Atmospheric circulation is often clustered in so‐called circulation regimes, which are persistent and recurrent patterns. For the Euro‐Atlantic sector in winter, most studies identify four regimes: the Atlantic Ridge, Scandinavian Blocking and the two phases of the North Atlantic Oscillation. These results are obtained by applying k‐means clustering to the first several empirical orthogonal functions (EOFs) of geopotential height data. Studying the observed circulation in reanalysis data, it is found that when the full field data are used for the k‐means cluster analysis instead of the EOFs, the optimal number of clusters is no longer four but six. The two extra regimes that are found are the opposites of the Atlantic Ridge and Scandinavian Blocking, meaning they have a low‐pressure area roughly where the original regimes have a high‐pressure area. This introduces an appealing symmetry in the clustering result. Incorporating a weak persistence constraint in the clustering procedure is found to lead to a longer duration of regimes, extending beyond the synoptic time‐scale, without changing their occurrence rates. This is in contrast to the commonly used application of a time‐filter to the data before the clustering is executed, which, while increasing the persistence, changes the occurrence rates of the regimes. We conclude that applying a persistence constraint within the clustering procedure is a better way of stabilizing the clustering results than low‐pass filtering the data.
Models incorporating delay have been frequently used to understand climate variability phenomena, but often the delay is introduced through an ad-hoc physical reasoning, such as the propagation time of waves. In this paper, the Mori-Zwanzig formalism is introduced as a way to systematically derive delay models from systems of partial differential equations and hence provides a better justification for using these delay-type models. The Mori-Zwanzig technique gives a formal rewriting of the system using a projection onto a set of resolved variables, where the rewritten system contains a memory term. The computation of this memory term requires solving the orthogonal dynamics equation, which represents the unresolved dynamics. For nonlinear systems, it is often not possible to obtain an analytical solution to the orthogonal dynamics and an approximate solution needs to be found. Here, we demonstrate the Mori-Zwanzig technique for a two-strip model of the El Niño Southern Oscillation (ENSO) and explore methods to solve the orthogonal dynamics. The resulting nonlinear delay model contains an additional term compared to previously proposed ad-hoc conceptual models. This new term leads to a larger ENSO period, which is closer to that seen in observations.
To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro-Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub-seasonal and interannual time-scales. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual time-scales relations between the occurrence rates of the regimes and the El Niño Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual time-scales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO-index where the signal strength in the observations is underestimated by a factor of 2 in the model. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index as we find poor predictability for the corresponding NAO− regime.
Abstract. Climate tipping elements are large-scale subsystems of the Earth that may transgress critical thresholds (tipping points) under ongoing global warming, with substantial impacts on biosphere and human societies. Frequently studied examples of such tipping elements include the Greenland Ice Sheet, the Atlantic Meridional Overturning Circulation, permafrost, monsoon systems, and the Amazon rainforest. While recent scientific efforts have improved our knowledge about individual tipping elements, the interactions between them are less well understood. Also, the potential of individual tipping events to induce additional tipping elsewhere, or stabilize other tipping elements is largely unknown. Here, we map out the current state of the literature on the interactions between climate tipping elements and review the influences between them. To do so, we gathered evidence from model simulations, observations and conceptual understanding, as well as archetypal examples of paleoclimate reconstructions where multi-component or spatially propagating transitions were potentially at play. Lastly, we identify crucial knowledge gaps in tipping element interactions and outline how future research could address those gaps.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.