| dc.description.abstract | A substantial segment of the error in numerical weather prediction and climate
projections comes from the intrinsic uncertainties of General Circulation Models
of the atmosphere. Stochastic physics schemes are one of the preferred methods to
represent the model uncertainty in Ensemble Prediction Systems, where different
realizations of the same forecast are created to quantify the probabilities of
different outcomes in the atmospheric flow.
Stochastic physics schemes have been successfully employed in medium-range and
seasonal forecasting systems, as they increase the skill of probabilistic
forecasts. Similarly it has been demonstrated than these schemes can improve
certain aspects of the model's climate. However, it is still not clear whether
they are a truthful representation of the model uncertainties they aim to
represent.
In this thesis, a collection of stochastic physics schemes are evaluated
using a seamless approach. It is found that they can improve the
representation of the tropical climate and extra-tropical cyclones, but they
degrade the individual representation of these processes deteriorating the
deterministic skill of the model. Some important features of the model can be
degraded by the stochastic physics schemes, like energy and moisture
conservation on climate scales. Some closures to the schemes are proposed and
successfully tested to remove or reduce some of the problems found. Alternative
approaches in the development of stochastic parametrizations are also
investigated.
Stochastic physics schemes have some benefits but still require further development to
produce a realistic representation of model error.
It is also recommended that evaluation methodologies
must be expanded to include process-based diagnostics to display the
realism of its perturbations. | en_GB |
