Increasing risks of multiple breadbasket failure under 1.5 and 2 °C global warming

Abstract

The increasingly interconnected global food system is becoming more vulnerable to production shocks owing to increasing global mean temperatures and more frequent climate extremes. Little is known, however, about the actual risks of multiple breadbasket failure due to extreme weather events. Motivated by the Paris Climate Agreement, this paper quantifies spatial risks to global agriculture in 1.5 and 2°C warmer worlds. This paper focuses on climate risks posed to three major crops-wheat, soybean and maize-in five major global food producing areas. Climate data from the atmosphere-only HadAM3P model as part of the "Half a degree Additional warming, Prognosis and Projected Impacts" (HAPPI) experiment are used to analyse the risks of climatic extreme events. Using the copula methodology, the risks of simultaneous crop failure in multiple breadbaskets are investigated. Projected losses do not scale linearly with global warming increases between 1.5 and 2°C Global Mean Temperature (GMT). In general, whilst the differences in yield at 1.5 versus 2°C are significant they are not as large as the difference between 1.5°C and the historical baseline which corresponds to 0.85°C above pre-industrial GMT. Risks of simultaneous crop failure, however, do increase disproportionately between 1.5 and 2°C, so surpassing the 1.5°C threshold will represent a threat to global food security. For maize, risks of multiple breadbasket failures increase the most, from 6% to 40% at 1.5 to 54% at 2°C warming. In relative terms, the highest simultaneous climate risk increase between the two warming scenarios was found for wheat (40%), followed by maize (35%) and soybean (23%). Looking at the impacts on agricultural production, we show that limiting global warming to 1.5°C would avoid production losses of up to 2 753 million (161 000, 265 000) tonnes maize (wheat, soybean) in the global breadbaskets and would reduce the risk of simultaneous crop failure by 26%, 28% and 19% respectively.

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

“…Despite tremendous efforts to quantify yield response to climate variation, substantial uncertainties still exist in the estimation of climate impacts on crop yield (Asseng et al 2013, Wheeler and von Braun 2013, Challinor et al 2014b, which can be attributed to the complexity of the relevant processes and the scarcity of relevant datasets. In general, two types of models have been widely used in the literature: statistical models (Lobell and Field 2007, Tebaldi and Lobell 2008, Zampieri et al 2017, Gaupp et al 2019, Leng and Hall 2019 and process-based models (Rosenzweig et al 2014, Waha et al 2015, Deryng et al 2016. Whilst each approach has its own strengths and weakness, only a few studies have compared the estimates between statistical models and crop models (Leng 2017a(Leng , 2017bAsseng 2017, Roberts et al 2017).…”

“…For the most part, the focus of these studies, and inter-comparison exercises such as the Agricultural Modelling Inter-comparison and Improvement Project (AgMIP); has been on understanding the potential future changes in average agricultural yields given future climate scenarios. There has been much less attention to the impacts of climate variation and extremes, though these effects can be particularly significant for global food systems if they result in large reductions in yield, interruptions to supply and price increases (Gaupp et al 2019). Empirically characterizing the variability in crop yields is more challenging than characterizing general trends, in particular because extreme events are by definition rare.…”

“…Some more studies have recently examined the effects of droughts on agricultural production (Leng and Hall 2019) and the impacts of concurrent droughts in different locations (Gaupp et al 2019). Although these studies have addressed large food producing regions, they have not sought to characterize both the spatial and temporal response of yields to climatic variability.…”

The risk and consequences of multiple breadbasket failures: an integrated copula and multilayer agent-based modeling approach

“…In the work of Jongman and colleagues (Jongman et al 2014), it was shown that large-scale atmospheric processes can result in strongly correlated extreme discharges across river basins leading to extreme flooding across large regions over Europe. Also in the case of drought events, the risk of simultaneous large-scale events that cause drought-induced crop losses in several regions at once are found to exhibit tail dependency (Prudhomme and Genevier 2011;Ratnam et al 2016;Vicente-Serrano and López-Moreno 2006;Gaupp et al 2019).…”

“…In 2010, droughts in Russia and floods in Pakistan that were physically connected through atmospheric blocking (Lau and Kim 2012) directly impacted breadbasket regions in these two countries and affected global food prices (Katsafados et al 2014). This phenomenon, known as "Multiple Breadbasket Failures" or MBBF, has recently been highlighted as a global security concern by climate experts, agriculture economics and food security experts (Janetos et al 2017;Gaupp et al 2020), and with climate change, is expected to get worse in the coming decades (Gaupp et al 2019).…”