CAFE60v1: A 60-year large ensemble climate reanalysis. Part I: System design, model configuration and data assimilation.

Author:

O’Kane Terence J.1,Sandery Paul A.1,Kitsios Vassili2,Sakov Pavel3,Chamberlain Matthew A.1,Collier Mark A.4,Fiedler Russell1,Moore Thomas S.1,Chapman Christopher C.1,Sloyan Bernadette M.1,Matear Richard J.1

Affiliation:

1. CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

2. CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia, and Laboratory for Turbulence Research in Aerospace and Combustion, Department of Mechanical and Aerospace Engineering Monash University, Clayton, Victoria 3800, Australia

3. Bureau of Meteorology, Docklands, Victoria, Australia

4. CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia

Abstract

AbstractWe detail the system design, model configuration and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1. CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multi-year climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in-situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are sub-sampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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