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Abstract High‐tide flooding—minor, disruptive coastal inundation—is expected to become more frequent as sea levels rise. However, quantifying just how quickly high‐tide flooding rates are changing, and whether some places experience more high‐tide flooding than others, is challenging. To quantify trends in high‐tide flooding from tide‐gauge observations, flood thresholds—elevations above which flooding begins—must be specified. Past studies of high‐tide flooding in the United States have used different data sets and approaches for specifying flood thresholds, only some of which directly relate to coastal impacts, which has lead to sometimes conflicting and ambiguous results. Here we present a novel method for quantifying, with uncertainty, high‐tide flooding thresholds along the United States coast based on sparsely available impact‐based flood thresholds. We use those newly modeled thresholds to make an updated assessment of changes in high‐tide flooding across the United States over the past few decades. From 1990–2000 to 2010–2020, high‐tide flooding rates almost certainly (probability ) increased along the United States East Coast, Gulf Coast, California, and Pacific Islands, while they very likely decreased along Alaska during that time; significant changes in high‐tide flooding rates between the two decades were not detected in Oregon, Washington, and the Caribbean. Averaging spatially, we find that high‐tide flooding rates probably more than doubled nationally between 1990–2000 and 2010–2020. Our approach lays a foundation for future studies to more accurately model high‐tide flood thresholds and trends along the global coastline. 

The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill, yet, quantifying the sources of skilful predictions is a long-standing challenge. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts. 

Abstract The U.S. coastlines have experienced rapid increases in occurrences of High Tide Flooding (HTF) during recent decades. While it is generally accepted that relative mean sea level (RMSL) rise is the dominant cause for this, an attribution to individual components is still lacking. Here, we use local sea-level budgets to attribute past changes in HTF days to RMSL and its individual contributions. We find that while RMSL rise generally explains > 84% of long-term increases in HTF days locally, spatial patterns in HTF changes also depend on differences in flooding thresholds and water level characteristics. Vertical land motion dominates long-term increases in HTF, particularly in the northeast, while sterodynamic sea level (SDSL) is most important elsewhere and on shorter temporal scales. We also show that the recent SDSL acceleration in the Gulf of Mexico has led to an increase of 220% in the frequency of HTF events over the last decade.