Changes in crop yields and their variability at different levels of global warming

Abstract

Abstract. An assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.

Global and regional impacts of climate change at different levels of global temperature increase

“…Arnell et al (2016b) presented 'damage functions' relating impact to change in global mean temperature, using scenarios constructed from CMIP3-generation climate models by pattern-scaling, and Arnell et al (2018) used a similar approach with CMIP5-generation climate models to assess the impacts avoided if low temperature targets are met. Schleussner et al (2016), Naumann et al (2018) and Ostberg et al (2018) estimated impacts using scenarios representing specific temperature targets constructed by 'time-slicing' (extracting the periods from a climate model simulation with the target mean change in global temperature). Seneviratne et al (2016) plotted global average temperature and precipitation extreme indices against global mean temperature change, derived from decadal means calculated from CMIP5 model output.…”

A rolling horizon simulation approach for managing demand with lead time variability

“…Finally, it is worth stressing that our forthcoming work intends to address the RH-SD-Java multilevel, multiproduct and supplier capacity constrained SC to test alternative lot-sizing techniques (least total cost, least unit cost, among others) and their application to different industrial or agri-food SC settings (Taylor and Fearne 2006). In this way, they will be able to better face the management of adverse demands which, for instance, this second sector presents suddenly or in the short or midterms, and result from external agents, like climate, natural disasters (Brown and Kshirsagar 2015;Ostberg et al 2018), oil prices and international situations (Anderson and Nelgen 2012;Zhang and Qu 2015), among others, especially those that are uncontrolled.…”

Surveying perceptions and practices of high-end climate change

“…Although recent research on impacts beyond 2°C is relatively sparse, the existing literature points to much more severe consequences compared to the historical period, 3 such as those described for flood risk (Alfieri et al 2015), sea-level rise (Golledge et al 2015;Cooper and Lemckert 2012), water scarcity (Schewe et al 2014) and heat stress (Sherwood and Huber 2010) and on sectors including agriculture (crops- Ostberg et al 2018, Parkes et al 2018, agricultural welfare-Stevanović et al 2016, freshwater (Koutroulis et al 2018) and groundwater (Portmann et al 2013). The IPCC AR5 (Oppenheimer et al 2014) includes a section on risks from > 4°C above pre-industrial levels, claiming that the evidence base has only recently become sufficient to allow for assessment.…”