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There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance

To respect the Paris agreement targeting a limitation of global warming below 2°C by 2100, and possibly below 1.5 °C, drastic reductions of greenhouse gas emissions are mandatory but not sufficient. Large-scale deployment of other climate mitigation strategies are also necessary. Among these, increasing soil organic carbon (SOC) stocks is an important lever because carbon in soils can be stored for long periods and land management options to achieve this already exist and have been widely tested. However, agricultural soils are also an important source of nitrous oxide (N2 O), a powerful greenhouse gas, and increasing SOC may influence N2 O emissions, likely causing an increase in many cases, thus tending to offset the climate change benefit from increased SOC storage. Here, we review the main agricultural management options for increasing SOC stocks. We evaluate the amount of SOC that can be stored as well as resulting changes in N2 O emissions to better estimate the climate benefits of these management options. Based on quantitative data obtained from published meta-analyses and from our current level of understanding, we conclude that the climate mitigation induced by increased SOC storage is generally overestimated if associated N2 O emissions are not considered but, with the exception of reduced tillage, is never fully offset. Some options (e.g, biochar or non-pyrogenic C amendment application) may even decrease N2 O emissions.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would

At the 21st session of the United Nations Framework Convention on Climate Change (UNFCCC, COP21), a voluntary action plan, the ‘4 per 1000 Initiative: Soils for Food Security and Climate’ was proposed under the Agenda for Action. The Initiative underlines the role of soil organic matter (SOM) in addressing the three-fold challenge of food and nutritional security, adaptation to climate change and mitigation of human-induced greenhouse gases (GHGs) emissions. It sets an ambitious aspirational target of a 4 per 1000 (i.e. 0.4%) rate of annual increase in global soil organic carbon (SOC) stocks, with a focus on agricultural lands where farmers would ensure the carbon stewardship of soils, like they manage day-to-day multipurpose production systems in a changing environment. In this paper, the opportunities and challenges for the 4 per 1000 initiative are discussed. We show that the 4 per 1000 target, calculated relative to global top soil SOC stocks, is consistent with literature estimates of the technical potential for SOC sequestration, though the achievable potential is likely to be substantially lower given socio-economic constraints. We calculate that land-based negative emissions from additional SOC sequestration could significantly contribute to reducing the anthropogenic CO2 equivalent emission gap identified from Nationally Determined Contributions pledged by countries to stabilize global warming levels below 2 °C or even 1.5 °C under the Paris agreement on climate. The 4 per 1000 target could be implemented by taking into account differentiated SOC stock baselines, reversing the current trend of huge soil CO2 losses, e.g. from agriculture encroaching peatland soils. We further discuss the potential benefits of SOC stewardship for both degraded and healthy soils along contrasting spatial scales (field, farm, landscape and country) and temporal (year to century) horizons. Last, we present some of the implications relative to non-CO2 GHGs emissions, water and nu

To keep global warming possibly below 1.5 ᵒC and mitigate adverse effects of climate change, agriculture, like all other sectors, will have to contribute to efforts in achieving net negative emissions by the end of the century. Cost-efficient distribution of mitigation across regions and economic sectors is typically calculated using a global uniform carbon price in climate stabilization scenarios. However, in reality such a carbon price would substantially affect food availability. Here, we assess the implications of climate change mitigation in the land use sector for agricultural production and food security using an integrated partial equilibrium modelling framework and explore ways of relaxing the competition between mitigation in agriculture and food availability. Using a scenario that limits global warming cost-efficiently across sectors to 1.5 ᵒC, results indicate global food calorie losses ranging from 110 to 285 kcal per capita and day in 2050 depending on the applied demand elasticities. This could translate into a rise in undernourishment of 80 to 300 million people in 2050. Less ambitious GHG mitigation in the land use sector reduces the associated food security impact significantly, however the 1.5 ᵒC target would not be achieved without additional reductions outside the land use sector. Efficiency of greenhouse gas (GHG) mitigation will also depend on the level of participation globally. Our results show that if non-Annex I countries decide not to contribute to mitigation action while other parties pursue their mitigation efforts to reach the global climate target, food security impacts in these non-Annex I countries will be higher than if they participate in a global agreement, as inefficient mitigation increases agricultural production costs and therefore food prices. Land-rich countries with a high proportion of emissions from land use change, such as Brazil, could reduce emissions with only a marginal effect on food availability. In contrast, agri

Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grass-biomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5° by 0.5°. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 1901–2012. The grass-biomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, rising CO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1 × 106 km2 in 1901 to 12.3 × 106 km2 in 2000, although the expansion pathway varies between different regions. ORCHIDEE-GM also simulated augmentation in global mean productivity and herbage-use efficiency over managed grassland during the 20th century, indicating a general intensification of grassland management at global scale but with regional differences. The gridded grassland management intensity maps are model dependent because they depend on modelled productivity. Thus specific attention was given to the evaluation of modelled productivity against a series of observations from site-level net primary productivity (NPP) measurements to two global satellite products of gross primary productivity (GPP) (MODIS-GPP and SIF data). Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle, and interannual variability of grassland productivity at global scale well and thus is appro

Several lines of evidence point to European managed grassland ecosystems being a sink of carbon. In this study, we apply ORCHIDEE-GM a process-based carbon cycle model that describes specific management practices of pastures and the dynamics of carbon cycling in response to changes in climatic and biogeochemical drivers. The model is used to simulate changes in the carbon balance (i.e., Net Biome Production, NBP) of European grasslands over 1991-2010 on a 25 km x 25 km grid. The modeled average trend of NBP is 1.8 - 2.0 g C m^-2 yr^-2 during the past two decades. Attribution of this trend suggests management intensity as the dominant driver explaining NBP trends in the model (36% - 43% of the trend due to all drivers). A major change in grassland management intensity has occurred across Europe resulting from reduced livestock numbers. This change has 'inadvertently' enhanced soil C sequestration and reduced N2O and CH4 emissions by 1.2 - 1.5 Gt CO2-equivalent, offsetting more than 7% of greenhoue gas emissions in the whole European agricultural sector during the period 1991-2010. Land-cover change, climate change and rising CO2 also make positive and moderate contributions to the NBP trend (between 24% and 31% of the trend due to all drivers). Change in nitrogen addition (including fertilization and atmospheric deposition) is found to have only marginal net effect on NBP trends. However, this may not reflect reality because our model has only a very simple parameterization of nitrogen-effects on photosynthesis. The sum of NBP trends from each driver is larger than the trend obtained when all drivers are varied together, leaving a residual - nn-attributed - term (22% - 26% of the trend due to all drivers) indicating negative interactions between drivers.

More than 100 countries pledged to reduce agricultural greenhouse gas (GHG) emissions (Richards et al., 2015a) in the 2015 Paris Agreement of the United Nations Framework Convention on Climate Change. Yet technical information about how much mitigation is needed in the sector versus how much is feasible remains poor. We identify a preliminary global target for reducing emissions from agriculture of ~1 GtCO2e/yr by 2030 to limit warming in 2100 to 2°C above pre-industrial levels. Yet plausible agricultural development pathways with mitigation co-benefits deliver only 21 to 40% of needed mitigation. The target indicates that more transformative technical and policy options will be needed, such as methane inhibitors and finance for new practices. A more comprehensive target for the 2°C limit should be developed to include soil carbon and agriculture-related mitigation options. Excluding agricultural emissions from mitigation targets and plans will increase the cost of mitigation in other sectors or reduce the feasibility of meeting the 2°C limit.