1- 1Nanyang Technological University, School of Physical and Mathematical Sciences and The Asian School of the Environment , Singapore
- 2State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Biases are often associated either to the presence of model structural errors or to a misrepresentation of the properties of initial condition errors (initial error biases or a bad representation of the initial error distribution). In the current work, the development of biases is addressed by considering a twin experiment in which the dominant initial condition uncertainties are imposed to the external forcing of a coupled ocean-atmosphere extratropical system in a perfectly controlled environment. The forcing is generated by a low-order 3-variable tropical model mimicking the dynamic of ENSO. No structural model errors are introduced and the statistical properties of the initial error are perfectly known. It is shown that even if this almost perfect setting, important biases are induced on seasonal-to-decadal forecasts, and hence unreliable (under-dispersive) ensembles.
More specifically, three main types of ensemble forecast experiments are performed: with random perturbations along the three Lyapunov vectors of the tropical model; along the two dominant Lyapunov vectors; and along the first Lyapunov vector only. When perturbations are introduced along all vectors, important forecasting biases, inducing a mismatch between the error of the ensemble mean and the error spread, are produced. Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector. Hence, perturbing along the dominant instabilities allows a reduced mean square error to be obtained at long lead times of a few years, as well as reliable ensemble forecasts across the whole time range. These very counterintuitive findings, reported in Vannitsem and Duan (2026), further underline the importance of appropriately controlling the initial condition error properties in the tropical components of models.
Reference
Vannitsem, S., Duan, W. A Note on the Role of the Initial Error Structure in the Tropics on the Seasonal-to-Decadal Forecasting Skill in the Extratropics. Adv. Atmos. Sci. 43, 157–169 (2026). https://doi.org/10.1007/s00376-025-4521-7
How to cite: Vannitsem, S. and Duan, W.: Sources of biases in climate prediction: role of initial condition uncertainties of external forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15785, https://doi.org/10.5194/egusphere-egu26-15785, 2026.
