Africa Group paper session, Monday 18 February 2008 Charlie Williams Climate modelling in AMMA Ruti, P. M., Hourding, F. & Cook, K. H. CLIVAR Exchanges, Vol 12, No. 2 April 2007 Title

from Parker et al (2007)

Aim: analyse model skill at different spatial/temporal scales, considering interactions between atmospheric dynamics & parameterisations – fact that AMMA (the African Monsoon Multidisciplinary Analysis) is multiscale allows evaluation of regional climate change predictions and improvement of physical schemes

W Africa characterised by strong meridional surface gradients of albedo & veg These are coupled to atmospheric circulations e.g. AEJ – this develops during monsoon season between 10°-15°N, & location is correlated with gradients in surface temperature, soil moisture, etc Synoptic variability during monsoon is dominated by AEWs (poss linked to AEJ) High seasonal variation e.g. abrupt latitudinal shift at onset of monsoon whereas slower/more progressive latitudinal shift at monsoon retreat So…structure/variability of basic large-scale features involves complex interactions with processes at different spatial & temporal scales, which all need to be simulated Similar to CASCADE?? Important issue: how well do current models simulate not only mean state, but also variability & related mechanisms Motivation

To address multiple scales characterising WAM, AMMA structured around 4 interacting spatial scales: 1.Global scale 2.Regional scale 3.Mesoscale 4.Local scale ** Typically GCMs can only resolve 1 and 2 ** Problem because interactions between AEWs & MCSs is key to WAM, producing rainfall at both mesoscale & local scale

Several papers aim to answer this, but few are intercomparison projects considering large number of models and different physical processes West African Monsoon Project (WAMP), funded by EU in the Fourth Framework ( ). Special emphasis placed upon: 1.Seasonal cycle – GCMs dont capture rainfall seasonal cycle, producing early onset of monsoon. Saharan heat low (important in determining meridional temp gradient) is overestimated, producing too many intense rainfall events 2.Interannual variability – suggested that WAM is influenced by ENSO forcing from Kelvin/Rossby waves, but ability of GCMs to reproduce wave dynamics appears weak 3.Synoptic & mesoscale weather systems – relevance of AEJ-AEW link highlighted by combination of observational data + RCMs. Interactions between convection & AEWs analysed – suggests convection propagates through AEW in obs but is more tied to trough of wave in GCMs How well do current models simulate WAM?

More recently, simulations undertaken for AR4 have been collated to form WCRP / CLIVAR IPCC AR4 Archive Example here is from Cook and Vizy (2006), demonstrating (in)ability of GCMs to simulate WAM climatology – figures show JJAS precip from , from 18 coupled GCMs In general, GCMs fail to capture correct precip patterns, or 20 th century drying trend over Sahel

Way forward… Two main intercomparison initiatives developed: 1.USA WAMME (West African Monsoon Modelling and Evaluation) – uses GCMs/RCMs to address issues regarding role of land-ocean- atmosphere interaction, land / water use, vegetation dynamics & aerosols on WAM development 2.European AMMA-MIP (AMMA-Modelling Intercomparison Project) – focuses on hydrological parameters & convection at seasonal / intraseasonal timescales