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Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., et al. (2015). Multimodel ensembles of wheat growth: many models are better than one. Glob. Chang. Biol., 21(2), 911–925.
Abstract: 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-of-season 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 (e-mean) 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 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.
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Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., et al. (2013). Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change, 3(9), 827–832.
Abstract: 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.
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Müller, C., Elliott, J., & Levermann, A. (2014). Food security: Fertilizing hidden hunger. Nat. Clim. Change, 4(7), 540–541.
Abstract: Atmospheric CO2 fertilization may go some way to compensating the negative impact of climatic changes on crop yields, but it comes at the expense of a deterioration of the current nutritional value of food.
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Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J. L., Li, T., et al. (2015). A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Agricultural and Forest Meteorology, 214-215, 483–493.
Abstract: Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
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Dass, P., Müller, C., Brovkin, V., & Cramer, W. (2013). Can bioenergy cropping compensate high carbon emissions from large-scale deforestation of high latitudes. Earth System Dynamics, 4(2), 409–424.
Abstract: Numerous studies have concluded that deforestation of the high latitudes result in a global cooling. This is mainly because of the increased albedo of deforested land which dominates over other biogeophysical and biogeochemical mechanisms in the energy balance. This dominance, however, may be due to an underestimation of the biogeochemical response, as carbon emissions are typically at or below the lower end of estimates. Here, we use the dynamic global vegetation model LPJmL for a better estimate of the carbon cycle under such large-scale deforestation. These studies are purely theoretical in order to understand the role of vegetation in the energy balance and the earth system. They must not be mistaken as possible mitigation options, because of the devastating effects on pristine ecosystems. For realistic assumptions of land suitability, the total emissions computed in this study are higher than that of previous studies assessing the effects of boreal deforestation. The warming due to biogeochemical effects ranges from 0.12 to 0.32 degrees C, depending on the climate sensitivity. Using LPJmL to assess the mitigation potential of bioenergy plantations in the suitable areas of the deforested region, we find that the global biophysical bioenergy potential is 68.1 +/- 5.6 EJ yr(-1) of primary energy at the end of the 21st century in the most plausible scenario. The avoided combustion of fossil fuels over the time frame of this experiment would lead to further cooling. However, since the carbon debt caused by the cumulative emissions is not repaid by the end of the 21st century, the global temperatures would increase by 0.04 to 0.11 degrees C. The carbon dynamics in the high latitudes especially with respect to permafrost dynamics and long-term carbon losses, require additional attention in the role for the Earth’s carbon and energy budget.
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