Forest ecosystem processes follow classic responses with age, peaking production around canopy closure and declining thereafter. Although age dynamics might be more dominant in certain regions over others, demographic effects on net primary production (NPP) and heterotrophic respiration (Rh) are bound to exist. Yet, explicit representation of ecosystem demography is notably absent in most global ecosystem models. This is concerning because the global community relies on these models to regularly update our collective understanding of the global carbon cycle. This paper aims to fill this gap in understanding by presenting the technical developments of a computationally-efficient approach for representing age-class dynamics within a global ecosystem model, the LPJ-wsl v2.0 Dynamic Global Vegetation Model. The modeled age-classes are initially created by fire feedbacks, wood harvesting, and abandonment of managed land, otherwise aging naturally until a stand-clearing disturbance is simulated or prescribed. In this paper, we show that the age-module can capture classic demographic patterns in stem density and tree height compared to inventory data, and that patterns of ecosystem function follow classic responses with age. We also present a few scientific applications of the model to assess the modeled age-class distribution over time and to determine the demographic effect on ecosystem fluxes relative to climate. Simulations show that, between 1860 and 2016, zonal age distribution on Earth was driven predominately by fire, causing a ~45-year difference in ages between boreal (50N-90N) and tropical (23S-23N) latitudes. Land use change and land management was responsible for an additional decrease in zonal age by -6 years in boreal and by -21 years in temperate (23N-50N) and tropical latitudes, with the anthropogenic effect on zonal age distribution increasing over time. A statistical model helped reduced LPJ-wsl complexity by predicting per-grid-cell annual NPP and Rh fluxes by three terms: precipitation, temperature and age-class; at global scales, R² was between 0.95 and 0.98. As determined by the statistical model, the demographic effect on ecosystem function was often less than 0.10 kg C m^-2 yr^-1 but as high as 0.60 kg C m^-2 yr^-1 where the effect was greatest. In eastern forests of North America, the demographic effect was of similar magnitude, or greater than, the effects of climate; demographic effects were similarly important in large regions of every vegetated continent. Spatial datasets are provided for global ecosystem ages and the estimated coefficients for effects of precipitation, temperature and demography on ecosystem function. The discussion focuses on our finding of an increasing role of demography in the global carbon cycle, the effect of demography on relaxation times (resilience) following a disturbance event and its implications at global scales, and a finding of a 40-Pg C increase in turnover from age dynamics at global scales. Whereas time is the only mechanism that increases ecosystem age, any additional disturbance not explicitly modeled will decrease age. This LPJ-based age-module therefore simulates the upper limit of age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.

These datasets contain raw data that were simulated by the LPJ-wsl v2.0 model <https://github.com/benpoulter/LPJ-wsl_v2.0> and R scripts to reproduce analyses and figures in the associated publication 

There is a general README text file in main folder of the archive. It provides detail description of the folder/file structure and of every file in the archive. For NetCDF files (*.nc), the pre-processing history is self-contained in the metadata and can be accessed via the command-line utility "ncdump -h {file.nc}", to print the history to screen. The R scripts are heavily annotated and a main header describes the contents and objectives of each script. The R scripts have been tested and can be run from within the scripts folder. All R scripts reference datasets in the data folder. The following R package libraries are required to succesfully run the scripts: ggplot2, gridExtra, ncdf4, plyr, raster, vioplot.