Multiple forms of knowledge, including scientific evidence, narrative scenarios and prospective pathways, inform the understanding of 1.5C. This report is informed by traditional evidence of the physical climate system and associated impacts and vulnerabilities of climate change, together with knowledge drawn from the perceptions of risk and the experiences of climate impacts and governance systems. Scenarios and pathways are used to explore conditions enabling goal-oriented futures while recognizing the significance of ethical considerations, the principle of equity, and the societal transformation needed. 1.2.3, 1.5.2
The assessed pathways describe integrated, quantitative evolutions of all emissions over the 21st century associated with global energy and land use and the world economy. The assessment is contingent upon available integrated assessment literature and model assumptions, and is complemented by other studies with different scope, for example, those focusing on individual sectors. In recent years, integrated mitigation studies have improved the characterizations of mitigation pathways. However, limitations remain, as climate damages, avoided impacts, or societal co-benefits of the modelled transformations remain largely unaccounted for, while concurrent rapid technological changes, behavioural aspects, and uncertainties about input data present continuous challenges. (high confidence) 2.1.3, 2.3, 2.5.1, 2.6, Technical Annex 2
Trends in intensity and frequency of some climate and weather extremes have been detected over time spans during which about 0.5C of global warming occurred (medium confidence). This assessment is based on several lines of evidence, including attribution studies for changes in extremes since 1950. 3.2, 3.3.1, 3.3.2, 3.3.3, 3.3.4
A.1.3. Trends in intensity and frequency of some climate and weather extremes have been detected over time spans during which about 0.5C of global warming occurred (medium confidence). This assessment is based on several lines of evidence, including attribution studies for changes in extremes since 1950. 3.3.1, 3.3.2, 3.3.3
Potential synergies and trade-offs between the sectoral portfolio of climate change mitigation options and the Sustainable Development Goals (SDGs). The SDGs serve as an analytical framework for the assessment of the different sustainable development dimensions, which extend beyond the time frame of the 2030 SDG targets. The assessment is based on literature on mitigation options that are considered relevant for 1.5C. The assessed strength of the SDG interactions is based on the qualitative and quantitative assessment of individual mitigation options listed in Table 5.2. For each mitigation option, the strength ofthe SDG-connection as well as the associated confidence of the underlying literature (shades of green and red) was assessed. The strength of positive connections (synergies) and negative connections (trade-offs) across all individual options within a sector (see Table 5.2) are aggregated into sectoral potentials for the whole mitigation portfolio. The (white) areas outside the bars, which indicate no interactions, have low confidence due to the uncertainty and limited number of studies exploring indirect effects. The strength of the connection considers only the effect of mitigation and does not include benefits of avoided impacts. SDG 13 (climate action) is not listed because mitigation is being considered in terms of interactions with SDGs and not vice versa. The bars denote the strength of the connection, and do not consider the strength of the impact on the SDGs. The energy demand sector comprises behavioural responses, fuel switching and efficiency options in the transport, industry and building sector as well as carbon capture options in the industry sector. Options assessed in the energy supply sector comprise biomass and non-biomass renewables, nuclear, carbon capture and storage (CCS) with bioenergy, and CCS with fossil fuels. Options in the land sector comprise agricultural and forest options, sustainable diets and reduced food waste, soil sequestration, livestock and manure management, reduced deforestation, afforestation and reforestation, and responsible sourcing. In addition to this figure, options in the ocean sector are discussed in the underlying report. 5.4, Table 5.2, Figure 5.2
Information about the net impacts of mitigation on sustainable development in 1.5C pathways is available only for a limited number of SDGs and mitigation options. Only a limited number of studies have assessed the benefits of avoided climate change impacts of 1.5C pathways for the SDGs, and the co-effects of adaptation for mitigation and the SDGs. The assessment of the indicative mitigation potentials in Figure SPM.4 is a step further from AR5 towards a more comprehensive and integrated assessment in the future.
D.6.3. Pathways that are consistent with sustainable development show fewer mitigation and adaptation challenges and are associated with lower mitigation costs. The large majority of modelling studies could not construct pathways characterized by lack of international cooperation, inequality and poverty that were able to limit global warming to 1.5C. (high confidence) 2.3.1, 2.5.1, 2.5.3, 5.5.2
After Hansen's testimony, other groups of scientists started to study what might constitute "catastrophic climate change," and many papers used 1 or 2 degrees Celsius as reference points to model what might happen to the Earth at different levels of warming. In the early 90s, governments began coming together to discuss ways to stave off climate change, and informed by these studies, world leaders often referenced either 2 or 1.5 C during their discussions.
