A.K. Gueye, S Janicot, A.M. Lezine LOCEAN, P. Braconnot LSCE Analysis of climate simulations over Africa at 6Ky, 4Ky and pre-industrial periods by using Self-Organizing Maps. Example of northern summer. A.K. Gueye, S Janicot, A.M. Lezine LOCEAN, P. Braconnot LSCE Objective. The objective is to analyse summer rainfall anomaly fields over West Africa for three contrasted periods, pre-industrial, and 4000 and 6000-year BP, and evaluate their links with regional and global atmospheric and oceanic fields. Data. IPSL/CM5A climate model simulations have been performed on 1000 years for the pre-industrial period (“PI”), 910 years for the 6000-year BP (“6K”) and 400 years for the 4000-year BP (“4K”). The variables used are rainfall, evaporation, surface temperature, mean sea level pressure and wind and specific humidity at 10 meters. The seasonal summer averages for each period have been computed and removed from the individual seasons of corresponding periods, and the three de-seasonalised data sets have been merged in a unique 2310 summers ensemble. Mean summer rainfall. The Figure on the left shows the mean pre-industrial rain-fall field (top, PI) and the difference 4K-PI and 6K-PI. The PI field shows the rain-belt located over West Africa in summer with maxima near the mountains of Cameroon and Fouta-Jalon. This simula-ted rainbelt is a bit southward of the observed one. At 4K, the anomaly field indi-cates a rainbelt more to the north in agreement with proxies data and the astronomic context of mid-Holocene. At 6K, a similar anomaly field is produced but with higher rain anomalies (be aware that the colour scales are different) in agreement too with proxies and context. Classification process. (Figures below) We first applied an unsupervised classification using a neural network based model, the SOM (Self Organizing Map, Kohonen, 1984). The aim was to summarize the information contained in the Learning Set by producing a small number of reference vectors (rvs) that are statistically representative of it. The large number of subsets provided by the SOM map allowed us to take into account the complexity of the dataset but may have prevented us from synthesizing some geophysical information embedded in the Learning Set, such as spatial or temporal specificities. To counteract this difficulty, we decided to aggregate this large number of subsets into a smaller number of types based on the similarities of the subsets using a hierarchical ascendant classification (HAC; Jain and Dubes, 1988) using the Ward distance for the intra-classes similarity. Rainfall and associated anomaly fields. (Figures above) The eight rainfall anomaly fields are shown on the left column. Note that this classification has been computed over the longitudes 20°W-20°E. A first generic rainfall pattern is the meridional dipole with either positive/negative anomalies over the Sahel/Guinea coast (C6, C8) or negative/positive anomalies (C1, C5). The second generic rainfall pattern is anomalies along the coast either positive (C2, C7) or negative (C3, C4). This is consistent with the location of the eight classes on the Self-Organizing Map below, the opposite dipole classes being at the two extremities of the map, the other classes in the middle of the map but well separated too, and the similar classes being adjacent. The dipole patterns are clearly associated with the northward and southward location of the low-level atmospheric winds, associated with consistent pressure anomalies, and bringing more or less moisture to the north (middle left column). The other rainfall patterns are organized at a smaller spatial scale with convergent/divergent wind fields located along the Guinean coast. At larger scale (middle right column for surface temperature anomalies and right column for mean sea level pressure anomalies), for the rainfall dipole classes, the C1 and C8 have a clear and consistent signal of pressure anomalies over North Africa while for the two other dipole classes C5 and C6, pressure anomalies are weaker over North Africa, C5 showing decreased pressure over the south tropical Atlantic that might be consistent with a southward location of the rainbelt. Surface temperature anomalies are well defined for the dipole classes in agreement with pressure anomalies and with extension over the eastern equatorial Atlantic. For the other classes where the rainfall signal is located over the coast, C2 and C4 has a clear and consistent signal of La Nina / El Nino in the eastern Pacific associated with opposite sea surface temperatures in the eastern equatorial Atlantic. The last two classes, C3 and C7, can be linked to consistent sea surface temperature anomalies present on the northern tropical Atlantic. First results. (Figures on the right) Eight classes have been selected through the SOM-HAC process. Their location on the Self-Organizing Map is shown on the right. Each hexagon represents a neuron and the eight coloured ensembles are the result of the HAC. The advantage of this process is to be able to display the respective proximity of all the classes on this map. More to the right is the projection of the eight classes as well as the three periods (PI, 4K, 6K). This projection helps to highlight the “proximities” of both classes and periods. Sub-groups (C1-C2-C5, C3-C4-C8, C6-C7) represent the HAC at the upper level. Test values in the table enables to quantify the representativeness of each period within each of the classes (for a specific period, a high positive value means a high occurrence of summers of this period in a class, and a low negative value a weak occurrence). We can see that PI is well represented in classes C2, C4, C5 and not in classes C1 and C8; 6K is well present in C1 and C8, and not in C2 and C4; 4K has no specific characterization, which is consistent with the fact that this period represents an intermediate climate between 6K and PI.