Supplementary Material
Climate Warming and Natural Disaster Management:
An Exploration of the Issues
By
D. Etkin, J. Medalye, K. Higuchi
David Etkin (corresponding author)
Disaster and Emergency Management
Faculty of Liberal Arts and Professional Studies
York University
4700 Keele St.
Toronto, Ontario
Canada, M3J 1P3
J. Medalye
Faculty of Political Science
York University
K. Higuchi
Adaptation and Impacts Research Division
Environment Canada
Abstract
This paper explores two issues that have been receiving increasing attention in recent decades, climate changeadaptation and natural disaster risk reduction. An examination of the similarities and differences between them reveals important linkages but also significant differences, including the spectrum of threats, time and spatial scales, the importance of local versus global processes, how risks are perceived, and degree of uncertainty. Using a risk perspective to analyze these issues, preferential strategies emerge related to choices of being proactive, reactive, or emphasizing risk management as opposed to the precautionary principle. The policy implications of this analysis are then explored, using Canada as a case study.
Key Words:
Climate change adaptation, disaster risk reduction, precautionary principle
Uncertainties in the projection of future climatic regimes
The climate system is a highly nonlinear chaotic dynamical-biogeochemical system composed of a huge number of interactive feedback processes (both negative and positive), resulting in an almost infinite number of degrees of freedom. At the present time there really is no comprehensive theory of climate. Instead, our understanding of the climate system (and thus our climate science) is based on models of climate partially constructed from various knowledge bases obtained partially from theoretical considerations, but also from empirical relationships. These empirical relationships result in formulations of various parameterizations that represent processes not resolved explicitly by the models, as well as processes we do not yet fully understand. Thus, any predictions of the evolution of possible climate states over the next 30-50 years are characterized by large uncertainties. These uncertainties increase as physical scale decreases, and are a crucial part of any risk assessment (Intergovernmental Panel on Climate Change-WG1, 2007).
Uncertainties in the prediction of future climate states have sources that can be broadly categorized into three classes: (1) an incomplete data base and knowledge of climate system theory, (2) the fundamental chaotic nature of the climate system that limits what we are able to predict at a theoretical level, and (3) human behavior and decision-making. In the first case, uncertainties can be reduced by further research to improve our state of knowledge of the system. (Intergovernmental Panel on Climate Change-WG1 2007) For example, an improved understanding of how cloud droplets of different sizes are formed is needed. Also required is a better understanding of how aerosols of different sizes and shapes influence cloud formation, as well as how they interact radiatively. Another important aspect of the climate system we have little understanding of is precipitation (as part of the global hydrological cycle), since it determines how much water vapour remains in the atmosphere (water vapour is the most significant greenhouse gas in the atmosphere, contributing about 60-70% to the natural greenhouse effect, and a slight variation in the amount can have a major impact on the radiative balance of the atmosphere (Mathez, 2009). This understanding of how the climate system operates can be increased with further study, thus reducing uncertainties originating from this source.
The second source of uncertainty is associated with the fundamental nature of the climate system. The climate system is a highly nonlinear chaotic dynamical system, with its time evolution sensitive to precise initial conditions (Lorenz, 1963). Even if every feedback process is included in climate models there would still exist fundamental, irreducible uncertainties in the way the climate system will evolve, because it is impossible to precisely know initial conditions; measurements cannot be made with infinite precision. A slight variation in the initial condition will send the climate model into a different temporal trajectory. This limitation in knowledge and predictability is somewhat analogous to the Heisenberg uncertainty principle in quantum mechanics[1] (of course, measurements of the global climate system are coarse, exacerbating this problem). Another important property of complex systems is the possibility of surprising outcomes. One example of this is ozone depletion in the stratosphere, an event that was not predicted but that had enormous potential consequences. There are several known mechanisms that could potentially “flip” the climate system into a new state including melting of ice sheets, slippage of the Antarctic Ice Sheet into the ocean and a shutdown of the ocean conveyer belt. These events do not appear likely within the near future but their probability is difficult to evaluate[2] (Russill and Nyssa, 2009; Meinshausen et al., 2009; Allen et al., 2009). The second source of uncertainty will always remain and cannot be eliminated since it is a fundamental property of the system.
