Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-ofseason variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables.Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (emean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 Accepted ArticleThis article is protected by copyright. All rights reserved. models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
A method (Barometric Process Separation, BaPS) was developed for the quantification of gross nitrification rates and denitrification rates in oxic soil using intact soil cores incubated in an isothermal gas tight system. Gross nitrification rates and denitrification rates are derived from measurements of changes (i) in air pressure within the closed system, which are primarily the result of the activities of nitrification (pressure decrease), denitrification (pressure increase), and respiration (pressure neutral), and (ii) of O2 and CO2 concentrations in the system. Besides these biological processes, the contribution of physicochemical dissolution of produced CO2 in soil water to the pressure changes observed is to be considered. The method allows collection of additional information about the contribution of nitrification and denitrification to N2O emission from soil, provided simultaneous measurements of N2O emission are performed. Furthermore, BaPS can be used to quantify the percentage of N2O lost from nitrification. The advantage of BaPS is that disturbance of the soil system is minimized compared with other methods such as the use of gaseous inhibitors (e.g., acetylene) or application of 15N compounds to the soil. We present the theoretical considerations of BaPS, results for nitrification rates, denitrification rates, and identification of soil N2O sources in a well‐aerated coniferous forest soil using BaPS. The suitability of BaPS as a method for determination of gross nitrification is demonstrated by validation experiments using the 15N‐pool dilution technique.
Six simulations with the Weather Research and Forecasting (WRF) model differing in planetary boundary layer (PBL) schemes and land surface models (LSMs) are investigated in a case study in western Germany during clear-sky weather conditions. The simulations were performed at 2 km resolution with two local and two nonlocal PBL schemes, combined with two LSMs (NOAH and NOAH-MP). Resulting convective boundary layer (CBL) features are investigated in combination with high-resolution water vapor differential absorption lidar measurements at an experimental area. Further, the simulated soil-vegetation-atmosphere feedback processes are quantified applying a mixing diagram approach. The investigation shows that the nonlocal PBL schemes simulate a deeper and drier CBL than the local schemes. Furthermore, the application of different LSMs reveals that the entrainment of dry air depends on the energy partitioning at the land surface. The study demonstrates that the impact of processes occurring at the land surface is not constrained to the lower CBL but extends up to the interfacial layer and the lower troposphere. With respect to the choice of the LSM, the discrepancies in simulating a diurnal change of the humidity profiles are even more significant at the interfacial layer than close to the land surface. This indicates that the representation of land surface processes has a significant impact on the simulation of mixing properties within the CBL.
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