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Free, publiclyaccessible full text available January 1, 2024

Free, publiclyaccessible full text available October 1, 2023

The modes of Pacific decadalscale variability (PDV), traditionally defined as statistical patterns of variance, reflect to first order the ocean's integration (i.e., reddening) of atmospheric forcing that arises from both a shift and a change in strength of the climatological (timemean) atmospheric circulation. While these patterns concisely describe PDV, they do not distinguish among the key dynamical processes driving the evolution of PDV anomalies, including atmospheric and ocean teleconnections and coupled feedbacks with similar spatial structures that operate on different timescales. In this review, we synthesize past analysis using an empirical dynamical model constructed from monthly ocean surface anomalies drawn from several reanalysis products, showing that the PDV modes of variance result from two fundamental lowfrequency dynamical eigenmodes: the North Pacific–central Pacific (NPCP) and Kuroshio–Oyashio Extension (KOE) modes. Both eigenmodes highlight how twoway tropical–extratropical teleconnection dynamics are the primary mechanisms energizing and synchronizing the basinscale footprint of PDV. While the NPCP mode captures interannual to decadalscale variability, the KOE mode is linked to the basinscale expression of PDV on decadal to multidecadal timescales, including contributions from the South Pacific.Free, publiclyaccessible full text available January 16, 2024

In multiobjective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the componentwise sum of the cost vectors of the edges of the former path is 'less than' the componentwise sum of the cost vectors of the edges of the latter path. The Paretooptimal solution set is the set of all undominated paths from a given start vertex to a given goal vertex. Its size can be exponential in the size of the graph being searched, which makes multiobjective search timeconsuming. In this paper, we therefore study how to find an approximate Paretooptimal solution set for a userprovided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Paretooptimal solution set, which enables the design of approximate multiobjective search algorithms that are efficient and produce small solution sets. We present such an algorithm in this paper, called A*pex. A*pex builds on PPA*, a stateoftheart approximate biobjective search algorithm (where there are only two cost components) but (1) makes PPA* more efficient for biobjective search and (2) generalizes it to multiobjective search for any numbermore »

The Paretooptimal frontier for a biobjective search problem instance consists of all solutions that are not worse than any other solution in both objectives. The size of the Paretooptimal frontier can be exponential in the size of the input graph, and hence finding it can be hard. Some existing works leverage a userspecified approximation factor ε to compute an approximate Paretooptimal frontier that can be significantly smaller than the Paretooptimal frontier. In this paper, we propose an anytime approximate biobjective search algorithm, called Anytime BiObjective A*ε (ABOA*ε). ABOA*ε is useful when deliberation time is limited. It first finds an approximate Paretooptimal frontier quickly, iteratively improves it while time allows, and eventually finds the Paretooptimal frontier. It efficiently reuses the search effort from previous iterations and makes use of a novel pruning technique. Our experimental results show that ABOA*ε substantially outperforms baseline algorithms that do not reuse previous search effort, both in terms of runtime and number of node expansions. In fact, the most advanced variant of ABOA*ε even slightly outperforms BOA*, a stateoftheart biobjective search algorithm, for finding the Paretooptimal frontier. Moreover, given only a limited amount of deliberation time, ABOA*ε finds solutions that collectively approximate the Paretooptimal frontier muchmore »