Abstract :
[en] 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.
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