Evaluating the role of time lag between climate extremes and socioeconomic impacts

In the last decades, climate-related extremes are becoming a recurrent feature in many hotspot regions, as southern Latin America, the Mediterranean, the sub-Saharan Africa, north-eastern China, and southern Australia. Depending on the type of the extreme, reported damages of single events – often on specific sectors – are becoming available with increasing detail, but this is limited to recent years and does not apply to all countries. Consequently, to estimate the socio-economic impacts of climate change, the usual approach is to regress climate variables versus indicators as the Gross Domestic Product (GDP), resulting in annual temperature being the major or the only driver for most sectors, with heterogeneous marginal influence of precipitation. In this presentation, we focus on the benefit of including climate extremes in such models, specifically – as single or compound events – heatwaves, cold waves, rainfall extremes, droughts, and snowfall. Using daily and high-resolution climate data and sub-national socioeconomic indicators over the past decades, we explore the role of the lagged effect between climate extremes and the economic response, represented by GDP, but also other indicators as the Human Development Index. To include the climate extremes in damage functions, a parameterization for such lagged effects is needed, but its explicit modelling is not free from uncertainties, which can be very large, to the point that they might suggest excluding some extremes over specific areas. We also present – in preliminary form – an SSP-based estimation of future impacts of climate extremes, using a set of different global damage functions, also tentatively including regional aggregation for specific climate extremes.