Predicting the mineral composition of dust aerosols: Insights from elemental composition measured at the Izaña Observatory.

Regional variations of dust mineral composition are fundamental to climate impacts but generally neglected in climate models. A challenge for models is that atlases of soil composition are derived from measurements following wet sieving, which destroys the aggregates potentially emitted from the soil. Aggregates are crucial to simulating the observed size distribution of emitted soil particles. We use an extension of brittle fragmentation theory in a global dust model to account for these aggregates. Our method reproduces the size-resolved dust concentration along with the approximately size-invariant fractional abundance of elements like Fe and Al in the decade-long aerosol record from the Izaña Observatory, off the coast of West Africa. By distinguishing between Fe in structural and free forms, we can attribute improved model behavior to aggregation of Fe and Al-rich clay particles. We also demonstrate the importance of size-resolved measurements along with elemental composition analysis to constrain models.

GISS-E2.1: Configurations and Climatology.

This paper describes the GISS-E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS-E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden-Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7-3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger.

BACKGROUND: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. OBJECTIVES: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. DATA AND METHODS: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January-May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. RESULTS: At the national level, a model using early incidence in December and averaged November-December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41). CONCLUSIONS: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic.