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Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values globally for the allocation processes. Therefore, this paper aims to evaluate the sensitivity of yield and HI of wheat and maize simulated with LPJ-GUESS to eight production allocation-related parameters and identify the most suitable parameter values for global simulations. The nitrogen demand reduction after anthesis, the minimum leaf carbon to nitrogen ratio (C:N) and the range of leaf C:N strongly affected carbon assimilation and yield, while the retranslocation of labile stem carbon to grains and the retranslocation rate of nitrogen and carbon from vegetative organs to grains after anthesis mainly influenced HI. A global database of observed HI for both crops was compiled for reference to constrain simulations before calibrating parameters for yield against reference data. Two high- and low-yielding maize cultivars emerged from the calibration, whilst spring and winter cultivars were found appropriate for wheat. The calibrated version of LPJ-GUESS improved the simulation of yield and HI at the global scale for both crops, providing a basis for future studies exploring crop production under different climate and management scenarios.

Making projections of ecological systems under environmental change is central to many disciplines. Process-based models aim to represent core ecological mechanisms governing ecosystem dynamics, which can then be valuable for projecting change under novel environmental conditions. Yet, as our ecological understanding evolves, updating parameter information can be challenging. In addition, classical statistical approaches to fitting functional relationships often miss the complexity of interacting, non-linear dynamics, which can limit the predictive capacity of models. As such, a growing body of work suggests that the integration of modern machine learning might help to improve the representation of key ecological dynamics within such process-based models. Here, we present a case study, using machine learning to identify key relationships between relative growth, biomass and mortality compared to classical regression methods. Our results suggest that the inclusion of a deep neural network (DNN) into a “theory-driven” process based global vegetation model can greatly improve model predictions of patch level forest structure and vegetation dynamics. This hybrid approach offers both the benefits of interpretability and physically-realistic structure, combined with the depth of information contained in big datasets and the flexibility of model machine learning.

Increasing crop productivity while keeping detrimental side-effects on the environment low is a major challenge for global agriculture. Cover crops (CCs), mostly grown during the fallow period and incorporated in soils, are expected to improve soil fertility and crop yields while reducing chemical fertilizer use, with climate change mitigation co-benefits. However, quantifying these ecosystem services across global agricultural lands remains uncertain. In this study we investigate how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects yields and cropland carbon and nitrogen balances using the dynamic global vegetation model LPJ-GUESS. Model performance is evaluated against observations from field trials worldwide as well as other published model-based estimates. LPJ-GUESS generally captures the observed enhanced soil carbon, reduced N leaching, and yield changes caused by CCs. We found that the combination of N-fixing CCs with no-tillage management could potentially increase soil carbon storage by 7% (+0.32 Pg C yr-1 in global croplands) while reducing N leaching by 41% (-7.3 Tg N yr-1) compared with bare fallows after 36 years of simulation. This integrated practice is accompanied by a 2% increase in total crop production (+37 million tonnes yr-1 including wheat, maize, rice, and soybean) in the last decade of the simulation. Legume cover cropping is found to contribute more to increasing the subsequent crop yields than non-legumes. The effects of CCs on crop productivity are highly dependent on the main food crop types, chemical fertilizer use, and management duration, with smallest yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long-term environmental sustainability without compromising crop production in global croplands.

Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. However, these models have so far not been benchmarked alongside each other.We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.

With climate change happening at a faster rate at high-latitudes than the global average, it is important to understand the warming-induced permafrost thaw effect on high-latitude GHG emissions. As permafrost soils contain nearly half of the global soil C pool a change to active layer depths could substantially increase GHG emissions from the soil and hence the concentrations in the atmosphere. Here we present a version of the dynamic vegetation model LPJ-GUESS updated to include a new multi-layer soil organic matter scheme that makes it possible to simulate organic matter dynamics at all soil depths. Together with improved soil physics, hydrology, and snow representation, this new version of LPJ-GUESS can closely simulate the current best estimates of Arctic soil C at depths (e.g. NCSCDv2.2) making it possible to simulate emissions of CO2, CH4, and N2O as the active layer thickens. We also present preliminary estimates of how the Arctic soil thermodynamics and biogeochemistry could change under different future scenarios, including overshoot scenarios, to see if the Arctic C balance will act as a net source or sink of greenhouse gases.

