Global Disturbance Regimes: Patterns, Climatic Drivers, and Carbon Budget Implications

Forest disturbances are fundamental drivers of accelerated ecosystem carbon turnover and massive carbon storage losses. At the landscape level, disturbance regimes characterized by their spatial extent (μ), frequency (α), and intensity (β) of disturbance histories, together with background mortality (K_b), are essential for understanding forest carbon sink dynamics. However, global patterns of these disturbance regimes and their climatic drivers, as well as their corresponding impacts on regional and global carbon budgets in the context of climate change, remain poorly understood.

Building upon our recently developed global dataset of these disturbance regimes data at 0.25° resolution, derived from high-resolution biomass observations (Wang et al., under review), this study aims to resolve the following three questions: (1) What disturbance characteristics have forests in different regions of the world experienced historically? (2) How do climate change and climate extremes influence these observed disturbance regimes? (3) To what extent do these disturbances directly or indirectly alter regional and global carbon budgets?

First, we applied K-Means clustering to the disturbance regime data using multivariate similarity. The optimal numbers of clusters were determined by the Elbow Method, allowing us to classify global ecosystems into 12 distinct disturbance-regime groups. The largest  cluster (17.83%) is primarily distributed in temperate regions, while specific biomes, such as wet tropical forests, are dominated by 3 clusters (14%) characterized by relatively high extent and frequency but low-intensity disturbance regimes. These groups exhibit strong spatial coherence, closely mapping onto distinct biomes and climate zones.  

Second, we developed cluster-specific Random Forest models to assess the primary climatic drivers associated with these regime types. Integrating ERA5 reanalysis data, we examined both long-term climatic means and a suite of extreme indices (e.g., heatwaves, precipitation anomalies). The feature importance of these variables reveals the distinct hierarchy of climatic drivers for each regime.  This analysis differentiates the influence of baseline climatic conditions from extreme events, helping to identify the specific environmental factors most strongly associated with disturbance dynamics in different global regions.

Third, we plan to examine the potential implications of these regimes for the carbon cycle. We will analyze the regional carbon budgets from atmospheric inversions, linking them to disturbance regime characteristics, specifically investigating how shifts in disturbance intensity and frequency relate to regions transitioning between carbon sources and sinks.

This study systematically addresses how global ecosystems can be functionally grouped by their disturbance regimes, identifies the specific climatic factors driving these patterns, and quantifies the impact of regime shifts on the carbon budget. By linking these elements, our findings provide essential empirical constraints for Earth System Models (ESMs), particularly for representing the stochastic nature of disturbances and predicting their feedback to the global carbon cycle in a changing climate.