read some of the cited reference papers. There was an interesting paper from the 1950's.
Good thing comical clowns like you, the OP and Hawkeye who think you're 'experts' in every field of science, don't rely on your scientific 'expertise' to make a living.
I read the methodology. It's very clearly explained in detail. I also read some of the cited reference papers. There was an interesting paper from the 1950's.
Good thing comical clowns like you, the OP and Hawkeye who think you're 'experts' in every field of science, don't rely on your scientific 'expertise' to make a living.
Good thing you don't rely on your personality for a job.
I read the methodology. It's very clearly explained in detail. I also read some of the cited reference papers. There was an interesting paper from the 1950's.
Good thing comical clowns like you, the OP and Hawkeye who think you're 'experts' in every field of science, don't rely on your scientific 'expertise' to make a living.
No. I'm a data specialist, among other things. The merhodolgy utilized is a gap fill from unreliable/missing piece data sets.
Try again
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is a latent variable representing ice dates, xi are the years of the time series, β0 is the intercept of the regression (ice date on year 0), β1 is the trend in ice date prior to any breakpoint (ice date per year), the ak are the breakpoints (k was either 1 or 2 for this study. Because the number of parameters increases with k, we limited k to avoid over-fitting the model), the βk+1 are the changes in the temporal trend at each of the k breakpoints compared to the trend prior to the breakpoint, and the εi are the errors. Note that the βk+1 parameters indicate the effect on ice date of years elapsed since the previous breakpoint once the breakpoint has passed.
