With its increasing record length and subsequent reduction in influence of shorter-term variability on measured trends, satellite altimeter measurements of sea level provide an opportunity to assess near-term sea level rise. Here, we use gridded measurements of sea level created from the network of satellite altimeters in tandem with tide gauge observations to produce observation-based trajectories of sea level rise along the coastlines of the United States from now until 2050. These trajectories are produced by extrapolating the altimeter-measured rate and acceleration from 1993 to 2020, with two separate approaches used to account for the potential impact of internal variability on the future estimates and associated ranges. The trajectories are used to generate estimates of sea level rise in 2050 and subsequent comparisons are made to model-based projections. It is found that observation-based trajectories of sea level from satellite altimetry are near or above the higher-end model projections contained in recent assessment reports, although ranges are still wide.

Changing climate manifests in many ways, with rising seas being one of the most tangible consequences. Sea-level rise increases coastal flood risk to communities, damages infrastructure, raises groundwater, impacts freshwater supplies, exacerbates coastal erosion, and threatens ecosystems and biodiversity. Beyond these direct impacts, the effects of sea-level rise are also felt across many sectors of our society, including public health, emergency preparedness, insurance, finance, real estate, urban planning, infrastructure design, marine commerce, ecosystem restoration, marine resource management and, national security, among others. The coastal counties of the United States produce more than $9 trillion in goods and services annually and are home to over 127 million people (NOAA, 2021). Over the past century, sea level relative to land has risen around the coastlines of the United States by over 0.3 m (1 foot) on average, with increases driven by a combination of rising sea level and movement of land (Figure 1; e.g., Hamlington et al., 2020;Sweet et al., 2017). As a direct consequence, flooding within U.S. coastal communities has doubled in frequency since 2000 and now averages two to six events per year (Sweet al., 2020). Additional direct impacts are ongoing and worsening and an increase in the indirect impacts listed above is expected to quickly follow. In short, there is an urgent need to take action and begin planning for ongoing and worsening sea-level rise. This urgency was recently conveyed at the national level in the United States. Within hours of taking the Oath of Office, President Biden rejoined the Paris climate agreement and made climate change a national priority. With the ambitious plan for environmental justice, President Biden's climate plan seeks to "build

Abstract. Understanding future impacts of sea-level rise at the local level is important for mitigating its effects. In particular, quantifying the range of sea-level rise outcomes in a probabilistic way enables coastal planners to better adapt strategies, depending on cost, timing and risk tolerance. For a time horizon of 100 years, frameworks have been developed that provide such projections by relying on sea-level fingerprints where contributions from different processes are sampled at each individual time step and summed up to create probability distributions of sea-level rise for each desired location. While advantageous, this method does not readily allow for including new physics developed in forward models of each component. For example, couplings and feedbacks between ice sheets, ocean circulation and solid-Earth uplift cannot easily be represented in such frameworks. Indeed, the main impediment to inclusion of more forward model physics in probabilistic sea-level frameworks is the availability of dynamically computed sea-level fingerprints that can be directly linked to local mass changes. Here, we demonstrate such an approach within the Ice-sheet and Sea-level System Model (ISSM), where we develop a probabilistic framework that can readily be coupled to forward process models such as those for ice sheets, glacial isostatic adjustment, hydrology and ocean circulation, among others. Through large-scale uncertainty quantification, we demonstrate how this approach enables inclusion of incremental improvements in all forward models and provides fidelity to time-correlated processes. The projection system may readily process input and output quantities that are geodetically consistent with space and terrestrial measurement systems. The approach can also account for numerous improvements in our understanding of sea-level processes.

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We apply two statistical techniques to satellite measurements to identify a relationship between terrestrial water storage (TWS) and El Niño-Southern Oscillation (ENSO). First, we modified and used the least-squares regression of a previous study using longer records. Second, we applied a cyclostationary empirical orthogonal function analysis (CSEOF). Although the CSEOF technique is distinct from the LSR in that it does not consider proxies, each method produces two modes (decadal and interannual), showing consistency with each technique in spatial pattern and its evolution amplitudes. We also compared the results obtained by the two methods for 30 watersheds, of which five watersheds were compared with previous studies. The combination of the two modes explains the total variance in most river-basins showing the role that interannual and decadal ENSO-related signals in understanding terrestrial water storage variability. The results show that the decadal mode, along with the interannual mode, also plays an important role in describing the local TWS.