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Abstract Our understanding and quantification of global soil nitrous oxide (N 2 O) emissions and the underlying processes remain largely uncertain. Here, we assessed the effects of multiple anthropogenic and natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change, and rising atmospheric CO 2 concentration, on global soil N 2 O emissions for the period 1861–2016 using a standard simulation protocol with seven process‐based terrestrial biosphere models. Results suggest global soil N 2 O emissions have increased from 6.3 ± 1.1 Tg N 2 O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N 2 O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N 2 O‐N/year to 3.3 Tg N 2 O‐N/year over the same period, accounting for 82% of the total increase. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N 2 O emissions since the 1970s. However, US cropland N 2 O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N 2 O emissions appear to have decreased by 14%. Soil N 2 O emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N 2 O‐N/year (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application, and climate change contributed 54%, 26%, 15%, and 24%, respectively, to the total increase. Rising atmospheric CO 2 concentration reduced soil N 2 O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N 2 O emissions, this study recommends several critical strategies for improving the process‐based simulations.
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Abstract Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time ( τ veg ), leaf C fraction (F leaf ), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (P LAI ), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τ veg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems. , Key Points A GPP‐traceability framework is established to diagnose the uncertainty sources of modeled GPP Large intermodel differences of modeled GPP result from their different representation of vegetation functional properties Positive bias in simulated GPP over the East Asian monsoon region could be attributed to the higher simulated CUE and SLA comparing with observations