 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 the Northern Hemisphere land temperatures. We'll run the regression. Here, EBM, target observation is temperature, we'll run. We see that the R squared value is 0.716. That means a fairly impressive just under 72% of the variation in Northern Hemisphere land temperatures is explained just using this energy balance model. If we look at the value of rho, the lag 1 auto correlation coefficient, 0.067 tells us the 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 model output instead of the EBM. But we'll put them both on the same scale here. One, this one. And you can see that it does provide, as we saw before, a fairly good fit to the data. It explains just under 72% of the variation of the data. If I turn off these plots and look at just the residuals from the model, we can see that the residuals still look pretty random and that doesn't appear to be a whole lot of structure. Although there's quite a bit of interannual variability, and perhaps we can explain some of the 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 we'll look at that next.