Speaker
Description
Multistability is a frequent feature in the climate system and leads to key challenges for our ability to predict how the system will respond to transient perturbations of the dynamics. As a particular example, the stably stratified atmospheric boundary layer is known to exhibit distinct flow regimes that are believed to be metastable. Numerical weather prediction and climate models encounter challenges in accurately representing these flow regimes and the transitions between them, leading to an inadequate depiction of regime occupation statistics. To improve theoretical understanding, stochastic conceptual models are used as a tool to systematically investigate what types of unsteady flow features may trigger abrupt transitions in the mean boundary layer state. The findings show that simulating intermittent turbulent mixing may be key in some cases, where transitions in the mean state follow from initial transient bursts of mixing.
Turbulent mixing is a parameterized process in atmospheric models, and the theory underpinning the parameterization schemes was developed for homogeneous and flat terrain, with stationary conditions. The parameterized turbulent mixing lacks key spatio-temporal variability that induces transient perturbations of the mean dynamics. This variability could be effectively included via stochastic parameterisation schemes, provided one knows how to define the strength or memory characteristics of random perturbations. Towards that goal, we use a systematic data-driven approach to quantify the uncertainty of parameterisations and inform us on how and when to incorporate uncertainty using stochastic models. To enable such a systematic data-driven approach, methods from entropy-based learning and uncertainty quantification were combined in a model-based clustering framework, where the model is a stochastic differential equation with piecewise constant parameters. As a result, stochastic parameterisation can be learned from observations. The method is able to retrieve a hidden functional relationship between the parameters of a stochastic model and the resolved variables. A reduced model is obtained, where the unresolved scales are expressed as stochastic differential equations whose parameters are continuous functions of the resolved variables. Using field measurements of turbulence, the stochastic modelling framework is able to uncover a stochastic parameterisation that represent unsteady mixing in difficult conditions. Such methodology will be explored for further derivation of stochastic parameterisations, and should help to quantify uncertainties in climate projections related to uncertainties of the unresolved scales dynamics.
