Abstract
Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
Highlights
Climate change impacts on agriculture are subject to large uncertainties from a variety of sources
The ensemble of crop model emulators projects consistently more negative impacts on average, so that the uncertainty range (±1 standard deviation, colored area in figure 2) of only three global gridded crop model (GGCM) overlaps the zero line (CARAIB, LPJ-GUESS, PROMET)
If random sets of climate models that constitute a similar share of the ensemble size (n = 4 of 34 for CMIP6, roughly equivalent of one in nine crop models), we find that results on the GCM- and GGCM-induced variance shares change less than if individual GGCMs are excluded in the first half of the 21st century, but can be affected strongly at the end of the century, suggesting that the distribution of changes in the GCM ensemble is more balanced in short-term projections than that within the GGCM ensemble
Summary
Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is associated with the severity of climate change itself, even for a fixed emission scenario. Climate models differ in mean projected changes over large regions but in the spatial patterns of those changes, with precipitation an especial concern In general CMIP6 is noticeably more sensitive to CO2 than CMIP5, largely due to the updated representation of aerosols (e.g. Wyser et al 2020).Given these wide uncertainties in climate projections, it is important to understand how they translate into uncertainties in potential impacts on crop yields
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