Predictability of the terrestrial carbon cycle

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Tracking effects of extreme drought on coniferous forests from space using dynamic habitat indices

Terrestrial ecosystems such as coniferous forests in Central Europe are experiencing changes in health status following extreme droughts compounding with severe heat waves. The increasing temporal resolution and spatial coverage of earth observation data offer new opportunities to assess these dynamics. Dense time-series of optical satellite data allow for computing Dynamic Habitat Indices (DHIs), which have been predominantly used in biodiversity studies. However, DHIs cover three aspects of vegetation changes that could be affected by drought: annual productivity, minimum cover, and seasonality. Here, we evaluate the health status of coniferous forests in the federal state of Hesse in Germany over the period 2017-2020 including the severe drought year of 2018 using DHIs based on the Normalized Difference Vegetation Index (NDVI) for drought assessment. To identify the most important variables affecting coniferous forest die-off, a series of environmental variables together with the three DHIs components were used in a logistic regression (LR) model. Each DHI component changed significantly across non-damaged and damaged sites in all years (p-value 0.05). When comparing 2017 to 2019, DHI-based annual productivity decreased and seasonality increased. Most importantly, none of the DHI components had reached pre-drought conditions, which likely indicates a change in ecosystem functioning. We also identified spatially explicit areas highly affected by drought. The LR model revealed that in addition to common environmental parameters related to temperature, precipitation, and elevation, DHI components were the most important factors explaining the health status. Our analysis demonstrates the potential of DHIs to capture the effect of drought events on Central European coniferous forest ecosystems. Since the spaceborne data are available at the global level, this approach can be applied to track the dynamics of ecosystem conditions in other regions, at larger spatial scales, and for other Land Use/Land Cover types.

Carbon cycle responses to climate change across China's terrestrial ecosystem: Sensitivity and driving process

Investigations into the carbon cycle and how it responds to climate change at the national scale are important for a comprehensive understanding of terrestrial carbon cycle and global change issues. Contributions of carbon fluxes to the terrestrial sink and the effects on climate change are still not fully understood. In this study, we aimed to explore the relationship between ecosystem production (GPP/SIF/NDVI) and net ecosystem carbon exchange (NEE) and to investigate the sensitivity of carbon fluxes to climate change at different spatio-temporal scales. Furthermore, we sought to delve into the carbon cycle processes driven by climate stress in China since the beginning of the 21st century. To achieve these objectives, we employed correlation and sensitivity analysis techniques, utilizing a wide range of data sources including ground-based observations, remote sensing observations, atmospheric inversions, machine learning, and model simulations. Our findings indicate that NEE in most arid regions of China is primarily driven by ecosystem production. Climate variations have a greater influence on ecosystem production than respiration. Warming has negatively impacted ecosystem production in Northeast China, as well as in subtropical and tropical regions. Conversely, increased precipitation has strengthened the terrestrial carbon sink, particularly in the northern cool and dry areas. We also found that ecosystem respiration exhibits heightened sensitivity to warming in southern China. Moreover, our analysis revealed that the control of terrestrial carbon cycle by ecosystem production gradually weakens from cold/arid areas to warm/humid areas. We identified distinct temperature thresholds (ranging from 10.5 to 13.7 °C) and precipitation thresholds (approximately 1400 mm yr-1) for the transition from production-dominated to respiration-dominated processes. Our study provides valuable insights into the complex relationship between climate change and carbon cycle in China.

Multi-objective optimization of saline water irrigation in arid oasis regions: Integrating water-saving, salinity control, yield enhancement, and CO2 emission reduction for sustainable cotton production

Brackish water stands as a promising alternative to mitigate freshwater scarcity in arid regions. However, its application poses potential threats to agricultural sustainability. There is a need to establish a clear understanding of the economic and ecological benefits. We conducted a two-year (2021-2022) field experiment to investigate the effects of four different irrigation water salinity levels on soil electrical conductivity, cotton yield, water use efficiency, CO2 emissions, and carbon sequestration. The salinity levels were designated as CK (0.85 g L-1), S1 (3 g L-1), S2 (5 g L-1), and S3 (8 g L-1). Results indicated that using irrigation water with high salinity (≥5 g L-1) led to the accumulation of salt in the soil, and a decrease in plant biomass and seed cotton yield. Compared to CK, the S3 treatment decreased by 18.72 % and 20.10 % in the respective two years. Interestingly, using brackish water (3 L-1 and 5 g L-1) decreased the rate and cumulative CO2 emissions, and increased the carbon emission efficiency and carbon sequestration by 0.098-0.094 kg kg-1 and 871-1859 kg ha-1 in 2021, 0.098-0.094 kg kg-1 and 617-1995 kg ha-1 in 2022, respectively. To comprehensively evaluate the tradeoff between economic and ecological benefits, we employed the TOPSIS method, and S1 was identified as the optimal irrigation salinity. Through fitting analysis, the most suitable irrigation salinity levels for 2021 and 2022 were determined as 3.52 g L-1 and 3.31 g L-1, respectively. From the perspective of water conservation, salinity management, yield improvement, and reduction of CO2 emissions, it is feasible to utilize brackish water for irrigation purposes, as long as the salinity does not exceed 3.52 g L-1 (first year) and 3.31 g L-1 (second year).

Beyond a deterministic representation of the temperature dependence of soil respiration

Soil CO2 efflux represents a complex interplay of biological and physical processes that result in the production and transfer of CO2 from soils to the atmosphere. Temperature has been widely recognized as a critical factor regulating soil CO2 efflux and is commonly utilized in deterministic empirical models to predict this important flux for the carbon cycle. This study introduces the Bernstein copula-based cosimulation (BCC) as a data-driven probabilistic approach to model the temperature-soil CO2 efflux relationship. The BCC accounts for the joint probability distribution and temporal dependence of soil CO2 efflux, which are often overlooked in deterministic models. The BCC was implemented as a proof of concept using two years of data on soil CO2 efflux conditioned by soil temperature in a temperate forest. The BBC accurately reproduced the original probability distribution, temporal dependency, and temperature-soil CO2 efflux relationship. Our findings show that a deterministic method, such as the commonly employed exponential relationship between soil CO2 efflux and temperature, is limited for comprehensively capturing the intricate nature of the temperature-soil CO2 efflux relationship. This is due to the confounding and interacting effects of environmental drivers beyond temperature, which are not fully accounted for in such a deterministic approach. Furthermore, the BCC revealed that the probability density between the joint cumulative probability of temperature and soil CO2 efflux is not constant, which raises the concern that deterministic approaches introduce incorrect assumptions for estimating temperature-soil CO2 relationship. In conclusion, we propose that probabilistic approaches hold promise for effectively depicting dependency relationships for soil CO2 efflux modeling, and for improving predictions of the effects of weather variability and climate change.

Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types

Accurate estimation and forecasts of net biome CO₂ exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.

Pool dynamics of time-dependent compartmental systems with application to the terrestrial carbon cycle

Compartmental models play an important role to describe the dynamics of systems that involve mass movements between different types of pools. We develop a theory to analyse the average ages of mass in different pools in a linear compartmental system with time-dependent (i.e. non-autonomous) transfer rates, which involves transit times that characterize the average time a particle has spent in a particular pool. We apply our theoretical results to investigate a nine-dimensional compartmental system with time-dependent fluxes between pools modelling the carbon cycle which is a modification of the Carnegie-Ames-Stanford approach model. Knowledge of transit time and mean age allows calculation of carbon storage in a pool as a function of time. The general result that has important implications for understanding and managing carbon storage is that the change in storage in different pools does not change monotonically through time: as rates change monotonically a pool which initially shows a decrease may then show an increase in storage or vice versa. Thus caution is needed in extrapolating even the direction of future changes in storage in carbon storage in different pools with global change.

Changes in the relationship between vapour pressure deficit and water use efficiency with the drought recovery time: A case study of the Yellow River Basin

Drought is a major driver of interannual variability in the gross primary productivity (GPP) of global terrestrial ecosystems, and drought recovery time has been widely used to assess ecosystem responses to drought. However, the response of the carbon-water coupled cycle to drought, especially changes in the correlation between drought intensity and carbon-water coupling throughout the recovery time, remains unclear. In this study, the Yellow River Basin (YRB) located mostly in drylands was the study area. We assessed the correlation between the standardized water vapour pressure deficit (VPD) and the water use efficiency of ecosystems (WUEe) and water use efficiency of canopies (WUEc) every month with the drought recovery time of GPP. We found that the drought intensity in the middle reach of the YRB (MYRB) was greater and the drought recovery time was longer than those in the upper reach (UYRB) and lower reach (LYRB) during the period from 2003 to 2017. In terms of the correlation between drought intensity and carbon-water coupling, the greater the VPD was, the lower the WUEc. In addition, the correlation of WUEc with VPD was higher than that of WUEe in most areas of the YRB, especially in the LYRB. On the watershed level, the correlation between the two types of WUE and VPD increased gradually with the recovery time, while the correlation between WUEc and VPD (mostly negative) changed more than the correlation between WUEe and VPD (mostly positive). Therefore, the response of WUEc to meteorological drought should be given more attention, especially during the middle and late stages of drought, since it exhibited an opposite signal compared to that of WUEe during drought recovery.

Microbial metabolic responses and CO2 emissions differentiated by soil water content variation in subarctic tundra soils

Recent rapid air temperature increases across the northern-latitude tundra have prolonged permafrost thawing and snow melting periods, resulting in increased soil temperature (T_s) and volumetric soil water content (SWC). Under prolonged soil warming at 8°C, Alaskan tundra soils were incubated in a microcosm system and examined for the SWC differential influence on the microbial decomposition activity of large molecular weight (MW) humic substances (HS). When one microcosm soil (AKC1-1) was incubated at a constant SWC of 41% for 90 days (T = 90) and then SWC was gradually decreased from 41% to 29% for another T = 90, the initial HS was partly depolymerized. In contrast, in AKC1-2 incubated at a gradually decreasing SWC from the initial 32% to 10% for T = 90 and then increasing to 27% for another T = 90, HS depolymerization was undetected. Overall, the microbial communities in AKC1-1 could maintain metabolic activity at sufficient and constant SWC during the initial T = 90 incubation. In contrast, AKC1-2 microbes may have been damaged by drought stress during the drying SWC regimen, possibly resulting in the loss of HS decomposition activity, which did not recover even after re-wetting to an optimal SWC range (20-40%). After T = 90, the CO₂ production in both treatments was attributed to the increased decomposition of small-MW organic compounds (including aerobic HS-degradative products) within an optimal SWC range. We expect this study to provide new insights into the early effects of warming- and topography-induced SWC variations on the microbial contribution to CO₂ emissions via HS decomposition in northern-latitude tundra soil.

The future of tundra carbon storage in Greenland - Sensitivity to climate and plant trait changes

The continuous change in observed key indicators such as increasing nitrogen deposition, temperatures and precipitation will have marked but uncertain consequences for the ecosystem carbon (C) sink-source functioning of the Arctic. Here, we use multiple in-situ data streams measured by the Greenland Ecosystem Monitoring programme in tight connection with the Soil-Plant-Atmosphere model and climate projections from the high-resolution HIRHAM5 regional model. We apply this modelling framework with focus on two climatically different tundra sites in Greenland (Zackenberg and Kobbefjord) to assess how sensitive the net C uptake will expectedly be under warmer and wetter conditions across the 21st century and pin down the relative contribution to the overall C sink strength from climate versus plant trait variability. Our results suggest that temperatures (5-7.7 °C), total precipitation (19-110 %) and vapour pressure deficit will increase (32-36 %), while shortwave radiation will decline (6-9 %) at both sites by 2100 under the RCP8.5 scenario. Such a combined effect will, on average, intensify the net C uptake by 9-10 g C m^-2 year^-1 at both sites towards the end of 2100, but Zackenberg is expected to have more than twice the C sink strength capacity of Kobbefjord. Our sensitivity analysis not only reveals that plant traits are the most sensitive parameters controlling the net C exchange in both sites at the beginning and end of the century, but also that the projected increase in the net C uptake will likely be similarly influenced by future changes in climate and existing local nutrient conditions. A series of experiments forcing realistic changes in plant nitrogen status at both sites corroborates this hypothesis. This work proves the unique synergy between monitoring data and numerical models to assist robust model calibration/validation and narrow uncertainty ranges and ultimately produce more reliable C cycle projections in understudied regions such as Greenland.

Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements

Separating the components of ecosystem-scale carbon exchange is crucial in order to develop better models and future predictions of the terrestrial carbon cycle. However, there are several uncertainties and unknowns related to current photosynthesis estimates. In this study, we evaluate four different methods for estimating photosynthesis at a boreal forest at the ecosystem scale, of which two are based on carbon dioxide (CO₂) flux measurements and two on carbonyl sulfide (COS) flux measurements. The CO₂-based methods use traditional flux partitioning and artificial neural networks to separate the net CO₂ flux into respiration and photosynthesis. The COS-based methods make use of a unique 5-year COS flux data set and involve two different approaches to determine the leaf-scale relative uptake ratio of COS and CO₂ (LRU), of which one (LRU_CAP) was developed in this study. LRU_CAP was based on a previously tested stomatal optimization theory (CAP), while LRU_PAR was based on an empirical relation to measured radiation. For the measurement period 2013-2017, the artificial neural network method gave a GPP estimate very close to that of traditional flux partitioning at all timescales. On average, the COS-based methods gave higher GPP estimates than the CO₂-based estimates on daily (23% and 7% higher, using LRU_PAR and LRU_CAP, respectively) and monthly scales (20% and 3% higher), as well as a higher cumulative sum over 3 months in all years (on average 25% and 3% higher). LRU_CAP was higher than LRU estimated from chamber measurements at high radiation, leading to underestimation of midday GPP relative to other GPP methods. In general, however, use of LRU_CAP gave closer agreement with CO₂-based estimates of GPP than use of LRUPAR. When extended to other sites, LRU_CAP may be more robust than LRU_PAR because it is based on a physiological model whose parameters can be estimated from simple measurements or obtained from the literature. In contrast, the empirical radiation relation in LRU_PAR may be more site-specific. However, this requires further testing at other measurement sites.

The Grain-for-Green project offsets warming-induced soil organic carbon loss and increases soil carbon stock in Chinese Loess Plateau

The dynamics of soil organic carbon (SOC) stock is a vital element affecting the climate, and ecological restoration is potentially an effective measure to mitigate climate change by enhancing vegetation and soil carbon stocks and thereby offsetting greenhouse gas emissions. The Grain-for-Green project (GFGP) implemented in Chinese Loess Plateau (LP) since 1999 is one of the largest ecological restoration projects in the world. However, the contributions of ecological restoration and climate change to ecosystem soil carbon sequestration are still unclear. In this study, we improved a soil carbon decomposition framework by optimizing the initial SOC stock based on full spatial simulation of SOC and incorporating the priming effect to investigate the SOC dynamics across the LP GFGP region from 1982 through 2017. Our results indicated that SOC stock in the GFGP region increased by 20.18 Tg C from 1982 through 2017. Most portion (15.83 Tg C) of the SOC increase was accumulated when the GFGP was initiated, with a SOC sink of 16.12 Tg C owing to revegetation restoration and a carbon loss of 0.29 Tg C due to warming during this period. The relationships between SOC and forest canopy height and investigations on the SOC dynamics after afforestation revealed that the accumulation rate of SOC could be as high as 24.68 g C m^-2 yr^-1 during the 70 years following afforestation, and that SOC could decline thereafter (-8.89 g C m^-2 yr^-1), which was mainly caused by warming. This study provides a new method for quantifying the contribution of ecological restoration to SOC changes, and also cautions the potential risk of LP SOC loss in the mature forest soil under future warming.

Remote sensing-based assessment of land degradation and drought impacts over terrestrial ecosystems in Northeastern Brazil

The spatio-temporal assessment of water and carbon fluxes in Brazil's Northeast region (NEB) allows for a better understanding of these surface flux patterns in areas with different vegetation physiognomies. The NEB is divided into four biomes: Amazon, Cerrado, Caatinga, and Atlantic Forest. Land degradation is a growing problem, particularly in susceptible areas of the Caatinga biome, such as the seasonally dry tropical forest. Furthermore, this region has experienced climatic impacts, such as severe droughts. Due to increasing human pressure, the Caatinga's natural land cover undergoes drastic changes, making it a region particularly vulnerable to desertification. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of evapotranspiration (ET) and gross primary production (GPP) were validated in two contrasting areas, dense Caatinga and sparse Caatinga, using eddy covariance (EC) data and then investigated their behavior over 21 years (2000-2021) for the NEB. MODIS products explained around 60% of the variations in ET and GPP, showing higher accuracy in dense Caatinga, while areas of sparse Caatinga presented the lowest GPP, indicating that land degradation has reduced the photosynthetic activity of the vegetation in this area. Based on the analysis of ET and GPP over 21 years, we observed a greater dependence of the sparse Caatinga on climate variability, demonstrating a stronger resilience of dense Caatinga to climate effects. In comparison with the other biomes of the NEB region, we found lower rates of ET and GPP in the Caatinga biome, with averages similar to the Sparse Caatinga. In comparison with the other biomes in the NEB region, we found the lowest averages of ET and GPP in the Caatinga biome, similar to values found in the sparse Caatinga. In forest areas, similar to the monitored DC, they allowed the Caatinga to behave closer to the other biomes present in the region.

