Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields 40 and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat 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 45 Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but 50 also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions 55 in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results

Impact of human population density on fire frequency at the global scale

Human impact on wildfires, a major earth system component, remains poorly understood. While local studies have found more fires close to settlements and roads, assimilated charcoal records and analyses of regional fire patterns from remote-sensing observations point to a decline in fire frequency with increasing human population. Here, we present a global analysis using three multi-year satellite-based burned-area products combined with a parameter estimation and uncertainty analysis with a non-linear model. We show that at the global scale, the impact of increasing population density is mainly to reduce fire frequency. Only for areas with up to 0.1 people per km2, we find that fire frequency increases by 10 to 20% relative to its value at no population. The results are robust against choice of burned-area data set, and indicate that at only very few places on earth, fire frequency is limited by human ignitions. Applying the results to historical population estimates results in a moderate but accelerating decline of global burned area by around 14% since 1800, with most of the decline since 1950

The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols

The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models

Increasing stomatal conductance inresponse to rising atmospheric CO2

Background and Aims: Studies have indicated that plant stomatal conductance (gs) decreases in response to elevated atmospheric CO2, a phenomenon of significance for the global hydrological cycle. However, gs increases across certain CO2 ranges have been predicted by optimisation models. The aim of this work was to demonstrate that under certain environmental condition, gs can increase in response to elevated CO2. Methods: When using (i) an extensive, up-to-date, synthesis of gs responses in FACE experiments, (ii) in situ measurements across four biomes showing dynamic gs responses to a CO2 rise of ~50ppm (characterising the change in this greenhouse gas over the past three decades) and (iii) a photosynthesis-stomatal conductance model, it is demonstrated that gs can in some cases increase in response to increasing atmospheric CO2. Key Results: Field observations are corroborated by an extensive synthesis of gs responses in FACE experiments showing that 11.8% of gs responses under experimentally elevated CO2 are positive. They are further supported by a strong data-model fit (r2=0.607) using a stomatal optimization model applied to the field gs dataset. A parameter space identified in the Farquhar-Ball-Berry photosynthesis-stomatal conductance model confirms field observations of increasing gs under elevated CO2 in hot dry conditions. It was shown that contrary to the general assumption, positive gs responses to elevated CO2, although relatively rare, are a feature of woody taxa adapted to warm, low-humidity conditions, and that this response is also demonstrated in global simulations using the Community Land Model (CLM4). Conclusions: The results contradict the over-simplistic notion that global vegetation always responds with decreasing gs to elevated CO2, a finding that has important implications for predicting future vegetation feedbacks on the hydrological cycle at the regional level.Irish Research CouncilScience Foundation Irelan

Biophysics and vegetation cover change: A process-based evaluation framework for confronting land surface models with satellite observations

This is the final version. Available on open access from Copernicus Publications via the DOI in this recordLand use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land-climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies.The study was funded by the FP7 LUC4C project (grant no. 603542

Wildfire air pollution hazard during the 21st century

Wildfires pose a significant risk to human livelihoods and are a substantial health hazard due to emissions of toxic smoke. Previous studies have shown that climate change, increasing atmospheric CO2, and human demographic dynamics can lead to substantially altered wildfire risk in the future, with fire activity increasing in some regions and decreasing in others. The present study re-examines these results from the perspective of air pollution risk, focussing on emissions of airborne particulate matter (PM2. 5), combining an existing ensemble of simulations using a coupled fire–dynamic vegetation model with current observation-based estimates of wildfire emissions and simulations with a chemical transport model. Currently, wildfire PM2. 5 emissions exceed those from anthropogenic sources in large parts of the world. We further analyse two extreme sets of future wildfire emissions in a socio-economic, demographic climate change context and compare them to anthropogenic emission scenarios reflecting current and ambitious air pollution legislation. In most regions of the world, ambitious reductions of anthropogenic air pollutant emissions have the potential to limit mean annual pollutant PM2. 5 levels to comply with World Health Organization (WHO) air quality guidelines for PM2. 5. Worst-case future wildfire emissions are not likely to interfere with these annual goals, largely due to fire seasonality, as well as a tendency of wildfire sources to be situated in areas of intermediate population density, as opposed to anthropogenic sources that tend to be highest at the highest population densities. However, during the high-fire season, we find many regions where future PM2. 5 pollution levels can reach dangerous levels even for a scenario of aggressive reduction of anthropogenic emissions

Air quality impacts of European wildfire emissions in a changing climate

Wildfires are not only a threat to human property and a vital element of many ecosystems, but also an important source of air pollution. In this study, we first review the available evidence for a past or possible future climate-driven increase in wildfire emissions in Europe. We then introduce an ensemble of model simulations with a coupled wildfire–dynamic-ecosystem model, which we combine with published spatial maps of both wildfire and anthropogenic emissions of several major air pollutants to arrive at air pollutant emission projections for several time slices during the 21st century. The results indicate moderate wildfire-driven emission increases until 2050, but there is a possibility of large increases until the last decades of this century at high levels of climate change. We identify southern and north-eastern Europe as potential areas where wildfires may surpass anthropogenic pollution sources during the summer months. Under a scenario of high levels of climate change (Representative Concentration Pathway, RCP, 8.5), emissions from wildfires in central and northern Portugal and possibly southern Italy and along the west coast of the Balkan peninsula are projected to reach levels that could affect annual mean particulate matter concentrations enough to be relevant for meeting WHO air quality targets

Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models

This is the final version. Available from European Geosciences Union (EGU) via the DOI in this record.Recent climate changes have increased fire-prone weather conditions in many regions and have likely affected fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled dynamic global vegetation models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socio-economic predictor variables and burned area. We applied this approach to monthly burned area time series for the period from 2005 to 2011 from satellite observations and from DGVMs from the "Fire Modeling Intercomparison Project" (FireMIP) that were run using a common protocol and forcing data sets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socio-economic variables (e.g. human population density). DGVMs broadly reproduce the relationships with climate variables and, for some models, with population density. Interestingly, satellite-derived responses show a strong increase in burned area with an increase in previous-season leaf area index and plant productivity in most fire-prone ecosystems, which was largely underestimated by most DGVMs. Hence, our pattern-oriented model evaluation approach allowed us to diagnose that vegetation effects on fire are a main deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately simulate the role of fire under global environmental change..European Space AgencyTU Wien Wissenschaftspreis 2015Gordon and Betty Moore FoundationEuropean Research Counci