Knowing the ice thickness distribution of a glacier is of fundamental importance for a number of applications, ranging from the planning of glaciological fieldwork to the assessments of future sea-level change. Across spatial scales, however, this knowledge is limited by the paucity and discrete character of available thickness observations. To obtain a spatially coherent distribution of the glacier ice thickness, interpolation or numerical models have to be used. Whilst the first phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX) focused on approaches that estimate such spatial information from characteristics of the glacier surface alone, ITMIX2 sought insights for the capability of the models to extract information from a limited number of thickness observations. The analyses were designed around 23 test cases comprising both real-world and synthetic glaciers, with each test case comprising a set of 16 different experiments mimicking possible scenarios of data availability. A total of 13 models participated in the experiments. The results show that the inter-model variability in the calculated local thickness is high, and that for unmeasured locations, deviations of 16% of the mean glacier thickness are typical (median estimate, three-quarters of the deviations within 37% of the mean glacier thickness). This notwithstanding, limited sets of ice thickness observations are shown to be effective in constraining the mean glacier thickness, demonstrating the value of even partial surveys. Whilst the results are only weakly affected by the spatial distribution of the observations, surveys that preferentially sample the lowest glacier elevations are found to cause a systematic underestimation of the thickness in several models. Conversely, a preferential sampling of the thickest glacier parts proves effective in reducing the deviations. The response to the availability of ice thickness observations is characteristic to each approach and varies across models. On average across models, the deviation between modeled and observed thickness increase by 8.5% of the mean ice thickness every time the distance to the closest observation increases by a factor of 10. No single best model emerges from the analyses, confirming the added value of using model ensembles.

Fram Strait is a hot spot of Arctic cold air outbreaks (CAOs), which typically occur within the northerly flow associated with a strong low tropospheric east‐west pressure gradient between Svalbard and Greenland. This study investigates the processes in the inner Arctic that thermodynamically precondition air masses associated with CAOs south of Fram Strait where they lead to negative potential temperature anomalies often in excess of 15 K. Kinematic backward trajectories from Fram Strait are used to quantify the Arctic residence time and to analyze the thermodynamic evolution of these air masses. Additionally, the study explores the importance of cyclonic tropopause polar vortices (TPVs) for CAO formation south of Fram Strait. Results from a detailed case study and the climatological analysis of the 100 most intense CAOs from Fram Strait in the ERA‐Interim period reveal that (i) air masses that cause CAOs (CAO air masses) reside longer in the inner Arctic compared to those that do not (NO‐CAO air masses), and they originate from climatologically colder regions; (ii) the 10‐day accumulated cooling is very similar for CAO and NO‐CAO air masses indicating that the transport history and northerly origin of the air masses is more decisive for the formation of an intense negative temperature anomaly south of Fram Strait than an enhanced inner Arctic diabatic cooling; (iii) 40% (29%) of the top 40 (100) CAOs are related to a TPV in the vicinity of Fram Strait; (iv) TPVs confine anomalously cold air masses within their associated low tropospheric cold dome leading to enhanced accumulated radiative cooling.

This study examines the European heatwaves' predictability at subseasonal timescales. Land surface feedbacks and tropical convection, due to their variability at intraseasonal timescales, are taken into consideration and their potential role in extending the predictability beyond the medium range (10 days) is explored. A classification of European heatwaves into five heatwave types is used to discriminate the effects of surface feedbacks and of tropical variability among the different heatwave types. The classification is computed in terms of circulation patterns. By inferring the near‐surface temperature through atmospheric circulation, we aim to identify the predictable component of the heatwave events. All five heatwave circulation patterns are characterized by persistent anticyclonic anomalies located over the region with maximum temperatures. We show that soil moisture deficit is not a required precondition for the occurrence of heatwaves over most of Europe. However, heatwave events over southern Europe exhibit some sensitivity to dry conditions. We use a simplified index to describe the dominant mode of tropical convection at intraseasonal timescales. The index, based on precipitation anomalies, represents the evolution of the Boreal Summer Intraseasonal Oscillation (BSISO). We find that episodes with strong BSISO amplitudes characterized by enhanced convection over India, Bay of Bengal and China sea favour the occurrence of heatwave events over Russia. The results highlight the role of tropical intraseasonal variability in enhancing the predictability of some extreme temperature events over Europe.

<p>                Heat waves have important impacts on society and our environment. In Europe for instance, the summer of 2003 caused upwards of 40000 fatalities. They also impact the crop production, ecosystems, and infrastructures. In a warming climate, heat wave intensity and frequency are likely to increase with potentially more dramatic consequences.</p><p>                Considering this, it is crucial to forecast such extreme events and therefore gain a better understanding of their triggering processes. The determination of these processes requires to identify heat wave patterns (timing and location) together with the correlated large-scale circulation patterns. This will enable to devise early warning systems, that could help mitigate the impact.</p><p>                This work is part of an ongoing PhD project focusing on improving the forecast of heat waves at sub-seasonal time scale. The main objectives are to evaluate the link between large scale weather patterns and severe warm events over Europe and measure current level of predictive skill. The first part will focus on defining an objective criteria to identify heat wave events in the ERA5 reanalaysis dataset from ECMWF. The identification of heat waves depends on three main criteria: temperature threshold, spatial and temporal extension. Meaning that the temperature should exceed a defined threshold over a large enough region and for a long enough period. We will consider daily means as well as maximum and minimum values of 2m temperature. We will identify the circulation patterns (persistent high pressure systems) associated with heat wave events and analyse the key differences with persistent high pressure systems that are not associated with heat waves.</p><p>                <strong>This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grand agreement No 813844</strong>.</p>