Asynchronous recovery of predators and prey conditions resilience to drought in a neotropical ecosystem

The predicted increase in the intensity and frequency of drought events associated with global climate change will impose severe hydrological stress to freshwater ecosystems, potentially altering their structure and function. Unlike freshwater communities' direct response to drought, their post-drought recovery capacities remain understudied despite being an essential component driving ecosystem resilience. Here we used tank bromeliad as model ecosystem to emulate droughts of different duration and then assess the recovery capacities of ecosystem structure and function. We followed macroinvertebrate predator and prey biomass to characterize the recovery dynamics of trophic structure (i.e. predator-prey biomass ratio) during the post-drought rewetting phase. We showed that drought significantly affects the trophic structure of macroinvertebrates by reducing the predator-prey biomass ratio. The asynchronous recovery of predator and prey biomass appeared as a critical driver of the post-drought recovery trajectory of trophic structure. Litter decomposition rate, which is an essential ecosystem function, remained stable after drought events, indicating the presence of compensatory effects between detritivores biomass and detritivores feeding activity. We conclude that, in a context of global change, the asynchrony in post-drought recovery of different trophic levels may impact the overall drought resilience of small freshwater ecosystems in a more complex way than expected.

Soil phosphorus drives plant trait variations in a mature subtropical forest

Earth system models are implementing soil phosphorus dynamic and plant functional traits to predict functional changes in global forests. However, the linkage between soil phosphorus and plant traits lacks empirical evidence, especially in mature forests. Here, we examined the soil phosphorus constraint on plant functional traits in a mature subtropical forest based on observations of 9943 individuals from 90 species in a 5-ha forest dynamic plot and 405 individuals from 15 species in an adjacent 10-year nutrient-addition experiment. We first confirmed a pervasive phosphorus limitation on subtropical tree growth based on leaf N:P ratios. Then, we found that soil phosphorus dominated multidimensional trait variations in the 5-ha forest dynamic plot. Soil phosphorus content explained 44% and 53% of the variance in the traits defining the main functional space across species and communities, respectively. Lastly, we found much stronger phosphorus effects on most plant functional traits than nitrogen at both species and community levels in the 10-year nutrient-addition experiment. This study provides evidence for the consistent pattern of soil phosphorus constraint on plant trait variations between the species and community levels in a mature evergreen broadleaf forest in the East Asian monsoon region. These findings shed light on the predominant role of soil phosphorus on plant functional trait variations in mature subtropical forests, providing new insights for models to incorporate soil phosphorus constraint in predicting future vegetation dynamics.

Enzymic moderations of bacterial and fungal communities on short- and long-term warming impacts on soil organic carbon

Microbial communities play critical roles in soil carbon-warming feedback, but our understanding of their linkages to soil carbon (C) pools in response to short- and long-term warming is deficient. Here, by conducting a meta-analysis of 150 studies, we show that short-term (<5 years) warming mainly affects soil labile carbon (LC) pools by changing bacterial community structure, while long-term (≥5 years) warming promotes the decomposition of recalcitrant C (RC) pools by increasing fungal biomass and decreasing actinobacterial biomass. Specifically, under short-term warming, significant increases in actinobacterial biomass (+15.9%) and the G+/G- ratio (+8.0%) were accompanied by an increase in carbon-degrading enzyme activities and a decrease in LC (-5.9%). Under long-term warming, the fungal biomass (+20.4%) and related POX (phenol oxidase) activity (+34.9%) increased significantly, while actinobacterial biomass (-20.1%), RC (-18.8%) and SOC (-6.7%) decreased. Meanwhile, we observed that warming impacts on soil microbial communities can be predicted by ecosystem type, the magnitude of warming, pH and elevation. Latitude and warming duration contributed the most to explaining the responses of LC and RC, respectively, across studies. Given that RC accounts for a substantial fraction of global soil C pools, the decline in RC pools greatly contributes to soil C degradation. Our findings suggest that different microbial groups may mediate the temporal dynamics of the decomposition of different soil C components and highlight that incorporating the temporal responses of soil microorganisms will improve predictions of the long-term dynamics of soil C pools in a warmer world.

Influence of Residue Type and Method of Placement on Dynamics of Decomposition and Nitrogen Release in Maize-Wheat-Mungbean Cropping on Permanent Raised Beds: A Litterbag Study

Decomposition influences carbon and nutrient cycling from crop residues. The nylon-mesh-bag technique was implied to study the decomposition and N-release dynamics from different crop residues under field conditions. The four types of residues were: maize (lower than 50% below the cob), wheat (lower than 25% of wheat stubbles), a whole mung bean residue, and a mixture of wheat + mung bean residue (1:1 ratio) put on the soil surface and in below the sub-surface. Decomposition and N release from both at-surface- and below-surface-placed residues were accurately described by a single-pool first-order exponential decay function as a function of thermal time (based on the accumulative daily mean temperature). The simple first-order exponential model met the criteria of goodness of fit. Throughout the decomposition cycle (one thermal year), the rate of decomposition as measured by a decrease in residue mass and the release of total N were statistically higher from the sub-surface compared to the surface-placed residue, irrespective of the residue type. At the end of the 150-day decomposition cycle, the release of total N was highest in mung bean (32.0 kg N ha−1), followed by maize (31.5 kg N ha−1) > wheat + mung bean (16.1 kg N ha−1), and the minimum (6.54 kg N ha−1) in wheat residue. Crop residues with a wider C/N ratio such as maize and wheat, when applied on the soil surface in conservation agriculture, caused the decomposition to occur at slower rates, thereby providing long-term beneficial effects on the soil thermal regime, soil moisture conservation, and C sequestration in North-West India.

Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling

Soil microorganisms determine the fate of soil organic matter (SOM), and their activities compose a major component of the global carbon (C) cycle. We employed a multisubstrate, DNA-stable isotope probing experiment to track bacterial assimilation of C derived from distinct sources that varied in bioavailability. This approach allowed us to measure microbial contributions to SOM processing by measuring the C assimilation dynamics of diverse microorganisms as they interacted within soil. We identified and tracked 1,286 bacterial taxa that assimilated 13C in an agricultural soil over a period of 48 d. Overall 13C-assimilation dynamics of bacterial taxa, defined by the source and timing of the 13C they assimilated, exhibited low phylogenetic conservation. We identified bacterial guilds composed of taxa that had similar 13C assimilation dynamics. We show that C-source bioavailability explained significant variation in both C mineralization dynamics and guild structure, and that the growth dynamics of bacterial guilds differed significantly in response to C addition. We also demonstrate that the guild structure explains significant variation in the biogeographical distribution of bacteria at continental and global scales. These results suggest that an understanding of in situ growth dynamics is essential for understanding microbial contributions to soil C cycling. We interpret these findings in the context of bacterial life history strategies and their relationship to terrestrial C cycling.

