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Anthropogenic activities have substantially enhanced the loadings of reactive nitrogen (Nr) in the Earth system since pre-industrial times1,2, contributing to widespread eutrophication and air pollution3-6. Increased Nr can also influence global climate through a variety of effects on atmospheric and land processes but the cumulative net climate effect is yet to be unravelled. Here we show that anthropogenic Nr causes a net negative direct radiative forcing of -0.34 [-0.20, -0.50] W m-2 in the year 2019 relative to the year 1850. This net cooling effect is the result of increased aerosol loading, reduced methane lifetime and increased terrestrial carbon sequestration associated with increases in anthropogenic Nr, which are not offset by the warming effects of enhanced atmospheric nitrous oxide and ozone. Future predictions using three representative scenarios show that this cooling effect may be weakened primarily as a result of reduced aerosol loading and increased lifetime of methane, whereas in particular N2O-induced warming will probably continue to increase under all scenarios. Our results indicate that future reductions in anthropogenic Nr to achieve environmental protection goals need to be accompanied by enhanced efforts to reduce anthropogenic greenhouse gas emissions to achieve climate change mitigation in line with the Paris Agreement.

Nitrous oxide (N2O) is a greenhouse gas and stratospheric ozone-depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N2O-depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at eight remote air sampling sites for modern and pre-industrial periods. Models show general agreement on the seasonal phasing of zonal-average N2O fluxes for most sites, but seasonal peak-to-peak amplitudes differ several-fold across models. The modeled seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN2O, inferred by the difference between the surface-troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ∼1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre-industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN2O seasonality. Considering the large model spread, in situ aN2O observations and atmospheric transport-chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon-nitrogen cycles under ongoing global warming.

Allometric equations are often used to estimate plant biomass allocation to different tissue types from easier-to-measure quantities. Biomass allocation, and thus allometric equations, often differs by species and sometimes varies with nutrient availability. We measured biomass components for five nitrogen-fixing tree species (Robinia pseudoacacia, Gliricidia sepium, Casuarina equisetifolia, Acacia koa, Morella faya) and three non-fixing tree species (Betula nigra, Psidium cattleianum, Dodonaea viscosa) grown in field sites in New York and Hawaii for 4-5 years and subjected to four fertilization treatments. We measured total aboveground, foliar, main stem, secondary stem, and twig biomass in all species, and belowground biomass in Robinia pseudoacacia and Betula nigra, along with basal diameter, height, and canopy dimensions. The individuals spanned a wide size range (

Terrestrial biosphere models are a key tool in investigating the role played by land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting instead to prescribe them using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth and the impacts of land use change, it prevents the simulation of climate-change-driven biome shifts, with implications for the projection of future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a new framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables in model intercomparison projects. After validating simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high-CO 2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected change in net land–atmosphere CO 2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate-change-driven biome shifts for projecting future terrestrial carbon sink. [ABSTRACT FROM AUTHOR]

Phosphorus (P) limits productivity in many ecosystems and has the potential to constrain the global carbon sink. The magnitude of these effects depends on how climate change and rising CO2 affect P cycling. Some effects are well established. First, P limitation often constrains CO2 fertilization, and rising CO2 often exacerbates P limitation. Second, P limitation and P constraints to CO2 fertilization are more common in warmer and wetter sites. Models that couple P cycling to vegetation generally capture these outcomes. However, due largely to differences between short-term and long-term dynamics, the patterns observed across climatic gradients do not necessarily indicate how climate change over years to decades will modify P limitation. These annual-to-decadal effects are not well understood. Furthermore, even for the well-understood patterns, much remains to be learned about the quantitative details, mechanisms, and drivers of variability. The interface between empirical and modeling work is particularly ripe for development. [ABSTRACT FROM AUTHOR]

Quantification of uncertainty in fluxes of energy, water, and CO 2 simulated by land surface models (LSMs) remains a challenge. LSMs are typically driven with, and tuned for, a specified meteorological forcing data set and a specified set of geophysical fields. Here, using two data sets each for meteorological forcing and land cover representation (in which the increase in crop area over the historical period is implemented in the same way), as well as two model structures (with and without coupling of carbon and nitrogen cycles), the uncertainty in simulated results over the historical period is quantified for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. The resulting eight (2×2×2) model simulations are evaluated using an in-house model evaluation framework that uses multiple observation-based data sets for a range of quantities. The simulated area burned, fire CO 2 emissions, soil carbon mass, vegetation carbon mass, runoff, heterotrophic respiration, gross primary productivity, and sensible heat flux show the largest spread across the eight simulations relative to their global ensemble mean values. Simulated net atmosphere–land CO 2 flux, a critical determinant of the performance of LSMs, is found to be largely independent of the simulated pre-industrial vegetation and soil carbon mass, although our framework represents the historical increase in crop area in the same way in both land cover representations. This indicates that models can provide reliable estimates of the strength of the land carbon sink despite some biases in carbon stocks. Results show that evaluating an ensemble of model results against multiple observations disentangles model deficiencies from uncertainties in model inputs, observation-based data, and model configuration. [ABSTRACT FROM AUTHOR]

