• Energy Research

Water-table drawdown across peatlands increases carbon dioxide (CO2) and reduces methane (CH4) emissions. The net climatic effect remains unclear. Based on global observations from 130 sites, we found a positive (warming) net climate effect of water-table drawdown. Using a machine-learning-based upscaling approach, we predict that peatland water-table drawdown driven by climate drying and human activities will increase CO2 emissions by 1.13 (95% interval: 0.88–1.50) Gt yr−1 and reduce CH4 by 0.26 (0.14–0.52) GtCO2-eq yr−1, resulting in a net increase of greenhouse gas of 0.86 (0.36–1.36) GtCO2-eq yr−1 by the end of the twenty-first century under the RCP8.5 climate scenario. This drops to 0.73 (0.2–1.2) GtCO2-eq yr−1 under RCP2.6. Our results point to an urgent need to preserve pristine and rehabilitate drained peatlands to decelerate the positive feedback among water-table drawdown, increased greenhouse gas emissions and climate warming. The climate impact of water-table drawdown in peatlands is unclear as carbon dioxide emissions increase and methane emissions decrease due to drying. This study shows decreasing water-table depth results in net greenhouse gas emissions from global peatlands, despite reducing methane emissions.

AbstractSoils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long‐term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site‐by‐site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha−1 year−1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi‐model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water‐related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi‐model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water‐related variables on SOC decomposition.Highlights The feasibility of the 4‰ target was studied at 16 long‐term agricultural experiments. An ensemble of soil organic carbon models was used to estimate the uncertainty of the predictions. On average across the sites, carbon input had to increase by 119% compared to initial conditions. High uncertainty of the simulations was mainly driven by water‐related variables.

AbstractAnthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta‐analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far‐reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

AbstractIncreasing atmospheric CO2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO2-enrichment experiments in woody ecosystems that measured total NPP and biomass. CO2 enrichment increased biomass increment by 1.05 ± 0.26 kg C m−2 over a full decade, a 29.1 ± 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO2 response of NPP (0.16 ± 0.03 kg C m−2 y−1) and the CO2-independent, linear slope between biomass increment and cumulative NPP (0.55 ± 0.17). An ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO2-independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO2 responses.