Thermal optima of gross primary productivity are closely aligned with mean air temperatures across Australian wooded ecosystems

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%-75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (T_opt ). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary-line analysis to eddy covariance flux observations to (a) derive ecosystem GPP-Ta relationships and T_opt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP-Ta relationships within and among ecoregions; (c) examine the relationship between T_opt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down-welling short-wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP-Ta relationship. GPP-Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem T_opt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna-wet and dry seasons). The shape of GPP-Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between T_opt and MDTa (Adjusted R² : 0.81; Slope: 1.08) with T_opt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems.

Patterns of post-drought recovery are strongly influenced by drought duration, frequency, post-drought wetness, and bioclimatic setting

Understanding vegetation recovery after drought is critical for projecting vegetation dynamics in future climates. From 1997 to 2009, Australia experienced a long-lasting drought known as the Millennium Drought (MD), which led to widespread reductions in vegetation productivity. However, vegetation recovery post-drought and its determinants remain unclear. This study leverages remote sensing products from different sources-fraction of absorbed photosynthetically active radiation (FPAR), based on optical data, and canopy density, derived from microwave data-and random forest algorithms to assess drought recovery over Australian natural vegetation during a 20-year period centered on the MD. Post-drought recovery was prevalent across the continent, with 6 out of 10 drought events seeing full recovery within about 6 months. Canopy density was slower to recover than leaf area seen in FPAR. The probability of full recovery was most strongly controlled by drought return interval, post-drought hydrological condition, and drought length. Full recovery was seldom observed when drought events occurred at intervals of 3 months or less, and moderately dry (standardized water balance anomaly [SWBA] within [-1, -0.76]) post-drought conditions resulted in less complete recovery than wet (SWBA > 0.3) post-drought conditions. Press droughts, which are long term but not extreme, delayed recovery more than pulse droughts (short term but extreme) and led to a higher frequency of persistent decline. Following press droughts, the frequency of persistent decline differed little among biome types but peaked in semi-arid regions across aridity levels. Forests and savanna required the longest recovery times for press drought, while grasslands were the slowest to recover for pulse drought. This study provides quantitative thresholds that could be used to improve the modeling of ecosystem dynamics post-drought.

Global process-based characterization factors of soil carbon depletion for life cycle impact assessment

Regionalization of land use (LU) impact in life cycle assessment (LCA) has gained relevance in recent years. Most regionalized models are statistical, using highly aggregated spatial units and LU classes (e.g. one unique LU class for cropland). Process-based modelling is a powerful characterization tool but so far has never been applied globally for all LU classes. Here, we propose a new set of spatially detailed characterization factors (CFs) for soil organic carbon (SOC) depletion. We used SOC dynamic curves and attainable SOC stocks from a process-based model for more than 17,000 world regions and 81 LU classes. Those classes include 63 agricultural (depending on 4 types of management/production), and 16 forest sub-classes, and 1 grassland and 1 urban class. We matched the CFs to LU elementary flows used by LCA databases at country-level. Results show that CFs are highly dependent on the LU sub-class and management practices. For example, transformation into cropland in general leads to the highest SOC depletion but SOC gains are possible with specific crops.

Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite-derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above-ground biomass. The adjusted simulation significantly reduced the above-ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation-constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

Soil carbon accumulation with increasing temperature under both managed and natural vegetation restoration in calcareous soils

Vegetation restoration has been proposed as an effective strategy for increasing soil organic carbon (SOC) sequestration. However, the responses of SOC to managed and natural vegetation restoration strategies at a large scale are poorly understood due to the varying SOC components and changing climatic conditions. Here, we measured bulk SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MOC) after 15 years of vegetation restoration along an elevation gradient with a corresponding temperature gradient in the calcareous soils of karst region, Southwest China. We compared managed plantation forest and naturally recovered shrubland vegetation restoration strategies, using cropland and mature forest as references. Overall, we found that the SOC and POC densities in both plantation forest and shrubland were significantly higher than in the cropland but lower than in the mature forest. There were no significant differences in the SOC pool between the plantation forest and shrubland. Furthermore, the relative changes in the SOC and POC densities increased with increasing mean annual temperature in the plantation forest and shrubland. Our results showed that both vegetation restoration strategies, characterized by higher soil microbial abundance and exchangeable Ca concentration, were beneficial to POC but not MOC accumulation, and sufficiently compensated SOC decomposition at lower elevation with higher MAT. Our results highlight the potential of both vegetation restoration strategies for promoting SOC accumulation in warmer karst regions and emphasize the necessity to understand soil carbon stabilization mechanisms in calcareous soils.

Long-term measurements in a mixed-grass prairie reveal a change in soil organic carbon recalcitrance and its environmental sensitivity under warming

Soil respiration, the major pathway for ecosystem carbon (C) loss, has the potential to enter a positive feedback loop with the atmospheric CO₂ due to climate warming. For reliable projections of climate-carbon feedbacks, accurate quantification of soil respiration and identification of mechanisms that control its variability are essential. Process-based models simulate soil respiration as functions of belowground C input, organic matter quality, and sensitivity to environmental conditions. However, evaluation and calibration of process-based models against the long-term in situ measurements are rare. Here, we evaluate the performance of the Terrestrial ECOsystem (TECO) model in simulating total and heterotrophic soil respiration measured during a 16-year warming experiment in a mixed-grass prairie; calibrate model parameters against these and other measurements collected during the experiment; and explore whether the mechanisms of C dynamics have changed over the years. Calibrating model parameters against observations of individual years substantially improved model performance in comparison to pre-calibration simulations, explaining 79-86% of variability in observed soil respiration. Interannual variation of the calibrated model parameters indicated increasing recalcitrance of soil C and changing environmental sensitivity of microbes. Overall, we found that (1) soil organic C became more recalcitrant in intact soil compared to root-free soil; (2) warming offset the effects of increasing C recalcitrance in intact soil and changed microbial sensitivity to moisture conditions. These findings indicate that soil respiration may decrease in the future due to C quality, but this decrease may be offset by warming-induced changes in C cycling mechanisms and their responses to moisture conditions.

