Quantifying topographic dynamics from 3D time series is essential for a broad range of geoscientific applications. However, data acquired by laser scanning often vary between epochs, e.g. in point density and coverage. These heterogeneities present a challenge for change detection, particularly across multi-temporal and multi-modal data. We propose a new approach that enhances the analysis of spatiotemporally irregular point clouds, particularly when the sampling frequency exceeds the analysis time scale. In particular, our method extends the Multiscale Model to Model Cloud Comparison (M3C2) algorithm through adaptive temporal aggregation. Driven by a local point density requirement, our method employs both a spatial neighborhood (as in standard M3C2) and a temporal neighborhood for each core point within the point cloud time series. If the neighborhood of a core point is too sparse for surface estimation, an iterative temporal search incorporates data from adjacent epochs until either the density requirement or a maximum temporal window is reached. This adaptive process ensures sufficient local point density while preventing globally fixed temporal aggregation. We evaluate our method on a multi-modal laser scanning dataset from Mont-Saint-Michel Bay, France, comprising 38 epochs spanning a decade at daily to seasonal intervals. Results demonstrate that our method increases change detection completeness by more than 13% compared to standard M3C2 and increases accuracy by 31% compared to commonly used fixed-window averaging. Our approach thereby enhances 3D change detection in complex real-world 4D datasets, enabling higher accuracy and completeness in the analysis of surface dynamics at variable spatiotemporal scales.