Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428in situstem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428in situstem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that somein situwaveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha⁻¹, average of 167 Mg ha⁻¹). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

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 (PCF) 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 (DL) 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 PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO₂), and methane flux (CH₄)—with high precision and minimal loss (i.e. ALT^RMSE: 1.327 cm [1969–2022]; CO₂ ^RMSE: 0.697 µmolCO₂m⁻²s⁻¹ [2003–2021]; CH₄ ^RMSE: 0.715 nmolCH₄m⁻²s⁻¹ [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Forest structure—the height, cover, vertical complexity, and spatial patterns of trees—is a key indicator of productivity variation across forested extents. During the 2017 and 2019 growing seasons, NASA’s Arctic-Boreal Vulnerability Experiment collected full-waveform airborne LiDAR using the land, vegetation and imaging sensor, sampling boreal and tundra landscapes across a variety of ecological regions from central Canada westward through Alaska. Here, we compile and archive a geo-referenced gridded suite of these data that include vertical structure estimates and novel horizontal cover estimates of vegetation canopy cover derived from vegetation’s vertical LiDAR profile. We validate these gridded estimates with small footprint airborne LiDAR, and link >36 million of them with stand age estimates from a Landsat time-series of tree-canopy cover that we confirm with plot-level disturbance year data. We quantify the regional magnitude and variability in site index, the age-dependent rates of forest growth, across 15 boreal ecoregions in North America. With this open archive suite of forest structure data linked to stand age, we bound current forest productivity estimates across a boreal structure gradient whose response to key bioclimatic drivers may change with stand age. These results, derived from a reduction of a large archive of airborne LiDAR and a Landsat time series, quantify forest productivity bounds for input into forest and ecosystem growth models, to update forecasts of changes in North America’s boreal forests by improving the regional parametrization of forest growth rates.