Quote:You clearly have no idea what conclusion I've arrived at concerning the topic. Where did I ever say that it's not a real phenomenon?


 

What is "significant?" Give me a number. Should we really be concerned with 1 degree of warming? The temperature in my house varies more than that from room to room. That's the effect of a doubling of CO2, and agrees with the measurements. The atmosphere is not a simple bell jar, and there are plenty of confounding factors, the main one being cloud cover which the models don't handle. There is also the problem that the temperatures rose at the same rate in the early 1900s when anthropogenic CO2 was not significant.


 

I also have a problem with the lies being perpetrated by the scientific community. Here is a quote from climate.nasa.gov:


 

 

If you go to NOAA.gov, they have a large collection of tide gauge records. None of them, some going back more than a century, show any change in the rate of sea level rise. There is no acceleration! The quote above is a flat out lie. As far as I know not a single Climate Scientist has publicly criticized it.

 

Not understanding what "significant" means makes it clear you don't have a background in science. If results are statistically significant it means that you accept or reject the null hypothesis (something like there is no warming or warming = 0). There are various test to determine the degree of significance usually based on the probability of having a 5% (p < 0.05, 95% confidence) chance of finding a difference as large or larger than the one in your study given that the null hypothesis is true. I'll give you a very basic illustration of how this works. 

 

Say I am interested in the effect of solar radiation on tropospheric warming, and using average temperatures over a span of 50 years as an endpoint. Say you took 100 temperature measurements for each of those 50 years (you might need to run a power calculation to determine if this is an adequate sample size).You could use other endpoints but I am using temperature as one in this example. 

 

HO (null hypothesis): Solar activity has no significant effect on tropospheric warming.

HAalternative hypothesis): Solar activity has a significant effect on tropospheric warming. 

 

So in this very very simplistic exercise you might run a ANOVA (this would probably be a bad test because the data is likely not normally distributed and the test is not very robust, Kruskal-Wallis, PERMANOVA, etc might be better). on the averages using a program like R.

 

The output will have a test statistic (F-value) and the degree of significance. Let's say we were testing at p < 0.05. If our results indicate p = 0.002, then we would reject the null hypothesis and accept the alternative. We reject the null because at the significance level of 0.05, the results obtained happen too frequently for us to be confident that solar activity has a effect on tropospheric warming. The opposite can also happen. If p = 0.06, the you would accept the null and the results would indicate that solar activity has no effect on tropospheric warming. Again this is very very basic. 

 

You could do other things like create a linear mixed model but I won't get into that.

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