Stochstic representation of model uncertainties in ECMWF's forecasting system

Our best estimates of future climate are based on the use of complex computer models that do not explicitly resolve the wide variety of spatio-temporal scales making up Earth's climate system. The non-linearity of the governing physical processes allows energy transfer between different scales, and many aspects of this complex behaviour can be represented by stochastic models. However, the theoretical basis for so doing is far from complete. Many uncertainties remain in predictions derived from climate models, yet governments are increasingly reliant on model predictions to inform mitigation and adaptation strategies. An overarching aim of climate scientists is to reduce the uncertainty in climate predictions and produce credible assessments of model accuracy. This programme focuses on two key themes that both require the close collaboration of mathematicians, statisticians and climate scientists in order to improve climate models and the interpretation of their output.

Read more at http://www.newton.ac.uk/programmes/CLP/index.html

Our best estimates of future climate are based on the use of complex computer models that do not explicitly resolve the wide variety of spatio-temporal scales making up Earth's climate system. The non-linearity of the governing physical processes allows energy transfer between different scales, and many aspects of this complex behaviour can be represented by stochastic models. However, the theoretical basis for so doing is far from complete. Many uncertainties remain in predictions derived from climate models, yet governments are increasingly reliant on model predictions to inform mitigation and adaptation strategies. An overarching aim of climate scientists is to reduce the uncertainty in climate predictions and produce credible assessments of model accuracy. This programme focuses on two key themes that both require the close collaboration of mathematicians, statisticians and climate scientists in order to improve climate models and the interpretation of their output. Read more at http://www.newton.ac.uk/programmes/CLP/index.html

Share Stochstic representation of model uncertainties in ECMWF's forecasting system

Sign up to save your podcasts

Stochstic representation of model uncertainties in ECMWF's forecasting system

Stochstic representation of model uncertainties in ECMWF's forecasting system

More shows like Mathematical and Statistical Approaches to Climate Modelling and Prediction

Philosophy

Public Lectures from the Faculty of Law, University of Cambridge

Plagues – Darwin College Lecture Series 2014

Faculty of Classics

Cambridge Institute of Criminology Podcast

Cambridge Pragmatism: A Research Workshop

LCIL International Law Centre Podcast

Medieval History Seminars

Social and Developmental Psychology Seminar Series

Criminology: 11th Evidence Based Policing Conference