Non-growing season carbon emissions in a northern peatland are projected to increase under global warming

Peatlands are important ecosystems that store approximately one third of terrestrial organic carbon. Non-growing season carbon fluxes significantly contribute to annual carbon budgets in peatlands, yet their response to climate change is poorly understood. Here, we investigate the governing environmental variables of non-growing season carbon emissions in a northern peatland. We develop a support-vector regression model using a continuous 13-year dataset of eddy covariance flux measurements from the Mer Blue Bog, Canada. We determine that only seven variables were needed to reproduce carbon fluxes, which were most sensitive to net radiation above the canopy, soil temperature, wind speed and soil moisture. We find that changes in soil temperature and photosynthesis drove changes in net carbon flux. Assessing net ecosystem carbon exchange under three representative concentration pathways, we project a 103% increase in peatland carbon loss by 2100 under a high emissions scenario. We suggest that peatland carbon losses constitute a strong positive climate feedback loop.

Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China

As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m−2 y−1. Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m−2 y−1. Furthermore, distinct advantages of GOSIF GPP (with R2 = 0.85–0.98, and RMSE = 0.87–2.66 g C m−2 d−1) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C3 and C4 plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.

Soil sampling and preparation for monitoring soil carbon

There is an urgent need for standardized monitoring of existing soil organic carbon stocks in order to accurately quantify potential negative or positive feedbacks with climate change on carbon fluxes. Given the uncertainty of flux measurements at the ecosystem scale, obtaining precise estimates of changes in soil organic carbon stocks is essential to provide an independent assessment of long-term net ecosystem carbon exchange. Here we describe the standard procedure to monitor the soil organic carbon stocks within the footprint of an eddy covariance flux tower, as applied at ecosystem stations of the Integrated Carbon Observation System. The objectives are i) to ensure comparability between sites and to be able to draw general conclusions from the results obtained across many ecosystems and ii) to optimize the sampling design in order to be able to prove changes in time using a reduced number of samples. When sampling a given site at two periods, the objective is generally to assess if changes occurred in time. The changes that can be detected (i.e., demonstrated as statistically significant) depend on several parameters such as the number of samples, the spatial sampling design, and the inherent within-site soil variability. Depending on these parameters, one can define the ‘minimum detectable change’ which is the minimum value of changed that can be statistically proved. Using simulation studies, we address the trade-off between increasing the number of samples and getting lower minimum detectable changes of soil organic carbon stocks.