Jul 12, 2013
Insight: young scientist predicts that he will retire before global climate models become good enough for use in agriculture
I am a young researcher with plenty of years ahead to solve some of the problems that society will face as a result of climate change. But it is disconcerting to discover that I will be retired by the time global climate models are of sufficient quality to plug directly into agricultural models. At least that is the finding of a recent article I lead authored recently in Environmental Research Letters (ERL).
The study took the latest global climate projections from CMIP5, which are the basis for the forthcoming Fifth Assessment Report of the IPCC, and examined how they have improved since the last version (CMIP3). Climate scientists aptly call the ability of global climate models (GCMs) to represent climate "skill". We found that errors in all GCMs were often larger than 2 °C for temperature and 20% for precipitation. This is particularly concerning since exceeding even "moderate limits" of temperature (+2 °C) and precipitation (–20%) can cause tropical crop yields to drop by 10 to 20%. So we could be talking about a massive under- or over-estimation of impacts, if we did not care about GCM errors.
The good news is we found that skill in certain areas has improved – for example, temperature skill in CMIP5 models is up by 5–15% compared with CMIP3. The problem is that this improvement is not fast enough. At this rate, we predict that it will take 5–30 years for the models to represent climate well enough so that they can directly be employed in agricultural impact analyses.
For precipitation the picture is bleaker – in around five years, prediction "skill" has improved by just 1–2%. That means 30–50 years of improvement are needed before we can directly plug these into agricultural impact studies. But if we waited until climate models are good enough for our purposes we would probably run into trouble, given the increasing need to adapt to climate change. A linear rate of improvement is a worst-case scenario, but significant investment is going into improving global climate predictions, and things may get significantly better in a shorter period of time as a new wave of models come online.
But this does not mean that I need to find a new job. It just means that uncertainty in climate prediction is here to stay. So we need to ensure that the science robustly takes this into account, and that decisions on how to tackle climate change are made within the context of uncertainty.
It is crucial that we learn to take decisions despite uncertainty. This could be done by identifying no-regret adaptation options – those that are a good idea no matter how the climate changes – or by using scientific evidence and explicit evaluations of uncertainty to make the best call. This is something decision-makers face constantly, and is nothing new. What we cannot afford, however, is to allow climate-projection uncertainty to be the focus of discussion. After all, there is no uncertainty that the climate is changing.
About the author
Andy Jarvis and Julian Ramirez-Villegas are part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), in Cali, Colombia.