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  1. Abstract

    Understanding contributions of climate and management intensifications to crop yield trends is essential to better adapt to climate changes and gauge future food security. Here we quantified the synergistic contributions of climate and management intensifications to maize yield trends from 1961 to 2017 in Iowa (United States) using a process-based modeling approach with a detailed climatic and agronomic observation database. We found that climate (management intensifications) contributes to approximately 10% (90%), 26% (74%), and 31% (69%) of the yield trends during 1961–2017, 1984–2013, and 1982–1998, respectively. However, the climate contributions show substantial decadal or multi-decadal variations, with the maximum decadal yield trends induced by temperature or radiation changes close to management intensifications induced trends while considerably larger than precipitation induced trends. Management intensifications can produce more yield gains with increased precipitation but greater losses of yields with increased temperature, with extreme drought conditions diminishing the yield gains, while radiation changes have little effect on yield gains from management intensifications. Under the management condition of recent years, the average trend at the higher warming level was about twice lower than that at the lower warming level, and the sensitivity of yield to warming temperature increased with management intensifications from 1961 to 2017. Due to such synergistic effects, management intensifications must account for global warming and incorporate climate adaptation strategies to secure future crop productions. Additional research is needed to understand how plausible adaptation strategies can mitigate synergistic effects from climate and management intensifications.

     
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  2. Abstract Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events. 
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