Ghosts from summers past: quantifying the role of vegetation legacy to climatic extremes

The last two decades have witnessed record-breaking drought and heat extremes during summer months across Europe, with wide-ranging consequences for biodiversity, public health, and the amount of carbon taken up by the land. Climate change is expected to increase the frequency, magnitude, and duration of future droughts and heat extremes. Yet, the associated timescale needed for affected ecosystems to recover from extreme events is largely unknown and rarely considered in evaluations of future climate change impacts.

Without this critical knowledge of recovery timescales, we are likely to underestimate ecosystem and associated services sensitivity to compound events (heatwaves coincident with drought), and/or repeated climate extremes. To robustly predict future impacts, we need new theories to represent our understanding of the timescales (months-to-years) over which the ecological legacy to meteorological extremes persists.

This project will determine legacy timescales at multiple spatial scales by applying state-of-the-art machine learning techniques to both manipulation experiments (rainfall/warming), field data, and satellite data covering recent, record-breaking European summer extremes. These insights into the scale (both time and spatial extent) of legacy persistence will be used to test four hypotheses that govern the vegetation's response to droughts and heat extremes.

This combination of statistical machine learning, alongside hypothesis-driven model development, will unlock critical new insights into the role of the past in dictating vegetation responses to future environmental extremes, reducing associated risk, and facilitating mitigation planning.

Our ultimate impact will be the modification of the land surface component of UKESM - the UK's new Earth System Model - facilitating a transformative improvement in our capacity to assess the future impact of climate extremes both across Europe and globally, critical to forecasting the future terrestrial carbon sink.