Satellite-derived Normalized Difference Vegetation Index (NDVI), a proxy of vegetation productivity, is known to be correlated with temperature in northern ecosystems. This relationship, however, may ...change over time following alternations in other environmental factors. Here we show that above 30°N, the strength of the relationship between the interannual variability of growing season NDVI and temperature (partial correlation coefficient RNDVI-GT) declined substantially between 1982 and 2011. This decrease in RNDVI-GT is mainly observed in temperate and arctic ecosystems, and is also partly reproduced by process-based ecosystem model results. In the temperate ecosystem, the decrease in RNDVI-GT coincides with an increase in drought. In the arctic ecosystem, it may be related to a nonlinear response of photosynthesis to temperature, increase of hot extreme days and shrub expansion over grass-dominated tundra. Our results caution the use of results from interannual time scales to constrain the decadal response of plants to ongoing warming.
Land surface models rarely incorporate the terrestrial phosphorus cycle and its interactions with the carbon cycle, despite the extensive scientific debate about the importance of nitrogen and ...phosphorus supply for future land carbon uptake. We describe a representation of the terrestrial phosphorus cycle for the ORCHIDEE land surface model, and evaluate it with data from nutrient manipulation experiments along a soil formation chronosequence in Hawaii. ORCHIDEE accounts for the influence of the nutritional state of vegetation on tissue nutrient concentrations, photosynthesis, plant growth, biomass allocation, biochemical (phosphatase-mediated) mineralization, and biological nitrogen fixation. Changes in the nutrient content (quality) of litter affect the carbon use efficiency of decomposition and in return the nutrient availability to vegetation. The model explicitly accounts for root zone depletion of phosphorus as a function of root phosphorus uptake and phosphorus transport from the soil to the root surface. The model captures the observed differences in the foliage stoichiometry of vegetation between an early (300-year) and a late (4.1 Myr) stage of soil development. The contrasting sensitivities of net primary productivity to the addition of either nitrogen, phosphorus, or both among sites are in general reproduced by the model. As observed, the model simulates a preferential stimulation of leaf level productivity when nitrogen stress is alleviated, while leaf level productivity and leaf area index are stimulated equally when phosphorus stress is alleviated. The nutrient use efficiencies in the model are lower than observed primarily due to biases in the nutrient content and turnover of woody biomass. We conclude that ORCHIDEE is able to reproduce the shift from nitrogen to phosphorus limited net primary productivity along the soil development chronosequence, as well as the contrasting responses of net primary productivity to nutrient addition.
Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI ...satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees.
Continental to global‐scale modeling of the carbon cycle using process‐based models is subject to large uncertainties. These uncertainties originate from the model structure and uncertainty in model ...forcing fields; however, little is known about their relative importance. A thorough understanding and quantification of uncertainties is necessary to correctly interpret carbon cycle simulations and guide further model developments. This study elucidates the effects of different state‐of‐the‐art land cover and meteorological data set options and biosphere models on simulations of gross primary productivity (GPP) over Europe. The analysis is based on (1) three different process‐oriented terrestrial biosphere models (Biome‐BGC, LPJ, and Orchidee) driven with the same input data and one model (Biome‐BGC) driven with (2) two different meteorological data sets (ECMWF and REMO), (3) three different land cover data sets (GLC2000, MODIS, and SYNMAP), and (4) three different spatial resolutions of the land cover (0.25° fractional, 0.25° dominant, and 0.5° dominant). We systematically investigate effects on the magnitude, spatial pattern, and interannual variation of GPP. While changing the land cover map or the spatial resolution has only little effect on the model outcomes, changing the meteorological drivers and especially the model results in substantial differences. Uncertainties of the meteorological forcings affect particularly strongly interannual variations of simulated GPP. By decomposing modeled GPP into their biophysical and ecophysiological components (absorbed photosynthetic active radiation (APAR) and radiation use efficiency (RUE), respectively) we show that differences of interannual GPP variations among models result primarily from differences of simulating RUE. Major discrepancies appear to be related to the feedback through the carbon‐nitrogen interactions in one model (Biome‐BGC) and water stress effects, besides the modeling of croplands. We suggest clarifying the role of nitrogen dynamics in future studies and revisiting currently applied concepts of carbon‐water cycle interactions regarding the representation of canopy conductance and soil processes.
In this study, we assessed the performance of discharge simulations by coupling the runoff from seven Dynamic Global Vegetation Models (DGVMs; LPJ, ORCHIDEE, Sheffield‐DGVM, TRIFFID, LPJ‐GUESS, ...CLM4CN, and OCN) to one river routing model for 16 large river basins. The results show that the seasonal cycle of river discharge is generally modeled well in the low and middle latitudes but not in the high latitudes, where the peak discharge (due to snow and ice melting) is underestimated. For the annual mean discharge, the DGVMs chained with the routing model show an underestimation. Furthermore, the 30 year trend of discharge is also underestimated. For the interannual variability of discharge, a skill score based on overlapping of probability density functions (PDFs) suggests that most models correctly reproduce the observed variability (correlation coefficient higher than 0.5; i.e., models account for 50% of observed interannual variability) except for the Lena, Yenisei, Yukon, and the Congo river basins. In addition, we compared the simulated runoff from different simulations where models were forced with either fixed or varying land use. This suggests that both seasonal and annual mean runoff has been little affected by land use change but that the trend itself of runoff is sensitive to land use change. None of the models when considered individually show significantly better performances than any other and in all basins. This suggests that based on current modeling capability, a regional‐weighted average of multimodel ensemble projections might be appropriate to reduce the bias in future projection of global river discharge.