Estimating Net Primary Productivity (NPP) and Debris-Fall in Forests Using Lidar Time Series

Temporal series of lidar, properly field-validated, can provide critical information allowing in-ferences about the dynamics of biomass and carbon in forest canopies. Forest canopies gain carbon through net primary production (NPP) and lose carbon through canopy component damage and death, such as fine and coarse woody debris and litterfall (collectively, debris-fall). We describe a statistical method to extract gamma distributions of NPP and debris-fall rates in forest canopies from lidar missions repeated through time and we show that the means of these distributions covary with ecologically meaningful variables: topography, canopy structure, and taxonomic composition. The method employed is the generalized method of moments that applies the R package gmm to uncover the distribution of latent variables. We present an example with eco-logical interpretations that support the method’s application to change in biomass estimated for a boreal forest in southcentral Alaska. The deconvolution of net change from remote sensing products as distributions of NPP and debris-fall rates can inform carbon cycling models of can-opy-level NPP and debris-fall rates.

Consumer regulation of the carbon cycle in coastal wetland ecosystems

Despite escalating anthropogenic alteration of food webs, how the carbon cycle in ecosystems is regulated by food web processes remains poorly understood. We quantitatively synthesize the effects of consumers (herbivores, omnivores and carnivores) on the carbon cycle of coastal wetland ecosystems, 'blue carbon' ecosystems that store the greatest amount of carbon per unit area among all ecosystems. Our results reveal that consumers strongly affect many processes of the carbon cycle. Herbivores, for example, generally reduce carbon absorption and carbon stocks (e.g. aboveground plant carbon by 53% and aboveground net primary production by 23%) but may promote some carbon emission processes (e.g. litter decomposition by 32%). The average strengths of these effects are comparable with, or even times higher than, changes driven by temperature, precipitation, nitrogen input, CO₂ concentration, and plant invasions. Furthermore, consumer effects appear to be stronger on aboveground than belowground carbon processes and vary markedly with trophic level, body size, thermal regulation strategy and feeding type. Despite important knowledge gaps, our results highlight the powerful impacts of consumers on the carbon cycle and call for the incorporation of consumer control into Earth system models that predict anthropogenic climate change and into management strategies of Earth's carbon stocks. This article is part of the theme issue 'Integrative research perspectives on marine conservation'.

Accelerated terrestrial ecosystem carbon turnover and its drivers

The terrestrial carbon cycle has been strongly influenced by human-induced CO₂ increase, climate change, and land use change since the industrial revolution. These changes alter the carbon balance of ecosystems through changes in vegetation productivity and ecosystem carbon turnover time (τ_eco ). Even though numerous studies have drawn an increasingly clear picture of global vegetation productivity changes, global changes in τ_eco are still unknown. In this study, we analyzed the changes of τ_eco between the 1860s and the 2000s and their drivers, based on theory of dynamic carbon cycle in non-steady state and process-based ecosystem model. Results indicate that τ_eco has been reduced (i.e., carbon turnover has accelerated) by 13.5% from the 1860s (74 years) to the 2000s (64 years), with reductions of 1 year of carbon residence times in vegetation (r_veg ) and of 9 years in soil (r_soil ). Additionally, the acceleration of τ_eco was examined at biome scale and grid scale. Among different driving processes, land use change and climate change were found to be the major drivers of turnover acceleration. These findings imply that carbon fixed by plant photosynthesis is being lost from ecosystems to the atmosphere more quickly over time, with important implications for the climate-carbon cycle feedbacks.

Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States

Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings (R ² = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.

Global Urban Carbon Networks: Linking Inventory to Modeling

Cities utilize and manipulate an immense amount of global carbon flows through their economic and technical activities. Here, we establish the carbon networks of eight global cities by tracking the carbon exchanges between various natural and economic components. The metabolic properties of these carbon networks are compared by combining flow-based and interpretative network metrics. We further assess the relations of these carbon metabolic properties of cities with their socioeconomic attributes that are deemed important in urban development and planning. We find that, although there is a large difference in city-level carbon balance and flow pattern, a similarity in intercomponent relationships and metabolic characteristicsdoes exist. Cities with lower per capita carbon emissions tend to have healthier metabolic systems with more cooperative resource allocation among various industries, which indicates that there may be synergy between urban decarbonization and carbon-containing resource system optimization. A combination of indicators from flow balance and network models is a promising scheme for linking sector-based carbon inventories to system-based simulations of carbon management efforts. With this done, we may be able to reduce the knowledge gap with respect to how various carbon flows in cities can be concertedly managed considering both the restraint from their climate mitigation goals as well as the impact on urban social and economic development.

High-throughput screening of environmental polysaccharide-degrading bacteria using biomass containment and complex insoluble substrates

Carbohydrate degradation by microbes plays an important role in global nutrient cycling, human nutrition, and biotechnological applications. Studies that focus on the degradation of complex recalcitrant polysaccharides are challenging because of the insolubility of these substrates as found in their natural contexts. Specifically, current methods to examine carbohydrate-based biomass degradation using bacterial strains or purified enzymes are not compatible with high-throughput screening using complex insoluble materials. In this report, we developed a small 3D printed filter device that fits inside a microplate well that allows for the free movement of bacterial cells, media, and enzymes while containing insoluble biomass. These devices do not interfere with standard microplate readers and can be used for both short- (24–48 h) and long-duration (> 100 h) experiments using complex insoluble substrates. These devices were used to quantitatively screen in a high-throughput manner environmental isolates for their ability to grow using lignocellulose or rice grains as a sole nutrient source. Additionally, we determined that the microplate-based containment devices are compatible with existing enzymatic assays to measure activity against insoluble biomass. Overall, these microplate containment devices provide a platform to study the degradation of complex insoluble materials in a high-throughput manner and have the potential to help uncover ecologically important aspects of bacterial metabolism as well as to accelerate biotechnological innovation.

