New Saharan wind observations reveal substantial biases in analysed dust‐generating winds

Alexander J Roberts,Philip D Rosenberg,Peter Knippertz,John H Marsham,Mark Bart,Luis Garcia‐Carreras,Matthew Hobby,Daniel Walker,James B Mcquaid,Douglas J Parker

doi:10.1002/asl.765

Abstract

AbstractFor the remote Sahara, the Earth's largest dust source, there has always been a near‐absence of data for evaluating models. Here, new observations from the Fennec project are used along with Sahelian data from the African Monsoon Multidisciplinary Analysis (AMMA) to give an unprecedented evaluation of dust‐generating winds in the European Centre for Medium‐Range Weather Forecasts ERA‐Interim reanalysis (ERA‐I). Consistent with past studies, near‐surface, high‐speed winds are lacking in ERA‐I and the diurnal variability is under‐represented. During the summer monsoon season, correlations of ERA‐I with observed wind‐speed are low (∼0.35 in Sahel and 0.25–0.4 in the Sahara). Fennec data show for the first time that: (1) correlations are reduced even in the Sahara, not directly influenced by the monsoon, (2) the systematic underestimation of observed winds by ERA‐I in the summertime Sahel extends into the central Sahara: potentially explaining the failure of global models to capture the observed global dust maximum that occurs over the summertime Sahara (such as CMIP5), and demonstrates that modelled winds must be improved if they are to capture this key feature of the climatology.

Highlights

The Sahara/Sahel is the world’s largest dust source but wind remains a key uncertainty in modelling emission (Knippertz and Todd, 2012)
The nocturnal low-level jet (NLLJ) is often underestimated in models (Fiedler et al, 2013; Largeron et al, 2015) and haboobs are missed by models with parametrized convection (Marsham et al, 2011)
The African Monsoon Multidisciplinary Analysis (AMMA) field campaign was an international project with the goal of improving the understanding of the West African Monsoon

Summary

Introduction

The Sahara/Sahel is the world’s largest dust source but wind remains a key uncertainty in modelling emission (Knippertz and Todd, 2012). The observational constraint on reanalyses can be insufficient, leading to significant errors (Agustí-Panareda et al, 2010; Garcia-Carreras et al, 2013; Roberts et al, 2015). This becomes something of circular problem, in that for regions with very few observations model developers, and researchers commonly use reanalyses as de-facto observations. This can be important when we consider the known limitations of models to capture key dust uplift mechanisms

Methods

Results

Conclusion