Higher Subseasonal Predictability of Extreme Hot European Summer Temperatures as Compared to Average Summers

Abstract

Summer temperatures in the last decades were increasingly characterized by persistent extremes, and there is evidence that this trend will continue in a warming climate. The exact timing of these extremes is less well known, and it is therefore crucial to consider their subseasonal predictability. We compare the prediction of summer 2m-temperature extremes in Europe with the prediction of average events for four subseasonal forecasting systems. We find higher prediction skill for warm extremes as compared to average events, with some regional dependence. The same is not true for cold extremes, indicating an asymmetry in the processes causing opposite summer temperature extremes. The forecast skill is strongly increased by the most severe and persistent events in the analyzed period. We hypothesize that the enhanced warm extreme skill is related to persistent flow patterns and land-atmosphere interaction. This could have implications for potentially enhanced predictability in a warming climate.

The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

“…Generally the moderate events in the bulk can be better predicted than events in the tails. This ordering is in contrast with the study of Wulff and Domeisen (2019), who found that European warm extremes in summer, exceeding the 90th percentile and at a 5 day temporal aggregation, are more predictable than moderate events between the 25th and 75th percentiles. They found this for the warm tail only, so they hypothesized that the emergent source of conditional predictability related to land-atmosphere feedbacks and large-scale circulation.…”

“…Note the different colour scale but our results support both schools of thought. The BSS curves (Figure 3) showed that tail events are harder to predict, and we also found no indication of increased predictability of summer warm extremes ( Figure 10; Wulff and Domeisen, 2019). On the other hand, the winter BSS ( Figure 8) displayed a regional signal in the upper quantiles (visible only at larger time aggregations) which we might relate to an emergent predictable phenomenon.…”

“…Studies have also tailored the aggregation to a single conditional source of predictability: rainfall events in Europe that are clustered in time due to large-scale dynamics (Economou et al, 2015;Pasquier et al, 2019;Yang and Villarini, 2019), or extreme temperatures occurring simultaneously within a spatial region due to large-scale flow or sea-surface temperatures (Stefanon et al, 2012;McKinnon et al, 2016;Vijverberg et al, 2020). The forecast skill of such derived predictands can be high, but it is conditional on the occurrence of the source mechanism, and might also lose validity for less or more extreme events (Wulff and Domeisen, 2019). To improve skill under all physical circumstances, statistical post-processing is often used to correct systematic biases and under-or over-dispersion.…”

Introduction to Special Collection: “Bridging Weather and Climate: Subseasonal‐to‐Seasonal (S2S) Prediction”

“…With amplified warming over land in recent decades, summer temperatures are increasingly characterized by persistent extreme heat and on S2S time scales; summer warm extremes are more predictable at weekly lead times than average and cold events (Wulff & Domeisen, ). The enhanced warm extreme skill is suggested to be related to persistent flow patterns and land‐atmosphere interactions during the summer season.…”

The 2018 summer heatwaves over northwestern Europe and its extended-range prediction

“…However, prediction of the occurrence of a given heatwave events over a specific location 3–4 weeks in advance remains a challenge. Slow varying drivers including Madden–Julian Oscillation, mid-latitude persistent flow regimes, and land–atmosphere interaction could be potential sources of the sub-seasonal predictability of European 2 m-temperature extremes 12 – 15 . Although severe and/or long-lasting heatwaves could be more predictable at the extended-range timescales, such as the 2010 Russian heatwave 14 , 15 , the skill of the prediction of blocking variability would largely control the forecast of the onset, duration, and amplitude of the heatwave 13 .…”