ALBERT

Description: Heat stress is projected to intensify with global warming, causing significant socioeconomic impacts and threatening human health. Wet-bulb temperature (WBT), which combines temperature and humidity effects, is a useful indicator for assessing regional and global heat stress variability and trends. However, the variations of European WBT and their underlying mechanisms remain unclear. Using observations and reanalysis datasets, we demonstrate a remarkable warming of summer WBT during the period 1958–2021 over Europe. Specifically, the European summer WBT has increased by over 1.08C in the past 64 years. We find that the increase in European summer WBT is driven by both near-surface warming temperatures and increasing atmospheric moisture content. We identify four dominant modes of European summer WBT variability and investigate their linkage with the large-scale atmospheric circulation and sea surface temperature anomalies. The first two leading modes of the European WBT variability exhibit prominent interdecadal to long-term variations, mainly driven by a circumglobal wave train and concurrent sea surface temperature variations. The last two leading modes of European WBT variability mainly show interannual variations, indicating a direct and rapid response to large-scale atmospheric dynamics and nearby sea surface temperature variations. Further analysis shows the role of global warming and changes in midlatitude circulations in the variations of summer WBT. Our findings can enhance the understanding of plausible drivers of heat stress in Europe and provide valuable insights for regional decision-makers and climate adaptation planning.

Description: A fundamental statistic of climate variability is its spatiotemporal correlation function. Its complex structure can be concisely summarized by a frequency-dependent measure of the effective spatial degrees of freedom (ESDOF). Here we present, for the first time, frequency-dependent ESDOF estimates of global natural surface temperature variability from purely instrumental measurements, using the HadCRUT4 dataset (1850-2014). The approach is based on a newly developed method for estimating the frequency-dependent spatial correlation function from gappy data fields. Results reveal a multicomponent structure of the spatial correlation function, including a large-amplitude short-distance component (with weak time scale dependence) and a small-amplitude long-distance component (with increasing relative amplitude toward the longer time scales). Two frequency-dependent ESDOF measures are applied, each responding mainly to either of the two components. Both measures exhibit a significant ESDOF reduction from monthly to multidecadal time scales, implying an increase of the effective spatial scale of natural surface temperature fluctuations. Moreover, it is found that a good approximation to the global number of equally spaced samples needed to estimate the variance of global mean temperature is given, at any frequency, by the greater one of the two ESDOF measures, decreasing from ;130 at monthly to ;30 at multidecadal time scales. Finally, the multicomponent structure of the correlation function together with the detected ESDOF scaling properties indicate that the ESDOF reduction toward the longer time scales cannot be explained simply by diffusion acting on stochastically driven anomalies, as it might be suggested f rom simple stochastic-diffusive energy balance models.

Description: Coastal upwelling, driven by alongshore winds and characterized by cold sea surface temperatures and high upper-ocean nutrient content, is an important physical process sustaining some of the oceans’ most productive ecosystems. To fully understand the ocean properties in eastern boundary upwelling systems, it is important to consider the depth of the source waters being upwelled, as it affects both the SST and the transport of nutrients toward the surface. Here, we construct an upwelling source depth distribution for parcels at the surface in the upwelling zone. We do so using passive tracers forced at the domain boundary for every model depth level to quantify their contributions to the upwelled waters. We test the dependence of this distribution on the strength of the wind stress and stratification using high-resolution regional ocean simulations of an idealized coastal upwelling system. We also present an efficient method for estimating the mean upwelling source depth. Furthermore, we show that the standard deviation of the upwelling source depth distribution increases with increasing wind stress and decreases with increasing stratification. These results can be applied to better understand and predict how coastal upwelling sites and their surface properties have and will change in past and future climates.