For an analysis of the recently released IPCC report, click HERE
For a syllabus on systems thinking for global issues, click HERE
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):
A Year of Weather 2013.This visualisation, comprised of imagery from the geostationary satellites of EUMETSAT, NOAA and the JMA, shows an entire year of weather across the globe during 2013, with audio commentary from Mark Higgins, Training Officer at EUMETSAT. The satellite data layer is superimposed over NASA's 'Blue Marble Next Generation' ground maps, which change with the seasons. Source: Mark Higgins, Training Officer at EUMETSAT, 27 January 2014
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):
CCSM CAM3 T341 Water Vapor and Precipitation Simulation. Today's generation of climate models are typically run at resolutions that cover the globe with a 256 by 128 longitude/latitude horizontal grid or about a 1.4 degree latitude-longitude grid at the equator. An experimental version of the CCSM CAM3 was run at a resolution much more similar to a global numerical weather prediction model (1024x512 grid points which is 0.35 degree global grid increment) where the solution was sampled hourly for an entire year. At this resolution, fine scale, transient systems such as hurricanes and typhoons become visible - something that is not seen in lower resolution experiments. Note: CCSM is sponsored by the National Science Foundation (NSF) and the U.S. Department of Energy (DOE). CCSM is a project within the Climate & Global Dynamics Division (CGD) of the Earth and Sun Systems Laboratory (ESSL) at the National Center for Atmospheric Research (NCAR). Source: CCSM CAM3 T341 Simulation Report, 11 January 2010
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.
ABOUT THE AUTHOR
Steve M. Easterbrook is a professor of Computer Science at the University of Toronto. Professor Easterbrook's research is focused on software engineering for climate system modeling, and the following is a list of recent publications:
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