Global pattern of ecosystem respiration tendencies and its implications on terrestrial carbon sink potential

As the largest component of carbon export from terrestrial ecosystems, ecosystem respiration (RECO) determines the carbon stock changes in terrestrial ecosystems. It is essential to accurately simulate the response of RECO to climate change. In this study, by constructing an optimal deep learning model for simulating global-scale RECO, we found that there is a 1–2 years' lagged response of RECO to changes in water conditions and an inconsistency in carbon input (NPP) and output (RECO) trends. The NPP growth trend in global terrestrial ecosystems is greater than that of RECO, with a trend showing increasing carbon sinks, particularly in the northern extra-tropics; while the carbon sink capacity of tropical regions has gradually saturated, showing that the changing trend of RECO is close to that of NPP, which poses a potential risk to the sustainable carbon sink capacity of global ecosystems in the future.

Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.

Changes in global terrestrial live biomass over the 21st century

Live woody vegetation is the largest reservoir of biomass carbon, with its restoration considered one of the most effective natural climate solutions. However, terrestrial carbon fluxes remain the largest uncertainty in the global carbon cycle. Here, we develop spatially explicit estimates of carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show that live biomass has removed 4.9 to 5.5 PgC year−1 from the atmosphere, offsetting 4.6 ± 0.1 PgC year−1 of gross emissions from disturbances and adding substantially (0.23 to 0.88 PgC year−1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.

Projecting long-term soil organic dynamics in Australia's rangelands

<p>Rangelands in Australia are vast and occupy more than 80% of the continental land area. They extend across arid, semi-arid, and the tropical regions with seasonal, variable rainfall in the north. They include diverse, relatively undisturbed grasslands, shrublands, woodlands and tropical savanna ecosystems. They represent Australia’s largest terrestrial carbon sink as they account for almost 70% of Australia's total soil organic carbon stock (Viscarra Rossel et al., 2014), more than all above-ground sources of carbon (native grasses, trees and shrubs) in these regions (Gifford et al., 1992). Here we have developed a novel space-time approach for projecting the long-term C dynamics of rangelands soils using Long Short-Term Memory (LSTM) deep learning neural networks. We further demonstrate how the networks might be interpreted and quantified the influence of explanatory variables on the spatiotemporal dynamics of soil C in these regions. Our results provide an improved ability to accurately model long-term carbon dynamics, which is needed to confidently predict changes in soil C from change in climate or anthropogenic disturbance. The information is critical for improving our understanding of soil C in these regions and for understanding the potential for sequestering C in the rangelands.</p>

Substantial decrease in CO2 emissions from Chinese inland waters due to global change

Carbon dioxide (CO2) evasion from inland waters is an important component of the global carbon cycle. However, it remains unknown how global change affects CO2 emissions over longer time scales. Here, we present seasonal and annual fluxes of CO2 emissions from streams, rivers, lakes, and reservoirs throughout China and quantify their changes over the past three decades. We found that the CO2 emissions declined from 138 ± 31 Tg C yr−1 in the 1980s to 98 ± 19 Tg C yr−1 in the 2010s. Our results suggest that this unexpected decrease was driven by a combination of environmental alterations, including massive conversion of free-flowing rivers to reservoirs and widespread implementation of reforestation programs. Meanwhile, we found increasing CO2 emissions from the Tibetan Plateau inland waters, likely attributable to increased terrestrial deliveries of organic carbon and expanded surface area due to climate change. We suggest that the CO2 emissions from Chinese inland waters have greatly offset the terrestrial carbon sink and are therefore a key component of China’s carbon budget.

Modeling FoRTE, the Forest Resilience Threshold Experiment

<p>Forested ecosystems represent a large yet uncertain fraction of the global terrestrial carbon sink. Their future state depends on a number of natural and anthropogenic influences; a particularly large uncertainty is how disturbance affects vegetation structure and ecosystem biogeochemistry.  We used the Ecosystem Demography model to explore the ecological and biogeochemical consequences of disturbance as part of the Forest Resilience Threshold Experiment (FoRTE), a dual modeling and manipulative field experiment investigating the effects of disturbance at different severities on a century-old deciduous forest. The field component was conducted at the University of Michigan Biological Station (UMBS), where stem girdling was applied to achieve four different severity levels of disturbance (0, 45, 65, and 85% gross defoliation) before the 2019 growing season. Since then, we have tracked the subsequent changes in vegetation and biogeochemistry. The modeling component attempted to simulate the FoRTE disturbance treatments within its framework. While we were able to instantiate a forest in ED with a similar climatology, soil characteristics, disturbance history, and vegetation of UMBS,  baseline ED is ultimately unable to reproduce the vegetation dynamics and carbon fluxes observed at the UMBS control plots. This is consistent with previous work where the model is not capable of matching observed carbon and vegetation dynamics. However, ED’s response to the disturbance treatments is consistent with observations from UMBS: in both the model and UMBS experimental results, we observed different resiliences and carbon cycle responses with respect to disturbance severity. These intriguing results point to both weaknesses and new possibilities in the modeling of ecosystems facing rising disturbances and climate change.</p>

Impacts of vegetation greenness on the sensitivity of terrestrial ecosystem productivity to flash drought in China

<p>Vegetation greening in the recent three decades significantly alters the carbon and water cycles over China. The response of terrestrial ecosystem productivity to flash droughts could be influenced by vegetation conditions and characteristics of flash droughts. However, it is still unclear that how the sensitivity of vegetation to flash drought varies with increasing leaf area index (LAI) across China. We use a land surface model and multiple satellite LAI products to assess the response of gross primary productivity (GPP) to flash droughts. Evapotranspiration is increased with increasing LAI and soil moisture is correspondingly decreased. Thus, the frequency, duration, and severity of flash droughts are all intensified from a water-budget perspective. The increasing LAI is contributed to the enhanced terrestrial carbon sink through increasing water use efficiency (WUE). The resistance and resilience of GPP to flash drought are also enhanced due to the increased LAI across various climates and vegetation types. These results refine the sensitivity of GPP to flash droughts in greening China and constrain the prognostic models to simulate the response of vegetation to droughts in changing environments.</p>