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Nitrogen (N) and phosphorus (P) limitation constrains the magnitude of terrestrial carbon uptake in response to elevated carbon dioxide and climate change. However, global maps of nutrient limitation are still lacking. Here we examined global N and P limitation using the ratio of site-averaged leaf N and P resorption efficiencies of the dominant species across 171 sites. We evaluated our predictions using a global database of N- and P-limitation experiments based on nutrient additions at 106 and 53 sites, respectively. Globally, we found a shift from relative P to N limitation for both higher latitudes and precipitation seasonality and lower mean annual temperature, temperature seasonality, mean annual precipitation and soil clay fraction. Excluding cropland, urban and glacial areas, we estimate that 18% of the natural terrestrial land area is significantly limited by N, whereas 43% is relatively P limited. The remaining 39% of the natural terrestrial land area could be co-limited by N and P or weakly limited by either nutrient alone. This work provides both a new framework for testing nutrient limitation and a benchmark of N and P limitation for models to constrain predictions of the terrestrial carbon sink.Spatial patterns in the phosphorus and nitrogen limitation in natural terrestrial ecosystems are reported from analysis of a global database of the resorption efficiency of nutrients by leaves.

Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1 m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world’s soils, their capacity to store carbon, and priority regions and actions for soil carbon management. Mineral-organic associations play a key role in soil carbon preservation. Here, Georgiou et al. produce global estimates of mineral-associated soil carbon, providing insight into the world’s soils and their capacity to store carbon

A meta-analysis and field data show that frequent fires in savannas and broadleaf forests decrease soil carbon and nitrogen over many decades; modelling shows that nitrogen loss drives carbon loss by reducing net primary productivity. Soil degradation fuelled by fire The patterns of naturally occurring fires have been altered, both spatially and temporally, as a result of climate and land-use changes. The long-term effects of fire frequency on soil carbon and nutrient storage and the resulting potential limitations on plant productivity remain poorly understood. On the basis of a meta-analysis and an independent dataset of additional field sites, this paper finds that frequent burning leads to soil carbon and nitrogen losses that emerge over decadal timescales. Furthermore, the authors use a model to suggest that the decadal losses of soil nitrogen as a result of more frequent burning could decrease the amount of carbon sequestered by net primary productivity. Fire frequency is changing globally and is projected to affect the global carbon cycle and climate 1 , 2 , 3 . However, uncertainty about how ecosystems respond to decadal changes in fire frequency makes it difficult to predict the effects of altered fire regimes on the carbon cycle; for instance, we do not fully understand the long-term effects of fire on soil carbon and nutrient storage, or whether fire-driven nutrient losses limit plant productivity 4 , 5 . Here we analyse data from 48 sites in savanna grasslands, broadleaf forests and needleleaf forests spanning up to 65 years, during which time the frequency of fires was altered at each site. We find that frequently burned plots experienced a decline in surface soil carbon and nitrogen that was non-saturating through time, having 36 per cent (±13 per cent) less carbon and 38 per cent (±16 per cent) less nitrogen after 64 years than plots that were protected from fire. Fire-driven carbon and nitrogen losses were substantial in savanna grasslands and broadleaf forests, but not in temperate and boreal needleleaf forests. We also observe comparable soil carbon and nitrogen losses in an independent field dataset and in dynamic model simulations of global vegetation. The model study predicts that the long-term losses of soil nitrogen that result from more frequent burning may in turn decrease the carbon that is sequestered by net primary productivity by about 20 per cent of the total carbon that is emitted from burning biomass over the same period. Furthermore, we estimate that the effects of changes in fire frequency on ecosystem carbon storage may be 30 per cent too low if they do not include multidecadal changes in soil carbon, especially in drier savanna grasslands. Future changes in fire frequency may shift ecosystem carbon storage by changing soil carbon pools and nitrogen limitations on plant growth, altering the carbon sink capacity of frequently burning savanna grasslands and broadleaf forests.

