10 Sep Assessing and forecasting of climate change risks and vulnerabilities to regional security, businesses and industries, and sustainable development
Climate change is a major challenge for society, particularly in terms of its capacity to take individual and collective decisions that will enable appropriate responses to address it. In many respects, it differs from other environmental problems facing modern civilization in its time scale and in its com-plex relationship between human activities, the embedded societal structures and interactions that are emerging between different environmental systems. Climate change is leading to cascade-like risks in technical installations, ecological systems, the economy and society, all of which are often interlinked and create the conditions for irreversible and unwanted exceeding of threshold values at various levels. Forecasting climate risks across sectors and in a way that is meaningful to decision-makers thus represents a major scientific challenge. Data mining and big data analytics is seen as very promising in terms of predicting risks associated with climate change.
The Intergovernmental Panel on Climate Change (IPCC) has described the benefits of a risk-based approach to better understand both the dynamic interactions of spatial and temporal determinants leading to specific impacts of climate change and the role of adaptation initiatives in managing corresponding risks. The most fundamental component for the analysis and prediction of climate risks is verifiable, up-to-date and comparable data and relevant modelling. Conventional approaches to risk forecasting and assessment are challenged by the substantial temporal and geospatial dynamics of climate change, by the enhancement of risks by certain societal settings and by the interaction of several risk factors. However, today big data from climate model simulations is increasingly being used to predict future trends in climate change and to assess the associated risks. There are various ways in which big data elements could contribute to improving the modelling of climate risks and impacts. New forms of data could be useful for the calibration of risk models and crowdsourcing and crowdsensing data collected for a specific purpose could be useful, as the assumption of constancy can be justified by reference to the user base. To manage their responses, stakeholders and policy makers need to forecast the potential local risk impacts of climate change at the county-to-city level. Part of this information could be derived by combining fine-grained climate risk assessments with AI-based big data analytics of weather extremes, property damage, health impairment and other variables. A number of big data analytics-based tools for screening climate risks are currently being developed, including the World Bank’s “Climate and Disaster Risk Screening Tool”, and many institutions are using them to better understand climate risk in their decision-making.
The primary challenge in forecasting the risks of global climate change is clearly the complexity and myriad of interacting factors. Each incremental change in greenhouse emissions and temperature gives rise to different responses in climatological, ecological, hydrological and other biophysical systems, varying from short term impacts on primary productivity to longer term effects such as rising sea levels, degradation or land formation , whereby the coupling of systems can lead to reactions that affect other systems, including feedback effects on climate. Some recent studies emphasise the changing nature of the three components of risk (hazard, exposure and vulnerability) and point to the need for the development of coherent guidance on strategies and methodologies that better account for the dynamic nature of the individual risk components and their interaction. This is particularly important since climate risks are not only a function of physical processes and shifting characteristics of climate systems but are also shaped by complex interactions with socio-economic drivers that can change and evolve within the macroscale conditions and may also change norms and values. With the driving forces and physical consequences of climate change being better known, researchers and practitioners are increasingly turning their focus on analysing these socio-economic drivers of climate change. This is also evident in a range of new data-based approaches that are currently being integrated into ongoing climate change risk assessment and management processes, considering the introduction of integrated models (i.e. Bayesian networks, agent-based models, artificial neural networks and expert systems) that are capable of incorporating multiple stressors and endpoints (i.e. socio-economic and environmental objectives and priorities), dealing with uncertainties and taking into account the impact of policy and adaptation in changing end states of the system.