• Energy Research

Improving agricultural sustainability is a global challenge, particularly for China’s high-input and low-efficiency cropping systems with environmental trade-offs. Although national strategies have been implemented to achieve Sustainable Development Goals in agriculture, the potential contributions of crop switching as a promising solution under varying future climate change are still under-explored. Here, we optimize cropping patterns spatially with the targets of enhancing agriculture production, reducing environmental costs, and achieving sustainable fertilization across the different climate scenarios. Compared with that maintains the historical cropping patterns, the optimal crop distributions under different climate scenario consistently suggest allocating the planting areas of maize and rapeseed to the other crops (rice, wheat, soybean, peanut and potato). Such crop switching can consequently increase crop production by 14.1%, with the reduction in environmental impacts (8.2% for leached nitrogen and 24.0% for irrigation water use) across three representative Shared Socio-economic Pathways (SSPs) from 2020 to 2100. The sustainable fertilization rates vary from 148-173 kg N ha-1 in 2030 to 213-253 kg N ha-1 in 2070, significantly smaller than the current rate (305 kg N ha-1). These outcomes highlight large potential benefits of crop switching and fertilizer management for improving China’s future agricultural sustainability.

AbstractAimVegetation dynamics and the competitive interactions involved are assumed to restrict the ability of species to migrate. But in most migration modelling approaches disturbance‐driven succession and competition processes are reduced to simple assumptions or are even missing. The aim of this study was to test a combination of a migration model and a dynamic vegetation model to estimate the migration of tree species controlled by climate, environment and local species dynamics such as succession and competition.LocationEurope.MethodsTo estimate the effect of vegetation dynamics on the migration of European beech and Norway spruce, we developed a post‐process migration tool (LPJ‐CATS). This tool integrates outputs of the migration model CATS and the dynamic vegetation model LPJ‐GUESS. The model LPJ‐CATS relies on a linear dependency between the dispersal kernel and migration rate and is based on the assumption that competition reduces fecundity.ResultsSimulating potential migration rates with the CATS model, which does not account for competition and disturbance, resulted in mean Holocene migration rates of 435 ± 55 and 330 ± 95 m year−1 for the two species Picea abies and Fagus sylvatica, respectively. With LPJ‐CATS, these mean migration rates were reduced to 250 ± 75 and 170 ± 60 m year−1 for spruce and beech, respectively. Moreover, LPJ‐CATS simulated migration pathways of these two species that generally comply well with those documented in the palaeo‐records.Main conclusionsOur ‘hybrid’ modelling approach allowed for the simulation of generally realistic Holocene migration rates and pathways of the two study species on a continental scale. It suggests that competition can considerably modify spread rates, but also the magnitude of its effect depends on how close climate conditions are to the niche requirements of a particular species.

AbstractEarth observation‐based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem‐level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables. The variability in field‐observed GPP, net primary productivity and solar‐induced fluorescence was better or equally well captured by our LRF‐based GPP when compared with six state‐of‐the‐art Earth observation‐based GPP products. Over the period 1982–2015, the LRF‐based average annual global terrestrial GPP budget was 121.8 ± 3.5 Pg C, with a detrended inter‐annual variability of 0.74 ± 0.13 Pg C. The strongest inter‐annual variability was observed in semi‐arid regions, but croplands in China and India also showed strong inter‐annual variations. The trend in global terrestrial GPP during 1982–2015 was 0.27 ± 0.02 Pg C year−1, and was generally larger in the northern than the southern hemisphere. Most positive GPP trends were seen in areas with croplands whereas negative trends were observed for large non‐cropped parts of the tropics. Trends were strong during the eighties and nineties but levelled off around year 2000. Other GPP products either showed no trends or continuous increase throughout the study period. This study benchmarks a first global Earth observation‐based model using an asymptotic light response function, improving simulations of GPP, and reveals a stagnation in the global GPP after the year 2000.

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 CO _2 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.

We have investigated the spatio-temporal carbon balance patterns resulting from forcing a dynamic global vegetation model with output from 18 climate models of the CMIP5 (Coupled Model Intercomparison Project Phase 5) ensemble. We found robust patterns in terms of an extra-tropical loss of carbon, except for a temperature induced shift in phenology, leading to an increased spring uptake of carbon. There are less robust patterns in the tropics, a result of disagreement in projections of precipitation and temperature. Although the simulations generally agree well in terms of the sign of the carbon balance change in the middle to high latitudes, there are large differences in the magnitude of the loss between simulations. Together with tropical uncertainties these discrepancies accumulate over time, resulting in large differences in total carbon uptake over the coming century (−0.97–2.27 Pg C yr ^−1 during 2006–2100). The terrestrial biosphere becomes a net source of carbon in ten of the 18 simulations adding to the atmospheric CO _2 concentrations, while the remaining eight simulations indicate an increased sink of carbon.

Abstract. Photosynthesis provides carbon for the synthesis of macromolecules to construct cells during growth. This is the basis for the key role of photosynthesis in the carbon dynamics of ecosystems and in the biogenic CO2 assimilation. The development of eddy-covariance (EC) measurements for ecosystem CO2 fluxes started a new era in the field studies of photosynthesis. However, the interpretation of the very variable CO2 fluxes in evergreen forests has been problematic especially in transition times such as the spring and autumn. We apply two theoretical needle-level equations that connect the variation in the light intensity, stomatal action and the annual metabolic cycle of photosynthesis. We then use these equations to predict the photosynthetic CO2 flux in five Scots pine stands located from the northern timberline to Central Europe. Our result has strong implications for our conceptual understanding of the effects of the global change on the processes in boreal forests, especially of the changes in the metabolic annual cycle of photosynthesis.