Climate Dynamics

, Volume 23, Issue 7–8, pp 745–760 | Cite as

An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

  • J. C. Hargreaves
  • J. D. Annan
  • N. R. Edwards
  • R. Marsh
Original Articles


We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO2 increase. The equilibrium surface air temperature rise for this CO2 increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.


Heat Transport Ensemble Member Moisture Transport CMIP Model Freshwater Flux 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



NRE is currently supported by the Swiss NCCR-Climate programme. RM is supported by the Natural Environment Research Council (NERC) Core Strategic Programme “Ocean Variability and Climate”. Supercomputer facilities and support were provided by JAMSTEC. This research was partly supported by the GENIE project (, which is funded by the NERC (NER/T/S/2002/00217) through the e-Science programme.


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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • J. C. Hargreaves
    • 1
    • 4
  • J. D. Annan
    • 1
    • 4
  • N. R. Edwards
    • 2
  • R. Marsh
    • 3
  1. 1.Frontier Research System for Global ChangeKanagawaJapan
  2. 2.Climate and Environmental Physics, Physics InstituteUniversity of BernBernSwitzerland
  3. 3.James Rennell DivisionSouthampton Oceanography CentreSouthamptonUK
  4. 4.Proudman Oceanographic LaboratoryLiverpoolUK

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