 Hello, this is Professor Neshaba, and I'm here to help you out with the second phase, the analysis phase of your GCM run. And hopefully what you're looking at here is that the GCM run completed successfully. And so then we can go back to the regular GCM file here. And so what I'm looking at over here is the run that we just completed, which you probably have to click on again. This blue, by the way, says that it's got the data on it. And let's just make sure that that's right. Yep, this is the based on sample control, year of 2017. Everything's fine on that. So now that we have this highlighted, we can go to this thing called Analyze Output icon. So I'm going to click on that. Now, the first window that comes up here is we kind of have to start on the left and we'll work our way to the right. There was only one year, if we had done multiple years, various years would have shown up here. But that one year is what we want. So I need to say I'd like the averages for that year. And it's going to do that. And as soon as it comes back, it's going to tell us that it's done. So now this appears here and we've got the averages for 2017. What we need to do next is go over to this window here and say what sort of things do we want to look at. I think I need to click on this. And you could get all of them if you want, but some of the things that you might be interested in are Snow and Ice coverage, total cloud cover, and the surface air temperature. So I'm going to click those three. These are all, we can just leave those as the default comes out. But we do need to say I want to extract the data. So I pressed on that button and now it's going through that whole year and it's extracted the data. And now this item up here appears. And I think you can press this view button, but on my machine I can just double click on that. Now what's going to happen is that it's going to launch Penoply. But if you're on a PC, it will launch something called Ava and it will look a little bit different. But here we are in the viewing software. And you can see that the three things that I asked for, Snow and Ice coverage, surface air temperature, and total cloud coverage has come here, come up here. So once you click on this, then you have the option of creating a plot, which I can do. Or you can double click. And here we are. Here is the surface air temperature according to that GCM for the year of 2017. And there's lots of things that we can look at here and let me just take you through a few of them. So one of them is that you can see that it's extracted the month of January. And you can see that in January the Arctic is cold and the Antarctic is not as cold as because it's summer in the southern hemisphere. If I wanted to look in our summer, you can see that that's reversed. And if I want to look at the whole average, then what this is telling us is that on average the Antarctic is colder than the Arctic and so on. Other things that are quite interesting here that I'm just going to point out right now is that the interpolation is turned on by default. But if you turn that off, what you can see is that these squares here are telling us what EDGCM actually used as its grid. And the thing that I just want to point out is that it's pretty big. In other words, GCM treats this whole block right here, if you like, as representing that part of Spain and the ocean above it. So it's not a super high resolution, high spatial resolution product. I'm going to turn that interpolate back on. Scale, things that are pretty interesting to work on here is I kind of like to have when we're talking about temperature. I like to center it on zero degrees, in this case Celsius, so I'm going to do that. And what this means, of course, is that everywhere in red is above freezing, which is the mid-latitudes. And everywhere sort of in blue cooler shades is below freezing. And let's see what other things we can do here. Now, there's also different projections, map projections that you can look at. That's the equirectangular. And let's see if I can get a more interesting one. Airy shows us the Earth this way. But I'm going to go back to the equirectangular. And so on, and you can label things differently as you wish. If you also want to get what's called a zonal average, what that means is that it will take every latitude and average across longitudes, then you can do that. I think you have to switch to map and then switch back to zonal averages to get that. So this is telling us that the surface air temperature here at minus 90 at South Pole is this temperature and average overall longitudes. And now we get up to the equator. We get up to the North Pole. So this is telling us that North is on average a little bit warmer than the South Pole. So that's that. And then the other things that we can look at is I'm going to go back to the panoply. And we can say, well, what about total cloud cover? So just double-clicked on that. And here we have a new view. Now in the case of total cloud cover, which usually more interesting to look at is not a color scale, but a scale that perhaps we could look at this gray scale. So this is telling us that where it's white, then it's more cloud cover. And what I want to do here is it's in a percentage and I want to go from no cloud to 100% cloud on this scale. And so as you can see here, the Sahara doesn't have very much cloud cover. Neither does the Southeast United States and so on. And so the other thing that we can look at here is we can also look at snow and ice. And snow and ice coverage also tells you some interesting features. So for example, in this case I think I want to fit to data and I want to center it on zero and maybe invert the colors here so that maybe not. So that is the introduction to the analysis.