Reducing uncertainty should be priority in climate modelling, say scientists
Despite modern scientific knowledge and computer power, building a perfect model of earth’s climate is impossible. Climate modellers face a choice: whether to include as many different processes that affect global temperatures as possible – interactions between the land, atmosphere, oceans, plants and ice cover, for example – or focus their efforts on a few of the most important ones.
Two European climate scientists have published a perspective article today in the journal Science, suggesting that when it comes to building climate models, scientists have their priorities wrong.
The piece argues that producing accurate temperature forecasts will require focussing less on representing every possible influence on climate and more on getting key processes right – most importantly, how clouds affect the rate of warming.
Early days of climate models
In 1963, Joseph Smagorinsky published a set of mathematical equations that became the building blocks of the first climate model.
The equations described the physics of how air circulates in the atmosphere, and were the earliest incarnation of what became known as General Circulation Models (GCMs).
As scientific understanding developed, scientists added many more physical processes – making the representation of atmospheric circulation more realistic. This marked the transition of the early models into more complex Global Climate Models.
Model evolution
Most recently, with the addition of more sophisticated interactions between terrestrial and ocean ecosystems, the latest generation are known as Earth System Models.
The authors explain a certain level of complexity is necessary to adequately represent earth’s climate system. Models also need to be complex enough to be useful in assessing natural climate variability, the role of human activity and possible mitigation strategies. As the authors say in the paper:
“The increase in complexity has greatly expanded the scope of questions to which [climate models] can be applied.”
But despite climate models becoming more sophisticated since their birth fifty years ago, many of the biggest uncertainties are still the same, the authors argue.
The most important of these uncertainties is how moisture circulates in the atmosphere and forms clouds. Clouds can have either a warming or cooling effect, depending on how high in the atmosphere they sit. As the authors say in the paper:
“An adequate description of basic processes like cloud formation, moist convection, and mixing is what climate models miss most.”
That’s not to say climate models aren’t a useful tool that increase what we know about how earth’s climate is likely to change in the future.
The complex nature of the climate system means some degree of uncertainty is unavoidable and most sources of uncertainty are quantified, which means using scientific knowledge to constrain them to within certain likely limits – what scientists sometimes call ‘known unknowns’.
Climate impacts
The uncertainty surrounding clouds has far reaching consequences. It affects estimates of what scientists call equilibrium climate sensitivity, which is the amount of warming per doubling of atmospheric carbon dioxide above pre-industrial levels. It also affects ocean heat uptake, patterns of rainfall and the carbon cycle.
There are complicated knock-on effects too. Incomplete understanding about how quickly the Arctic is warming limits our ability to predict how fast permafrost will melt, and how soon it will release methane into the atmosphere, leading to further warming.
To account for uncertainty, the Intergovernmental Panel on Climate Change (IPCC) provides a range of possible temperatures for each emissions scenario rather than a single prediction.
But as models have become more complicated, the overall uncertainty has increased, say the authors. Better representation of the fundamentals might help address this issue, they argue.
For example, the image below shows how the uncertainty around each scenario is bigger in the IPCC’s new generation of models (right) compared to the last one (left).
Image - Knutti _and _Sedlacek _SRES _vesus _RCPs (note)
Source: Knutti & Sedlacek (2012).
Where do we go from here?
Scientists should focus their efforts on getting a better representation of the interplay between moisture, circulation and cloud formation, rather than trying to encompass all known influences on the climate, say the authors – each of which adds a bit more to the overall uncertainty.
Uncertainty in model projections is no reason to delay action on climate change mitigation or adaptation. As a recent study by Knutti and Sedlacek puts it:
“More research uncovers a picture that is more complicated; thus, uncertainty can grow with time…but these should not prevent those working on climate impacts, mitigation and adaptation from making decisions”.
There’s no doubt that if emissions continue to rise, so will global temperature – but exactly how much warming we’ll see in the next century is harder to pin down. When it comes to the optimal number of known climate influences to include in models, could less be more?