Huo, W., Drews, A., Martin, T., & Wahl, S. (2024). Impacts of North Atlantic model biases on natural decadal climate variability. Journal of Geophysical Research: Atmospheres, 129, e2023JD039778. https://doi.org/10.1029/2023JD039778

Plain Language Summary:
A long-standing cold bias in the North Atlantic in climate models could be reduced by increasing the horizontal resolution, but we are often limited by computational resources. Here we embedded a nest with 1/10° resolution in the ocean in the North Atlantic in a global chemistry-climate model (called FOCI-VIKING10). It can largely reduce the North Atlantic cold bias (roughly 50%) and correct the path of the North Atlantic current (NAC). North Atlantic Oscillation (NAO) subdecadal variability (a period of 8 years) can be simulated by FOCI-VIKING10 when the representations of the NAO-forced anomalies and the ocean feedback are improved by alleviating the biases. The reported NAO-like responses to the 11-year solar cycle are confirmed in the 9-member ensemble mean with FOCI (non-eddying). Although the solar signals are also found in a single member with FOCI-VIKING10, we cannot rule out the aliasing of the internal variability in this single short member. For detecting a weak or varying signal of external forcings like the 11-year solar cycle in this study, a large ensemble with a “coarse” resolution model is favorable over a single realization with a “presumably” better model.

Ho-Tran, L., Fiedler, S. A climatology of weather-driven anomalies in European photovoltaic and wind power production. Commun Earth Environ 5, 63 (2024). https://doi.org/10.1038/s43247-024-01224-x

Summary : "In this research we analyze the spatiotemporal variability of photovoltaic (PV) and wind power production in Europe associated with a classification of 29 weather patterns. The results identify specific weather patterns linked to anomalously low PV plus wind power production and how they depend on the region, installed capacity, and event duration."

Kanngießer, F., & Fiedler, S. (2024). “Seeing” beneath the clouds—Machine-learning-based reconstruction of North African dust plumes. AGU Advances, 5, e2023AV001042. https://doi.org/10.1029/2023AV001042

Abstract: Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to cloud-masked, gray-scaled images, which were derived from false color images indicating elevated dust plumes in bright magenta. The images were obtained from the Spinning Enhanced Visible and Infrared Imager instrument onboard the Meteosat Second Generation satellite. We find up to 15% of summertime observations in West Africa and 10% of summertime observations in Nubia by satellite images miss dust plumes due to cloud cover. We use the new dust-plume data to demonstrate a novel approach for validating spatial patterns of the operational forecasts provided by the World Meteorological Organization Dust Regional Center in Barcelona. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but once trained, the reconstruction is computationally inexpensive. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases.