2022, Kemp, Luke, Xu, Chi, Depledge, Joanna, Ebi, Kristie L., Gibbins, Goodwin, Kohler, Timothy A., Rockström, Johan, Scheffer, Marten, Schellnhuber, Hans Joachim, Steffen, Will, Lenton, Timothy M.

2022, Kemp, Luke, Xu, Chi, Depledge, Joanna, Ebi, Kristie L., Gibbins, Goodwin, Kohler, Timothy A., Rockström, Johan, Scheffer, Marten, Schellnhuber, Hans Joachim, Steffen, Will, Lenton, Timothy M.

2022, Khaykin, Sergey, Podglajen, Aurelien, Ploeger, Felix, Grooß, Jens-Uwe, Tence, Florent, Bekki, Slimane, Khlopenkov, Konstantin, Bedka, Kristopher, Rieger, Landon, Baron, Alexandre, Godin-Beekmann, Sophie, Legras, Bernard, Sellitto, Pasquale, Sakai, Tetsu, Barnes, John, Uchino, Osamu, Morino, Isamu, Nagai, Tomohiro, Wing, Robin, Baumgarten, Gerd, Gerding, Michael, Duflot, Valentin, Payen, Guillaume, Jumelet, Julien, Querel, Richard, Liley, Ben, Bourassa, Adam, Clouser, Benjamin, Feofilov, Artem, Hauchecorne, Alain, Ravetta, François

The eruption of the submarine Hunga volcano in January 2022 was associated with a powerful blast that injected volcanic material to altitudes up to 58 km. From a combination of various types of satellite and ground-based observations supported by transport modeling, we show evidence for an unprecedented increase in the global stratospheric water mass by 13% relative to climatological levels, and a 5-fold increase of stratospheric aerosol load, the highest in the last three decades. Owing to the extreme injection altitude, the volcanic plume circumnavigated the Earth in only 1 week and dispersed nearly pole-to-pole in three months. The unique nature and magnitude of the global stratospheric perturbation by the Hunga eruption ranks it among the most remarkable climatic events in the modern observation era, with a range of potential long-lasting repercussions for stratospheric composition and climate.

2022, Tzallas, Vasileios, Hünerbein, Anja, Stengel, Martin, Meirink, Jan Fokke, Benas, Nikos, Trentmann, Jörg, Macke, Andreas

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).

2022, Kemp, Luke, Xu, Chi, Depledge, Joanna, Ebi, Kristie L., Gibbins, Goodwin, Kohler, Timothy A., Rockström, Johan, Scheffer, Marten, Schellnhuber, Hans Joachim, Steffen, Will, Lenton, Timothy M.

[no abstract available]

2022, Braun, Tobias, Kraemer, K. Hauke, Marwan, Norbert

Couplings in complex real-world systems are often nonlinear and scale dependent. In many cases, it is crucial to consider a multitude of interlinked variables and the strengths of their correlations to adequately fathom the dynamics of a high-dimensional nonlinear system. We propose a recurrence-based dependence measure that quantifies the relationship between multiple time series based on the predictability of their joint evolution. The statistical analysis of recurrence plots (RPs) is a powerful framework in nonlinear time series analysis that has proven to be effective in addressing many fundamental problems, e.g., regime shift detection and identification of couplings. The recurrence flow through an RP exploits artifacts in the formation of diagonal lines, a structure in RPs that reflects periods of predictable dynamics. Using time-delayed variables of a deterministic uni-/multivariate system, lagged dependencies with potentially many time scales can be captured by the recurrence flow measure. Given an RP, no parameters are required for its computation. We showcase the scope of the method for quantifying lagged nonlinear correlations and put a focus on the delay selection problem in time-delay embedding which is often used for attractor reconstruction. The recurrence flow measure of dependence helps to identify non-uniform delays and appears as a promising foundation for a recurrence-based state space reconstruction algorithm.