References
- Allen, M., Kettleborough, J. and Stainforth, D. 2002. Model error in weather and climate forecasting. In: Proceedings of the 2002 ECMWF Predictability Seminar, ECMWF, Reading, UK,275-294.
- Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Re v. 129, 2884–2902.
- Andronova, N. G. and Schlesinger, M. E. 2001. Objective estimation of the probability density function for climate sensitivity. J. Geophys. Res. 108(D8), 22 605–22 611.
- Annan, J. D., Hargreaves, J. C., Edwards, N. R. and Marsh, R. 2004. Parameter estimation in an intermediate complexity Earth System Model using an ensemble Kalman filter. Ocean Modelling, in press.
- Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. On the analysis scheme in the ensemble Kalman filter. Mon. Wea. Re v. 126, 1719–1724.
- Derber, J. 1989. A variational continuous assimilation scheme. Mon. Wea. Re v. 117, 2437–2446.
- Dickinson, R. P. and Gelinas, R. J. 1976. Sensitivity analysis of ordinary differential equation systems—a direct method. J. Comput. Phys. 21, 123–143.
- Dias, D. W., Canale, R. P. and Meier, P. G. 1992. Development of Bayesian Monte Carlo techniques for water quality model uncertainty. Ecological Modelling 62, 149–162.
- Evensen, G. 1994a. Inverse methods and data assimilation in non-linear ocean models. Physica D 77, 108–129.
- Evensen, G. 1994b. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), 10 143–10 162.
- Evensen, G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367.
- Gregory, J. M., Stouffer, R. J., Raper, S. C. B., Stott, P. A. and Rayner, N. A. 2002. An observationally based estimate of the climate sensitivity. J. Climate 15 (22), 3117–3121.
- Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. 82D, 33–45.
- Keppenne, C. L. 2000. Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Wea. Re v. , 128, 1971–1981.
- Knutti, R., Stocker, T. F., Joos, F. and Plattner, G.-K. 2002. Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416, 719–723.
- Lea, D. J., Allen, M. R. and Haine, T. W. N. 2000. Sensitivity analysis of the climate of a chaotic system. Tellus 52A, 523–532.
- Lea, D. J., Haine, T. W. N., Allen, M. R. and Hansen, J. A. 2002. Sensitivity analysis of the climate of a chaotic ocean circulation model. Q. J. R. Meteorol. Soc. 128 (586), 2587–2605.
- Lorenz, E. N. 1963. Deterministic non-periodic flow. J. Atmos. Sc i. 20, 130–141.
- Miller, R. N., Ghil, M. and Gauthiez, F. 1994. Advanced data assimilation in strongly non-linear dynamical systems-1. Atmos. Sci. 51 (8), 1037–1056.
- Moolenaar, H. E. and Stelien, F. M. 2004. Finding the effective parameter perturbations in atmospheric models: the Lorenz63 model as a case study. Tellus 56A, 47–55.
- Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. 1994. Numerical Recipes in Fortran: The Art of Scientific Computing. Cambridge University Press, Cambridge.