Quantification of uncertainty in fluxes of energy, water, and CO2 simulated by land surface models (LSMs) remains a challenge. LSMs are typically driven with, and tuned for, a specified meteorological forcing data set and a specified set of geophysical fields. Here, using two data sets each for meteorological forcing and historical land cover reconstruction, as well as two model structures (with and without coupling of carbon and nitrogen cycles), the uncertainty in simulated results over the historical period is quantified for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. The resulting eight (2 x 2 x 2) equally probable model simulations are evaluated using an in-house model evaluation framework that uses multiple observations-based data sets for a range of quantities. Among the primary global energy, water, and carbon related fluxes and state variables, simulated area burned, fire CO2 emissions, soil carbon mass, vegetation biomass, runoff, heterotrophic respiration, gross primary productivity, and sensible heat flux show the largest spread across the eight simulations relative to their mean. Simulated net atmosphere-land CO2 flux, which is considered a critical determinant of the performance of LSMs, is found to be largely independent of the simulated pre-industrial vegetation and soil carbon mass. This indicates that models can provide reliable estimates of the strength of the land carbon sink despite biases in carbon stocks. Results show that evaluating an ensemble of model results against multiple observations allows to disentangle model deficiencies from uncertainties in model inputs, observation-based data, and model configuration. [ABSTRACT FROM AUTHOR]

Forests are a significant CO2 sink. However, CO2 sequestration in forests is radiatively offset by emissions of nitrous oxide (N2O), a potent greenhouse gas, from forest soils. Reforestation, an important strategy for mitigating climate change, has focused on maximizing CO2 sequestration in plant biomass without integrating N2O emissions from soils. Although nitrogen (N)‐fixing trees are often recommended for reforestation because of their rapid growth on N‐poor soil, they can stimulate significant N2O emissions from soils. Here, we first used a field experiment to show that a N‐fixing tree (Robinia pseudoacacia) initially mitigated climate change more than a non‐fixing tree (Betula nigra). We then used our field data to parameterize a theoretical model to investigate these effects over time. Under lower N supply, N‐fixers continued to mitigate climate change more than non‐fixers by overcoming N limitation of plant growth. However, under higher N supply, N‐fixers ultimately mitigated climate change less than non‐fixers by enriching soil N and stimulating N2O emissions from soils. These results have implications for reforestation, suggesting that N‐fixing trees are more effective at mitigating climate change at lower N supply, whereas non‐fixing trees are more effective at mitigating climate change at higher N supply. [ABSTRACT FROM AUTHOR]

Nitrogen (N)‐fixing trees fulfil a unique and important biogeochemical role in forests through their ability to convert atmospheric N2 gas to plant‐available N. Due to their high N fixation rates, it is often assumed that N‐fixing trees facilitate neighbouring trees and enhance forest growth. This assumption is supported by some local studies but contradicted by others, leaving the overall effect of N‐fixing trees on forest growth unresolved.Here we use the US Forest Service's Forest Inventory and Analysis database to evaluate the effects of N‐fixing trees on plot‐scale basal area change and individual‐scale neighbouring tree demography across the coterminous US.First we discuss the average trends. At the plot and individual scales, N‐fixing trees do not affect the relative growth rates of neighbouring trees, but they facilitate recruitment and inhibit survival rates, suggesting that they are drivers of tree turnover in the coterminous US. At the plot scale, N‐fixing trees facilitate the basal area change of non‐fixing neighbours.In addition to the average trends, there is wide variation in the effect of N‐fixing trees on forest growth, ranging from strong facilitation to strong inhibition. This variation does not show a clear geographical pattern, though it does vary with certain local factors. N‐fixing trees facilitate forest growth when they are likely to be less competitive: under high N deposition and high soil moisture or when neighbouring trees occupy different niches (e.g. high foliar C:N trees and non‐fixing trees).Synthesis. N‐fixing trees have highly variable effects on forest growth and neighbour demographics across the coterminous US. This suggests that the effect of N‐fixing trees on forest development and carbon storage depends on local factors, which may help reconcile the conflicting results found in previous localized studies on the effect of N‐fixing trees on forest growth. [ABSTRACT FROM AUTHOR]