Daniel Lea, Met Office

The new Met Office Coupled Atmosphere/Land/Ocean/Sea-Ice Data Assimilation System

Isabelle Mirouze, Matthew J. Martin, Robert R. King, Adrian Hines, David Walters and Michael Thurlow


The Met Office has developed a weakly-coupled data assimilation (DA) system using a global coupled model configuration. The aim of coupled DA is to produce a more consistent analysis for coupled forecasts which may lead to less initialisation shock and improved forecast performance.

The coupled model used has the UM atmospheric model at 60 km horizontal resolution on 85 vertical levels, the NEMO ocean model at ~25 km horizontal resolution on 75 vertical levels, and the sea-ice model CICE. The coupled model is corrected using two separate 6-hour window data assimilation systems: a 4D-Var for the atmosphere with associated soil moisture content nudging and snow analysis schemes on the one hand, and a 3D-Var FGAT for the ocean and sea-ice on the other hand. The background information in the DA systems comes from a previous 6-hour forecast of the coupled model.

13 month experiments run include 1) a full atmosphere/land/ocean/sea-ice coupled DA run, 2) an atmosphere-only run forced by OSTIA SSTs and sea-ice with atmosphere and land DA, and 3) an ocean-only run forced by atmospheric fields from run 2 with ocean and sea-ice DA. In addition, 5-day and 10-day forecast runs, have been produced from initial conditions generated by either run 1 or a combination of runs 2 and 3.

The performance of the coupled DA is found to be similar to the existing separate ocean and atmosphere DA systems. The coupled model has some biases which do not affect the uncoupled models. E.g. precipitation and run off errors affecting the ocean salinity. This does, however, highlight a particular benefit of coupled data assimilation in it gives direct information about the coupled model short term biases. By identifying the biases and developing solutions this will improve the short range coupled forecasts, but may also improve the coupled model on climate timescales.