Quantifying uncertainty in estimates of mineral dust flux: An intercomparison of model performance over the Bodélé Depression, northern Chad
Mineral dust aerosols play an important role in the climate system. Coupled climate‐aerosol models are an important tool with which to quantify dust fluxes and the associated climate impact. Over the last decade or more, numerous models have been developed, both global and regional, but to date, the...
Gespeichert in:
Veröffentlicht in: | Journal of Geophysical Research: Atmospheres 2008-12, Vol.113 (D24), p.n/a |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Mineral dust aerosols play an important role in the climate system. Coupled climate‐aerosol models are an important tool with which to quantify dust fluxes and the associated climate impact. Over the last decade or more, numerous models have been developed, both global and regional, but to date, there have been few attempts to compare the performance of these models. In this paper a comparison of five regional atmospheric models with dust modules is made, in terms of their simulation of meteorology, dust emission and transport. The intercomparison focuses on a 3‐day dust event over the Bodélé depression in northern Chad, the world's single most important dust source. Simulations are compared to satellite data and in situ observations from the Bodélé Dust Experiment (BoDEx 2005). Overall, the models reproduce many of the key features of the meteorology and the large dust plumes that occur over the study domain. However, there is at least an order of magnitude range in model estimates of key quantities including dust concentration, dust burden, dust flux, and aerosol optical thickness. As such, there remains considerable uncertainty in model estimates of the dust cycle and its interaction with climate. This paper discusses the issues associated with partitioning various sources of model uncertainty. |
---|---|
ISSN: | 0148-0227 2169-897X 2156-2202 2169-8996 |
DOI: | 10.1029/2008JD010476 |