Summer Research

I recently started working for the summer, with Andrew Weaver’s research group at the University of Victoria. If you’re studying climate modelling in Canada, this is the place to be. They are a fairly small group, but continually churn out world-class research.

Many of the projects here use the group’s climate model, the UVic ESCM (Earth System Climate Model). I am working with the ESCM this summer, and have previously read most of the code, so I feel pretty well acquainted with it.

The climate models that most people are familiar with are the really complex ones. GCMs (General Circulation Models or Global Climate Models, depending on who you talk to) use high resolution, a large number of physical processes, and relatively few parameterizations to emulate the climate system as realistically as possible. These are the models that take weeks to run on the world’s most powerful supercomputers.

EMICs (Earth System Models of Intermediate Complexity) are a step down in complexity. They run at a lower resolution than GCMs and have more paramaterizations. Individual storms and wind patterns (and sometimes ocean currents as well) typically are not resolved – instead, the model predicts the statistics of these phenomena. Often, at least one component (such as sea ice) is two-dimensional.

The UVic ESCM is one of the most complex EMICs – it really sits somewhere between a GCM and an EMIC. It has a moderately high resolution, with a grid of 3.6° longitude by 1.8° latitude (ten thousand squares in all), and 19 vertical layers in the ocean. Its ocean, land, and sea ice component would all belong in a GCM. It even has a sediment component, which simulates processes that most GCMs ignore.

The only reason that the UVic model is considered an EMIC is because of its atmosphere component. This part of the model is two-dimensional and parameterizes most processes. For example, clouds aren’t explicitly simulated – instead, as soon as the relative humidity of a region reaches 85%, the atmospheric moisture falls out as rain (or snow). You would never see this kind of atmosphere in a GCM, and it might seem strange for scientists to deliberately build an unrealistic model. However, this simplified atmosphere gives the UVic ESCM a huge advantage over GCMs: speed.

For example, today I tested out the model with an example simulation. It ran on a Linux cluster with 32 cores, which I accessed remotely from a regular desktop. It took about 7 minutes of real time to simulate each year and record annual averages for several dozen variables. In comparison, many GCMs take an entire day of real time to simulate a year, while running on a machine with thousands of cores. Most of this work is coming from the atmospheric component, which requires short time steps. Consequently, cutting down on complexity in the atmosphere gives the best return on model efficiency.

Because the UVic model is so fast, it’s suitable for very long runs. Simulating a century is an “overnight job”, and several millennia is no big deal (especially if you run it on WestGrid). As a result, long-term processes have come to dominate the research in this lab: carbon cycle feedbacks, sensitivity studies, circulation in the North Atlantic. It simply isn’t feasible to simulate these millennial-scale processes on a GCM – so, by sacrificing complexity, we’re able to open up brand new areas of research. Perfectly emulating the real world isn’t actually the goal of most climate modelling.

Of course, the UVic ESCM is imperfect. Like all models, it has its quirks – an absolute surface temperature that’s a bit too low, projections of ocean heat uptake that are a bit too high. It doesn’t give reliable projections of regional climate, so you can only really use globally or hemispherically averaged quantities. It’s not very good at decadal-scale projection. However, other models are suitable for these short-term and small-scale simulations: the same GCMs that suffer when it comes to speed. In this way, climate models perform “division of labour”. By developing many different models of varying complexity, we can make better use of the limited computer power available to us.

I have several projects lined up for the summer, and right now I’m reading a lot of papers to familiarize myself with the relevant sub-fields. There have been some really cool discoveries in the past few years that I wasn’t aware of. I have lots of ideas for posts to write about these papers, as well as the projects I’m involved in, so check back often!

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