Risk analysis carried out in this study using sWBGT finds hundreds of millions of people to be additionally affected by heat stress at each (successively higher) warming level. This result is consistent in magnitude with other recent studies, such as Matthews et al. (2017) who project 350 million more megacity region inhabitants to be exposed to deadly heat by 2050 for an end of century warming level of 1.5 C. Andrews et al. (2018) also project hundreds of millions of people to be exposed to extreme heat for warming levels of 1.5 C and above. As has been shown in other multi-sectoral impacts, studies which include humid heat metrics (e.g. Byers et al. 2018) projected heat exposure is most pronounced in the tropics, and as such, we identify benefits of reduced exposure associated with limiting warming in low-latitude regions. Our study focuses on applying targeted climate scenario data to calculate global (combined urban and rural) population heat stress using the sWBGT metric. While sWBGT can produce an overestimate of heat exposure risk during cloudy or windy conditions and vice versa, Willett and Sherwood (2012) argue that changes in solar radiation and wind speed are unlikely to impact significantly on global patterns. Population impacts of exposure to heat stress will depend on the activity of the person concerned and the choices that they make.
We estimate a continuous increase in global drought risk and find hundreds of millions of people to be additionally affected by drought at each (successively higher) warming level, which is well aligned with previous research (Prudhomme et al. 2014; Smirnov et al. 2016; Lehner et al. 2017; Arnell et al. 2018; Naumann et al. 2018; Liu et al. 2018). A direct comparison in terms of affected people and impact avoided with any of these studies, however, is complicated due to the use of different drought indices, metrics and population data. For example, Naumann et al. (2018) also use the SPEI-12, but they use event length and magnitude as a measure of impact, while Smironv et al. (2016) use the SPEI-24 but calculate the number of people affected. This shows that more studies are needed that use sets of indices, metrics and data that intersect with already existing ones to enable a better comparison. Other studies are based on SPI which is easier to calculate, but by definition excludes evapotranspiration meaning that the drought estimation is conservative as it fails to allow for the important role of temperature rise in contributing to potential evapotranspiration.
The additional exposure to fluvial flooding risk (Figure 2) is mostly evident in West Africa, India and parts of central East Africa, aligning with the identification of hotspots with multi-sector risk in West Africa and South Asia. Our study also shows that large areas of inhabitants in sub-Saharan Africa and southern Asia would be exposed to Q100 floods at the higher degree warming. Thus, our findings agree mostly with previous studies such as Piontek et al. (2014), Gosling and Arnell (2016) and Byers et al. (2018) who projected that the poor and vulnerable populations in Africa and southern Asia would be disproportionately impacted by multi-sectors impacts.
The constantly reducing crop yields we obtain under increasing global temperature align well with results in the existing literature using both process-based models and statistical models (see SM), although compared to these studies, our results appear to be conservative. Projections for regional changes in crop yields are consistent with a previous study (Schleussner et al. 2016) identifying Africa, SE Asia, and C&S America as hotspots for projected declines in yield and indeed for projected avoided risks if warming is limited to 1.5 C rather than 2 C (Figure 2). Equally, they are well aligned with the hotspots identified by Byers et al. (2018) and Piontek et al. (2014). Similar to Arnell et al. (2016), our results indicate that reductions of maize yield in all regions and soybean yields are projected to potentially increase in Europe, North America and Australasia but to decline in other regions. In case of wheat, we project declines in all regions, while Arnell et al. (2016) obtained mixed results. We suspect that this is due to the different types of wheat that were analysed. For rice, our projections indicate strong losses in Africa and South-East Asia but increasing yields in Europe and Australasia. Limiting global warming to 1.5 C rather than 2 C would provide benefits for most regions across the globe, particularly in the Americas, Europe and Africa (Figure 2), which is also in line with the findings of Arnell et al. (2016). Overall, our results suggest an inequality in risk of crop yield loss between the Northern and Southern hemispheres and especially tropical and non-tropical regions. The main limitation of the models used here is that they are based on unevenly spaced national data and that the area harvested was assumed to remain constant so that potential future land use change is not accounted for.