The effect of stochastically perturbed parametrisation tendencies (SPPT) on rapidly ascending air streams

Abstract

The stochastically perturbed parametrisation tendency (SPPT) scheme is a well-established technique in ensemble forecasting to address model uncertainty by introducing perturbations into the tendencies provided by the physics parametrisations. The magnitude of the perturbations scales with the local net parametrisation tendency, resulting in large perturbations where diabatic processes are active. Rapidly ascending air streams, such as warm conveyor belts (WCBs) and organized tropical convection, are often driven by cloud diabatic processes and are therefore prone to such perturbations. This study investigates the effects of SPPT and initial condition perturbations on rapidly ascending air streams by computing trajectories in sensitivity experiments with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system, which are set up to disentangle the effects of initial conditions and physics perturbations. The results demonstrate that SPPT systematically increases the frequency of rapidly ascending air streams. The effect is observed globally, but is enhanced in regions where the latent heating along the trajectories is larger. Despite the frequency changes, there are only minor modifications to the physical properties of the trajectories due to SPPT. In contrast to SPPT, initial condition perturbations do not affect WCBs and tropical convection systematically. An Eulerian perspective on vertical velocities reveals that SPPT increases the frequency of strong upward motions compared with experiments with unperturbed model physics. Consistent with the altered vertical motions, precipitation rates are also affected by the model physics perturbations. The unperturbed control member shows the same characteristics as the experiments without SPPT regarding rapidly ascending air streams. We make use of this to corroborate the findings from the sensitivity experiments by analyzing the differences between perturbed and unperturbed members in operational ensemble forecasts of ECMWF. Finally, we give an explanation of how symmetric,This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Northern Hemisphere extratropical cyclone biases in ECMWF subseasonal forecasts

“…Moreover, it could be related to the representation of microphysical processes. Pickl et al (2022) showed that ECMWF's stochastically perturbed parameterisation tendency (SPPT) scheme systematically increases the number of rapidly ascending airstreams, although the SPPT perturbations are symmetric around zero (a similar behavior has recently been found for the stochastically perturbed parametrisations (SPP) scheme, which is currently under development at ECMWF: Deinhard & Grams, 2023). As rapidly ascending airstreams play an important role for cyclone intensification (e.g., Binder et al, 2016), the SPPT could thus contribute to a higher maximum cyclone intensity through the same mechanism.…”

Warm conveyor belts as amplifiers of forecast uncertainty

“…Further, Pickl et al . (2022) found sensitivities of WCBs to stochastic perturbations of physical processes. This indicates that WCBs can act as a direct source of forecast uncertainty, additionally to the propagation and amplification of pre‐existing forecast errors through upscale error growth.…”

Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar0)

“…As the scale of cloud microphysical processes responsible for precipitation formation is too small to be explicitly resolved in NWP models, parameterizations are used to calculate the integrated effects on the resolved prognostic variables. These parameterization schemes, however, are still associated with large uncertainties that can influence the representation of atmospheric dynamics including airmass ascent and formation of precipitation in NWP models (Leutbecher and Palmer, 2008;Ollinaho et al, 2017;Pickl et al, 2022).…”