The third source of uncertainty derives from human actions and reactions to climate change (Dessai and Hulme, 2004), such as how much greenhouse gas will be emitted into the atmosphere in the future. Climate models are used to produce different climate change scenarios due to specified future greenhouse gas emissions; political and social decisions as well as technological developments will largely determine how society reacts. It is extremely difficult (perhaps impossible) to predict how human societies will react to climate change risk. Thus, we will always have uncertainties associated with climate predictions.
Climate scientists generally agree that they have a high degree of confidence in the ability of their global climate models to simulate the present state of the globally-averaged climate (such as global mean surface temperature and precipitation), as well as in the models’ ability to forecast globally-averaged mean climate over the next 30-50 years due to scenarios of prescribed emissions of anthropogenic greenhouse gases (Intergovernmental Panel on Climate Change -WG1, 2007). But these same levels of confidence do not extend to regional or local spatial scales. Indeed, one can almost say that there is very little “faith” in the climate forecasts at smaller levels. However, it is at these spatial scales that individuals and human communities act (either proactively or reactively) to adapt to climate change (Intergovernmental Panel on Climate Change-WG2, 2007).
Although one may not have “faith” in the quantitative forecast of regional climate change by the climate models (i.e., in the actual magnitude of change), one can have a higher level of confidence in the qualitative trend of climate change (i.e., is it getting warmer or cooler, and if so, roughly by how much?). On a regional level there is significantly more disagreement among the climate modelsin simulating the present climate, as compared to global averages, and more so in the forecasts of climate change. The resulting wide range of possible future regional climates means that effective adaptation strategies must be flexible.
Modeling Risk
Risk models have been used to analyze potential impacts and inform decision makersin both the climate change (for example, Williamson et al., 2011;ISDR, 2008) and disaster management communities (Blaikie et al., 1994). Historically the two issues were not been strongly linked, though this is happening with increased frequency (Australian Greenhouse Office, 2005; International Strategy for Disaster Reduction[3]). It should be noted that the word “risk” has often been used differently in the two communities. In much of the climate change literature it refers to the likelihood of some negative outcome – sometimes a physical event that has the potential to cause harm (e.g. the risk of sea level rise or heat waves) and sometimes an environmental or social impact (e.g. extinction risk or risk of increased deaths). Within the disaster management community the most common interpretation of risk is as a potentially harmful event that is a function of both vulnerability (V) and hazard (H) (i.e. R=VxH). This risk definition is discussed at length in Wisner et al. (2004) and is used in their Pressure and Release (PAR) Model of disaster. This model portrays disaster as an event that is damaging to society as a result of the complex interaction between hazard and vulnerability. Vulnerability is portrayed as a multifaceted dynamic process beginning with root causes, which then create dynamic pressures that result in unsafe conditions. Hazard is portrayed in a simpler manner, as a trigger that exposes vulnerability. These notions have been adopted institutionally; the UN International Strategy on Disaster Reduction (ISDR) uses the phrase “disaster risk reduction” in their official terminology[4] and makes use of the PAR model (UN, 2006).
Figure 1 (Etkin, 2009) presents a modified version of the PAR model, which will be used to illustrate linkages between the climate change and disaster management issues. This modified model makes significant changes to the original model; specifically (a) hazard is portrayed as having a level of dynamic complexity similar to vulnerability, and (b) hazard and vulnerability are linked through box 1 (endogenous root causes). An example of a common root cause is “economic systems and development”, which can result in environmental degradation that makes many hazards worse, but that also creates systems of critical infrastructure that lack resilience when short term profit-making is emphasized. This link allows the model to include climate change (Box 4: Chains of Cause & Effect) as a dynamic factor that affects many hazards, but that also links it to some of the root causes that also influence disaster risk. Exogenous causes that are natural in origin are separated into a separate box, since climate changes as a result of both human and natural causes (Intergovernmental Panel on Climate Change -WG1, 2007). The “Pressure” part of the model is shown by the direction of the arrows in Figure 1 where hazard triggers vulnerability, resulting in a damaging event that may be considered a disaster, depending upon a variety of factors including cultural filters. Cultural filters are important because of the different importance given to events by different communities. The “Release” part of the PAR model comes into play if the arrows related to cause and effect reverse direction; by reducing either hazard or vulnerability the “pressure” in the system is lessened. This is where strategies related to disaster management, adaptation and climate change mitigation come into play. This model can also be used to show how climate change fits into a disaster risk perspective – thus its relevance to the current discussion.