Global meta-analysis on the responses of soil extracellular enzyme activities to warming

Soil enzymes play critical roles in the decomposition of organic matter and determine the availability of soil nutrients, however, there are significant uncertainties in regard to how enzymatic responses to global warming. To reveal the general response patterns and controlling factors of various extracellular enzyme activities (EEA), we collected data from 78 peer-reviewed papers to investigate the responses of extracellular enzyme activities (EEA), including β-1,4-glucosidase (BG), β-d-cellobiosidase (CBH), β-1,4-xylosidase (XYL), leucine amino peptidase (LAP), N-acetyl-glucosaminidase (NAG), urease (URE), phosphatase (PHO), peroxidase (PER), phenol oxidase (POX), and polyphenol oxidase (PPO), to experimental warming. Our results showed that warming treatments increased soil temperature by 1.9 °C on average. The oxidative EEA, calculated as the sum of PER, POX and PPO, was on average stimulated by 9.4% under warming. However, the responses of C acquisition EEA (the sum of BG, CBH and XYL), N acquisition EEA (the sum of LAP, NAG and URE), and P acquisition EEA to warming had large variations across studies. The warming effects on C, N, P acquisition EEA and oxidative EEA tended to increase with soil warming magnitude and duration as well as the mean annual temperature. The response of C acquisition EEA to warming was positively correlated with fungal biomass, while that of P acquisition EEA had positive relationships with fungi: bacteria ratios. The response of oxidative EEA was negatively correlated with the abundance of gram-positive bacterial biomass. Our results suggested that warming consistently stimulated oxidative EEA, but had diverse effects on hydrolytic EEA, which were dependent on the warming magnitude or duration, or environmental factors. The observed relationships between changes in microbial traits and extracellular enzymes suggested that microbial compositions drive changes in enzyme decomposition under warming. Thus, incorporation of microbial modification in biogeochemistry models is essential to better predict ecosystem carbon and nutrient dynamics.

Stabilization of carbon sequestration in a Chinese desert steppe benefits from increased temperatures and from precipitation outside the growing season

The carbon (C) dynamics of desert steppes play an important role in the C budget of temperate steppes. Using the Terrestrial Ecosystem Regional model (TECO-R) model for desert steppes, we examined the dynamics and potential driving mechanisms for C stocks at different temporal and spatial scales from 2000 to 2017 in northern China. The ecosystem C density averaged 2.73 kg C m^-2 and soil organic C accounted for 91.6%. The grassland biome stored 2.85 kg C m^-2, which is higher than the shrub biome (2.19 kg C m^-2). The ecosystem storage increased by an average of 27.75 g C m^-2 yr^-1, with the fastest increase in the southeastern part of the study area. The grassland biome storage increased by an average of 33.54 g C m^-2 yr^-1, versus 25.74 g C m^-2 yr^-1 for the shrub biome. The desert steppe C stock totaled 288.29 Tg C, and increased at 3.09 Tg C yr^-1. An average of >45% of the aboveground biomass was browsed by livestock. The growing season precipitation was significantly positively correlated with changes in the C stock. Increasing temperature was negatively correlated with the C stock, especially for soil carbon. Precipitation was an important driving factor, but warming interacted with precipitation to affect C sequestration during the growing season. Outside the growing season, the increased precipitation and temperature stabilized C sequestration in the desert steppe. This improved understanding of feedbacks between the desert steppe's C cycle and climate will improve predictions of C dynamics in terrestrial ecosystems and of the impacts of climate change.

Detailed global modelling of soil organic carbon in cropland, grassland and forest soils

Assessments of the global carbon (C) cycle typically rely on simplified models which consider large areas as homogeneous in terms of the response of soils to land use or consider very broad land classes. For example, "cropland" is typically modelled as an aggregation of distinct practices and individual crops over large regions. Here, we use the process-based Rothamsted soil Carbon Model (RothC model), which has a history of being successfully applied at a global scale, to calculate attainable SOC stocks and C mineralization rates for each of c. 17,000 regions (combination of soil type and texture, climate type, initial land use and country) in the World, under near-past climate conditions. We considered 28 individual crops and, for each, multiple production practices, plus 16 forest types and 1 grassland class (total of 80 classes). We find that conversion to cropland can result in SOC increases, particularly when the soil remains covered with crop residues (an average gain of 12 t C/ha) or using irrigation (4 t C/ha), which are mutually reinforcing effects. Attainable SOC stocks vary significantly depending on the land use class, particularly for cropland. Common aggregations in global modelling of a single agricultural class would be inaccurate representations of these results. Attainable SOC stocks obtained here were compared to long-term experiment data and are well aligned with the literature. Our results provide a regional and detailed understanding of C sequestration that will also enable better greenhouse gas reporting at national level as alternatives to IPCC tier 2 defaults.

Land carbon models underestimate the severity and duration of drought's impact on plant productivity

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0-6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO₂.

Long-term straw decomposition in agro-ecosystems described by a unified three-exponentiation equation with thermal time

Understanding drivers of straw decomposition is essential for adopting appropriate management practice to improve soil fertility and promote carbon (C) sequestration in agricultural systems. However, predicting straw decomposition and characteristics is difficult because of the interactions between many factors related to straw properties, soil properties, and climate, especially under future climate change conditions. This study investigated the driving factors of straw decomposition of six types of crop straw including wheat, maize, rice, soybean, rape, and other straw by synthesizing 1642 paired data from 98 published papers at spatial and temporal scales across China. All the data derived from the field experiments using little bags over twelve years. Overall, despite large differences in climatic and soil properties, the remaining straw carbon (C, %) could be accurately represented by a three-exponent equation with thermal time (accumulative temperature). The lignin/nitrogen and lignin/phosphorus ratios of straw can be used to define the size of labile, intermediate, and recalcitrant C pool. The remaining C for an individual type of straw in the mild-temperature zone was higher than that in the warm-temperature and subtropical zone within one calendar year. The remaining straw C after one thermal year was 40.28%, 37.97%, 37.77%, 34.71%, 30.87%, and 27.99% for rice, soybean, rape, wheat, maize, and other straw, respectively. Soil available nitrogen and phosphorus influenced the remaining straw C at different decomposition stages. For one calendar year, the total amount of remaining straw C was estimated to be 29.41 Tg and future temperature increase of 2 °C could reduce the remaining straw C by 1.78 Tg. These findings confirmed the long-term straw decomposition could be mainly driven by temperature and straw quality, and quantitatively predicted by thermal time with the three-exponent equation for a wide array of straw types at spatial and temporal scales in agro-ecosystems of China.

Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation

With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes.

Novel 3D geometry and models of the lower regions of large trees for use in carbon accounting of primary forests

There is high uncertainty in the contribution of land-use change to anthropogenic climate change, especially pertaining to below-ground carbon loss resulting from conversion of primary-to-secondary forest. Soil organic carbon (SOC) and coarse roots are concentrated close to tree trunks, a region usually unmeasured during soil carbon sampling. Soil carbon estimates and their variation with land-use change have not been correspondingly adjusted. Our aim was to deduce allometric equations that will allow improvement of SOC estimates and tree trunk carbon estimates, for primary forest stands that include large trees in rugged terrain. Terrestrial digital photography, photogrammetry and GIS software were used to produce 3D models of the buttresses, roots and humus mounds of large trees in primary forests dominated by Eucalyptus regnans in Tasmania. Models of 29, in situ eucalypts were made and analysed. 3D models of example eucalypt roots, logging debris, rainforest tree species, fallen trees, branches, root and trunk slices, and soil profiles were also derived. Measurements in 2D, from earlier work, of three buttress 'logs' were added to the data set. The 3D models had high spatial resolution. The modelling allowed checking and correction of field measurements. Tree anatomical detail was formulated, such as buttress shape, humus volume, root volume in the under-sampled zone and trunk hollow area. The allometric relationships developed link diameter at breast height and ground slope, to SOC and tree trunk carbon, the latter including a correction for senescence. These formulae can be applied to stand-level carbon accounting. The formulae allow the typically measured, inter-tree SOC to be corrected for not sampling near large trees. The 3D models developed are irreplaceable, being for increasingly rare, large trees, and they could be useful to other scientific endeavours.

Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models

Many global change stresses on terrestrial and marine ecosystems affect not only ecosystem services that are essential to humankind, but also the trajectory of future climate by altering energy and mass exchanges with the atmosphere. Earth system models, which simulate terrestrial and marine ecosystems and biogeochemical cycles, offer a common framework for ecological research related to climate processes; analyses of vulnerability, impacts, and adaptation; and climate change mitigation. They provide an opportunity to move beyond physical descriptors of atmospheric and oceanic states to societally relevant quantities such as wildfire risk, habitat loss, water availability, and crop, fishery, and timber yields. To achieve this, the science of climate prediction must be extended to a more multifaceted Earth system prediction that includes the biosphere and its resources.

Global patterns of drought recovery

Drought, a recurring phenomenon with major impacts on both human and natural systems, is the most widespread climatic extreme that negatively affects the land carbon sink. Although twentieth-century trends in drought regimes are ambiguous, across many regions more frequent and severe droughts are expected in the twenty-first century. Recovery time-how long an ecosystem requires to revert to its pre-drought functional state-is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO₂ fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth's climate system) and second, that drought impacts (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by long-term field warming

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon-climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming. Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO₂ respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.

Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions

Soil organic carbon (SOC) dynamics are regulated by the complex interplay of climatic, edaphic and biotic conditions. However, the interrelation of SOC and these drivers and their potential connection networks are rarely assessed quantitatively. Using observations of SOC dynamics with detailed soil properties from 90 field trials at 28 sites under different agroecosystems across the Australian cropping regions, we investigated the direct and indirect effects of climate, soil properties, carbon (C) inputs and soil C pools (a total of 17 variables) on SOC change rate (r_C , Mg C ha^-1  yr^-1 ). Among these variables, we found that the most influential variables on r_C were the average C input amount and annual precipitation, and the total SOC stock at the beginning of the trials. Overall, C inputs (including C input amount and pasture frequency in the crop rotation system) accounted for 27% of the relative influence on r_C , followed by climate 25% (including precipitation and temperature), soil C pools 24% (including pool size and composition) and soil properties (such as cation exchange capacity, clay content, bulk density) 24%. Path analysis identified a network of intercorrelations of climate, soil properties, C inputs and soil C pools in determining r_C . The direct correlation of r_C with climate was significantly weakened if removing the effects of soil properties and C pools, and vice versa. These results reveal the relative importance of climate, soil properties, C inputs and C pools and their complex interconnections in regulating SOC dynamics. Ignorance of the impact of changes in soil properties, C pool composition and C input (quantity and quality) on SOC dynamics is likely one of the main sources of uncertainty in SOC predictions from the process-based SOC models.

Ecosystem functioning is enveloped by hydrometeorological variability

Terrestrial ecosystem processes, and the associated vegetation carbon dynamics, respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Long-term variability of the terrestrial carbon cycle is not yet well constrained and the resulting climate-biosphere feedbacks are highly uncertain. Here we present a comprehensive overview of hydrometeorological and ecosystem variability from hourly to decadal timescales integrating multiple in situ and remote-sensing datasets characterizing extra-tropical forest sites. We find that ecosystem variability at all sites is confined within a hydrometeorological envelope across sites and timescales. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. However, simulation results with state-of-the-art process-based models do not reflect this long-term persistent behaviour in ecosystem functioning. Accordingly, we develop a cross-time-scale stochastic framework that captures hydrometeorological and ecosystem variability. Our analysis offers a perspective for terrestrial ecosystem modelling and paves the way for new model-data integration opportunities in Earth system sciences.