The performance analysis of a parallel application can be a difficult task. Specially in the case that this application is an operational atmospheric-chemistry model there can be multiple performance bottlenecks caused from different fields. Although the exascale era is coming, the applications are not ready to take advantage of all the new technologies and programming models. It is needed to improve our model in order to simulate higher resolutions and scale more efficient. In this article we describe the approaches that we follow for the performance analysis of an atmospheric-chemistry global model called NMMB/BSC chemical transport model and the identification of various bottlenecks by using the Paraver tool. We present the differences between some model configurations depending on the usage of extra modules and we study eight different topics that limit the scalability of the model. These topics include categories that there is no need for code modification such as mapping, processor affinity and more in depth analysis with hardware counters and load imbalance issues. The final results show the directions that we should follow in order to improve our model.