Extreme weather and climate events and their impacts can occur in complex combinations, an interaction shaped by physical drivers and societal forces. In these situations, governance, markets and other decision-making structures-together with population exposure and vulnerability-create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Various anthropogenic actions can also directly affect the severity of events, further complicating these feedback loops. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them.

The best available scientific evidence based on observations from long-term monitoring networks indicates that climate change is occurring, although the effects differ regionally.

No abstract

The Bulletin 17B framework assumes that the annual peak flow data included in a flood frequency analysis are from a homogeneous population. However, flood frequency analysis over the western United States is complicated by annual peak flow records that frequently contain annual flows generated from distinctly different flood generating mechanisms. These flood series contain multiple zero flows and/or potentially influential low floods (PILFs) that substantially deviate from the overall pattern in the data. Moreover, they often also contain extreme flood events representing different hydrometeorologic agents. Among the different flood generating mechanisms, atmospheric rivers (ARs) are responsible for large, regional‐scale floods. The spatial and fractional contribution of ARs in annual peak flow data is examined based on 1375 long‐term U.S. Geological Survey (USGS) streamgage sites with at least 30 years of data. Six main areas in which flooding is impacted by ARs at varying degrees were found throughout the western United States. The Pacific Northwest and the northern California coast have the highest fraction of AR‐generated peaks (∼80–100%), while eastern Montana, Wyoming, Utah, Colorado, and New Mexico have nearly no impacts from ARs. The individual regions of the central Columbia River Basin in the Pacific Northwest, the Sierra Nevada, the central and southern California coast, and central Arizona all show a mixture of 30–70% AR‐generated flood peaks. Analyses related to the largest flood peaks on record and to the estimated annual exceedance probabilities highlight the strong impact of ARs on flood hydrology in this region, together with marked regional differences.

For many climate change impacts such as drought and heat waves, global and national frameworks for climate services are providing ever more critical support to adaptation activities. Coastal zones are especially in need of climate services for adaptation, as they are increasingly threatened by sea level rise and its impacts, such as submergence, flooding, shoreline erosion, salinization and wetland change. In this paper, we examine how annual to multi-decadal sea level projections can be used within coastal climate services (CCS). To this end, we review the current state-of-the art of coastal climate services in the US, Australia and France, and identify lessons learned. More broadly, we also review current barriers in the development of CCS, and identify research and development efforts for overcoming barriers and facilitating their continued growth. The latter includes: (1) research in the field of sea level, coastal and adaptation science and (2) cross-cutting research in the area of user interactions, decision making, propagation of uncertainties and overall service architecture design. We suggest that standard approaches are required to translate relative sea level information into the forms required to inform the wide range of relevant decisions across coastal management, including coastal adaptation.

An approach to analyze high-end sea level rise is presented to provide a conceptual framework for high-end estimates as a function of time scale, thereby linking robust sea level science with stakeholder needs. Instead of developing and agreeing on a set of high-end sea level rise numbers or using an expert consultation, our effort is focused on the essential task of providing a generic conceptual framework for such discussions and demonstrating its feasibility to address this problem. In contrast, information about high-end sea level rise projections was derived previously either from a likely range emerging from the highest view of emissions in the Intergovernmental Panel on Climate Change assessment (currently the Representative Concentration Pathway 8.5 scenario) or from independent ad hoc studies and expert solicitations. Ideally, users need high-end sea level information representing the upper tail of a single joint sea level frequency distribution, which considers all plausible yet unknown emission scenarios as well as involved physical mechanisms and natural variability of sea level, but this is not possible. In the absence of such information we propose a framework that would infer the required information from explicit conditional statements (lines of evidence) in combination with upper (plausible) physical bounds. This approach acknowledges the growing uncertainty in respective estimates with increasing time scale. It also allows consideration of the various levels of risk aversion of the diverse stakeholders who make coastal policy and adaptation decisions, while maintaining scientific rigor.

Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine-copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre-processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.

This article has been republished with minor changes. These changes do not impact the academic content of the article. Disclosure statementNo potential conflict of interest was reported by the authors.