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The climate numerical models require a considerable amount of computing power. The modern parallel architectures provide the needed computing power to perform scientific simulations at acceptable resolutions. However, the efficiency of the exploitation of the parallel architectures by the climate models is often poor. Several factors influence the parallel efficiency such as the parallel overhead due to the communications among concurrent tasks, the memory contention among tasks on the same computing node, the load balancing and the tasks synchronization. The work here described aims at addressing two of the factors influencing the efficiency: the communications and the memory contention. The used approach is based on the optimal mapping of the tasks on the SMP nodes of a parallel cluster. The best mapping can heavily influence the time spent for communications between tasks belonging to the same node either to different nodes. Moreover, if we consider that each parallel task will allocate different amount of memory, the optimal tasks mapping can balance the total amount of main memory allocated on the same node and hence reduce the overall memory contention. The climate model taken into consideration is PELAGOS025 made by coupling the NEMO oceanic model with the BFM biogeochemical model. It has been used in a global configuration with a horizontal resolution of 0.25◦. Three different mapping strategies have been implemented, analyzed and compared with the standard allocation performed by the local scheduler. The parallel architecture used for the evaluation is an IBM iDataPlex with Intel SandyBridge processors located at the CMCC’s Supercomputing Center.