Multisectoral climate impact hotspots in a warming world

Abstract

The impacts of global climate change on different aspects of humanity's diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.

Keywords: ISI-MIP; coinciding pressures; differential climate impacts.

Fig. 1.

Threshold crossing temperatures with respect to the reference period GMT for the four sectoral metrics: discharge (A), crop yields (B), risk of severe ecosystem change (C), and LTS of malaria (D). Areas in white do not cross the respective threshold. The gray color indicates regions which are either masked out [discharge, Γ, crop yields (only regions where the maize, wheat, soy, and rice are currently cultivated are considered)], or where malaria is already endemic (D). An agreement of 50% of all GIM-GCM combinations on threshold crossing is required for consideration in the analysis.

Fig. 2.

Multisectoral hotspots of impacts for two (orange) and three (red) overlapping sectors in the strict assessment, with 50% of GIM-GCM combinations agreeing on the threshold crossing in each sector, for a GMT change of up to 4.5 °C. Which sectors overlap depends on the location and can be discerned from the sectoral patterns in Fig.1. An overlap of all four sectors does not occur in the strict assessment. Regions in light gray are regions where no multisectoral overlap is possible at all because of sectoral restrictions as shown in Fig.1. The dark gray shows the additional regions affected by multisectoral pressures under the worst-case assessment, where a minimum of 10% of all sectoral GIM-GCM combinations have to agree on the threshold crossing.

Fig. 3.

Cumulative fraction of global land area (excluding Antarctica; for crop yields the relevant area is the maximum crop area as covered today by the four staple crops: maize, wheat, soy, and rice) having crossed the respective sectoral thresholds up to the given ΔGMT for discharge (A), crop yields (B), risk of severe ecosystem changes (C), and LTS (D). Black boxes show the uncertainty across impact models, and red boxes indicate the uncertainty across GCMs. Each box indicates the interquartile range, the thick line shows the median, and the whiskers extend over the whole range of the distribution of all GCMs/GIMs at that temperature bin. Note the different ranges on the y axis for each panel.

Fig. 4.

Cumulative fraction of the global area (brightly tinted bars, excluding Antarctica) and population (lightly tinted bars) affected by the thresholds being crossed at ΔGMT in at least two (red), three (orange), and four (blue) overlapping sectors. (Left) The strict case (agreement of at least 50% of GIM-GCM combinations on the threshold crossing). (Right) The worst-case (agreement of at least 10% of GIM-GCM combinations on the threshold crossing and the sectoral crossing temperature is the 10th percentile of all crossing temperatures) assessment. Overlap of four sectors does not occur in the strict case. Population is held constant at the year 2000 levels.