Terrestrial ecosystems currently sequester about one third of anthropogenic CO2 emissions each year, an important ecosystem service that dampens climate change. The future fate of this net uptake of CO2 by land based ecosystems is highly uncertain. Most ecosystem models used to predict the future terrestrial carbon cycle share a common architecture, whereby carbon that enters the system as net primary production (NPP) is distributed to plant compartments, transferred to litter and soil through vegetation turnover and then re-emitted to the atmosphere in conjunction with soil decomposition. However, while all models represent the processes of NPP and soil decomposition, they vary greatly in their representations of vegetation turnover and the associated processes governing mortality, disturbance and biome shifts. Here we used a detailed second generation dynamic global vegetation model with advanced representation of vegetation growth and mortality, and the associated turnover. We apply an emulator that describes the carbon flows and pools exactly as in simulations with the full model. The emulator simulates ecosystem dynamics in response to 13 different climate or Earth system model simulations from the Coupled Model Intercomparison Project Phase 5 ensemble under RCP8.5 radiative forcing. By exchanging carbon cycle processes between these 13 simulations we quantified the relative roles of three main driving processes of the carbon cycle; (I) NPP, (II) vegetation dynamics and turnover and (III) soil decomposition, in terms of their contribution to future carbon (C) uptake uncertainties among the ensemble of climate change scenarios. We found that NPP, vegetation turnover (including structural shifts, wild fires and mortality) and soil decomposition rates explained 49%, 17% and 33%, respectively, of uncertainties in modelled global C-uptake. Uncertainty due to vegetation turnover was further partitioned into stand-clearing disturbances (16%), wild fires (0%), stand dynamics (7%), reproduction (10%) and biome shifts (67%) globally. We conclude that while NPP and soil decomposition rates jointly account for 83% of future climate induced C-uptake uncertainties, vegetation turnover and structure, dominated by biome shifts, represent a significant fraction globally and regionally (tropical forests: 40%), strongly motivating their representation and analysis in future C-cycle studies.

Global vegetation models and terrestrial carbon cycle models are widely used for projecting the carbon balance of terrestrial ecosystems. Ensembles of such models show a large spread in carbon balance predictions, ranging from a large uptake to a release of carbon by the terrestrial biosphere, constituting a large uncertainty in the associated feedback to atmospheric CO2 concentrations under global climate change. Errors and biases that may contribute to such uncertainty include ecosystem model structure, parameters and forcing by climate output from general circulation models (GCMs) or the atmospheric components of Earth system models (ESMs), e.g. as prepared for use in IPCC climate change assessments. The relative importance of these contributing factors to the overall uncertainty in carbon cycle projections is not well characterised. Here we investigate the role of climate model-derived biases by forcing a single global ecosystem-carbon cycle model, with original climate outputs from 15 ESMs and GCMs from the CMIP5 ensemble. We show that variation among the resulting ensemble of present and future carbon cycle simulations propagates from biases in annual means of temperature, precipitation and incoming shortwave radiation. Future changes in carbon pools, and thus land carbon sink trends, are also affected by climate biases, although to a smaller extent than the absolute size of carbon pools. Our results suggest that climate biases could be responsible for a considerable fraction of the large uncertainties in ESM simulations of land carbon fluxes and pools, amounting to about 40% of the range reported for ESMs. We conclude that climate bias-induced uncertainties must be decreased to make accurate coupled atmosphere-carbon cycle projections.

Background The aim of this study was to explore changes in liver volume and intrahepatic fat in morbidly obese patients during 4 weeks of low-calorie diet (LCD) before surgery and to investigate if these changes would facilitate the following laparoscopic gastric bypass. Methods Fifteen female patients (121.3 kg, BMI 42.9) were treated preoperatively in an open study with LCD (800–1,100 kcal/day) during 4 weeks. Liver volume and fat content were assessed by magnetic resonance imaging and spectroscopy before and after the LCD treatment. Results Liver appearance and the complexity of the surgery were scored at the operation. Eighteen control patients (114.4 kg, BMI 40.8), without LCD were scored similarly. Average weight loss in the LCD group was 7.5 kg, giving a mean weight of 113.9 kg at surgery. Liver volume decreased by 12% ( p  < 0.001) and intrahepatic fat by 40% ( p  < 0.001). According to the preoperative scoring, the size of the left liver lobe, sharpness of the liver edge, and exposure of the hiatal region were improved in the LCD group compared to the controls (all p  < 0.05). Conclusions The overall complexity of the surgery was perceived lower in the LCD group ( p  < 0.05), due to improved exposure and reduced psychological stress (both p  < 0.05). Four weeks of preoperative LCD resulted in a significant decrease in liver volume and intrahepatic fat content, and facilitated the subsequent laparoscopic gastric bypass as scored by the surgeon.

