We'll go from a minimum of 1 to a maximum of 5.
And that's the black curve is a global average ice cover, and it is decreasing significantly as the Earth is warming. Again, as we might expect and as we have seen in the observations. Global ice cover is decreasing over time fairly dramatically. Northern hemisphere snow cover, where we have widespread records, is decreasing significantly as the model projects it too.
And of course surface temperatures are warming. So we can look at various variables other than surface temperature in this model and get some sense of what else is going-- what else is going on in this model. What else does this model project that we could look to the observations and see if the things that the models project should happen as we increase CO2 concentrations are indeed happening. And why don't we take a look at global mean precipitation.
And global mean precipitation is increasing.
It's wetter over the oceans than it is over land, and the global average, the average of land and ocean regions is the black curve. If we zoom in on that curve, OK, that gives us an idea of how precipitation is changing. Precipitation, in this case, is expressed as millimeters per day. So in the global average, precipitation is increasing. That is also one of the robust projections of climate models. As we increase the surface temperature of the Earth, warmer oceans evaporate more water vapor into the atmosphere. The rate of evaporation increases. And to conserve water, that means that the rate of precipitation has to increase as well.
In other words, we get a more vigorous hydrological cycle-- faster evaporation and faster precipitation of water out of the atmosphere. And so the models project that global precipitation should increase, but what we saw previously in the observations was that, in fact, precipitation is a variable where there are very large regional differences. Certain regions become wetter, but other regions become drier. And so this is a case where looking at the global average, looking at a single time series, is going to be somewhat misleading. We really need to go to the actual spatial patterns of response in the models to see if we can make sense of what the model is projecting, and so that's what we'll do next.
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.
Gregory and J. Thus the computer calculates none of the cloud's details. Global ice cover is decreasing over time fairly dramatically. Bryan a , p. The differences in the mean number of wet days between resolutions are most pronounced in the mid and high latitudes of the Northern Hemisphere in DJF, as well as in JJA. All this was based on a few equations that could be written on one sheet of paper. Figure 3: Global-mean surface temperature, relative to average, from observations black line and from 58 simulations orange lines by 14 global climate models reported on in the IPCC Fourth Assessment.
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 to , 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-- , '09, '10, '11, ' 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 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. So I've extracted the last five years of the simulation, the period through , the average for that five-year period.
And I'm now going to read it into the Data Browser, where it's available to plot. Then as a baseline, I am going to take the first five years, the average over the first five years of the simulation, read that into the Data Browser. And what I'm going to do next is to take the difference. Let me select Surface Air Temperature.
We're going to take the difference between the last five years and the first five years of the simulation. And that'll give us the spatial pattern of the trend in temperature over the globe. So I want Data one minus Data two. It's going to plot that out for me. Unfortunately, it uses a nonsensical color scale. So I am going to change the color scale so that it is more meaningful. So the warm colors all indicate warming. The cold colors would indicate cooling. Of course, the entire globe is warming in this simulation, warming more at high latitudes, particularly in the northern hemisphere, where, as we know, sea ice is decreasing markedly.
And so that ice albedo feedback is kicking in, giving us that additional warming at high latitudes in the northern hemisphere.
And there's some interesting structure in the southern hemisphere, as well, perhaps a little bit more warming over the land regions than over most of the ocean regions, although the variations are fairly small. So that's the projected surface air temperature pattern.
Of course, as I've said before, this is a fairly primitive climate model by modern standards. And a lot of the more interesting variations in ocean circulation and atmospheric circulation that give us more complicated patterns of surface temperature changes in the projections of current state-of-the-art climate models are not really resolved in this relatively primitive model. But that's what the surface pattern of warming looks like. And we can now look at the patterns of some other variables. So now I've selected Precipitation. And we're going to look at the change in precipitation over the surface of the globe.
Differenced-- the first five years minus-- the last five years minus the first five years of the simulation.
OK-- Data one minus Data two. And this is what we get. Again, let's use a more sensible scale. So we'll go from minus 3.
So we can see that overall, precipitation is increasing over the globe, as we already saw when we plotted out the global average precipitation. But there are some strong regional variations. There is a concentration of increased precipitation in a band near the equator.
The largest increases in precipitation are near the equator, where we have the Intertropical Convergence Zone rising motion in the atmosphere. And since the surface is warmer and there's more water vapor in the atmosphere, we get more rainfall in the region where rainfall tends to occur, which is this Intertropical Convergence Zone.
But as we go to the subtropics, we can see large patches of white. And what that's indicating is that in fact, in the descending branch of the Hadley circulation, where we tend to see deserts, that region is actually expanding poleward somewhat. And so that region of drying is now expanding towards the poles. And that's why we see these large areas of at least small decreases in rainfall in subtropical regions, contrasting with the large increases we see closer to the equator.
And then as we get into higher latitudes again, you can start to see that there's a larger tendency, again, for increased precipitation in the subpolar latitudes. And that is associated with a migration poleward of the mid-latitude band of frontal precipitation in both hemispheres. So when we look at the pattern of rainfall, there's a far richer pattern of regional variation that tells us that, in fact, if we want to understand projected changes in rainfall, it's important not to just look at global averages or hemispheric averages. But look at the underlying pattern of changes, which is fairly complex, even in this case.
But of course, this is a relatively simple model. Encyclopedia of Remote Sensing Edition. Contents Search. Atmospheric General Circulation Models. Reference work entry First Online: 29 December How to cite. Definition Atmospheric general circulation models atmospheric GCMs are mathematical models based on numerically discretized versions of differential equations that describe the atmospheric physics and dynamics, which are utilized to simulate the global atmospheric circulation.
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