neue Veröffentlichung:

Bayr, T., Lübbecke, J. F., & Fiedler, S. (2024). Is El Niño-Southern Oscillation a tipping element in the climate system? Geophysical Research Letters, 51, e2023GL107848. https://doi.org/10.1029/2023GL107848

Abstract:
El Niño-Southern Oscillation (ENSO) is the dominant mode of climate variability with far-reaching impacts. El Niños, the warm events, occur in different flavors. In particular extreme Eastern Pacific (EP) El Niño events are associated with heavy precipitation events and extreme droughts, thus cause large socio-economic impacts in the Pacific region and beyond. In the observational record, they are quite rare so far. Here we present experiments of one climate model, which suggests that in a warmer climate ENSO may lock-in in a different state, in which nearly each El Niño is an extreme EP El Niño. On the other hand in a colder climate ENSO may lock-in in a state with nearly no extreme EP El Niños. In both climates this would have huge consequences for the socio-economic impacts of ENSO. Against this background we raise the discussion if ENSO may be a tipping element in the climate system.

Latif, M., Martin, T., & Bielke, I. (2024). Regional variation in extratropical North Atlantic air-sea interaction 1960–2020. Geophysical Research Letters, 51, e2024GL108174. https://doi.org/10.1029/2024GL108174

Abstract:
The relationship between the sea-surface temperature (SST) and air-sea heat exchange, is studied over the extratropical North Atlantic (NA) for late boreal winter during 1960–2020. This relationship provides information about the roles of atmospheric and oceanic processes in driving SST variations. We consider two regions, the central-midlatitude (35°N–50°N) and subpolar (50°N–60°N) NA. Over both regions, the atmosphere tends to drive the variations on the short interannual timescales. On the longer decadal timescales and over the central-midlatitude NA, oceanic processes tend to drive the SST anomalies that in turn influence the air-sea heat exchange. Air-sea heat exchange over the subpolar NA is mostly driven by the North Atlantic Oscillation on interannual and decadal timescales, which is the leading mode of internal atmospheric variability in winter. This study suggests that the atmosphere is more sensitive to SST over the central-midlatitude than subpolar NA.

Luiz, E. W., & Fiedler, S. (2024) Global climatology of low-level-jets: Occurrence, characteristics, and meteorological drivers. Journal of Geophysical Research: Atmospheres, 129, e2023JD040262. https://doi.org/10.1029/2023JD040262

Abstract: In this study, we investigated low-level jets (LLJs), and strong winds in the lower atmosphere that have significant environmental and societal impacts. Using a comprehensive weather data set spanning from 1992 to 2021, we created a global map of LLJs, categorizing them into three regions: non-polar land (LLLJ), polar land (PLLJ), and coastal (CLLJ). LLJs associated with temperature inversions were very common over land, but PLLJs were much more frequent. PLLJs were also the strongest and lowest. Coastal LLJs were prevalent on west coastlines and exhibited changing vertical temperature characteristics. On average, LLJs occurred 21% of the time globally, with higher frequencies over land (32%) compared to the ocean (15%). Regional trends in LLJ frequency and intensity varied, with some areas experiencing more intense LLJs without changes in frequency while others presented an increase in both. The study emphasized the uncertainty surrounding the influence of LLJs on climate and weather extremes in a warming world, with future research aiming at exploring LLJ trends and their broader implications.

Nworgu, U. C., H.C. Nnamchi & N. Rosario (2024). Divergent future change in South Atlantic Ocean Dipole impacts on regional rainfall in CMIP6 models. Environmental Research: Climate. 3, https://doi.org/10.1088/2752-5295/ad3a0e. 