The difference is found at the marginsThe terrestrial biosphere absorbs about a quarter of all anthropogenic carbon dioxide emissions, but the amount that they take up varies from year to year. Why? Combining models and observations, Ahlstrom et al. found that marginal ecosystems-semiarid savannas and low-latitude shrublands-are responsible for most of the variability. Biological productivity in these semiarid regions is water-limited and strongly associated with variations in precipitation, unlike wetter tropical areas. Understanding carbon uptake by these marginal lands may help to improve predictions of variations in the global carbon cycle.Science, this issue p. 895 The growth rate of atmospheric carbon dioxide (CO2) concentrations since industrialization is characterized by large interannual variability, mostly resulting from variability in CO2 uptake by terrestrial ecosystems (typically termed carbon sink). However, the contributions of regional ecosystems to that variability are not well known. Using an ensemble of ecosystem and land-surface models and an empirical observation-based product of global gross primary production, we show that the mean sink, trend, and interannual variability in CO2 uptake by terrestrial ecosystems are dominated by distinct biogeographic regions. Whereas the mean sink is dominated by highly productive lands (mainly tropical forests), the trend and interannual variability of the sink are dominated by semi-arid ecosystems whose carbon balance is strongly associated with circulation-driven variations in both precipitation and temperature.

Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr−1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr−1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.

Forests have seen a strong greening trend worldwide, and previous studies have attributed this mainly to land‐use conversions such as afforestation. However, for the greening of existing forests, the role of human interventions is unclear. Here we paired neighboring natural and planted forests in Southern China to minimize the differences between the forest types and analyzed the vegetation index EVI2 from Landsat over 1987 to 2021. The EVI2 trends observed in natural forests can be seen as mainly responses to large‐scale environmental changes, whereas the difference between the forest types represents the impact caused by human interventions. We found that though the mean EVI2 of planted forests was comparable to that of natural forests, the greening trends were overall 7.0% lower in planted forests. Our results suggest that human interventions associated with planted forests did not accelerate their greening, indicating the necessity for refined policies to enhance future forest greening. Plain Language Summary General greening of forests has been reported worldwide. While previous studies have tended to attribute forest greening to land‐use conversions such as re‐ and afforestation, the role of human interventions for existing forests is still unclear. The greening trend of natural forests are mainly impacted by environmental changes, whereas those in planted forests are also influenced by human interventions. Therefore, the comparison of greening trends between the two forest types can help quantitatively distinguish the role of human interventions. Here, we paired spatially adjacent natural forests with planted forests in Southern China, and performed a pairwise comparisons of greenness and its trend between the forest types over 1987 to 2021 based on Landsat satellite series. It was found that although their mean greenness was similar, the greening trends of planted forests were 7.0% lower than the natural forests. Thus, human interventions may lead to a weakened greening trend while environmental changes were likely the main driver of greening of existing forests in Southern China. Such distinction of drivers is key for our understanding of the impact of environmental changes, land use and land use change, and for designing policies that put us on a pathway to a more sustainable future. Key Points Planted forests showed slightly lower greenness than paired natural forests in Southern China from 1987 to 2021 Over the past 40 years, the greening trends of planted forests were overall 7.0% lower than natural forests in Southern China Environmental change, and not human interventions, was likely the dominant cause of observed greening in Southern China

Anthropogenic land use and land cover changes (LULCC) have a large impact on the global terrestrial carbon sink, but this effect is not well characterized according to biogeographical region. Here, using state-of-the-art Earth observation data and a dynamic global vegetation model, we estimate the impact of LULCC on the contribution of biomes to the terrestrial carbon sink between 1992 and 2015. Tropical and boreal forests contributed equally, and with the largest share of the mean global terrestrial carbon sink. CO 2 fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO 2 fertilization and nitrogen deposition, LULCC and meteorological forcing) caused a reduction and an increase, respectively, in the terrestrial carbon sink for tropical and boreal forests. These diverging trends were not observed when applying a conventional LULCC dataset, but were also evident in satellite passive microwave estimates of aboveground biomass. These datasets thereby converge on the conclusion that LULCC have had a greater impact on tropical forests than previously estimated, causing an increase and decrease of the contributions of boreal and tropical forests, respectively, to the growing terrestrial carbon sink. Combining Earth observation data and dynamic global vegetation models, the authors show that anthropogenic land use and land cover change has caused a reduction in the contribution to the terrestrial carbon sink for tropical forests but an increase for boreal forests between 1992 and 2015.