Global variability in belowground autotrophic respiration in terrestrial ecosystems

Belowground autotrophic respiration (RA) is one of the largest but most highly uncertain carbon flux components in terrestrial ecosystems. However, RA has not been explored globally before and still acts as a black box in global carbon cycling currently. Such progress and uncertainty motivate the development of a global RA dataset and understanding its spatial and temporal patterns, causes, and responses to future climate change. We applied the random forest (RF) algorithm to upscale an updated dataset from the Global Soil Respiration Database (v4) - covering all major ecosystem types and climate zones with 449 field observations, using globally gridded temperature, precipitation, soil and other environmental variables. We used a 10-fold cross validation to evaluate the performance of RF in predicting the spatial and temporal pattern of RA. Finally, a globally gridded RA dataset from 1980 to 2012 was produced with a spatial resolution of 0.5 degrees x 0.5 degrees (longitude x latitude) and a temporal resolution of 1 year (expressed in g C m(-2) yr(-1); grams of carbon per square meter per year). Globally, mean RA was 43.8 +/- 0.4 PgC yr(-1), with a temporally increasing trend of 0.025 +/- 0.006 PgC yr(-2) from 1980 to 2012. Such an incremental trend was widespread, representing 58% of global land. For each 1 degrees C increase in annual mean temperature, global RA increased by 0.85 +/- 0.13 PgC yr(-2), and it was 0.17 +/- 0.03 PgC yr(-2) for a 10 mm increase in annual mean precipitation, indicating positive feedback of RA to future climate change. Precipitation was the main dominant climatic driver controlling RA, accounting for 56% of global land, and was the most widely spread globally, particularly in dry or semi-arid areas, followed by shortwave radiation (25 %) and temperature (19 %). Different temporal patterns for varying climate zones and biomes indicated uneven responses of RA to future climate change, challenging the perspective that the parameters of global carbon stimulation are independent of climate zones and biomes. The developed RA dataset, the missing carbon flux component that is not constrained and validated in terrestrial ecosystem models and Earth system models, will provide insights into understanding mechanisms underlying the spatial and temporal variability in belowground vegetation carbon dynamics. The developed RA dataset also has great potential to serve as a benchmark for future data-model comparisons.