S IMULATING THE IMPACTS OF CLIMATE CHANGE

While the use of climate data may be of use to develop a broad understanding of possible ways in which a system may be affected under a changing climate, the question that really needs to be answered is: how will my system be affected by these changes in climate, and thus the ability to meet business objectives? In other words, we need to understand our system sensitivity to changes in the key driving variables, both climate and non-climate. If we have an accurate understanding of our system sensitivity then this provides powerful information which we can use to inform the development of adaptation strategies and actions.

As described in chapter 3, to obtain this system understanding we need a causal model, which describes either quantitatively, or qualitatively, the relationships and inter-relationships between the driving variables that are relevant to the functioning of a given system. This section provides a discussion of some of the main methods and tools that can be used to develop an understanding of how a given system functions.

4.5.1 Impact models

A powerful way of obtaining more detailed information on the way in which a given climate sensitive system may respond to changes in climate is through the use of environmental modelling tools, which are sometimes also referred to as climate impact models (Challinor et al. 2009). These models may be developed on the basis of empirical relationships (statistical models), or an understanding of fundamental physical processes (process models), and are

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used to model various environmental systems and economic sectors, e.g. water, agriculture, forestry, coastal systems, energy, food. Impact models will typically be driven by climate variables, and other system relevant variables, and may offer some scope for the inclusion of socio-economic variables. These models may be used to perform sensitivity analyses to try and understand better the way in which a given system responds to climate, and should ideally be able to simulate or integrate the action of adaptation actions or strategies on the system function (Lempert & Groves 2010). These kinds of models may already be used operationally in the water and energy sectors, for example.

Figure 4.4 The effect of changes in temperature distribution on extremes. Different changes in temperature distributions between present and future climate and their effects on extreme values of the distributions: a) effects of a simple shift of the entire distribution toward a warmer climate; b) effects of an increased temperature variability with no shift of the mean;

and c) effects of an altered shape of the distribution, in this example an increased asymmetry toward the hotter part of the distribution. Source: Lavell et al. (2012).

4.5.2 Model sensitivity analysis

A model sensitivity analysis investigates the way in which the model output responds to variation in the model inputs, and can be used to determine the relative importance of the

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model parameters in driving variation in the model output (Saltelli et al. 2005). For example, a crop growth model could be used to investigate the effect of temperature on crop yields.

If we have a model of a system, it will be possible to conduct sensitivity experiments, where we explore a wide range of variation in the climate and non-climate parameters, and analyse how the system (model) responds. This could be done with modelled climate data, or a less resource intensive approach would be to generate synthetic climate data.

Sensitivity analysis can be used to help identify those factors which have most influence on the functioning of a system, and thus can be very instructive in the search for adaptation actions or strategies (Dessai & Hulme 2007). Moreover, it may be possible to explore the efficacy of potential adaptation actions across a large range of possible futures, using a sensitivity analysis. This would serve to highlight where, or under what conditions, a given adaptation strategy may be sub-optimal or less robust. This kind of information would be of great value in the process of selecting adaptation strategies.

Model sensitivity analysis provides a powerful means of learning about the functioning of a system, and is less resource intensive than generating a large climate model ensemble. The results of a model sensitivity analysis can also be used to generate what are known as impact response surfaces.

4.5.3 Impact response surfaces

Model sensitivity analyses can lead to the identification of a set of model parameters which are most influential in driving the system response. Using this approach it may be possible to generate a functional relationship between a small set of model parameters, and the system response (Fronzek et al. 2010). In so doing, it is possible to generate what is known as a climate impact response surface, which represents this functional relationship. This system response could be a threshold value of high relevance to a given business objective.

For example, it may be necessary to have a certain water level in a river to allow transportation of manufactured goods. An impact response surface could be used to investigate how often in the future this threshold level would be met, and thus help support decisions in relation to developing adaptation measures. An example of an impact response surface is shown in figure 4.5.

This method provides a rapid and quickly updatable and reusable tool for understanding the effects of changes in climate on the system response. As new climate data sets become available they can simply be plotted over the surface (assuming no change in the functional relationship) and analyse if anything has changed. This attractive feature of reusability, and being easily updatable as new climate information becomes available or as business objectives change, means that this approach may represent a good return on any investment made to develop such tools, and is easily incorporated and used as part of an iterative review process of climate risk management.

These response surfaces serve to highlight visually when business objectives may be vulnerable, or alternatively where a particular opportunity may become worth exploiting.

Challenges arise when this response surface cannot be defined with less than four parameters, because one of the major attractions of response surfaces is the rapid assessment that visualisation provides. With more than three parameters visualisation becomes difficult.

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As such, this approach is most suitable for systems whose response is driven very strongly by a few climate variables (Prudhomme et al. 2010). Also, the accuracy of these surfaces compared to a full modelling approach should be assessed and reported. Also consideration of the uncertainty in the modelled response itself needs to be considered in the sense that a different impact model may give a different surface.

Figure 4.5 An example impact response surface for Lake Mälaren in Sweden. Diagonal black lines are the likelihood in percent of summer water level being below the target operating threshold for a consecutive period of 50 days for the change in summer temperature and precipitation. Climate projections for the time period 2031-2050 are shown as probability density plots in the coloured area, which encloses approximately 90% of all projected outcomes. The coloured dots are projections from regional climate models.

Clearly in this example there is a high likelihood, based on the approach taken, that this threshold will be under threat, and they would need to find ways to adapt. Source: van der Linden & Mitchell (2009).