25 Sep 2023
 | 25 Sep 2023

Towards a process-oriented understanding of the impact of stochastic perturbations on the model climate

Moritz Deinhard and Christian M. Grams

Abstract. Stochastic parametrisation techniques have been used by operational weather centres for decades to produce ensemble forecasts and to represent uncertainties of the forecast model. Their use has been demonstrated to be highly beneficial, as it increases the reliability of the forecasting system and reduces systematic biases. Despite the random nature of the perturbation techniques, the response of the model can be nonlinear and the mean state of the model can change. In this study, we attempt to provide a process-based understanding how stochastic model perturbations affect the model climate. Previous work has revealed sensitivities of the occurrence of diabatically driven, rapidly ascending air streams to the stochastically perturbed parametrisation tendencies (SPPT) scheme. Such strongly ascending air streams are linked to different weather phenomena, such as precipitation and upper-tropospheric ridge building in the midlatitudes, which raises the question whether these processes are also influenced by stochastic perturbations.

First, we analyse if rapidly ascending air streams also show sensitivities to a different perturbation technique - the stochastically perturbed parametrisations (SPP) scheme, which directly represents parameter uncertainty in parametrisations and has recently been developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). By running a set of sensitivity experiments with the Integrated Forecasting System (IFS) and by employing a Lagrangian detection of ascending air streams, we show that SPP results in a systematic increase of the occurrence of ascending air parcel trajectories compared to unperturbed simulations. This behaviour is very similar to that of SPPT, albeit some regional differences are apparent. We further show that the one-sided response to the stochastic forcing cannot be attributed to a single process (e.g. convection parametrisation), but rather that perturbations to different parametrisations have similar effects.

Thereafter, we link the frequency changes of ascending air streams to closely related weather phenomena. Whereas the signal of increased ascending motion is directly transmitted to global precipitation sums for all analysed schemes, changes to the amplitude of the upper-level Rossby wave pattern are more subtle. In agreement with the trajectory analysis, both SPPT and SPP increase the waviness of the upper-level flow and thereby reduce a systematic bias of the model, even though the order of magnitude is small.

Our study presents a coherent process chain that enables to understand how stochastic perturbations systematically affect the model climate. We argue that weather systems which are characterised by threshold behaviour on the one hand and that serve as a dynamical hinge between spatial scales on the other hand can convert zero-mean perturbations into an asymmetric response and project it onto larger scales.

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Moritz Deinhard and Christian M. Grams

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1938', Anonymous Referee #1, 28 Nov 2023
  • RC2: 'Comment on egusphere-2023-1938', Anonymous Referee #2, 22 Jan 2024
  • AC1: 'Comment on egusphere-2023-1938', Moritz Deinhard, 04 Feb 2024
Moritz Deinhard and Christian M. Grams
Moritz Deinhard and Christian M. Grams


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Short summary
Stochastic perturbations are a frequently used technique to represent model uncertainties in numerical weather prediction. While such schemes are beneficial for the forecast skill, they can also change the mean state of the model. We focus on how different schemes modulate rapidly ascending air streams, such as warm conveyor belts, and whether the changes to these weather systems are projected onto larger scales. We thereby provide a process chain how perturbations affect the model climate.