Computers crash, freeze, corrupt documents, and otherwise make us swear at them every day. At such moments I briefly blow my own fuse, and my computer becomes my enemy – until I remember it’s revolutionised how I work, communicate and access information. But knowing how easily they can go wrong – and how easily a small, overlooked, mistake in a piece of software can cause unexpected problems later – makes me cautious. That extends to writing this blog, when I often wonder just how much we can rely on the computer models used so widely by scientists studying global warming. So this year I’ve been asking researchers questions like: Why even use models? How can we trust that they’re accurate? How should we understand what they come up with?
These questions go deep into how science works, using evidence from what people see, or experiments we conduct, to build or knock down ideas. The best evidence is directly measured, in as much detail as possible. Today that’s available in some cases, but not all, and we can’t go back in time to get data over the long time periods that might be ideal. For example, this previously limited our understanding of global warming’s effect on tropical cyclones, Bruno Chatenoux from the Global Change and Vulnerability Unit at the United Nations Environment Program in Geneva, Switzerland told me in February. “Formal detection of trends in the existing records is challenged by data quality issues and record length,” he told me. “Model projections suffer less from this, but have other challenges, such as whether they are accurately representing all of the relevant physical processes.”
And while there are a lot of processes to represent, researchers have worked hard to establish them, underlined Xuefeng Cui from Beijing Normal University, China, in July. “Climate models have been developed by groups of scientists to include atmosphere, oceanography, land, biology, chemistry, physics, computing science for about 40 years,” he said. “They have a solid scientific foundation and model the climate system in reasonable resolution.”
This kind of model’s not about beauty – or is it?
Richard Turco from the University of California, Los Angeles, who I spoke to for an environmentalresearchweb article published in March, worked with early atmospheric chemistry simulations when doing his PhD in the late 1960s. “The typical computer code might consist of one or two big boxes of punch cards that had to be lugged around to the computer centre,” he recalled. “Occasionally, unfortunately, we would drop the boxes and scramble the programs up, so we had to reassemble them. You’d drop off your cards one day and maybe come back the next day to pick up the output. Things today are much faster, obviously. You want to run a program, you push a button. And nowadays the chemistry, dynamics, and aerosol microphysics, have been packaged together, making an atmosphere coupled with an ocean and maybe a biosphere model as well. The advances have been astounding during my career.”
Such advances also bring with them a need for caution to avoid the modern equivalent of a scrambled set of cards, Richard added. “There’s a danger in that people are running models in the same way you would use a complex instrument to make a measurement, where you don’t really understand its innards,” he said. “The models have become more complex and the users have got more remote from the details. You need a team of engineers and technicians who are constantly testing and maintaining the model.” He underlined that this is being done, for example by comparing different models and making sure they get similar results from the same inputs, as well as direct maintenance.
The most important of such efforts to “cross-calibrate” models are the Coupled Model Intercomparison Projects that have fed into the major Intergovernmental Panel on Climate Change (IPCC) assessment reports. But scientists across the world also often check how well models simulate past climate data, like temperature, when they’re run with actual conditions, such as greenhouse gas concentrations. Sometimes simulating the past can be used to check how well models represent different parts of the climate system, such as the shrinking Arctic ice. That’s what Muyin Wang from the University of Washington did in September, finding clear evidence that human greenhouse gas emissions are speeding its loss. “It’s important to be able to reproduce past climate and variations to be confident in models’ predictions,” Muyin told me. “If you are interviewing someone for a job, you look at their resumé, to see if they did a good job in the past. Then you know that they can do the job going forward. It’s a similar idea here, if models can simulate the past climate, then they’re the models we want to use in the projection.”
The uncertain facts of life
At other times scientists go on from modelling historic climate to try and understand the “uncertainty” range of future climate projections, like Dan Rowlands from Oxford University, UK, did. In March, he told me how his team ran 10,000 simulations of the past, each treating basic processes slightly differently, in one climate model. The scientists then threw out the simulations that didn’t match with history, before simulating the future and finding a wider range, predicting slightly higher temperatures on average. To do this, they used spare time donated by the general public on 30,000 computers in the climateprediction.net scheme. We can only speculate whether the owners were on good terms with their machines at the time.
Together these efforts help ensure models’ accuracy, establish trust in their output and improve our understanding of the basic science. They also provide projections like those in the IPCC’s last report that covered an uncertainty range of temperatures likely with climate change. In one example, if we use a mixture of renewable and fossil fuels, models project temperatures between 1.7 and 4.1°C higher than 100 years previously by the end of the 21st century. You might like a narrower uncertainty range – and it’s possible scientists might be able to give us one. But Xuefeng Cui stressed that these model projections always have uncertainties, and shouldn’t be regarded as exact predictions.
Last month, two other researchers also told me how uncertainty in climate models is a “fact of life”. In one, Clara Deser from the US National Center for Atmospheric Research (NCAR) in Boulder, Colorado even told me where the accuracy limit might be. In the other, Paul Higgins from the American Meteorological Society in Washington DC found that the ‘carbon cycle’ of chemicals moving through living creatures and the environment could increase model uncertainty. But that shouldn’t stop us from acting to fight climate change, he emphasised. “If you go out driving, the very fact that what’s going to happen to you while you’re out driving is uncertain is why you buckle your seatbelt,” he said. “If you waited until you were sure you were going to be in an accident, it would be too late and you would have managed the risk very poorly. Quantifying uncertainty helps us to understand risk.”
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Higgins, P., & Harte, J. (2012). Carbon Cycle Uncertainty Increases Climate Change Risks and Mitigation Challenges Journal of Climate, 25 (21), 7660-7668 DOI: 10.1175/JCLI-D-12-00089.1