Key Points
The seasonal cycle of river discharge is well reproduced in low and middle latitudes
DGVMs generally underestimate annual mean discharge
Most DGVMs correctly reproduce observed interannual variability of discharge
The magnitude of the nitrogen (N) limitation of terrestrial carbon (C) storage over the 21st century is highly uncertain because of the complex interactions between the terrestrial C and N cycles. We ...use an ensemble approach to quantify and attribute process‐level uncertainty in C‐cycle projections by analysing a 30‐member ensemble representing published alternative representations of key N cycle processes (stoichiometry, biological nitrogen fixation (BNF) and ecosystem N losses) within the framework of one terrestrial biosphere model. Despite large differences in the simulated present‐day N cycle, primarily affecting simulated productivity north of 40°N, ensemble members generally conform with global C‐cycle benchmarks for present‐day conditions. Ensemble projections for two representative concentration pathways (RCP 2.6 and RCP 8.5) show that the increase in land C storage due to CO2 fertilization is reduced by 24 ± 15% due to N constraints, whereas terrestrial C losses associated with climate change are attenuated by 19 ± 20%. As a result, N cycling reduces projected land C uptake for the years 2006–2099 by 19% (37% decrease to 3% increase) for RCP 2.6, and by 21% (40% decrease to 9% increase) for RCP 8.5. Most of the ensemble spread results from uncertainty in temperate and boreal forests, and is dominated by uncertainty in BNF (10% decrease to 50% increase for RCP 2.6, 5% decrease to 100% increase for RCP 8.5). However, choices about the flexibility of ecosystem C:N ratios and processes controlling ecosystem N losses regionally also play important roles. The findings of this study demonstrate clearly the need for an ensemble approach to quantify likely future terrestrial C–N cycle trajectories. Present‐day C‐cycle observations only weakly constrain the future ensemble spread, highlighting the need for better observational constraints on large‐scale N cycling, and N cycle process responses to global change.
An ensemble of carbon–nitrogen (C–N) models representing alternative, plausible assumptions on N fixation, ecosystem N loss and N requirements for plant growth and soil organic matter formation is used in a consistent framework to quantify process‐related uncertainty. The results clearly show that limited N availability on land consistently attenuates the C‐cycle responses to global change and in consequence reduces projected future land C storage. Uncertainty in our understanding of future responses of BNF, next to uncertainty in terrestrial stoichiometry is the main cause for the ensemble spread.
Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate ...from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO
2
enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle.
► We review the current generation of global terrestrial carbon–nitrogen cycle models. ► N deposition moderately increases current terrestrial C storage in mid-high latitude ecosystems. ► N ...limitation reduces future C sequestration due to CO
2 fertilisation more than climate warming reduces N limitation. ► This leads to lower land C sequestration and larger projected rates of climate change with C–N climate models than with C-cycle only climate models. ► There is some support for a small positive interaction between terrestrial N
2O emissions and climate.
Interactions between the terrestrial carbon (C) and nitrogen (N) cycles shape the response of ecosystems to global change. The limitation of ecosystem C storage due to N availability, and the response of N
2O emissions to environmental conditions and N addition have been intensively studied at the site level. However, their contribution to biosphere–climate interactions at regional to global scales remains unclear. A growing number of global terrestrial biogeochemical models provide a means to scale ecological understanding of the nitrogen cycle to regional and global scales with the ultimate aim to investigate the magnitude of nitrogen cycling effects on global biogeochemistry, as well as their indirect consequences for biogeophysical land-atmosphere interactions. Key challenges to modelling the coupled terrestrial carbon–nitrogen cycles arise from the need to account for micro-scale processes to represent and quantify important N fluxes, uncertainties in the representation of key carbon–nitrogen cycle couplings at the ecosystem scale, and vagaries in the available observations to constrain global models.
The new generation of carbon–nitrogen cycle models suggests that reactive nitrogen deposition is associated with a moderate increase in current terrestrial carbon sequestration, providing a small climate cooling effect. The models further unanimously demonstrate that nitrogen cycling reduces both global carbon sequestration due to CO
2 fertilisation and the carbon losses associated with climate change on land, in sum leading to an acceleration of carbon accumulation in the atmosphere relative to C-cycle only models. A recent study furthermore suggests a moderate positive interaction between terrestrial N
2O emissions and recent climatic changes, although the atmospheric increase in N
2O over the last few decades appears to be mostly associated with anthropogenic Nr additions to the terrestrial biosphere. At least some of these models can be used to assess the biogeophysical effects of N cycling through altered albedo and changed sensible and latent heat fluxes, but no study so far has assessed these consequences explicitly.
Atmospheric monitoring of high northern latitudes (above 40°N) has shown an enhanced seasonal cycle of carbon dioxide (CO₂) since the 1960s, but the underlying mechanisms are not yet fully ...understood. The much stronger increase in high latitudes relative to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. We found that the latitudinal gradient of the increasing CO₂ amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results underscore the importance of climate–vegetation–carbon cycle feedbacks at high latitudes; moreover, they indicate that in recent decades, photosynthetic carbon uptake has reacted much more strongly to warming than have carbon release processes.
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.
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