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analysed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models’ sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

Over the past half-century, drastically increased chemical fertilizers have entered agricultural ecosystems to promote crop production on the Yangtze Plain, potentially enhancing agricultural nutrient sources for eutrophication in freshwater ecosystems. However, long-term trends of nitrogen dynamics in terrestrial ecosystems and their impacts on eutrophication changes in this region remain poorly studied. Using a process-based ecosystem model, we investigated the temporal and spatial patterns of nitrogen use efficiency (NUE) and nitrogen leaching on the Yangtze Plain from 1979–2018. The agricultural NUE for the Yangtze Plain significantly decreased from 50 % in 1979 to 25 % in 2018, with the largest decline of NUE in soybean, rice, and rapeseed. Simultaneously, the leached nitrogen from cropland and natural land increased, with annual rates of 4.5 and 0.22 kg N ha−1 yr−2, respectively, leading to an overall increase of nitrogen inputs to the 50 large lakes. We further examined the correlations between terrestrial nutrient sources (i.e., the leached nitrogen, total phosphorus sources, and industrial wastewater discharge) and the satellite-observed probability of eutrophication occurrence (PEO) at an annual scale and showed that PEO was positively correlated with the changes in terrestrial nutrient sources for most lakes. Agricultural nitrogen and phosphorus sources were found to explain the PEO trends in lakes in the western and central part of the Yangtze Plain, and industrial wastewater discharge was associated with the PEO trends in eastern lakes. Our results revealed the importance of terrestrial nutrient sources for long-term changes in eutrophic status over the 50 lakes of the Yangtze Plain. This calls for region-specific sustainable nutrient management (i.e., nitrogen and phosphorus applications in agriculture and industry) to improve the water quality of lake ecosystems.

Over the past half century, drastically increased chemical fertilizer have entered agricultural ecosystems to promote the crop production on the Yangtze Plain, potentially enhancing agricultural nutrient sources for eutrophication in freshwater ecosystems. However, long-term trends of nitrogen dynamics in terrestrial ecosystems and their impacts on eutrophication changes in this region remain poorly studied. Using a process-based ecosystem model, we investigated the temporal and spatial patterns of nitrogen use efficiency (NUE) and nitrogen leaching on the Yangtze Plain from 1979 to 2018. The agricultural NUE for the Yangtze Plain significantly decreased from 50 % in 1979 to 25 % in 2018, with the largest decline of NUE in soybean, rice and rapeseed. Simultaneously, the leached nitrogen from cropland and natural land increased with annual rates of 4.5 kg N ha-1 yr-2 and 0.22 kg N ha-1 yr-2, respectively, leading to an overall increase of nitrogen inputs to the fifty large lakes. We further examined the correlations between terrestrial nutrient sources (i.e., the leached nitrogen, total phosphorus sources, and industrial wastewater discharge) and the satellite-observed probability of eutrophication occurrence (PEO) at an annual scale, and showed that PEO was positively correlated with the changes in terrestrial nutrient sources for most lakes. Agricultural nitrogen and phosphorus sources were found to explain the PEO trends in lakes in the western and central part of Yangtze Plain, and industrial wastewater discharge was associated with the PEO trends in eastern lakes. Our results revealed the importance of terrestrial nutrient sources for long-term changes in eutrophic status over the fifty lakes of the Yangtze Plain. This calls for sustainable nitrogen applications in agriculture systems to improve water quality of local lake ecosystems.

Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change, projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water-, and mortality-related parameters, while predictive uncertainties are dominantly induced by environmental drivers and parameters related to water and mortality. The importance of environmental drivers for predictive uncertainty increases with increasing temperature. Moreover, most of the interactions of model inputs (environmental drivers and parameters) are between environmental drivers themselves or between parameters and environmental drivers. In conclusion, our study highlights the importance of environmental drivers not only as contributors to predictive uncertainty in their own right but also as modifiers of sensitivities and thus uncertainties in other ecosystem processes. Reducing uncertainty in mortality-related processes and accounting for environmental influence on processes should therefore be a focus in further model development.

