 OK, so we've now gone to the maps option. Previously, we looked at time series, which give us a single number averaged over some region of the globe, typically the entire globe, or the oceans, or the land regions. But we can also look at the detailed latitudinal and longitudinal structure of these trends in various variables produced from the model. The easiest way to do that is to pick, say, a five-year period at the beginning of the simulation and a five-year period at the end of the simulation so that we average out some of the year-to-year fluctuations. And we have an early baseline that we can compare to some later average in the model. And I've already computed averages for various variables here, snow cover, precipitation, soil moisture, surface air temperature, ocean mixed layer temperature. I've computed those all for a base period of 1958 to 1963, the first five years of the model simulation. And now what I'm going to do is calculate the spatial patterns of those variables for the last five years of the simulation. So let's do that right now, 2008, 9, 10, 11, 12. We're going to calculate the averages over that period. And it's doing that right now. You can see it going through the individual months for each of those five years. And it's going to calculate the averages of all those variables for each of the grid boxes in this model. And the model is fairly low resolution. So as we'll see, the individual latitude, longitude grid boxes are in the order of like seven degrees latitude and longitude. So it's a pretty coarse description of the surface. State-of-the-art climate models today are run at much higher resolutions. But this is an earlier climate model. And at that time, it was necessary to resolve the Earth's surface into fairly coarse latitude, longitude grid boxes to run the models efficiently. So now we're going to take a look at the spatial patterns of some of these variables. And in particular, the difference between the most recent five years and the first five years of the simulation, giving us a sense of how the model simulation is projecting changes over time and over the surface of the Earth.