Abstract:
The South Atlantic Ocean Dipole (SAOD) exerts strong influence on climate variability in parts of Africa and South America. Here we assess the ability of an ensemble of 35 state-of-the-art coupled global climate models to simulate the SAOD impacts on regional rainfall for the historical period (1950–2014), and their future projections (2015–2079). For both periods we consider the peak phase of the dipole in austral winter. Observational analysis reveals four regions with spatially coherent SAOD impacts on rainfall; Northern Amazon, Guinea Coast, Central Africa, and Southeast Brazil. The observed rainfall response to the SAOD over Northern Amazon (0.31 mm d), Guinea Coast (0.38 mm d), and Southeast Brazil (0.12 mm d) are significantly underestimated by the modeled ensemble-mean response of 0.10 ± 0.15 mm d, 0.05 ± 0.15 mm d, −0.01 ± 0.04 mm d, respectively. A too southerly rain belt in the ensemble, associated with warmer-than-observed Atlantic cold tongue, leads to better performance of models over Central Africa (46% simulate observations-consistent SAOD-rainfall correlations) and poor performance over the Guinea Coast (only 5.7% simulate observations-consistent SAOD-rainfall correlations). We also find divergent responses among the projections of ensemble members precluding a categorical statement on the future strength of the SAOD-rainfall relationship in a high-emissions scenario. Our results highlight key uncertainties that must be addressed to enhance the value of SAOD-rainfall projections for the affected African and South American countries.

Savita, A., Kjellsson, J., Pilch Kedzierski, R., Latif, M., Rahm, T., Wahl, S., and Park, W.: Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps, Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, 2024.

Summary: "We examine the impact of horizontal resolution and model time step on the climate of the OpenIFS version 43r3 atmospheric general circulation model. A series of sim- ulations for the period 1979–2019 are conducted with vari- ous horizontal resolutions (i.e. ∼ 100, ∼ 50, and ∼ 25 km) while maintaining the same time step (i.e. 15 min) and us- ing different time steps (i.e. 60, 30, and 15 min) at 100 km horizontal resolution. We find that the surface zonal wind bias is significantly reduced over certain regions such as the Southern Ocean and the Northern Hemisphere mid-latitudes and in tropical and subtropical regions at a high horizon- tal resolution (i.e. ∼ 25 km). Similar improvement is evident too when using a coarse-resolution model (∼ 100 km) with a smaller time step (i.e. 30 and 15 min). We also find im- provements in Rossby wave amplitude and phase speed, as well as in weather regime patterns, when a smaller time step or higher horizontal resolution is used. The improvement in the wind bias when using the shorter time step is mostly due to an increase in shallow and mid-level convection that enhances vertical mixing in the lower troposphere. The en- hanced mixing allows frictional effects to influence a deeper layer and reduces wind and wind speed throughout the tropo- sphere. However, precipitation biases generally increase with higher horizontal resolutions or smaller time steps, whereas the surface air temperature bias exhibits a small improvement over North America and the eastern Eurasian continent. We argue that the bias improvement in the highest-horizontal- resolution (i.e. ∼ 25 km) configuration benefits from a combination of both the enhanced horizontal resolution and the shorter time step. In summary, we demonstrate that, by re- ducing the time step in the coarse-resolution (∼ 100 km) OpenIFS model, one can alleviate some climate biases at a lower cost than by increasing the horizontal resolution."

Nnamchi, H.C., Diallo, I. Inconsistent Atlantic Links to Precipitation Extremes over the Humid Tropics. Earth Systems Environment (2024). https://doi.org/10.1007/s41748-023-00370-0

Abstract:
This study investigates extreme wet and dry conditions over the humid tropics and their connections to the variability of the tropical ocean basins using observations and a multi-model ensemble of 24 state-of-the-art coupled climate models, for the 1930–2014 period. The extreme wet (dry) conditions are consistently linked to Central Pacific La Niña (Eastern Pacific El Niño), the weakest being the Congo basin, and homogeneous patterns of sea surface temperature (SST) variability in the tropical Indian Ocean. The Atlantic exhibits markedly varying configurations of SST anomalies, including the Atlantic Niño and pan-Atlantic decadal oscillation, with non-symmetrical patterns between the wet and dry conditions. The oceanic influences are associated with anomalous convection and diabatic heating partly related to variations in the strength of the Walker Circulation. The observed connection between the Amazon basin, as well as the Maritime continent, and the Indo-Pacific variability are better simulated than that of the Congo basin. The observed signs of the Pacific and Indian SST anomalies are reversed for the modelled Congo basin extreme conditions which are, instead, tied to the Atlantic Niño/Niña variability. This Atlantic–Congo basin connection is related to a too southerly location of the simulated inter-tropical convergence zone that is associated with warm SST biases over the Atlantic cold tongue. This study highlights important teleconnections and model improvements necessary for the skillful prediction of extreme precipitation over the humid tropics.

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.