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Information on climate change impacts is abundant, particularly in relation to well known threats in Australia such as bushfires and floods. All levels of government, as well as research and training institutions, industry bodies and NGOs are involved in the production and analysis of information related to climate impacts and adaptation.

This includes information on the impacts of climate change, guidance material in the form of best practice manuals, tools, information networks, courses and workshops.

(Although we note issues of coordination and access in this regard, and refer to the current NCCARF Leading Adaptation Practices project.) Governments also have a role in developing guidance to improve the quality and consistency of information.

Production of information is often collaborative with a number of different organisations involved and funding opportunities from many different sources (see Case Study 3). Many recent initiatives led by different organisations are outlined in

“Australia’s fifth national communication on climate change” (Australian Government 2010).

While information abounds, local information on climate impacts is often lacking, is not publically available or is not used (Wenger et al. 2012). Downscaling climate models and projections has significant limitations at present. Alternatively, studies are issue or sector specific, and fail to make the links within and between sectors which is so crucial to avoid maladaptive outcomes (Hussey and Pittock 2012). For example, Foerster (2012) makes the point that it is important to acknowledge that there are trade-offs associated with decision making in managing climate risk. Using Victoria’s decision after the devastating 2009 bushfires to provide for a strong prioritisation of human safety over other concerns in planning provisions, Foerster (2012) warns that such a decision may lead to unwanted environmental externalities:

“Of particular concern is the potential for development to continue in fire-prone areas but on the condition that vegetation is cleared to mitigate fire risks. The management of fire risks through vegetation removal can lead to increased carbon emissions, biodiversity loss, and other forms of land and water degradation” (p. 333). Purely on a public safety and asset protection basis, the efficacy of both focused and broad-scale fuel reduction in such cases is contested (Gibbons et al 2012).

Climate adaptation research focuses on assessing the possible impacts of climate change; identifying vulnerable sectors or communities in society; and proposing strategies to increase our resilience to those impacts. However, three issues in this domain pose particular problems for providing accurate, policy-relevant information for decision-making. The first of these issues is the high level of uncertainty around the magnitude and location of climate impacts. Much has been written on the

uncertainties surrounding climate science, and the IPCC has dedicated much thought to how uncertainties can be accurately and consistently accounted for in the provision of climate information (IPCC 2010). More recently, Hallegatte et al. (2012) consider the challenge of “deep” uncertainty in investment decision-making, which they define as “a situation in which analysts do not know or cannot agree on (1) models that relate to key forces that shape the future, (2) probability distributions of key variables and parameters in these models, and/or (3) the value of alternative outcomes” (p.2). The authors argue that climate change is a clear example of “very deep uncertainty”, because historical weather and climate data can no longer be trusted to provide an accurate picture of the future. There are three major sources of uncertainty:

x Future emissions of greenhouse gas emissions (‘policy uncertainty’), which are linked to demographic and socio-economic evolutions, to available technologies, to values and preferences (e.g. development models) and to policies. This uncertainty is linked to scientific uncertainty (what futures are possible?), but also to a policy uncertainty, which is a positive uncertainty that represents our ability to choose our future

x Scientific uncertainty (‘epistemic uncertainty’), which is created by our imperfect knowledge of the functioning of the climate system and of affected systems. It is for instance the uncertainty on the response of the global mean temperature to a given quantity of GHGs (including “climate sensitivity” i.e. the increase in global mean temperature for a doubling of CO2 concentration in the atmosphere), but also uncertainty in the regional effects of global warming, and the uncertainty on the reaction of affected systems, such as lakes, glaciers and ecosystems

x Natural variability (‘aleatory uncertainty’), i.e. the fact that global climate variables have their own dynamics, linked to the chaotic behaviour of the climate system. Climate models provide information of statistical nature (averages, variance, likelihood to exceed thresholds etc.), but they do not provide forecasts, i.e. deterministic prediction of the future. In other terms, they can estimate the average number of rainy days in the summers of 2060s, but do not say anything about the ‘any given day’ or even any specific summer (Hallegatte et al. 2012: 6-7)

The extent to which one or other of these uncertainties is significant depends on the scale of assessment. At a global level, and over the short term, natural variability and scientific uncertainty in the models play the largest role while future GHG emissions are relatively minor. However, at a regional scale, natural variability plays a more important role, and climate model uncertainty is still large, and policy uncertainty pertaining to GHG emissions is moderately important. As Hallegatte et al. (2012: 8) explain “It shows that when looking at one country or one region, it is much more difficult to predict future climates, regardless of future progress in our understanding

of climate change: natural variability means that the climate signal is more difficult to extract”. At the local scale, “downscaling techniques” are used to predict future climate, though this technique is based on historical data which can sometimes be difficult to obtain, and even where long time series are available the technique assumes that the statistical relationship between the climate data and local climate phenomena will remain valid in a future climate (Hallegatte et al. 2012: 8). These three uncertainties combined make it all the more difficult for decision-makers to assess investments for long term climate resilience.

