Dominik Büeler

Synchronous wildfires (wildfires occurring in multiple locations within a week) pose challenges to coordinated firefighting efforts between European countries. Despite their potentially severe impacts, their occurrence and relationship with predictable atmospheric circulation patterns are poorly understood. We analyze synchronous wildfires across ten European regions over the past two decades and attribute their seasonal occurrences to seven year-round weather regimes. We find that weather regimes modulate the occurrence and spatial extent (the number of regions involved) of synchronous wildfires primarily by driving warm and dry meteorological anomalies, suggesting that these events may become more frequent in a warming world. Specifically, Atlantic Ridge promotes wildfire synchronicity between multiple pairs of European regions in summer, and European Blocking is the most likely regime co-occurring with synchronous wildfires spanning large spatial domains (or involving more than five regions) in spring and fall. Our findings could support early warning systems for synchronous wildfire emergencies in Europe because these weather regimes are predictable on subseasonal timescales.

The rapid intensification of permafrost warming and thawing in the Swiss Alps due to anthropogenic climate change is well observed and documented, but the response to atmospheric temperature variability on shorter timescales of days to a season is less explored. Here, we address this research question for an ice-poor permafrost slope on the Swiss mountain peak Schilthorn. Using Swiss Permafrost Monitoring Network (PERMOS) observations, we provide evidence that the year-to-year variability of total atmospheric heat over the snow-free summer period largely determines the year-to-year variability of total heat diagnosed in the active layer from the start of the snow-free summer period until the subsequent spring, since the snowpack effectively decouples the ground from the atmosphere during the rest of the year. With the help of idealized sensitivity simulations with the land surface model SNOWPACK, we further demonstrate that late-summer-to-early-autumn heatwaves increase ground heat at the end of the snow-free period until the subsequent spring more than early-summer heatwaves do, even if the total atmospheric heat over the snow-free period remains the same. The reason is that late atmospheric heat occurs closer to the return date of the insulating snowpack and can thus be better retained by the ground than early heat, which is lost back into the atmosphere long before the snowpack comes back. In essence, we provide evidence that hot summers with individual heatwaves in late summer and early autumn might pose the largest risk for a deepening of the active layer and thus for permafrost warming and thawing, at least for ice-poor permafrost investigated here. Running ground surface models such as SNOWPACK with output from subseasonal and seasonal atmospheric prediction models might thus be beneficial for early warnings of ground temperature and permafrost anomalies.

Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.

The operational use of subseasonal atmospheric predictions remains a major challenge due to the intermittency of skill on these timescales. Often only specifically trained users can keep track of so‐called “windows of forecast opportunity” (WFOs). WFOs are periods during which prediction skill is enhanced because specific states of the atmosphere, ocean, or land surface temporarily enhance predictability. Here, we propose a novel method to combine simultaneously active WFOs into a single opportunity index, which can be used operationally like a traffic‐light system and without expert knowledge to anticipate enhanced or reduced subseasonal prediction skill. For predictions of two‐weekly and monthly mean temperature anomalies in Switzerland during summer—a region and season with particularly low predictability—we demonstrate that skill can nearly double for a high opportunity index compared with a low one. The use of such an index could thus advance the year‐round operational usability of subseasonal predictions in many other regions of the world.

Extratropical cyclones are the main cause of extreme surface weather events in the Mediterranean such as heavy precipitation, floods, severe winds, and dust storms. However, the accuracy in predicting the timing, location, and intensity of such events is often insufficient, which is typically related to errors in cyclone position, propagation, and intensity. In this two-part study we use operational ensemble forecasts from the European Centre for Medium-Range Weather Forecasts to quantify the predictability of extreme surface weather conditions linked to Mediterranean cyclones. We apply an object-based approach to attribute events of extreme precipitation and surface winds to Mediterranean cyclones. Thereby, objects of extreme surface weather are identified at grid points that exceed the seasonal 99th percentile of these parameters and matched to cyclones based on their distance to the cyclone center. In this first part, we introduce the probabilistic method and three illustrative case studies of Mediterranean cyclones that occurred between November 2022 and September 2023, including the infamous Storm Daniel as well as Storms Denise and Jan. We find that the cyclones as well as their attributed objects of extreme surface weather are predicted well for lead times ≤ 48 h. However, for longer lead times there is large case-to-case variability in the ensemble performance. Predictions of extreme surface weather objects are found to be more uncertain (i) for smaller and less coherent objects, (ii) if the associated cyclone is captured by fewer ensemble members, and (iii) during the earlier stage of the cyclones' lifecycle. The methodological development and its application documented in this paper provide the basis for a multi-year investigation of the predictability of extreme weather linked to Mediterranean cyclones in the second part of this study.