| dc.description.abstract | Land–atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land–atmosphere coupling. One of the linkages in land–atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes, which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, northeast Asia, and southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hot spot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidence for the extreme climate change accelerated by surface energy partitioning. | |
