The effects of regional orography on the West African monsoon system The West African monsoon system (WAM) is characterised by the monsoon onset, which is an abrupt latitudinal shift of the inter-tropical convergence zone (ITCZ) and its associated rainfall maxima, from a quasi-stationary location at 5° N in May–June to another quasi- stationary in July–August (Le Barbé et al, 2002). Numerical studies with global climate models (GCMs) and linear models have suggested a possible interaction between the North African large-scale orography and the WAM at the time of the onset. Due to the lack of regional scale details within the above mentioned models, the mechanisms responsible for these interactions remain not as clearly understood as the role of other regional mountain ranges. However, the recent and wide development of high spatial resolution regional climate models (RCMs) has now provided an opportunity to further study the influence of orographic forcing on the climate in West Africa. The main objective of this study is to further investigate the influence of orographic forcing on the West African climate with a high resolution RCM, focusing on the abrupt northward shift of the ITCZ in summer. Regional climate model The RCM used here is HadRM3P developed by the Hadley Centre. This model is used in the regional climate modeling system “Providing Regional Climates for Impact Studies (PRECIS)” (Jones et al., 2004). HadRM3P is similar to its immediate predecessor, HadRM3H, for which an extensive description has been given by Hudson and Jones (2002), and Frei et al. (2003). The main differences reside in the details of the representation of dynamical and convective cloud, and thresholds associated with the formation of precipitation. HadRM3P has a horizontal resolution of 0.44° latitude x 0.44° longitude (~50 km), 19 hybrid vertical levels and a time-step of 5 min. Experimental design Seven, one-year long integrations of the RCM are performed for the year Each simulation is driven by the ECMWF reanalysis ERA-15 and two domains are used for the integrations, one standard and one extended (Fig. 1). Table 1 summarises the integrations we have conducted, which consist of one control and six sensitivities experiments. In the first four experiments, the highlands of Fouta-Djalon, Cameroon, Bauchi, and Hoggar-Air-Tibesti have been removed from the simulation, one at the time, by replacing the height of the topography in grid boxes with the mean height of the areas adjacent to the highland. The fifth experiment corresponds to an enhanced representation of the topography by doubling the RCM horizontal resolution from 50 km to 25 km. In the last experiment, the eastern boundary of the model domain is shifted eastwards, allowing the inclusion of orographic features of North East Africa such as the Darfur and the Ethiopian highlands. 1. Atmospheric circulations The orographic forcing influences the features of the mean summer atmospheric circulations (Fig. 2). The Hoggar-Air-Tibesti complex controls the northernmost excursion of the south west monsoon (SWM) flow over the land and its exclusion moves the ITF northward by 2°. The shift of the eastern lateral boundary eastwards, so that the orographic features of East Africa are included, strengthens the AEJ, TEJ and the SWM flow significantly. 2. Monsoon pre-onset The latitude of the ITF — represented by the northern boundary of the 925-hPa zonal wind zero isoline — is generally a good indicator of the meteorological signal associated with the monsoon preonset. The date of the pre-onset can be defined as the date when the 925-hPa zonal wind component averaged over 10° W–10° E equals zero, going from negative (Harmattan wind) to positive values (SWM flow). Figure 3 depicts the time series of daily outgoing longwave radiation (OLR) and 925-hPa zonal wind at 15° N, filtered to remove variability below 10 days. The exclusion of orographic forcing clearly does not modify the date of the preonset (early April) but does modify the magnitude and the variability of deep convection and zonal wind. 3. Monsoon onset Figure 4 shows the time-latitude diagrams of daily rainfall values, averaged over the longitudes 10° W–10° E. The rapid shift of the ITCZ is identified in all the experiments around the end of June. The orographic forcing influences the rainfall distribution over the Sahel in June-September. This influence is particularly strong when the Hoggar-Air-Tibesti and the Cameroon highlands are excluded, and the extended domain is used. 4. Sensitivity to model resolution The sensitivity of the simulated West African monsoon to model resolution is detected in the representation of mesoscale convective systems (MCSs). Figure 5 illustrates the time-longitude diagram of daily rainfall from 15° W–15° E and at 15° N, and from June to September. The monsoon is developing rapidly from the second half of June and there is a band of high convective activity. The daily rainfall increases with the increase of the horizontal resolution. However, the influence of model resolution is less important than that of the domain size shifted in the eastwards direction. The West African orography influences the south west monsoon flow, the intensity of deep convection and rainfall. Over the Sahel, the influence of the Hoggar-Air- Tibesti complex on the West African monsoon is stronger than that of Fouta-Djalon, Bauchi Plateau, and Cameroon highlands. The increase of the model horizontal resolution from 50 km to 25 km leads to an increase in the simulated convective system in June-September. The simulated West African monsoon is particularly sensitive to the extension of the eastern boundary eastwards, so as to include orographic features of North East Africa. In the future, further work is needed to understand the influence of North East Africa orography and domain size on simulated convective systems. A series of model integrations over the extended domain, where the Darfur and Ethiopian highlands are removed, are planned. References: Le Barbé, L., T. Lebel, and D. Tapsoba, 2002: variability in West Africa during the years J. Climate, 15, Hudson, D. and R. Jones, 2002: Regional climate model simulation of present-day and future climate of southern Africa. Hadley Centre Technical Note, 39, 41pp. Frei, C., J. Christensen, M. Dequé, R. Jones, and P. Vidale, 2003: Daily precipitation statistics in regional climate model. Evaluation and intercomparison for the European Alps. J. Geophys. Res., 108, Jones, R.G., Noguer, M., Hassell, D.C., Hudson, D., Wilson, S.S., Jenkins, G.J., Mitchell, J.F.B.: 2004, Generating high resolution climate change scenarios using PRECIS, Met Office Hadley Centre, Exeter, UK/UNDP, New York, USA: 35pp. Background Methodology Results Method The method used here is the one-way nesting of a limited area climate model into a coarse resolution GCM. The principle behind this technique is that given a detailed representation of physical processes, and high spatial resolution that resolves complex orography, land-sea contrast, and land-use, a limited area model can generate realistic regional climate information consistent with the driving large-scale circulation, supplied either by global reanalysis data or a GCM. Figure 1: Model domains and distribution of orographic height (m) ExperimentsOrographic featuresHorizontal resolution Domain CTRLAll include50 kmstandard NOFOUNo Fouta-Djalon50 kmstandard NOCAMNo Cameroon50 kmstandard NOBAUNo Bauchi50 kmstandard NOHOGNo Hoggar-Air-Tibesti50 kmstandard HIRESAll include25 kmstandard EAFRAll include50 kmextended Table 1: Conducted RCM experiments Figure 2: Mean JJA cross-vertical section of zonal wind component Figure 3: Time series of daily zonal wind and OLR, averaged over 10° W–10° E along 15° N Figure 4: Time-latitude diagrams of daily rainfall, averaged over 10° W–10° E Figure 5: Time-longitude diagrams of daily rainfall along 15° N Conclusions and future plans It is worth noting that the control experiment has been first compared against the 1988 RCM outputs extracted from a previous 12-year ( ) continuous integration of HadRM3P. The results reveal no significant differences between the two RCM integrations. Therefore, the results presented here are considered to be unaffected by the model spinup. © Crown copyright /0356 Met Office and the Met Office logo are registered trademarks Met Office Hadley Centre FitzRoy Road Exeter Devon EX1 3PB United Kingdom Tel: Fax: Wilfran Moufouma-Okia and David Hassell Hadley Centre for Climate Prediction and Research