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Interactions Between Mean Sea Level, Tide, Surge, Waves and Flooding: Mechanisms and Contributions to Sea Level Variations at the Coast
Coastal areas epitomize the notion of ‘at-risk’ territory in the context of climate change and sea level rise (SLR). Knowledge of the water level changes at the coast resulting from the mean sea level variability, tide, atmospheric surge and wave setup is critical for coastal flooding assessment. This study investigates how coastal water level can be altered by interactions between SLR, tides, storm surges, waves and flooding. The main mechanisms of interaction are identified, mainly by analyzing the shallow water equations. Based on a literature review, the orders of magnitude of these interactions are estimated in different environments. The investigated interactions exhibit a strong spatiotemporal variability. Depending on the type of environments (e.g., morphology, hydrometeorological context), they can reach several tens of centimeters (positive or negative). As a consequence, probabilistic projections of future coastal water levels and flooding should identify whether interaction processes are of leading order, and, where appropriate, projections should account for these interactions through modeling or statistical methods.
Journal Article
Extreme sea levels at different global warming levels
The Paris agreement focused global climate mitigation policy on limiting global warming to 1.5 or 2 °C above pre-industrial levels. Consequently, projections of hazards and risk are increasingly framed in terms of global warming levels rather than emission scenarios. Here, we use a multimethod approach to describe changes in extreme sea levels driven by changes in mean sea level associated with a wide range of global warming levels, from 1.5 to 5 °C, and for a large number of locations, providing uniform coverage over most of the world’s coastlines. We estimate that by 2100 ~50% of the 7,000+ locations considered will experience the present-day 100-yr extreme-sea-level event at least once a year, even under 1.5 °C of warming, and often well before the end of the century. The tropics appear more sensitive than the Northern high latitudes, where some locations do not see this frequency change even for the highest global warming levels.Combining previous estimates in a multimethod approach, extreme sea levels are assessed under global warming levels of 1.5–5 °C at over 7,000 coastal sites worldwide. By 2100 or before, about 50% of locations exhibit present-day 100-year extreme sea levels at least once per year, even at 1.5 °C of warming.
Journal Article
Statistical Downscaling of Seasonal Forecasts of Sea Level Anomalies for U.S. Coasts
Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns. Plain Language Summary Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. In this study, we explored a method to provide such information on much smaller spatial scales, which better correspond to local coastal sea level measurements by tide gauges. We developed an efficient way to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer‐scale coastal ocean observations to the original coarser model predictions. By testing our approach on past forecasts (“hindcasts”) from existing climate forecast systems, we found that we could improve forecasts for different local regions along both the U.S. West and East Coasts. Key Points Sea level prediction from relatively coarse operational forecasts can be enhanced to finer coastal scales using statistical downscaling Downscaling can be determined by multivariate linear regression trained from high‐resolution reanalysis and its coarse‐grained counterpart This downscaling method significantly improves skill compared to bilinearly interpolated hindcasts at several U.S. tide gauge locations
Journal Article
Are we getting smarter? : rising IQ in the twenty-first century
\"The 'Flynn effect' is a surprising finding, identified by James R. Flynn, that IQ test scores have significantly increased from one generation to the next over the past century. Flynn now brings us an exciting new book which aims to make sense of this rise in IQ scores and considers what this tells us about our intelligence, our minds and society.\"--Provided by publisher.
Book
Global Mean Sea Level Rise Inferred From Ocean Salinity and Temperature Changes
Barystatic sea level rise (SLR) caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but detection of this signal has been hindered by sparse data coverage and the small trends compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001 to 2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13,000 ± 3,000 Gt from 2001 to 2019, implying a barystatic SLR of 2.0 ± 0.5 mm/yr. Combined with SLR of 1.3 ± 0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4 ± 0.6 mm/yr from 2001 to 2019. These results provide an important validation of remote‐sensing measurements of ocean mass changes, global SLR, and global ice budgets. Plain Language Summary Global sea level rise (SLR) is caused by heating of the ocean, and by the input of freshwater from the melting of glaciers and ice caps. Global freshwater input to the oceans from melting ice during the 21st century has primarily been tracked by satellites that measure changes in the mass of the ocean. Here, we show that trends in global SLR can also be accurately tracked by global observations of ocean salinity changes, as freshwater runoff from melting ice enters the ocean and dilutes ocean salinity. These results show that ocean salinity measurements are critical for monitoring global sea level changes, particularly as polar warming intensifies and the melting of ice sheets accelerates. Key Points A new full‐depth ocean salinity product yields robust global freshening trend of (35 ± 10) × 10−6 yr−1 from 2001 to 2019 Combined with estimates of sea ice loss, this freshening implies that ocean mass rose by 13,000 ± 3,000 Gt from 2001 to 2019 Sea level rise derived from ocean temperature and salinity measurements is 3.4 ± 0.6 mm/yr, confirming the satellite altimetry trend
Journal Article
A terrible thing to waste : environmental racism and its assault on the American mind
Demonstrates how environmental racism influences the racial IQ gap and explains what needs to be done to remedy its effects on marginalized communities.
Book
Contrasted influence of climate modes teleconnections to the interannual variability of coastal sea level components–implications for statistical forecasts
Sea level variations at the coast can have drastic environmental and socio-economic impacts in particular in the context of an ever-increasing coastal population and anthropogenic climate change. Regional to global climate variability influences all these factors and exerts a strong control on the coastal sea level over a wide range of time scales. Here, we focus on understanding interannual changes which is paramount to improve interannual forecasting systems as well as to constrain and reduce uncertainties on the secular trend in global mean sea level. We consider the coastal total water level (TWL) as the compound effect of three main components: the wave setup, mean regional sea level anomaly (
i.e.,
steric and ocean circulation influences) and atmospheric surge (
i.e.,
influence of local wind and surface atmospheric pressure). To understand their variability at a global scale, we focus on the effect of four climate modes that affect the major oceanic basins: the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and the Southern Annual Mode (SAM). The contrasted regional influence of these different climate modes on the interannual variations of TWL components are quantified. Results suggest that even if the regional mean sea level is overall the main contributor to the interannual variations of TWL variations at the coast and mostly related to ENSO, the contributions from wave setup and atmospheric surge are not negligible in particular at high latitudes and mostly related to the NAO in the Northern Atlantic and to the SAM in the Southern Hemisphere. Such influences from the NAO and SAM can be seen far away from their extratropical regions of action due to their atmospheric forcing of ocean waves that can significantly propagate their imprint towards tropical areas. Implications for interannual to decadal forecasts of the coastal TWL and related hazards are discussed in the light of regression statistical models and the climate modes own predictability.
Journal Article