Model Intercomparison Projects (MIPs) provide standardised computer simulations of the Earth system, offering unique opportunities to systematically detect and assess features and dynamics, such as abrupt shifts, across diverse models and variables. Recent advances combine time-series analysis with spatiotemporal clustering to identify dynamically connected regions within individual datasets. Yet, extending this notion of connectivity across the model and variable dimensions of MIP output remains an open challenge. Here, we present a conceptual workflow that addresses this by introducing two aggregation strategies for "detect-then-cluster" pipelines: "Detect-Cluster-Aggregate-Cluster" (DCAC) and "Detect-Aggregate-Cluster" (DAC), enabling systematic synthesis of spatiotemporal signals across multiple datasets. These aggregation algorithms are evaluated and tuned using a customisable Analytic Hierarchy Process (AHP) framework, which allows users to encode prior knowledge about dataset reliability. In anticipation of output from the Tipping Points Modelling Intercomparison Project (TIPMIP) and other MIPs within the Coupled Model Intercomparison Project (CMIP), we implement the proposed aggregation methods using the "Tipping and Other Abrupt Events Detector" (TOAD) package. To demonstrate feasibility, we apply the methods to CMIP6 simulations of Amazon rainforest dynamics, detecting and clustering abrupt vegetation shifts first across multiple variables, where a shared signal indicates a coherent ecosystem response, and then across multiple models, where a shared signal reflects model alignment. Our case study reveals that this aggregation helps distinguish such shared behaviour from dynamics that are specific to individual variables or models, patterns that typically remain obscured when datasets are analysed in isolation. These results illustrate that conclusions about abrupt dynamics depend critically on how information is synthesised across time, space, models, and variables. While showcased here in the context of tipping points, the proposed aggregation framework provides a structured and transferable foundation for multimodel and multivariate risk assessments of diverse Earth-system processes within MIPs.