A few weeks ago, Mark Higgins, from EUMETSAT, posted this wonderful video of satellite imagery of planet earth for the whole of the year 2013. The video superimposes the aggregated satellite data from multiple satellites on the top of NASA’s ‘Blue Marble Next Generation’ ground maps, to give a consistent picture of large scale weather patterns (Original video here – be sure to listen to Mark’s commentary):

When I saw the video, it reminded me of something. Here’s the output from the CAM3, the atmospheric component of the global climate model CESM, run at very high resolution (Original video here):

I¬†find it fascinating to play these two videos at the same time, and observe how the model captures the large scale weather patterns of the planet. The comparison isn’t perfect, because the satellite data measures the cloud temperature (the colder the clouds, the whiter they are shown), while the climate model output shows total water vapour & rain (i.e. warmer¬†clouds are a lot more visible, and precipitation is shown in orange). This means the tropical regions look much drier in the satellite imagery than they do in the model output.

But even so, there are some remarkable similarities. For example, both videos clearly show the westerlies, the winds that flow from west to east at the top and bottom of the map (e.g. pushing rain across the North Atlantic to the UK), and they both show the trade winds, which flow from east to west, closer to the equator. Both videos also show how cyclones form in the regions between these wind patterns. For example, in both videos, you can see the typhoon season ramp up in the Western Pacific in August and September – the model has two hitting Japan in August, and the satellite data shows several hitting China in September. The curved tracks of these storms are similar in both models. If you look closely, you can also see the daily cycle of evaporation and rain over South America and Central Africa in both videos – watch how these regions appear to pulse each day.

I find these similarities remarkable, because none of these patterns are coded into the climate model – they all emerge as a consequence of getting the basic thermodynamic properties of the atmosphere right. Remember also that a climate model is not intended to forecast the particular weather of any given year (that would be impossible, due to chaos theory). However, the model simulates a “typical” year on planet earth. So the specifics of where and when each storm forms do not correspond to anything that actually happened in any given year. But when the model gets the overall patterns about right, that’s a pretty impressive achievement.


  1. On occasion when someone is trashing climate models, Eli points out that they were originally called Global Circulation Models and one of the things that gave them credence was that they got the circulation right. As you point out, this is amazing.

  2. This is fascinating. A close look finds the north Atlantic a little quiet in the model. A guess from this unqualified member of the peanut gallery is that the complexity of interactions with land are greater there..

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  4. Susan: That’s an interesting observation, and is exactly the kind of question the modellers spend their time exploring. A modeller would start from an observation that the model seems a little off in some region and explore what’s going on. First, it might be an artefact of the comparison (remember the two videos aren’t showing exactly the same measurements, so there might be more going on there in the model than it looks). Also, remember that the map projection distorts the land surface as you get closer to the arctic regions, which will exaggerate motion in regions like the N. Atlantic. So we’d better check that the apparent difference isn’t because of such reasons. Once we’ve eliminated those, we ought to check how the model run was set up. I haven’t checked, but this run probably used the conditions as they were either in the pre-industrial era, or the mid-20th Century – it’s certainly not a run that simulates the climate changed conditions we find ourselves in today. So that might lead me to wonder if this is a result of climate change. I’d then do the run again, this time with 21st century conditions (more greenhouse gases, for example), and see if the difference disappears. I’d also go back to older satellite data and see if the difference appears in data from say 30 years ago. After all that, if I’m still convinced the model is not capturing something that appears in the real data, I’ll form a hypothesis for why not, and try modifying the model to test this hypothesis. It’s through this kind of process that the models gradually evolve to be better and better.

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  6. Steve, thanks for posting this. I use that CCSM video all the time in lectures.
    Is there a place where one can download Mark Higgins’s video?

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