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.
Highlights
Climate change exerts a substantial and direct impact on food security and hunger risk by altering the global patterns of precipitation and temperature which determine the location of arable land (Parry et al, 2005; Rosenzweig et al, 2014) as well as the quality (Müller et al, 2014) and quantity (Müller and Robertson, 2014; Lobell et al, 2012; van der Velde et al, 2012) of crops comprising most of the world food supply
Scaled climate projections have been used as input for different impact models (Ostberg et al, 2013; Stehfest et al, 2014) to achieve greater flexibility in terms of the range of emissions scenarios considered in climate impact studies. Building upon such a framework, we present a method to extend the capacity of crop yield impact projections by relating simulated crop yield changes to two highly aggregated quantities – global mean temperature change ( GMT) and atmospheric CO2 concentration – by means of simplified function
We propose three methods to generate these patterns based on the available complex model simulations, and describe the related approaches to estimate Global gridded crop models (GGCMs)- and global climate model (GCM)-specific yield changes for new GMT trajectories not originally covered by GCM–crop-model simulations
Summary
Climate change exerts a substantial and direct impact on food security and hunger risk by altering the global patterns of precipitation and temperature which determine the location of arable land (Parry et al, 2005; Rosenzweig et al, 2014) as well as the quality (Müller et al, 2014) and quantity (Müller and Robertson, 2014; Lobell et al, 2012; van der Velde et al, 2012) of crops comprising most of the world food supply. Rosenzweig et al, 2014), which in turn are a prerequisite for assessing potential changes in prices (Nelson et al, 2014) and food security (Parry et al, 2005) These process-based crop yield projections rely on spatially explicit realizations of the driving weather variables such as temperature, precipitation, radiation, and humidity, often at daily resolution, as provided by computationally expensive global climate model (GCM) simulations. Scaled climate projections have been used as input for different impact models (Ostberg et al, 2013; Stehfest et al, 2014) to achieve greater flexibility in terms of the range of emissions scenarios considered in climate impact studies Building upon such a framework, we present a method to extend the capacity of crop yield impact projections by relating simulated crop yield changes to two highly aggregated quantities – global mean temperature change ( GMT) and atmospheric CO2 concentration (pCO2) – by means of simplified function.
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