Over the past two decades, lakes in Yangtze Plain have suffered from serious eutrophication, in some regions with increased frequency of cyanobacteria blooms over years. In this study, we investigated the underlying causes of eutrophication using a combination of process-based ecosystem modelling and statistical data analysis. We found that terrestrial nutrient exports with runoff have significantly increased from 1979 to 2018 in Yangtze Plain, directly linked to the enhanced usage of chemical fertilizer for crops. Based on statistical analyses of environmental variables, terrestrial nutrient exports and satellite-observed probability of eutrophication occurrence (PEO), we separated the studied fifty lakes into five classes with similarities in environmental and nutrient variations, and attributed key factors in controlling the temporal changes of PEO. The results showed that the satellite-observed PEO trends in five classes could be largely linked to the terrestrial nutrient exports and environmental changes. Specifically, we found agricultural activities can explain the observed eutrophication trends in western lakes where lake catchments are dominated with arable and natural land, and the reduced discharge of industrial wastewater was found to be linked to the declining trends in eutrophication for eastern lakes where the green growth of industrialization were promoted from 2003 to 2011. These findings highlight the importance of sustainable management of agriculture and industrialization to overcome eutrophication issues in this region.

Land biosphere processes are of central importance to the climate system. Specifically, biological processes interact with the atmosphere through a variety of feedback loops that modulate energy, water and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land-atmosphere interactions. Dynamic Global Vegetation Models (DGVMs) attempt to capture these land surface processes, and are increasingly incorporated into Earth System Models (ESMs), which makes it possible to study the coupled dynamics of the land-biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water and CO2 between the land-ecosystem and the atmosphere. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modeled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types, but the emergent ecosystem composition and structure are consistent between the two versions. We find that the choice of stomatal conductance model has a major impact on the model's predictions. The new LSM implementation described in this work lays the foundation for using the well established LPJ-GUESS DGVM as an alternative LSM in coupled land-biosphere-climate studies, where an accurate representation of ecosystem processes is essential.

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

International audience; 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. CO2 fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO2 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.

Improved agricultural management plays a vital role in protecting soils from degradation in eastern Africa. Changing practices such as reducing tillage, fertilizer use, or cover crops are expected to enhance soil organic carbon (SOC) storage, with climate change mitigation co-benefits, while increasing crop production. However, the quantification of cropland management effects on agricultural ecosystems remains inadequate in this region. Here, we explored seven management practices and their potential effects on soil carbon (C) pools, nitrogen (N) losses, and crop yields under different climate scenarios, using the dynamic vegetation model LPJ-GUESS. The model performance is evaluated against observations from two long-term maize field trials in western Kenya and reported estimates from published sources. LPJ-GUESS generally produces soil C stocks and maize productivity comparable with measurements and mostly captures the SOC decline under some management practices that is observed in the field experiments. We found that for large parts of Kenya and Ethiopia, an integrated conservation agriculture practice (no-tillage, residue and manure application, and cover crops) increases SOC levels in the long term (+11 % on average), accompanied by increased crop yields (+22 %) in comparison to the conventional management. Planting nitrogen-fixing cover crops in our simulations is also identified as a promising individual practice in eastern Africa to increase soil C storage (+4 %) and crop production (+18 %), with low environmental cost of N losses (+24 %). These management impacts are also sustained in simulations of three future climate pathways. This study highlights the possibilities of conservation agriculture when targeting long-term environmental sustainability and food security in crop ecosystems, particularly for those with poor soil conditions in tropical climates.

Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts., There are big uncertainties in the contribution of irrigation to crop yields. Here, the authors use Bayesian model averaging to combine statistical and process-based models and quantify the contribution of irrigation for wheat and maize yields, finding that irrigation alone cannot close yield gaps for a large fraction of global rainfed agriculture.

