Seamless model assessment

Exploiting the seamless nature of the Met Office Unified Model across space and timescales to assess and, where possible, help improve the simulation of processes within the model.

The idea of seamless prediction, whereby a single model family can be used for prediction across a range of timescales, is at the heart of the Met Office strategy for weather and climate forecasting. A key benefit of such a system is that it allows us to learn about climate model performance and errors. The seamless model assessment team acts as a focal point for this work, facilitating analysis across space and timescales and investigating specific science questions.

Key aims

  • To improve modelling of regional climate, climate variability and extremes through the use of higher resolution models.

  • To understand error growth and biases in climate models through the use of shorter range predictions.

  • To understand and develop solutions to maintain the accurate representation of processes across the model hierarchy.

Current projects

High resolution climate modelling

This project involves developing higher resolution versions of our coupled climate model, in order to investigate the impact of resolving small-scale processes on the model mean climate, variability and extremes. It forms part of the Met Office/NERC Joint Weather and Climate Research Programme (JWCRP-Met Office, JWCRP-NERC).

Extreme rainfall processes across space and time scales

This project involves the analysis of extreme rainfall processes, and model deficiencies in the representation of these, across space and time scales. A key aspect is developing a very high resolution (1.5 km) version of the Met Office Unified Model, over a region of the UK, for use in climate change studies.

Coupled model error growth in the Madden-Julian Oscillation (MJO)

The MJO is an important phenomenon that links weather and climate. This project is to analyse the MJO error in various configurations of the Met Office Unified Model to gain understanding of the causes of model biases.

Development of stochastic physics parametrizations

Development and adaptation of current stochastic physics schemes to offset the absence of small scale processes in lower resolution versions of climate models, and thus include some of the missed variability of subgrid processes.

Transpose-AMIP experiments

Transpose-AMIP is an international project to run climate models in weather forecast mode initialised from NWP analyses. This allows an assessment of the fast physical processes operating in climate models using detailed observations of particular meteorological events.

Last updated: 29 July 2014