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\"An exploration of the responses that sea level rise demands along the West Coast\"-- Provided by publisher.

Sea‐level change threatens the U.S. East Coast. Thus, it is important to understand the underlying causes, including ocean dynamics. Most past studies emphasized links between coastal sea level and local atmospheric variability or large‐scale circulation and climate, but possible relationships with local ocean currents over the shelf and slope remain largely unexplored. Here we use 7 years of in situ velocity and sea‐level data to quantify the relationship between northeastern U.S. coastal sea level and variable Shelfbreak Jet transport south of Nantucket Island. At timescales of 1–15 days, southern New England coastal sea level and transport vary in anti‐phase, with magnitude‐squared coherences of ∼0.5 and admittance amplitudes of ∼0.3 m Sv−1. These results are consistent with a dominant geostrophic balance between along‐shelf transport and coastal sea level, corroborating a hypothesis made decades ago that was not tested due to the lack of transport data. Plain Language Summary Sea‐level rise is an imminent threat to coastal communities worldwide, including the U.S. East Coast. Therefore, it is crucial to understand the processes driving regional sea‐level change. While past studies documented how coastal sea level may be influenced by large‐scale ocean circulation, less attention has been paid to the role of more local currents over the shelf and slope. Here we explore the relationship between coastal sea level along the northeastern U.S. and the Shelfbreak Jet, a current that flows along the shelfbreak from the Labrador Sea to Cape Hatteras (North Carolina). From 7 years of in situ data of both current velocities and water levels, we see that as coastal sea level rises, Shelfbreak Jet transport increases westward (and vice versa) on timescales of days to weeks. Our results lay the groundwork for understanding relationships between coastal sea level and local ocean dynamics elsewhere. Key Points Daily Shelfbreak Jet transports and Southern New England coastal sea levels are anti‐correlated during 2014–2022 The observed relationship between these two variables is consistent with geostrophic balance For this region, coastal sea levels are more sensitive to local ocean dynamics than to large‐scale circulation

\"Water floods through cities along coastlines and islands all around the world. And the rising water has already started pushing people from their homes. What on Earth is causing these rising seas? As Earth's climate is changing, raising temperatures are melting ice and increasing sea levels. Uncover the problems of climate change, explore the impact of rising sea levels, and dive into what we can do to help. Approachable text with engaging images brings this timely topic to life\"-- Provided by publisher.

Glacial isostatic adjustment (GIA) modeling is not only useful for understanding past relative sea‐level change but also for projecting future sea‐level change due to ongoing land deformation. However, GIA model predictions are subject to a range of uncertainties, most notably due to uncertainty in the input ice history. An effective way to reduce this uncertainty is to perform data‐model comparisons over a large ensemble of possible ice histories, but this is often impossible due to computational limitations. Here we address this problem by building a deep‐learning‐based GIA emulator that can mimic the behavior of a physics‐based GIA model while being computationally cheap to evaluate. Assuming a single 1‐D Earth rheology, our emulator shows 0.54 m mean absolute error on 150 out‐of‐sample testing data with <0.5 s emulation time. Using this emulator, two illustrative applications related to the calculation of barystatic sea level are provided for use by the sea‐level community. Plain Language Summary Piecing together the history of ice sheet change during past glacial cycles is not only important for understanding past sea‐level change but also for predicting how ongoing glacial rebound contributes to future sea‐level change. Traditionally, a physics‐based “sea‐level model” is used to predict the sea‐level change associated with a particular reconstruction of past ice sheet change and compare the results with geological records of past sea level. However, a fundamental limitation of this approach is the need to compute sea‐level change for a large number of plausible ice histories, which is often prohibited by the computational resources required to repeatedly solve the complex physical equations. In this paper, we describe a machine‐learning‐based statistical model that can mimic the behavior of a physics‐based sea‐level model. This statistical model is computationally cheap and we demonstrate that it is able to accurately predict global sea‐level change for a suite of 150 “unseen” ice histories. Our statistical model predicts sea‐level change 100–1,000 times faster than a physics‐based model, making it an ideal tool for investigating and improving our understanding of global ice sheet change. Key Points The first attempt to build a deep‐learning based Glacial isostatic adjustment (GIA) emulator that can accurately predict global sea‐level change based on a given ice model This emulator (GEORGIA) can predict global sea‐level change history within 0.5 s with minor emulation error This GIA emulator along with two illustrative applications are available for use by the wider sea‐level community

The book provides a concise and interdisciplinary outlook on the impacts of climate change on coastal areas and how coastal communities adapt to them. The first chapter analyses how sea level rise, changing ocean conditions, or increased climate variability and the socio-environmental context of the coastal zone leads to vulnerable communities. The second chapter addresses adaptation strategies and tools, and gives some examples of their application around the world. The third chapter describes participative action research projects undertaken in New Brunswick and how this community based approach has enabled communities to increase their climate resilience.

