The examples discussed above show that PEA statements are made with respect to a counterfactual situation, the definition of the counterfactual is however different in each case leading to differing results. Furthermore these results are valid for a particular definition of the extreme event in question. Below we will explore the potential implications of these issues for policy, and then discuss the potential to promote science-policy dialogue to frame attribution questions.
Defining the counterfactual
Figure 1 gives very different results for the increase in risk depending on whether an annual or decadal approach is used. Based on a single year it answers the question “given all other conditions being equal, how has the risk of occurrence of such an extreme event changed as a result of anthropogenic emissions”? Comparing whole decades instead of single years smoothes the interannual variability of large-scale oscillations in the SSTs, thus answering the question “given all predictable (long term) things being equal, how has the risk changed due to the global mean temperature increase and increase in GHG forcing?”. The former approach addresses the event conditioned on the SSTs, and the latter analyses the climatological shift. Another approach to PEA, which has so far not been employed with large enough ensembles to attribute extreme events but is probably the most promising, is to use SSTs from seasonal forecasts instead of observed SSTs for the season of interest. This will provide an answer to the question “given all predictable (short term) conditions being equal, how has the risk of occurrence of such an extreme event changed as a result of anthropogenic emissions”? The latter approach will eliminate the unpredictable noise for the given year from the assessment of changes in risk.
It is worth noting that if based on temperatures in a temperate climate, e.g., the UK, Fig. 1 would look very different (see Otto et al. 2013, Fig. 5). In particular the discrepancy between the return times for a single year and those for a whole decade would be much smaller. This suggests as a rule of thumb that in any given summer the daily variability of night time temperatures is much higher in a UK climate compared to East Africa, the interannual variability is much smaller and only weakly correlated to large scale teleconnection systems like ENSO or the Atlantic Meridional Oscillation (AMO). Thus the importance of clearly stating how the research question is framed exactly becomes again particulaly apparent in the sub-tropical and tropical climates of Africa.
For an assessment of the anthropogenic influence on African climate overall, the use of decadal or longer simulations has the potential to better quantify the overall changes in risk, which is relevant for long term adaptation planning. However single year simulations (either with observed or seasonal forecast SSTs) will give a better assessment of how anthropogenic climate change altered the risk of specific events occurring. This allows us to make use of observed responses to extremes to help plan the adaptation to anthropogenic changes. The new approach using seasonal forecast SSTs may be particularly advantageous here, as estimating the predictable change in risk instead of the actual will be much easier to communicate.
Thus from an decision-making point of view we are looking at two different problems: the first is assessment of a specific observed event which gives guidance on how to build resilience to more or less of given this event and its impacts and responses; the second is an assessment of climatological shifts which could be used to provide more general guidance on adaptation responses. This distinction refers only to using the information to build resilience to future risks, which is the dominant focus in the UNFCCC processes on adaptation, including the recent negotiations on loss and damage (UNFCCC 2012). However, the annual approach, providing information about specific events, could also provide information about the causes of past events, with potential applications for liability and compensation (Allen 2003). This could be highly controversial (James et al. 2014), and at the time of writing the concrete aims and implementation of the WIM are undetermined. Scientists are acutely aware that simply providing information may have unpredictable consequences, including diverting attention away from building resilience towards a “search for the guilty” (Hulme et al. 2011). ‘The appropriate response is for science-policy dialogue to understand how to effectively blend and use scientific and local knowledge. The interaction between hazards and evolving vulnerability, including how vulnerability may change as stakeholders respond to new information, is critical in determining overall risk.
It is paramount that the scientific community communicates the exact framing of the research question they aim to answer, but it is also important to identify the questions decision-makers and stakeholders want and need answers to.
Defining an event
In Fig. 1 the attributable risk of exceeding 19 °C is much larger than the attributable risk of exceeding 18.5 °C. The inferred influence of climate change is different depending on which arbitrary threshold is chosen.Footnote 2
If these studies are to be useful beyond academic interest, the definition of the event, and the dependence of the results on the choice of threshold must be made explicit. This leads to questions about which risks really matter for practical decisions. Is a 1 in 100 year event or a 1 in 1000 year event more important? This is likely to depend on the stakeholder and the application. Whilst the most rare, high magnitude events might receive more attention in the media, in developing countries less rare events can still lead to large scale damages. It is likely that the questions which information is needed to answer will depend on spatial and temporal factors relating to the vulnerability of people, such as where they are located geographically. The answers will also be determined by people’s existing experience of extreme events, and by social dimensions, e.g., gender, ethnicity and age (Blaikie et al. 1994).
Furthermore, the meteorological extreme event is only the first step in a potentially multi-step assessment of loss and damage due to anthropogenic climate change, ranging e.g., from attributing large scale atmospheric dynamics, over precipitation above a threshold, to river flow and finally inundated crop land or properties.
Promoting science-policy dialogue
Framing research questions for attribution studies which are useful in an applied context is therefore not something scientists can do alone. Nor can stakeholders embark on this without an understanding of the science. A dialogue between science and policy is required should PEA provide scientific evidence for questions of adaptation and loss and damage. Co-production of knowledge occurs when scientists and stakeholders come together to generate new knowledge and technologies jointly through processes of learning. Co-production works when there are clearly defined boundaries and the means to overcome asymmetric power dynamics (Jasanoff 2006). To initiate this process it is important to build on existing mechanisms (e.g., participatory action and scenario development planning), engage with novel approaches (e.g., serious gaming, Mendler de Suarez et al. 2012) and to develop new platforms such as Rainwatch-AfClix.Footnote 3 Each of these different “mechanisms” presents opportunities to discuss PEA in an African context and initiate a dialogue.
The dialogue might be most productive if links are made between policy communities in disaster risk reduction and climate change adaptation. This need for broadening sectorial engagement is demonstrated by the SREX report (Field et al. 2012), which calls for “new balance…to be struck between taking measures to reduce risk, transfer risk (e.g., through insurance) and effectively prepare for and manage the impacts of disasters in a changing climate. This balance will require a stronger emphasis on anticipation and risk reduction (Mitchell and van Aalst 2011). Understanding the evidence from attribution studies of extreme events relative to attribution of long term changes in climate could be important in shaping progress. A poor understanding of the links between hazards, climate change and vulnerability may lead to scarce resources being allocated to the wrong actions today based on a poor understanding of climate-related adverse impacts (e.g., devoting resources ‘only’ to climatological hazard as opposed to investing in development, e.g., education, health etc.), then that could leave future generations worse off (UNFCCC 2012). It is vital that policy-makers understand the uncertainties in the evidence that is available. It might be that many stakeholders will take the view that simulated evidence, with current relatively low-resolution climate models, is not robust enough in the context of tools and information relevant to adaptation and loss and damage. This need to be clearly communicated as it means that attribution results of extreme weather events would not be usable to address damages from events other than the so-called slow-onset events such as sea-level rise for at least another decade when high resolution models may be available to be used for running large ensembles. Although such slow-onset events in context of the loss and damage agenda are highly predictable events, they cannot include other comparable but unpredictable slowly evolving events like multi-month droughts. While concentrating on predictable slow-onset events in developing countries for consideration under “loss and damage” might be good news for the Maldives, this outcome is likely to raise serious problems for land-locked African countries assuming that impacts of human-induced climate change are addressed.
Another important issue is how decision frameworks are determined by relationships between different actor groups and governments, which are in-turn often influenced by power dynamics and asymmetry of information (i.e., moral hazard). Clearly a blueprint “one fits all” approach might not be desirable. The discussion needs to start now; to find out which questions the scientific community should aim to answer if scientific evidence is thought in determining the pertinent climate risks.