The occurrence of cold spells over different regions of North America has been linked to windy extremes over western Europe. These events – termed pan-Atlantic extremes – are mediated by an anomalous state of the North Atlantic storm track. While it is known that the occurrence of European windstorms is modulated by the state of the storm track, the relative contribution of the North American cold spells to European wind extremes is not easy to quantify. In this study, cold spells over two regions of North America are clustered with respect to the evolution of the large-scale circulation over the North Atlantic. The contribution of cold spells to the European wind extremes is then ascertained using circulation analogs, so that different states of the North Atlantic storm track can be compared for days with and without cold spells. Consistent with previous work, two main pathways emerge from the analysis, called “zonal” and “wavy” for simplicity. For a wavy pathway, North American cold spell occurrence is associated with more frequent European wind extremes than expected from the state of the North Atlantic storm track. For the other pathways, on the other hand, the anomalous state of the storm track was able to account alone for the more frequent wind extremes than climatology observed across Europe, with no or little ascertainable contribution from the cold spells. This analysis clarifies the causality of wintertime pan-Atlantic extremes and how these link to different atmospheric dynamical pathways.

Hydrological extremes, such as droughts and floods, can trigger a complex web of compound and cascading impacts due to interdependencies between coupled natural and social systems. However, current decision-making processes typically only consider one impact and disaster event at a time, ignoring causal chains, feedback loops, and conditional dependencies between impacts. Analyses capturing these complex patterns across space and time are thus needed to better inform effective adaptation planning. This perspective paper aims to bridge this critical gap by presenting methods for assessing the dynamics of the multi-sector compound and cascading impacts (CCI) of hydrological extremes. We discuss existing challenges, good practices, and potential ways forward. Rather than pursuing a single methodological approach, we advocate for methodological pluralism. We see complementary roles for analyses building on quantitative (e.g. data-mining, systems modeling) and qualitative methods (e.g. mental models, qualitative storylines). We believe the data-driven and knowledge-driven methods provided here can serve as a useful starting point for understanding the dynamics of both high-frequency CCI and low-likelihood but high-impact CCI. With this perspective, we hope to foster research on CCI to improve the development of adaptation strategies for reducing the risk of hydrological extremes.

Insufficient in-situ observations from the Antarctic marginal ice zone limit our understanding and description of relevant mechanical and thermodynamic processes that regulate the seasonal sea ice cycle. Here we present high-resolution thermal images of the ocean surface and complementary measurements of atmospheric variables that were acquired underway during one austral winter and one austral spring expedition in the Atlantic and Indian sectors of the Southern Ocean. Skin temperature data and ice cover images were used to estimate the partitioning of the heterogeneous surface and calculate the heat fluxes to compare with ERA5 reanalyses. The winter marginal ice zone was composed of different but relatively regularly distributed sea ice types with sharp thermal gradients. The surface-weighted skin temperature compared well with the reanalyses due to a compensation of errors between the sea ice fraction and the ice floe temperature. These uncertainties determine the dominant source of inaccuracy for heat fluxes as computed from observed variables. In spring, the sea ice type distribution was more irregular, with alternation of sea ice cover and large open water fractions even 400 km from the ice edge. The skin temperature distribution was more homogeneous and did not produce substantial uncertainties in heat fluxes. The discrepancies relative to reanalysis data are however larger than in winter and are attributed to biases in the atmospheric variables, with the downward solar radiation being the most critical.

In this study, the relationship between the interannual variability of Arctic-midlatitude thermal gradient (AMG) and the winter atmospheric blocking frequency in the Ural region (UBF) is investigated in the ERA5 reanalysis product from 1940 to 2023. In particular, the paper focuses on the large-scale atmospheric circulation patterns associated with high UBF concomitant to weak AMG and vice versa, revisiting the more common and documented relationship connecting intense Ural blocking activity to strong AMG. Results show that displacements of the atmospheric blocking from the Ural region towards the Arctic lead to anomalous southerly thermal advections at polar latitudes and stronger AMG. On the other hand, high blocking frequency co-occurring in the Ural, Greenland and Chukotka regions lead to weaker AMG by limiting northward heat advections towards the Arctic region. These findings highlight a more complex picture of the role of subpolar atmospheric circulation in controlling the AMG.

We present an application of quantile generalised additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near-) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co-occurring wet or windy extremes in Western Europe, which we collectively term Pan-Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrisation of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterise the co-occurrence statistics and investigate the role of possible dynamical drivers of the Pan-Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak-over-threshold models.

Winter cold spells over North America have been correlated with European wind extremes, but the physical mechanisms behind such “pan-Atlantic” compound extremes have not been clarified yet. In this study, we propose that pan–Atlantic cold and windy extremes occur following two possible dynamical pathways. The first one involves the propagation of a Rossby wave train from the Pacific Ocean, associated with windstorms over north-western Europe in the 5-10 days after the cold spell peak. The second is associated with a high-latitude anticyclone over the North Atlantic and an equatorward-shifted jet, leading to windstorms over south-western Europe already in the days preceding the cold spell peak. European windstorms are thus consistently tied to North American cold spells according to the different flow configuration. The analysis underscores that seemingly similar surface extremes may be driven by different processes, and that overlooking these subtleties and conflating them together could lead to misleading conclusions.