Spatial Variability of Nocturnal Stability Regimes in an Operational Weather Prediction Model

Abstract

Forecast errors in near-surface temperatures are a persistent issue for numerical weather prediction models. A prominent example is warm biases during cloud-free, snow-covered nights. Many studies attribute these biases to parametrized processes such as turbulence or radiation. Here, we focus on the contribution of physical processes to the nocturnal temperature development. We compare model timestep output of individual tendencies from parametrized processes in the weather prediction model AROME-Arctic to measurements from Sodankylä, Finland. Thereby, we differentiate between the weakly stable boundary layer (wSBL) and the very stable boundary layer (vSBL) regimes. The wSBL is characterized by continuous turbulent exchange within the near-surface atmosphere, causing near-neutral temperature profiles. The vSBL is characterized by a decoupling of the lowermost model level, low turbulent exchange, and very stable temperature profiles. In our case study, both regimes occur simultaneously on small spatial scales of about 5 km. In addition, we demonstrate the model’s sensitivity towards an updated surface treatment, allowing for faster surface cooling. The updated surface parametrization has profound impacts on parametrized processes in both regimes. However, only modelled temperatures in the vSBL are impacted substantially, whereas more efficient surface cooling in the wSBL is compensated by an increased turbulent heat transport within the boundary layer. This study demonstrates the utility of individual tendencies for understanding process-related differences between model configurations and emphasizes the need for model studies to distinguish between the wSBL and vSBL for reliable model verification.

Simulating the Unsteady Stable Boundary Layer With a Stochastic Stability Equation

“…This occurs when a layer near the surface becomes driven by radiation and soil thermal transport, while the surface turbulent heat flux is too weak to sustain the energy demand of the surface (Van de Wiel et al., 2012). In NWP, the decoupling can occur in very localized regions with a high spatial variability, and the positive feedback between weakening turbulence and radiative cooling can lead to further rapid cooling in decoupled regions (Kähnert et al., 2022). To avoid such decoupling and so‐called runaway cooling to become unphysically important in models, operational parameterization schemes have implemented rather high levels of turbulent mixing (Cuxart et al., 2006; Derbyshire, 1999; Louis, 1979).…”

A New Stable Boundary Layer Parameterization for Numerical Weather Prediction Models: A Heat Flux Budget Approach

“…According to Mahrt (2014) the "fundamental features of the very stable boundary layer still remain a mystery". In this context, the simulation of the very SBL and the representation of transitions from and to such a regime by planetary boundary layer parameterizations is a major micrometeorological challenge that limits the quality of the representation of the mean state of the atmosphere near the surface in both numerical weather prediction (NWP) and climate models today {(van de Battisti et al 2017;Baas et al 2019;Holdsworth and Monahan 2019;Lapo et al 2019;Maroneze et al 2019Maroneze et al , 2021Lorenz et al 2022;Kähnert et al 2022). At the same time, the weakly SBL is well described by Monin-Obukhov similarity theory and, for this reason, is comparatively well simulated and represented by numerical planetary boundary layer (PBL) parametrization schemes (Mahrt 2014).…”

Simulating the unsteady stable boundary layer with a stochastic stability equation

“…This occurs when a layer near the surface becomes driven by radiation and soil thermal transport, while the surface turbulent heat flux is too weak to sustain the energy demand of the surface (Van de Wiel et al, 2012). In NWP, the decoupling can occur in very localised regions with a high spatial variability, and the positive feedback between weakening turbulence and radiative cooling can lead to further rapid cooling in decoupled regions (Kähnert et al, 2022). To avoid such decoupling and so-called runaway cooling to become unphysically important in models, operational parameterisation schemes have implemented rather high levels of turbulent mixing (Louis, 1979;Derbyshire, 1999;Cuxart et al, 2006).…”