 Okay, before we go on, let's actually do the formal regression using only the single predictor of the energy balance model simulation. As a predictor of Northern Hemisphere land temperatures, run the regression. We see the R squared value is 0.716, that means we explain a fairly impressive just under 72% of the variation in Northern Hemisphere land temperatures using just the result of that model simulation. If you look at the value of rho, the lag 1 auto correlation coefficient, 0.057, that tells us that auto correlation of the residuals doesn't appear to be a problem. So let's go back to the plot, and now we're going to plot the regression result, model output. We'll convert that to a line plot, and you can see that it does provide, as we saw before, a fairly good fit to the data, explains just under 72% of the variation in the data. And if we look at the residuals from that regression, model residuals, they look pretty random. There doesn't seem to be a whole lot of structure. Although there is quite a bit of interannual variability, and perhaps we can explain some of that interannual variability through two other predictors, the El Nino phenomenon and the North Atlantic Oscillation phenomenon, two internal climate modes that influence Northern Hemisphere land temperatures. So let's take a look at that next.