Biological nitrogen fixation (BNF) from grain legumes is significant importance in global agricultural ecosystems. Crops with BNF capability are expected to support the need to increase food production while reducing nitrogen (N) fertilizer input for agriculture sustainability, but quantification of N fixing rates and BNF crop yields remains inadequate. Here we incorporate two legume crops (soybean and faba bean) with BNF into a dynamic vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator). The performance of this new implementation is evaluated against observations from a range of water and N management trials. LPJ-GUESS generally captures the observed response to these management practices on legume biomass production, soil N uptake and N fixation, despite some deviations from observations in some cases. Globally, the simulated BNF is dominated by soil moisture and temperature, as well as amounts of N fertilizer addition. Annual inputs through BNF are modelled to be 11.6±2.2 Tg N for soybean and 5.6±1.0 Tg N for all pulses, with a total fixation of 17.2±2.9 Tg N yr-1 for all grain legumes during the period 1981–2016 on global scale. Our estimates show a good agreement with some previous statistical estimates but are relatively high compared to some estimates for pulses. This study highlights the importance of accounting for legume N fixation process when modelling C-N interactions in agricultural ecosystems, particularly when it comes to account for the combined effects of climate and land-use change on global terrestrial N cycle.

Arctic environmental change has induced shifts in high latitude plant community composition and stature with impli-cations for Arctic carbon cycling and energy exchange. Two major components of high latitude ecosystems undergoing change is the advancement of trees into treeless tundra and the increased abundance and size of shrubs. How future changes in key climatic and environmental drivers will affect distributions of major ecosystem types is an active area of research. Dynamic Vegetation Models (DVMs) offer a way to investigate multiple and interacting drivers of vegeta-tion distribution and ecosystem function. We employed the LPJ-GUESS DVM over a subarctic landscape in northern Sweden, Torneträsk. Using a highly resolved climate dataset we downscaled CMIP5 climate data from three Global Climate Models and two 21st century future scenarios (RCP2.6 and RCP8.5) to investigate future impacts of climate change on these ecosystems. We also performed three model experiments where we factorially varied drivers (climate, nitrogen deposition and [CO2]) to disentangle the effects of each on ecosystem properties and functions. We found that treelines could advance by between 45 and 195 elevational meters in the landscape until the year 2100, depending on the scenario. Temperature was a strong, but not the only, driver of vegetation change. Nitrogen availability was identi-fied as an important modulator of treeline advance. While increased CO2 fertilisation drove productivity increases it did not result in any range shifts of trees. Treeline advance was realistically simulated without any temperature depend-ence on growth, but biomass was overestimated. As nitrogen was identified as an important modulator of treeline ad-vance, we support the idea that accurately representing plant-soil interactions in models will be key to future predic-tions Arctic vegetation change.

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change.

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

Earth 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., A novel modelling approach for Earth observation of global gross primary production (GPP) of vegetation was introduced. The model improved simulations of GPP, and revealed a stagnation in the global GPP after the year 2000.

Humans have dramatically increased atmospheric CO2 concentration as well as biologically available nitrogen (N). Nitrogen is an essential nutrient for vegetation growth and N availability represents a limiting factor on carbon (C) sequestration by the terrestrial ecosystems. While there is a large infrastructure for measurements to constrain the C cycle, data to constrain the N cycle are less readily available. Using a combination of remote sensing products (MODIS), canopy N concentration data (ICP forest), plant functional type and environmental variables including soil, climate (WorldClim) and elevation (EU-DEM), we generated a canopy N map across European forests using a random forest statistical method (hereafter RF canopy N map).Most current Global Vegetation Models (GVMs) have integrated C and N cycles, to account for the link between C and N for plant growth and respiration. Leaf N concentration is also important for other biomass compartments as N allocations are prescribed relative to leaf N. The objective of this study is to compare canopy N of two GVMs, O-CN and LPJ-GUESS, and the RF canopy N map in European forests.The obtained canopy N maps show contrasting spatial patterns. The RF canopy N map shows higher canopy N values, i.e. between 1.8 and 2.2 %N, in mid-western and eastern Europe, while showing lower values, i.e. 1.2 and 1.6 %N, around the Mediterranean region and in the south of Sweden. The canopy N map obtained from the O-CN simulation shows relatively lower canopy N values, ranging from 1.0 to 1.8 %N, in central and northern Europe, while in the Mediterranean region the values are higher, between 1.8 and 2.4 %N. Similar to the RF map, the LPJ-GUESS canopy N map shows relatively higher canopy N values in mid-western Europe compared to southern and northern Europe, however, the LPJ-GUESS canopy N values show little spatial variation in the Mediterranean region. Also, the LPJ-GUESS values are higher, with canopy N values ranging between 2.0 and 2.8 %N in mid-western Europe, and canopy N values ranging between 1.6 and 1.8 %N in the Mediterranean region.The analysis yields insight into spatial differences in RF canopy N and canopy N predicted by GVMs, with especially a mismatch in arid and warm regions.

