The combination of multiple climate hazards (e.g. extreme hot-dry heat waves) that contribute to societal or environmental risk are the so-called compound extreme events. Such events are responsible for many of the most severe weather- and climate related impacts that can negatively affect different socio-economic sectors. Nevertheless, most of the on-going international activities devoted to the study of extremes focus on climate indices which describe either hot or dry events whilst there is an acknowledged need for developing more complex indicators which account for combined hot and dry conditions. Among others, this is of paramount importance for the agriculture sector, since compound events have been already found to reduce yield responses and shorten crop growth cycles in different regions of the world.
The aim of COMPOUND is therefore to develop the first high-resolution dataset of projected compound extreme indices (up to 2100) most relevant for agriculture. To do this, we will first identify a core set of multivariate indicators which best allow us to characterize the combined effect that extreme hot and dry conditions may have on crops in different regions of the world. Afterwards, based on the state-of-the-art regional climate simulations provided by the CORDEX-CORE initiative, we will project the future evolution of these compound indicators at approximately 25km spatial resolution, up to 2100. We will be able to quantify important aspects such as the change in frequency and the temporal trends that are expected for the considered compound extreme events according to different warming levels (e.g. 1.5ºC, 2ºC, 3ºC).
COMPOUND will address a number of issues which are nowadays recognized as scientific challenges. In particular, we will assess the advantages and limitations of univariate vs. multivariate bias adjustment methods for regional climate models in the context of compound indices, both for the historical simulations and future projections.