RESUMEN
The El Niño-Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1-3, yet, quantifying the sources of skilful predictions is a long-standing challenge4-7. 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 possible8-10, 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.
RESUMEN
American Samoa is experiencing rapid relative sea level rise due to increases in global sea level and significant post-2009 earthquake land subsidence, endangering homes and critical infrastructure. Wave and water-level observations collected over a fringing reef at Faga'itua Bay, American Samoa, in 2017 reveal depth-limited shoreline sea-swell wave heights over the range of conditions sampled. Using field data to calibrate a one-dimensional, phase-resolving nonhydrostatic wave model (SWASH), we examine the influence of water level on wave heights over the reef for a range of current and future sea levels. Assuming a fixed reef bathymetry, model results predict rising sea levels will escalate nearshore extreme water levels that are dominated by an increase in nearshore sea-swell wave heights. Model results provide insight into how and at what reef depths rising sea levels reduce reef capacity to dissipate wave energy, compounding shoreline threats. This study aims to bring increased attention to the immediate threats to American Samoa's way of life, and to demonstrate the utility of SWASH for extrapolating wave transformation to future sea level.
RESUMEN
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.
RESUMEN
Atmospheric rivers (ARs) cause inland hydrological impacts related to precipitation. However, little is known about coastal hazards associated with these events. We elucidate high-tide floods (HTFs) and storm surges during ARs on the US West Coast during 1980-2016. HTFs and ARs cooccur more often than expected from chance. Between 10% and 63% of HTFs coincide with ARs on average, depending on location. However, interannual-to-decadal variations in HTFs are due more to tides and mean sea-level changes than storminess variability. Only 2-15% of ARs coincide with HTFs, suggesting that ARs typically must cooccur with high tides or mean sea levels to cause HTFs. Storm surges during ARs reflect local wind, pressure, and precipitation forcing: meridional wind and barometric pressure are primary drivers, but precipitation makes secondary contributions. This study highlights the relevance of ARs to coastal impacts, clarifies the drivers of storm surge during ARs, and identifies future research directions.
RESUMEN
Sea-level rise (SLR) induced flooding is often envisioned as solely originating from a direct marine source. This results in alternate sources such as groundwater inundation and storm-drain backflow being overlooked in studies that inform planning. Here a method is developed that identifies flooding extents and infrastructure vulnerabilities that are likely to result from alternate flood sources over coming decades. The method includes simulation of flood scenarios consisting of high-resolution raster datasets featuring flood-water depth generated by three mechanisms: (1) direct marine flooding, (2) storm-drain backflow, and (3) groundwater inundation. We apply the method to Honolulu's primary urban center based on its high density of vulnerable assets and present-day tidal flooding issues. Annual exceedance frequencies of simulated flood thresholds are established using a statistical model that considers predicted tide and projections of SLR. Through assessment of multi-mechanism flooding, we find that approaching decades will likely feature large and increasing percentages of flooded area impacted simultaneously by the three flood mechanisms, in which groundwater inundation and direct marine flooding represent the most and least substantial single-mechanism flood source, respectively. These results illustrate the need to reevaluate main sources of SLR induced flooding to promote the development of effective flood management strategies.