The Global Gridded Crop Model Intercomparison: Data and modeling protocols for Phase 1 (v1.0)

We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record

Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management

Present day greenhouse gases could cause more frequent and longer Dust Bowl heatwaves

Substantial warming occurred across North America, Europe and the Arctic over the early twentieth century1, including an increase in global drought2, that was partially forced by rising greenhouse gases (GHGs)3. The period included the 1930s Dust Bowl drought4,5,6,7 across North America’s Great Plains that caused widespread crop failures4,8, large dust storms9 and considerable out-migration10. This coincided with the central United States experiencing its hottest summers of the twentieth century11,12 in 1934 and 1936, with over 40 heatwave days and maximum temperatures surpassing 44 °C at some locations13,14. Here we use a large-ensemble regional modelling framework to show that GHG increases caused slightly enhanced heatwave activity over the eastern United States during 1934 and 1936. Instead of asking how a present-day heatwave would behave in a world without climate warming, we ask how these 1930s heatwaves would behave with present-day GHGs. Heatwave activity in similarly rare events would be much larger under today’s atmospheric GHG forcing and the return period of a 1-in-100-year heatwave summer (as observed in 1936) would be reduced to about 1-in-40 years. A key driver of the increasing heatwave activity and intensity is reduced evaporative cooling and increased sensible heating during dry springs and summers

Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies

Cytotoxic Withanolide Constituents of Physalis longifolia

Fourteen new withanolides, 1–14, named withalongolides A–N, respectively, were isolated from the aerial parts of Physalis longifolia together with eight known compounds (15–22). The structures of compounds 1–14 were elucidated through spectroscopic techniques and chemical methods. In addition, the structures of withanolides 1, 2, 3, and 6 were confirmed by X-ray crystallographic analysis. Using a MTS viability assay, eight withanolides (1, 2, 3, 7, 8, 15, 16, and 19) and four acetylated derivatives (1a, 1b, 2a, and 2b) showed potent cytotoxicity against human head and neck squamous cell carcinoma (JMAR and MDA-1986), melanoma (B16F10 and SKMEL-28), and normal fetal fibroblast (MRC-5) cells with IC50 values in the range between 0.067 and 9.3 μM

Ocean and land forcing of the record-breaking Dust Bowl heat waves across central United States

International audienceThe severe drought of the 1930s Dust Bowl decade coincided with record-breaking summer heatwaves that contributed to the socioeconomic and ecological disaster over North America's Great Plains. It remains unresolved to what extent these exceptional heatwaves, hotter than in historically forced coupled climate model simulations, were forced by sea surface temperatures (SSTs) and exacerbated through human-induced deterioration of land cover. Here we show, using an atmospheric-only model, that anomalously warm North Atlantic SSTs enhance heatwave activity through an association with drier spring conditions resulting from weaker moisture transport. Model devegetation simulations, that represent the widespread exposure of bare soil in the 1930s, suggest human activity fueled stronger and more frequent heatwaves through greater evaporative drying in the warmer months. This study highlights the potential for the amplification of naturally occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in a warmer world

Withanolides and related steroids

Since the isolation of the first withanolides in the mid-1960s, over 600 new members of this group of compounds have been described, with most from genera of the plant family Solanaceae. The basic structure of withaferin A, a C28 ergostane with a modified side chain forming a δ-lactone between carbons 22 and 26, was considered for many years the basic template for the withanolides. Nowadays, a considerable number of related structures are also considered part of the withanolide class; among them are those containing γ-lactones in the side chain that have come to be at least as common as the δ-lactones. The reduced versions (γ and δ-lactols) are also known. Further structural variations include modified skeletons (including C27 compounds), aromatic rings and additional rings, which may coexist in a single plant species. Seasonal and geographical variations have also been described in the concentration levels and types of withanolides that may occur, especially in the Jaborosa and Salpichroa genera, and biogenetic relationships among those withanolides may be inferred from the structural variations detected. Withania is the parent genus of the withanolides and a special section is devoted to the new structures isolated from species in this genus. Following this, all other new structures are grouped by structural types. Many withanolides have shown a variety of interesting biological activities ranging from antitumor, cytotoxic and potential cancer chemopreventive effects, to feeding deterrence for several insects as well as selective phytotoxicity towards monocotyledoneous and dicotyledoneous species. Trypanocidal, leishmanicidal, antibacterial, and antifungal activities have also been reported. A comprehensive description of the different activities and their significance has been included in this chapter. The final section is devoted to chemotaxonomic implications of withanolide distribution within the Solanaceae. Overall, this chapter covers the advances in the chemistry and biology of withanolides over the last 16 years.Fil: Misico, Rosana Isabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos Aplicados a la Química Orgánica (i); ArgentinaFil: Nicotra, V.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; ArgentinaFil: Oberti, Juan Carlos María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; ArgentinaFil: Barboza, Gloria Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Farmacia; ArgentinaFil: Gil, Roberto Ricardo. University Of Carnegie Mellon; Estados UnidosFil: Burton, Gerardo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos Aplicados a la Química Orgánica (i); Argentin

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling — nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output — to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO 2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios ( < 10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.Michael Glotter, Joshua Elliott, David McInerney, Neil Best, Ian Foster, and Elisabeth J. Moye