Consider a variable $Y$ (e.g., temperature). Suppose that we were able to estimate this variable each year for the past $N$ years using some type of model. This means we have access to annual estimated values for Y (denoted as $Y_1, \ldots, Y_N$) and associated standard errors $S_1,\ldots,S_N$. The goal is to produce a point forecast for the value of $Y$ at the time N+1 which incorporates the uncertainty present in the estimated annual values $Y_1,\ldots,Y_N$.
One way to proceed is to use Monte Carlo simulation to create an ensemble of $B$=100,000 (or a large enough number) of synthetic time series obtained by shifting each of the original values $Y_1,\ldots,Y_N$ by a random z score (i.e., Gaussian white noise), scaled by the standard error $S_t$. (This approach assumes that $Y_1,\ldots,Y_N$ are independent.)
Each synthetic series can then be used to produce (I) a point forecast of $Y$ at time $N+1$ and (II) an interval forecast of $Y$ at time $N+1$.
My question is:
How do we summarize the information conveyed by the simulated point forecasts and interval forecasts to quantify the uncertainty present in $Y_1,\ldots,Y_N$ and its impact on the forecasting output?
For point forecasts, it makes sense to report the simulation distribution of the point forecasts. But what aspect of this distribution captures the uncertainty (e.g., spread)?
For interval forecasts, it is not clear (at least not to me) how to proceed. Is there any way to quantify uncertainty in the forecasting input (i.e., $Y_1,\ldots,Y_N$) when it comes to width and/or coverage of these intervals? (Maybe by using some type of retrospective performance of the prediction procedure?)