Figure 1: Modified Pressure and Release (PAR) Model (Etkin, 2009)
Disaster management is essentially the formal and informal structures, processes and sets of actions taken by society to reduce risk over a large range of scales. Typically “comprehensive disaster management” is conceptually framed within four[5] “pillars”, these being mitigation, preparedness, response and recovery (Canton, 2007). The term mitigation can be somewhat confusing – within the climate change community it refers to the reduction of greenhouse gas emissions, while in the disaster management community it refers to adaptive actions taken prior to a disastrous event that are intended to reduce risk; examples include land-use planning (non-structural) and dykes (structural). With respect to Figure 1, disaster management could address any of the boxes 1 through 5. Characteristically, however, society has not dealt with root causes such as gaps in wealth and access to power (which can be extraordinarily difficult to address) but rather has focused either on engineering solutions designed to control hazards (such as through the use of dykes and dams, or cloud seeding) or the creation and enforcement of controls to make “unsafe conditions” less unsafe (such as through the use of building codes and flood proofing; Mileti, 1999). For this reason it is often suggested that overall, social vulnerability is increasing (e.g. Slovic, 2000). In the past, issues in disaster management related to climate change have traditionally been ignored in risk assessments, though there were exceptions such as the effect of climate change on permafrost and mine tailings ponds (EBA, 2004). Increasingly it is being included in risk assessments.
Though Figure 1 is presented from a macro perspective, it can and should be used over a large range of scales, with smaller scale interactions being nested within larger ones (Figure 2)[6]. Figure 2 illustrates one of the important comparisons between disaster management and climate change. Reducing greenhouse gas emissions tends to be a process that begins at international levels (e.g. Kyoto Protocol) and works its way downward to smaller scales (Schipper and Pelling, 2006), though it must be noted that the actions of individuals, communities and cities are cumulatively very important. Adapting to climate change, by contrast, primarily involves local decisions since it is at that scale that hazards and community characteristics play a pivotal role. Similarly disaster management, particularly in the response phase, begins at smaller scales with larger entities gaining authority as smaller communities are progressively overwhelmed. For example, one of the principles of emergency management used by the Canadian Government is that if individuals are unable to cope, governments respond progressively, as their capabilities and resources are needed (it must be acknowledged that the above is something of an oversimplification, since many disaster management strategies occur at national scales). This has implications for institutions designed to address these risks, in terms of the scales they need to operate in.
Figure 2: Scales of Disaster Risk Reduction, Mitigation of Climate change and Adaptation to Climate change.
6: References
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Australian Greenhouse Office (2005). Climate change risk and vulnerability: Promoting an efficient adaptation response in Australia. Australia: Australian Government, Department of the Environment and Heritage
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Schipper, L & Pelling, M. (2006). Disaster risk, climate change and international development: scope for, and challenges to, integration. Disasters, 30(1), 19-38.
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EtkinPage 14/5/2019
[1]In the sense that predictions are probabilistic, not deterministic.
[2] Some scientists, such as Jim Hansen, believe that the system is very close to such a point - "There are tipping points in the climate system, which we are very close to, and if we pass them, the dynamics of the system take over and carry you to very large changes which are out of your control.'' (June 24, 2008, Toronto Star, Earth Near Tipping Point, Climatologist Warns, by Tim Harper)
[3]The ISDR has put together a working group on this topic that has produced a number of action oriented reports, available for download at their website Its importance is reflected in the following quote: “Over the period 1995-2004, a total of 2,500 million people were affected by disasters, with losses of 890,000 dead and US$ 570 billion costs. Most disasters (75%) are related to weather extremes” (ISDR)
[4]
[5] Increasingly prevention is being considered a fifth pillar.
[6] In this sense it is similar to the nested time and space scale cycles described by Gunderson and Holling (2002) in their discussion on adaptive cycles.