Holocene sea‐level reconstructions from tidal marshes are commonly derived from proxy indicators that have a consistent and quantifiable relationship with tidal elevation. While microfossils are most commonly employed, using multiple indicators leads to more robust reconstructions. We explore the utility of elemental geochemistry obtained through x‐ray fluorescence as a proxy indicator in tidal marshes at Port Alberni, British Columbia, Canada and Willapa Bay, Washington, United States. The elemental composition of bulk surface sediment collected from 141 stations along 10 transects was determined using an ITRAX Core Scanner. Partitioning Around Medoids cluster analysis on the elemental data distinguished between tidal flat, low marsh, and high marsh zones at both locations, similar to zones established from previously published microfossil (foraminifera, diatoms) data sets on the same samples. The elemental composition of low elevation samples from the tidal flat is dominated by lithogenic (Si, K, Ti, Fe) and biogenic (Sr) elements, whereas higher elevation samples have high proportions of organic content (Br, incoherent and coherent scattering ratio). Principal Component Analysis points to differences in organic versus inorganic content, a function of tidal elevation, as the main driver of geochemistry‐derived zones. Approximately 70% of the elemental variability within both marshes is controlled by the inorganic content, as indicated by lithogenic and biogenic elements versus organic content. The elemental composition of bulk surface sediment from two regions spaced ∼300 km apart shows a promising relationship with tidal elevation over a wider spatial scale and highlights the potential of this proxy for use in sea‐level reconstructions. Plain Language Summary In modern tidal wetland environments, the distribution of marsh vegetation and microfossil species is controlled by tidal inundation, for which elevation is the key indicator. The modern relationship between microfossils and tidal elevation is well established and has been applied to tidal marsh sediment to reconstruct sea‐level change through time from marshes around the world. In this study, we show that the relationship between the elemental composition of modern marsh sediment and tidal elevation at two separate locations is comparable to the well‐established relationship between microfossils and tidal elevation. Low‐elevation marsh areas (frequently inundated by tides) are dominated by mineral‐forming elements (e.g., Si, Ti, Fe), whereas high‐elevation areas (less frequently inundated by tides) contain abundant organic‐related elements (e.g., C, N, Br). This relationship is evident at tidal marshes at Port Alberni, British Columbia, Canada and Willapa Bay, Washington, United States, located ∼300 km apart, indicating similar processes controlling elemental distributions at both sites. Results from this study highlight the potential of elemental geochemistry for reconstructing sea‐ and land‐level change in coastal marshes. Key Points Elemental geochemistry of modern tidal marsh sediment shows a consistent relationship with tidal elevation at local spatial scales Elemental composition along a tidal marsh transect is predominantly controlled by the inorganic and organic content of the sediment The use of lower abundance elements contained within tidal marsh sediment warrants exploration

\"The Western Louisiana and Texas coast is vulnerable to sea-level rise due to low gradients, high subsidence, and depleted sediment supply. This Memoir describes the response of coastal environments to variable rates of sea-level rise and sediment supply during Holocene to modern time. This volume is a wake-up call about the potential magnitude of coastal change over decadal to centennial time scales\"-- Provided by publisher.

The results from a carefully implemented GPS analysis, using a strategy adapted to determine accurate vertical station velocities, are presented. The stochastic properties of our globally distributed GPS position time series were inferred, allowing the computation of reliable velocity uncertainties. Most uncertainties were several times smaller than the 1–3 mm/yr global sea level change, and hence the vertical velocities could be applied to correct the long tide gauge records for land motion. The sea level trends obtained in the ITRF2005 reference frame are more consistent than in the ITRF2000 or corrected for Glacial‐Isostatic Adjustment (GIA) model predictions, both on the global and the regional scale, leading to a reconciled global rate of geocentric sea level rise of 1.61 ± 0.19mm/yr over the past century in good agreement with the most recent estimates.

A family living on a cliff by the sea experiences rising water levels and a powerful storm, forcing them to relocate.

Dynamic sea‐level change (ΔDSL)$({\\Delta }\\mathrm{D}\\mathrm{S}\\mathrm{L})$is a key process in shaping the pattern of future sea‐level rise. CMIP6 models predict a range of ΔDSL${\\Delta }\\mathrm{D}\\mathrm{S}\\mathrm{L}$under 1% increase of CO2${\\text{CO}}_{2}$per year. We analyze this CMIP6 spread into contributions from: (a) surface flux change (dF)$(\\mathrm{d}\\mathrm{F})$and (b) model sensitivity to it (Φ)$({\\Phi })$ . Specifically, we force the pre‐industrial simulation of an ocean model with space‐ and time‐varying dF$\\mathrm{d}\\mathrm{F}$diagnosed from different CMIP6 models (one at a time). The CMIP6 spread is thus decomposed into a flux‐driven spread and a residual; the latter is linked to model spread of Φ${\\Phi }$ . We improve upon previous studies by: (a) deriving the perturbed forcing ensemble using an ocean‐only setup and (b) comparing it with the CMIP6 ensemble for both variance and correlation. This reveals distinct roles of surface forcing in driving the CMIP6 spread in different regions. In the North Pacific, differences in windstress forcing primarily explain the CMIP6 spread, while in the North Atlantic, differences in model sensitivity are more important. For the latter region, although buoyancy forcing drives a ΔDSL${\\Delta }\\mathrm{D}\\mathrm{S}\\mathrm{L}$spread there, it correlates poorly with the CMIP6 spread. In the Southern Ocean, differences in both surface forcing and model sensitivity are important for explaining the CMIP6 spread. The surface forcing affects the spread along 40°S via windstress and the spread around the Antarctic via buoyancy flux. Plain Language Summary Climate model simulations provide important information to support planning for future sea‐level rise. Contemporary climate models exhibit large differences in simulated regional sea‐level change under strong CO2${\\text{CO}}_{2}$emission. These model differences can be analyzed in terms of: (a) model differences in simulated surface flux changes (heat, freshwater and wind) and (b) model differences in simulated ocean response to a given surface flux change. We find that model differences in surface flux changes explain most of model diversity in sea‐level change in the North Pacific and part of that in the Southern Ocean, but little of that in the North Atlantic. These results pave the way for reducing sea‐level projection uncertainties in future research. Key Points CMIP6 spread of dynamic sea‐level projections results from both surface forcing and model sensitivity to it Windstress forcing explains the CMIP6 spread in the North Pacific, while model sensitivity is more important in the North Atlantic In the Southern Ocean, both surface forcing and model sensitivity are important for explaining the CMIP6 spread