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Tundra soils account for 50% of global stocks of soil organic carbon (SOC), and it is expected that the amplified climate warming in high latitude could cause loss of this SOC through decomposition. Decomposed SOC could become hydrologically accessible, which increase downstream dissolved organic carbon (DOC) export and subsequent carbon release to the atmosphere, constituting a positive feedback to climate warming. However, DOC export is often neglected in ecosystem models. In this paper, we incorporate processes related to DOC production, mineralization, diffusion, sorption-desorption, and leaching into a customized arctic version of the dynamic ecosystem model LPJ-GUESS in order to mechanistically model catchment DOC export, and to link this flux to other ecosystem processes. The extended LPJ-GUESS is compared to observed DOC export at Stordalen catchment in northern Sweden. Vegetation communities include flood-tolerant graminoids (Eriophorum) and Sphagnum moss, birch forest and dwarf shrub communities. The processes, sorption-desorption and microbial decomposition (DOC production and mineralization) are found to contribute most to the variance in DOC export based on a detailed variance-based Sobol sensitivity analysis (SA) at grid cell-level. Catchment-level SA shows that the highest mean DOC exports come from the Eriophorum peatland (fen). A comparison with observations shows that the model captures the seasonality of DOC fluxes. Two catchment simulations, one without water lateral routing and one without peatland processes, were compared with the catchment simulations with all processes. The comparison showed that the current implementation of catchment lateral flow and peatland processes in LPJ-GUESS are essential to capture catchment-level DOC dynamics and indicate the model is at an appropriate level of complexity to represent the main mechanism of DOC dynamics in soils. The extended model provides a new tool to investigate potential interactions among climate change, vegetation dynamics, soil hydrology and DOC dynamics at both stand-alone to catchment scales.

Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051–2080 than in 1971–2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

In this study, a global vegetation model (LPJ-GUESS) is forced with spatial information (Nomenclature of Units for Territorial Statistics (NUTS) 2 level) of land-use intensity change in the form of nitrogen (N) fertilization derived from a model chain which informed the Common Agricultural Policy Regionalized Impact (CAPRI) model. We analysed the combined role of climate change and land-use intensity change for trade-offs between agricultural yield and N leaching in the European Union under two plausible scenarios up until 2040. Furthermore, we assessed both driver importance and uncertainty in future trends based on an alternative land-use intensity dataset derived from an integrated assessment model. LPJ-GUESS simulated an increase in wheat and maize yield but also N leaching for most regions when driven by changes in land-use intensity and climate under RCP 8.5. Under RCP 4.5, N leaching is reduced in 53% of the regions while there is a trade-off in crop productivity. The most important factors influencing yield were CO2 (wheat) and climate (maize), but N application almost equaled these in importance. For N leaching, N application was the most important factor, followed by climate. Therefore, using a constant N application dataset in the absence of future projections has a substantial effect on simulated ecosystem responses, especially for maize yield and N leaching. This study is a first assessment of future N leaching and yield responses based on projections of climate and land-use intensity. It further highlights the importance of accounting for changes in future N applications and land-use intensity in general when evaluating environmental impacts over long time periods.

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase II experimental protocol and its simulation data archive. Twelve crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (``CTWN'') for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions, but is largest in high-latitude regions where crops may be grown in the future.

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.