This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.
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.
Abstract. In an effort led by the University Centre in Svalbard (UNIS), with support from the Norwegian Meteorological Institute (MET Norway), the Isfjorden Weather Information Network (IWIN) is under development in the Isfjorden region, central Svalbard. The network substantially expands upon the relatively sparse existing operational network of weather stations and consists of compact and cost-efficient all-in-one weather stations permanently installed on lighthouses around Isfjorden and onboard small tourist cruise ships trafficking the fjord from the spring to the autumn. All data from the network are freely available online in near real-time via MET Norway’s data portals (https://doi.org/10.21343/ebrw-w846). The IWIN data are highly valuable for scientific purposes such as atmospheric boundary layer research, validation and development of numerical weather prediction models and assimilation in these, as well as planning and safe conduction of outdoor activities in the region.
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