Precision monitoring of the subsurface carbon-dioxide plume ensures long-term, sustainable geological carbon storage. Carrigan et al. (2013) and Yang et al. (2014) showed that electrical resistivity tomography (ERT) can accurately map the evolution of the CO2 saturation during geological carbon storage. To better monitor the CO2 plume migration in a storage reservoir, we develop an unsupervised spatiotemporal clustering to process the CO2 saturation maps derived from the ERT measurements acquired over 80 days by Carrigan et al. (2013). Using dynamic time wrapping (DTW) K-means clustering, four distinct clusters were identified in the CO2-storage reservoir. The four clusters exhibit a Davies-Bouldin (DB) index of 0.71, a Calinski-Harabasz (CH) index of 262791, and a DTW-silhouette score of 0.58. Unlike traditional clustering methods, the DTW K-means incorporates a temporal distance metric. Traditional clustering methods, such as Euclidean K-means, agglomerative and meanshift clustering, exhibit a lower performance with DB index of 0.83, 0.95, and 1.01, respectively, and CH index of 157866, 131593, and 69438, respectively. Subsequent statistical analysis indicates that contrast stretching and fast-Fourier transform are strong geophysical signatures of the spatiotemporal evolution of CO2 plume. We also identified a strong correlation between injection flow rate and the spatial evolution of regions with high CO2 content. Finally, the previously computed spatiotemporal clusters were further clustered to discover distinct temporal sequences emerging with respect to the overall CO2 plume distribution in the subsurface. Six distinct temporal clusters of CO2 plume evolution were detected over a period of 2 months. A tensor-based feature extraction was critical for processing the ERT data acquired over 80 days to capture both the temporal and spatial components relevant to the evolution of CO2 plume in the storage reservoir.