That anomalous summer has put climate change back at the top of the political agenda but despite that, no one is comfortable doing actual weather and climate forecasting.
Well, almost no one. Professor Sergej S. Zilitinkevich of the Finnish Meteorological Institute (FMI) wants to revise the way physics treats turbulence in the atmosphere and ocean and help increase understanding of the important consequences for weather and climate modeling and prediction. Turbulent flow is difficult because it is non-linear. It's chaotic so current solutions rely on making it linear in really small steps and then using black box magic. That lacks of physics knowledge is why climate science has not been more rigorous in its predictive power. But help could be on the way.
“Turbulence is the key to the atmospheric ‘machine’,” says Prof. Zilitinkevich. “We cannot understand weather systems if we do not understand the connections between their parts.”
For the last century, turbulence has been understood in oversimplified form, based on an assumption that it could be split into two parts: ‘mean flow’ - organized motion which can be analyzed using classical mechanics - and then ‘turbulence’ - chaotic motion which must be analyzed using statistical methods. This approach works fine for engineering applications but in the field of geophysical turbulence – such as climate and weather – that is lacking. In the atmosphere or ocean, the density of the medium changes with height. This leads to stratification, instability and phenomena such as convection. The classical paradigm has not been able to deal with these phenomena satisfactorily.
“We are now seeing a scientific revolution in this field,” says Zilitinkevich. “Atmospheric turbulence can now be seen as having three parts: regular flow, chaotic turbulence and self-organised structures.”
Self-organization leads to long-lived structures, such as convective cells or rolls in the atmosphere or ocean. This new understanding means that both researchers and operational modelers need to account for these different types of movements and their role in energy and matter exchange in the atmosphere and ocean.
“Heat exchange between the upper ocean and lower atmosphere is controlled by turbulence,” explains Zilitinkevich. “Most thermal energy is in the ocean not the atmosphere, but we experience climate anthropocentrically as a characteristic of the near-surface part of the atmosphere, the atmospheric ‘planetary boundary layer’ (PBL).” His PBL-PMES project aims to revise thoroughly the physics used to model PBLs. He says this will lead to better understanding of heat exchange between land, sea and air, but researchers will also gain insight into phenomena like shallow stable atmospheric PBLs which trap smog and pollution in the air above cities.
Zilitinkevich hopes his research will to lead to radical changes in scientific understanding of weather and climate and in the success of forecasting models. “Within a decade, we should have incomparably better weather and climate predictions,” he says. “Microclimates, such as local climate change due to land-use change, will be modelled with greater accuracy.”
The new theoretical framework will then be implemented in modern weather-forecasting and air-pollution models. Until recently, one of the biggest limiting factors in weather prediction has been the spatial resolution of the models, restricted by the power of supercomputers. But new improved physics means it is now the models that need to be revised. “We are co-operating with a very good network of operational weather-modeling groups around Europe,” says Zilitinkevich. “By the end of next year we hope to have some practical results from the Finnish Meteorological Institute – and we are also working with MétéoFrance and the Danish Meteorological Institute.”