Making decisions in the light of uncertainty from global climate models

 

Models are important in all sciences. They are created to synthesize knowledge and quantify effects through mathematical equations. Global climate models are created based on the fundamental laws of physics, as they attempt to translate natural cycles of the earth into mathematical models that are able to depict a generalized image of future changes. The extent of accuracy of these models, however, are still in question. As global climate models grossly generalize natural cycles and fail to draw connections among the many existing relationships between natural processes, different climate models with different assumptions generate varying results, sometimes these results are even contradictory. Another limitation of climate models is that they are unable to generate reliable results on a small scale. While the use of local meteorological data to create regional climate models is possible, downscaling climate models does not increase the level of accuracy of these depictions. For the case of Nepal, modeling is particularly difficult due to the complex topography of the country, with elevations ranging from 0 – 8000 meters within an extremely small area. Given the inability to model topographical changes in such an area, GCMs generate results with high uncertainty for Nepal.

 

Figure 1. The uncertainty in designing adaptation responses using climate models

 

 

 

 

Although climate models are constantly being improved by scientists, including more intricate natural processes, along with increasing levels of computational power, the results that they generate will not provide with a high enough degree of confidence to allow precise adaptation decision-making. As we have seen in the IDS Nepal report, predictions for precipitation change range from -30% to +100% in annual rainfall, and temperature increases range from +2 C to +6 C based on different emissions scenarios. Does this mean that climate models are should not be incorporated into any decision-making processes relating to mitigation and adaptation responses due to its high level of uncertainty? Absolutely not. While small-scale and short-term predictions have high levels of uncertainty, climate models are able to predict a general view of the long-run. Given such uncertainty about the future, the approach of policymakers should not be focused on specific adaptation plans for a specific climate scenario, but more of a risk-prevention approach for a wide range of scenarios that may happen. As the cost of prevention is always lower than the cost of addressing the problem once it has happened, the risk minimization approach will help policymakers design effective generalized mitigation plans that decrease the vulnerability of communities to climate change. These policies or projects should focus on increasing the adaptive capacity of communities, as well as their livelihoods, to make them less insecure in the light of possible shocks induced by climate change. For example, while the degree of sea-level rise is still uncertain, the predictions generated by GCMs have shown that sea levels will rise. Instead of debating on whether the science of these models is sound, policymakers should focus on increasing the resiliency of coastal communities by creating buffer zones between the shore and residential areas. As GCMs predict the general trend that the Indian Monsoon is changing in the future, resulting in more intense rainfall and longer dry spells in certain areas of Nepal, policy design should focus on increasing human security, especially food and water security, of communities susceptible to climate shocks. Thus, climate models should not be taken with a grain of salt, but they can be one of the many tools used to inform policy-makers on designing effective adaptation strategies. Given such uncertainty about the future, a generalized risk-prevention approach, where policies are implemented to increase the adaptability of vulnerable communities for a wide-range of conditions, should be considered.

 

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