Description: Giant mineral dust particles (〉75 μm in diameter) found far from their source have long puzzled scientists. These wind-blown particles affect the atmosphere’s radiation balance, clouds, and the ocean carbon cycle but are generally ignored in models. Here, we report new observations of individual giant Saharan dust particles of up to 450 μm in diameter sampled in air over the Atlantic Ocean at 2400 and 3500 km from the west African coast. Past research points to fast horizontal transport, turbulence, uplift in convective systems, and electrical levitation of particles as possible explanations for this fascinating phenomenon. We present a critical assessment of these mechanisms and propose several lines of research we deem promising to further advance our understanding and modeling.

Description: Due to the harshness and inaccessibility of desert regions, the uncertainties concerning the processes of dust mobilization at the surface, airborne transport, and sedimentation are still considerable, limiting the ability to perform model simulations. In June 2011 a comprehensive data set of ground-based and airborne in-situ measurements and remote sensing observations was acquired within the Fennec/LADUNEX field campaign in the western Sahara region. Here, we evaluate the ability of the state-of-the-art Lagrangian particle dispersion model FLEXPART, newly fitted with a dust mobilization capability, to simulate dust transport in this region. We investigate a case where a large Mesoscale Convective System (MCS) triggered dust emissions in central Mali, which subsequently moved as a large cold-pool dust front towards northern Mauritania. Specifying dust mobilization for this case is shown to be an important obstacle to simulating dust transport during this event, since neither the MCS nor the associated cold pool causing dust emission are represented in the meteorological analysis. Obtaining a realistic dust transport simulation for this case therefore requires an inversion approach using a manual specification of the dust sources supported by satellite imagery. When compared to in-situ and remote-sensing data from two aircraft, the Lagrangian dust transport simulations represent the overall shape and evolution of the dust plume well. While accumulation and coarse mode dust are well represented in the simulation, giant mode particles are considerably underestimated. Our results re-emphasize that dust emission associated with deep moist convection remains a key issue for reliable dust model simulations in northern Africa.

Description: Operational forecasts using direct output from numerical weather prediction models exhibit poor skill over northern tropical Africa compared to simple climatology-based forecasts. A recent study found potential in using Spearman’s rank correlations of gridded rainfall estimates from TRMM to predict July-September tropical African rainfall. Using the satellite-based gridded GPM-IMERG product from 2001-2019, we build on this approach using the Coefficient of Predictive Ability (CPA) developed for improved variable selection for statistical models and expanding up to 3-day lags over tropical Africa and the Atlantic Ocean. High CPAs straddling the zonally oriented rainbelt are attributed to large-scale drivers causing coherent spatio-temporal anomalies. Low CPAs over the rainbelt centre indicate the lack of dominant forcing, high stochasticity, or both. The coherent-linear-propagation factor (coh) introduced at every grid point quantifies the coherence of the identified rainfall by summarising the extent to which lagged CPAs reflect propagation with constant phase speed and direction. High coh over the Sahel suggests African easterly waves’ dominance. Stochastic precipitation driven by small-scale processes causes low coh over the rainbelt. We train a statistical model using the rainfall linked to the identified CPAs and compare it with three benchmarks: a climatology-based forecast, the ECMWF 1-day ensemble prediction-system forecast and its statistically post-processed output. The statistical model is outperformed only by the post-processed output and only in the western Sahel and central Africa. However, the Diebold-Mariano test for forecast significance suggests no significant differences between the statistical and post-processed forecasts making the former a cheaper alternative over the analysis region.

Description: Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to disentangle due to highly non-linear interactions between different components of the WAM. We propose a new approach to this problem by emulating a full-blown numerical model, the ICON model of the German Weather Service, through statistical surrogate models. The ICON model was run during the rainy seasons in four years in a nested limited-area mode. The uncertainty contributions of six selected model parameters were investigated. To this end, we employed a sampling strategy to obtain model parameter combinations for a manageable number of ICON model runs. Surrogate models were then constructed to describe a relationship between the model parameters and selected Quantities of Interest (e.g. characteristics of the African and Tropical easterly jets or the Saharan heat low) to employ sensitivity and parameter studies. For better interpretation a local parameter analysis based on the output fields was conducted using the same setup. Results reveal the complex nature of the WAM system and indicate for which parameters (and thus processes) uncertainties need to be reduced to lower the spread in the outputs. Among the considered parameters, the entrainment rate and the terminal fall velocity of ice show the greatest effects, where larger values lead to a decrease of cloud cover and precipitation, and to an intensification of the Saharan heat low, despite distinct regional differences. The evaporative soil surface also shows a significant effect, mostly on temperature and cloud cover.