Forecasting Permafrost Carbon Dynamics in Alaska with GeoCryoAI

Abstract

Complex non-linear relationships exist between the permafrost thermal state, active layer thickness, and terrestrial carbon cycle dynamics In Arctic and boreal Alaska. The rate, magnitude, and extent of permafrost degradation remain uncertain, with an increasing recognition of the importance of abrupt thaw mechanisms. Similarly, large uncertainties in the rate, magnitude, timing, location, and composition of the permafrost carbon feedback complicate this issue. The challenge of monitoring sub-surface phenomena, such as the soil temperature and soil moisture profiles, with remote sensing technology further complicates the situation. There is an urgent need to understand how and to what extent permafrost degradation is destabilizing the Alaskan carbon balance and to characterize the feedbacks involved. We employ our artificial intelligence (AI)-driven model GeoCryoAI to quantify permafrost thaw dynamics and greenhouse gas emissions in Alaska. GeoCryoAI uses a hybridized multimodal deep learning architecture of stacked convolutionally layered memory-encoded bidirectional recurrent neural networks and 12.4 million parameters to simultaneously ingest and analyze 13.1 million in situ measurements (i.e., CALM, GTNP, ABoVE ReSALT, FLUXNET, NEON), 8.06 billion remote sensing airborne observations (i.e., UAVSAR, AVIRIS-NG), and 7.48 billion process-based modeling outputs (i.e., SIBBORK-TTE, TCFM-Arctic) with disparate spatiotemporal sampling and data densities. This framework introduces ecological memory components and effectively learns subtle spatiotemporal covariate complexities in high-latitude ecosystems by emulating permafrost degradation and carbon flux dynamics across Alaska with high precision and minimal loss (RMSE: 1.007cm, 0.694nmolCH4m-2s-1, 0.213µmolCO2m-2s-1). GeoCryoAI captures abrupt and persistent changes while providing a novel methodology for assimilating contemporaneous information on scales from individual sites to the pan-Arctic. Our approach overcomes traditional model inefficiencies and seamlessly resolves spatiotemporal disparities.