Here are a couple of base R suggestions, one for where the weights are integers but not too large and the second for where the weights are simply positive
# example data
df <- data.frame(temp=c(50,20,10,40), weight=c(3,1,4,2))
# unweighted empirical CDF
plot.ecdf(df$temp,
main="unweighted ecdf")
# weighted empirical CDF if weights are positive integers or counts
plot.ecdf(rep(df$temp, df$weight),
main="weighted ecdf 1 - using counts")
# weighted empirical CDF if weights are positive
dfsorted <- df[order(df$temp), ]
dfsorted$cumfreq <- cumsum(dfsorted$weight) / sum(dfsorted$weight)
dfsorted2 <- dfsorted[rep(1:nrow(df), each=2),]
dfsorted2$cumfreq <- c(0,dfsorted2$cumfreq[-2*nrow(df)])
plot(dfsorted2$temp, dfsorted2$cumfreq, type="l",
main="weighted ecdf 2 - general weights", xlab="temp", ylab="cumfreq")
So the unweighted ecdf looks like
and the first weighted ecdf looks like
and the second weighted ecdf looks like
In very broad terms I'd question the value of this. It is easy to concoct examples in which correlations are similar but the relationships between variables are different -- and in which correlations are different but the relationships between variables are similar. I write not only as someone interested in statistics but also as someone whose main applications are with environmental data.
Also, what you are proposing to do doing puts enormous weight on correlations as a catch-all summary measure, which necessarily cannot do justice to nonlinearities, clustering, outliers, etc., which are commonplace with environmental data. An analysis of analyses is not out of the question, but the great risk is that each analysis step is a step away from the data you are trying to understand.
Yet another negative: It is difficult to make sense of your graphs without labelling which correlation is which using the names of the variables. You have presumably 91 correlations, but labelling them all will just be confusing; labelling none of them will just be uninformative.
Suggesting a positive alternative would depend on a deeper acquaintance with your scientific objectives, but if these were my data I would start with a single pooled multivariate analysis of three regions and then see whether regions cluster in some low-dimensional subspace. PCA does indeed spring to mind if your variables are mostly or all measured variables.
You name yourself as a R user, but your graphs look like to me like Excel defaults. I suggest that your graphs should show bounds of $[-1,1]$ on both axes; shift the $x$ axis with its numeric labels away from the middle of the graph; and use open or hollow symbols such as "o" rather than solid symbols.
P.S. In statistics, parameters and variables are not alternative terms. Your parameters are all variables.
Whether you are a student or a professional, you might benefit from finding a friendly local statistician, or someone in your field with more statistical experience, to talk to.
(LATER) If you are determined to do this, an extension of @Dualinity's approach to parallel coordinate plots might help.
Best Answer
In order of your questions:
pnorm.Here is some code based on your questions which I believe should help:
Running more simulations would provide a better fit. Here is the result of the exact same code using $n = 10,000$.
Update
To show how using 10,000 observations makes the results very close, I will redo the plots with two line types and thicker lines to show they are both there. I will also change the empirical to red for contrast. The blue will remain the true CDF.