It is well-established that positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land-atmosphere feedback mechanisms, disrupt the global carbon cycle, and accelerate climate change. Permafrost dynamics are relevant to the global community because the distribution of this frozen ground substrate characterizes nearly 23 million square kilometers of the northern latitudes. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impact. Current earth system models do not account for permafrost carbon feedback mechanisms; we are exploring, simulating, and quantifying this limitation with field-scale surveys and numerical modeling, image processing, and machine learning at scale across the tundra and taiga ecosystems (TTE). This research seeks to identify, interpret, and explain the causal links and feedback sensitivities attributed to permafrost degradation and terrestrial carbon cycling asymmetry with in situ observations, remote sensing imagery, modeling and reanalysis products, and a hybridized multimodal deep learning ensemble of recurrent, convolutionally-layered, memory-based networks (GeoCryoAI). Preliminary metrics obtained from mirroring freeze-thaw dynamics and soil carbon flux across four subdomain in Alaska yield a root mean square error of 6.3637 and 4.7973, respectively. More specifically, this data-driven modeling ensemble is composed of a convolutional neural network-filtered (CNN) long short-term memory-encoded (LSTM) recurrent neural network that integrates teacher learning from in situ observations while embedding satellite-based measurements and time series datasets into a network of activation functions and processing layers. These outputs are then trained within a variational autoencoder framework (VAE) that encodes and imputes proper decoding protocol necessary for generative adversarial training, benchmarking, and reconstructing synthetic time series data for gap-filling and feature learning. Ongoing work demonstrates the fidelity of monitoring active layer thickness (ALT) variability as a sensitive, silent-but-pronounced harbinger of change; a unique signal for characterizing and forecasting permafrost degradation, soil carbon flux, and other biogeochemical drivers facilitating land cover change and earth system feedbacks. These multimodal approaches to knowledge discovery will not only improve sensitivity analyses and disentangle the spatial processes and causal links behind drivers of change, but also reconcile disparate estimations and below-ground uncertainty across the Arctic system.