Multi-model ensemble mean of projected changes in December, January and February (DJF) and June, July and August (JJA) surface air temperature for the period 2016–2035 relative to 1986–2005 under RCP4.5 scenario (left panels). RCP4.5 refers to representative concentration pathway 4.5, one of four greenhouse gas atmospheric concentration trajectories that are used by the IPCC. It is the second most conservative of the four trajectories): 2016–2035. The right panels show an estimate of the model-estimated internal variability (standard deviation of 20-year means). Hatching in left-hand panels indicates areas where projected changes are small compared to the internal variability (i.e., smaller than one standard deviation of estimated internal variability), and stippling indicates regions where the multi-model mean projections deviate significantly from the simulated 1986–2005 period (by at least two standard deviations of internal variability) and where at least 90 % of the models agree on the sign of change. The number of models considered in the analysis is listed in the top-right portion of the panels; from each model one ensemble member is used
Turning to the second challenge, how does this and other, more complicated, modelling of the future climate relate to lived experience? The answer is, not a lot, for reasons ranging from difficulties in communicating the complexity to the public to the public not wishing to engage, which we discuss in Chap. 7. This issue extends, moreover, beyond the physical science basis as many contemporary modellers attempt to predict the human impacts of climate change in relation to vulnerability, risk and resources such as water, food and agriculture. These human impacts potentially link directly with lived experience, and there is little doubt that such models provide useful and often essential information, but public engagement remains limited.
Climate change problems are multi-scale and, as Cash and Moser (2000) show, such challenges require a linking of science and policy across scales. They thus argue for a recognition of scale and cross-scale dynamics in understanding and addressing global environmental change. This argument can be carried over to a human scale of lived experience in order to communicate more effectively between the more general and bigger models and the local scale of climate change. Again, Chap. 7 explores further these issues.
So what is the point of deciphering this maze of lived experience, particularly as unlike ‘pure’ scientific knowledge we cannot easily offer numbers and statistical analysis of ‘lived experience’? Even if we attempt to codify it and create patterns using non-quantitative means, there is little doubt that there will be immense methodological problems (see Sect. 4.5.1). This has been shown in other subject studies where lived experiences have played a critical role in understanding issues, but have not always been logically acted upon being clouded in emotions. One example is domestic violence, a very personal and emotive subject that defies methodological logic (see for instance, Pratt-Eriksson et al’s (2014) study which attempts to incorporate Ricouer’s (2005) use of narratives to record lived experiences).
We can thus only hope to draw generalisations. Yet, it is important to narrate and generalise lived experience of climate change for three reasons, to: (i) contribute to an inclusive definition of climate change that considers it a social phenomenon as well as a physical one (Chap. 1, Sect. 1.2); (ii) understand the diversity of perspectives and interests on the challenge and why citizens, communities and countries respond as they do; (iii) shape public policy that is seen as legitimate by citizens. Insights provided can only add to existing knowledge on climate change and influence policy and practice in an inclusive way.
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