Variations of carbon allocation and turnover time across tropical forests

Abstract

Aim The carbon sink in tropical forests is a highly uncertain component of the global carbon budget. An understanding of the processes controlling this sink requires better quantification of carbon allocation, stocks and turnover times. Location Tropical forests. Time period 2010–2017. Major taxa studied Tropical forest ecosystem. Methods We develop a novel data assimilation system using satellite‐based annual above‐ground biomass derived from L‐band vegetation optical depth with 25 km × 25 km grid spacing, together with leaf area, to constrain the 25 km × 25 km carbon allocation patterns of net primary productivity (NPP) into the wood, leaf and root pools, and their turnover times. Our average data‐driven estimates of these variables are broadly consistent with independent ground‐based estimates of NPP allocation and wood turnover from forest inventory plots. Results In tropical forest, on average, the NPP allocation into wood (0.30 ± 0.04) is significantly higher than that into leaves (0.24 ± 0.07). From the wet to dry tropics, forest NPP allocation into both wood and leaves declines slightly. The turnover times of forest leaf pools exhibit little spatial variation, whereas the turnover times of wood pools in Africa (median and interquartile range: 50.1‐4.0+5.5 years) are slightly longer than those in South America (48.2‐3.3+4.0 years) and Southeast Asia (48.3‐3.1+5.4 years). Our datasets reveal emergent trade‐offs across climatic and vegetation gradients between growth and life span/turnover for both wood and leaves. The spatial gradients of NPP allocation to wood/leaves are associated with canopy height, adjusted by climate condition and nutrient acquisition. The spatial gradients of wood and leaf turnover times are influenced mainly by climate and leaf characteristics. Main conclusions Our data‐driven estimates of carbon allocation and turnover times provide a basis for more detailed exploration of these mechanisms in field studies. This highlights that improved model representation of carbon allocation and turnover is necessary for more accurate prediction of future carbon dynamics.

Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño

“…We use a simple box model driven by our estimates of AGB changes, MODIS NPP product (i.e., MOD17A3HGF), MODIS leaf area index (LAI) product (i.e., MOD15A2), and NPP allocation fractions from Yang et al. ( 32 ), with specific leaf area from Butler et al. ( 33 ), to estimate the drought-induced extra mortality up to 2019 (see details in SI Appendix , Text S2 and Fig.…”

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability

“…Model benchmarking and optimization studies that have been performed in dryland regions indicate considerable model-data discrepancies in vegetation dynamics, C and water fluxes (Dahlin et al, 2015;Exbrayat et al, 2018;Haverd et al, 2013;Lawal et al, 2019;MacBean et al, 2015;Renwick et al, 2019;Trudinger et al, 2016;Teckentrup et al, 2021 in review;Traore et al, 2014;Yang et al, 2021;Whitley et al, 2016). Whitley et al (2016) evaluated six TBMs at five savanna flux tower sites along the Northern Australian Tropical Transect and found that accurately representing both tree and grass phenology in TBMs was crucial for simulating seasonal dynamics of leaf area index (LAI) and gross primary productivity (GPP).…”

“…If the spread in posterior parameter values across PFTs seen in the DA experiments presented here is real, it may mean that traditional PFT categories do not represent dryland species well. Instead, high spread in posterior parameter values grouped by current PFT groupings may indicate that new PFTs need to be developed specifically for dryland species, or that certain parameters vary more across different species, or across biomes, latitudes and continents within each PFT(Dahlin et al, 2017;Yang et al, 2021). In addition, high spread in posterior parameter values may point to temporal variation in traits Barron-Gafford et al (2012).…”

Nitrogen limitation information retrieved from data assimilation

“…All of the observations which contain the basic C and N condition can be used to evaluate the N limitation by a C-only model and the coupled C-N model with data assimilation technique. In addition, there have been studies doing inverse analysis using global data products (Bloom et al, 2016;Yang et al, 2021), it will become possible to estimate the global nutrient limitation distribution by our method with increasing global data products with high reliability.…”