Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land-atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast permafrost carbon feedback dynamics – active layer thickness (ALT), carbon dioxide flux (CO2), and methane flux (CH4) – with high precision and minimal loss (i.e., ALTRMSE: 1.327cm [1969-2022]; CO2 RMSE: 0.697µmolCO2m-2s-1 [2003-2021]; CH4 RMSE: 0.715nmolCH4m-2s-1 [2011-2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the permafrost carbon feedback, and our model is the first characterization of these dynamics across space and time.