The AMMA Land Surface Model Intercomparison Project (ALMIP)
Boone, Aaron; de Rosnay, Patricia; Balsamo, Gianpaolo; Beljaars, Anton; Chopin, Franck; Decharme, Bertrand; Delire, Christine; Ducharne, Agnes; Gascoin, Simon; Grippa, Manuela; Guichard, Francoise; Gusev, Yeugeniy; Harris, Phil; Jarlan, Lionel; Kergoat, Laurent; Mougin, Eric; Nasonova, Olga; Norgaard, Anette; Orgeval, Tristan; Ottlé, Catherine; Poccard-Leclercq, Isabelle; Polcher, Jan; Sandholt, Inge; Saux-Picart, Stephane; Taylor, Christopher 
ORCID: https://orcid.org/0000-0002-0120-3198; Xue, Yongkang.
2009
The AMMA Land Surface Model Intercomparison Project (ALMIP).
Bulletin of the American Meteorological Society, 90 (12).
1865-1880.
https://doi.org/10.1175/2009BAMS2786.1
Abstract/Summary
The rainfall over West Africa has been characterized by extreme variability in the last half-century, with prolonged droughts resulting in humanitarian crises. There is, therefore, an urgent need to better understand and predict the West African monsoon (WAM), because social stability in this region depends to a large degree on water resources. The economies are primarily agrarian, and there are issues related to food security and health. In particular, there is a need to better understand land–atmosphere and hydrological processes over West Africa because of their potential feedbacks with the WAM. This is being addressed through a multiscale modeling approach using an ensemble of land surface models that rely on dedicated satellite-based forcing and land surface parameter products, and data from the African Multidisciplinary Monsoon Analysis (AMMA) observational field campaigns. The AMMA land surface model (LSM) Intercomparison Project (ALMIP) offline, multimodel simulations comprise the equivalent of a multimodel reanalysis product. They currently represent the best estimate of the land surface processes over West Africa from 2004 to 2007. An overview of model intercomparison and evaluation is presented. The far-reaching goal of this effort is to obtain better understanding and prediction of the WAM and the feedbacks with the surface. This can be used to improve water management and agricultural practices over this region
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