The second issue that is problematic in climate adaptation policy-making relates to a scale misfit between what can be provided by climate models and what is needed by decision-makers (this is more or less of a problem depending on how climate adaptation is ‘framed’, see Section 3.2). As described in the discussion above, climate models are susceptible to policy, epistemic and aleatory uncertainties which increase in magnitude the closer one gets to the ‘grass clippings’. The models are simply not capable of providing forecasts at the local level: there is a resolution of

~50km for physical downscaling and ~10km for statistical downscaling. In other words, the finer-scale the modelling is, the greater the uncertainty. The consequence of this limitation is an absence of knowledge at the scale at which decisions are made: most notably, the local scale. This is developed further in Case Studies 3, 5 and 7.

A third challenge for policy-makers concerns the myriad actors and ‘end-users’

involved in adaptation strategies. Adaptation is a nation-wide process, and decisions need to be made within all levels of government, in businesses (small, medium and large), by individuals, communities, associations, within and between whole sectors, and involving scientific and other types of experts. The multitude of actors involved makes climate adaptation as a policy problem infinitely more complex. In the first instance, and as explored in Section 3.2, all of those actors will hold several different interpretations of the meaning and purpose of ‘climate adaptation’, such that arriving at a shared understanding of the ‘problems’ and ‘solutions’ is very difficult (Fünfgeld and McEvoy 2011: 17). The large number of actors that have a ‘stake’ in climate adaptation also makes identifying, funding and disseminating information relevant to those individuals actors extremely difficult - a difficulty endured mostly by those responsible for providing climate-relevant information and analysis (governments and research agencies/institutions). The provision of information raises interesting questions. For example, to what extent is information generated within the private sector protected as a matter of competitive advantage? When climate resilient research is undertaken through tax-payer research funding should it be open-access (and thus not buried in academic publications with expensive subscription fees)? If so, how can and should that information be shared? As a matter of principle, it would seem appropriate that any research funded by the Australian tax-payer that contributes to the resilience of Australians and the Australian economy should be accessible to all, yet there are many grey areas (see Case Study 3).

There is also uncertainty over the efficacy of possible policy interventions, for example of a disaster warning system, of an education campaign on heat management in households, or of a policy assessment procedure triggered by insertion of climate adaptation consideration in statutory objects (Dovers and Hezri 2010).

Combined, the three challenges of uncertainty, scale and ‘audience’ render policy-making for climate adaptation a somewhat nebulous task and readily prone to politicisation. Ultimately any form of uncertainty adds to the likelihood that decisions, decision-making processes and the data informing these two will be contested.

Contestation in the climate change arena has been subject to considerable analysis over the past two decades, much of it related to identifying and understanding the implications of the dominant discourses that influence how problems are perceived (if perceived as problems at all) and hence how they should be resolved (and specifically through what actions) (see Dryzek 1997 and Heazle 2010 for example).

Suffice to say here that contestation is essentially a political process in terms of what different stakeholders define as important, how politicians choose, set and promote particular agendas and how they then act with bureaucrats to allocate resources to implement agenda-laden policy in an environment where stakeholders’ competing interests may not be resolved but become latent (i.e. subjected to domination by political elites and acquiescence in that domination) (Lukes 2005).

Uncertainty and contestation are not only problematic for defining policy objectively, they also impede clarity around questions like “How much adaptation is enough?”

and “What are the indicators of success?” These are questions that keep bureaucrats awake at night as if consensus on these can be reached through traditional positivist analysis; yet these questions are normative and context specific.

The dimensions of success are therefore diverse (i.e. in terms of economic, ecological, social and institutional outcomes) and can be conflicting (Moser and Boykoff 2013). Resolution to these questions is, once again, ultimately a political process.

Notwithstanding the contested nature of climate change policy, as the next section reveals, climate adaptation as a policy problem can be more - or less - complicated, depending on how it is framed.