Attributing decadal climate variability in coastal sea-level trends

Decadal sea-level variability masks longer-term changes due to natural and anthropogenic drivers in short-duration records and increases uncertainty in trend and acceleration estimates. When making regional coastal management and adaptation decisions, it is important to understand the drivers of these changes to account for periods of reduced or enhanced sea-level change. The variance in decadal sea-level trends about the global mean is quantified and mapped around the global coastlines of the Atlantic, Pacific, and Indian oceans from historical CMIP6 runs and a high-resolution ocean model forced by reanalysis data. We reconstruct coastal, sea-level trends via linear relationships with climate mode and oceanographic indices. Using this approach, more than one-third of the variability in decadal sea-level trends can be explained by climate indices at 24.6 % to 73.1 % of grid cells located within 25 km of a coast in the Atlantic, Pacific, and Indian oceans. At 10.9 % of the world's coastline, climate variability explains over two-thirds of the decadal sea-level trend. By investigating the steric, manometric, and gravitational components of sea-level trend independently, it is apparent that much of the coastal ocean variability is dominated by the manometric signal, the consequence of the open-ocean steric signal propagating onto the continental shelf. Additionally, decadal variability in the gravitational, rotational, and solid-Earth deformation (GRD) signal should not be ignored in the total. There are locations such as the Persian Gulf and African west coast where decadal sea-level variability is historically small that are susceptible to future changes in hydrology and/or ice mass changes that drive intensified regional GRD sea-level change above the global mean. The magnitude of variance explainable by climate modes quantified in this study indicates an enhanced uncertainty in projections of short- to mid-term regional sea-level trend

Can we resolve the basin-scale sea-level trend budget from GRACE ocean mass?

Understanding sea level changes at a regional scale is important for improving local sea level projections and coastal management planning. Sea level budget (SLB) estimates derived from the sum of observation of each component close for the global mean. The sum of steric and Gravity Recovery and Climate Experiment (GRACE) ocean mass contributions to sea level calculated from measurements does not match the spatial patterns of sea surface height trends from satellite altimetry at 1° grid resolution over the period 2005–2015. We investigate potential drivers of this mismatch aggregating to subbasin regions and find that the steric plus GRACE ocean mass observations do not represent the small-scale features seen in the satellite altimetry. In addition, there are discrepancies with large variance apparent at the global and hemispheric scale. Thus, the SLB closure on the global scale to some extent represents a cancelation of errors. The SLB is also sensitive to the glacial isostatic adjustment correction for GRACE and to altimery orbital altitude. Discrepancies in the SLB are largest for the Indian-South Pacific Ocean region. Taking the spread of plausible sea level trends, the SLB closes at the ocean-basin scale ( ) but with large spread of magnitude, one third or more of the trend signal. Using the most up-to-date observation products, our ocean-region SLB does not close everywhere, and consideration of systematic uncertainties diminishes what information can be gained from the SLB about sea level processes, quantifying contributions, and validating Earth observation systems

The scope of the Kalman filter for spatio-temporal applications in environmental science

The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations

Sources of sea level variability on the shelf of the U.S. Eastern seaboard from Sentinel-3A SRAL

Poster at European Space Agency (ESA) 2022 Living Planet Symposium, 23-27 May 2022, Bon

Separating GIA signal from surface mass change using GPS and GRACE data

The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by climate change and in order to better understand them using the aforementioned geodetic observations, glacial isostatic adjustment (GIA) signal should be accounted first. The default approach is to use forward GIA models that use uncertain ice-load history and approximate Earth rheology to predict GIA, yielding large uncertainties. The proliferation of contemporary, global, geodetic observations and their coverage have therefore enabled estimation of data-driven GIA solutions. A novel framework is presented that uses geophysical relations between the vertical land motion (VLM) and geopotential anomaly due to GIA and PDSMC to express GPS VLM trends and GRACE geopotential trends as a function of either GIA or PDSMC, which can be easily solved using least-squares regression. The GIA estimates are data-driven and differ significantly from forward models over Alaska and Greenland

From Sea Level Rise to COVID-19:Extending a Bayesian Hierarchical Model to unfamiliar problems with the 4D-Modeller framework

<p>The recently completed European Research Council project "Global Mass" (www.globalmass.eu) aimed to reconcile the global sea-level budget as measured through a variety of satellite and in-situ data sources using a space-time Bayesian Hierarchical Model (BHM). The BHM uses Gaussian latent processes to estimate the contribution and uncertainty of different physical processes such as land hydrology, ocean thermal expansion, and glacier melt, to ongoing sea-level rise. Each process has a unique spatial and temporal length scale, which can be provided as a prior or inferred from the observations within the model. The BHM can separate the physical process sources represented in the data, model the stationarity of these processes, and estimate their uncertainty globally. A particular strength of the BHM is its ability to estimate and separate the different processes, from data with disparate spatial and temporal sampling and for observations that are influenced by multiple processes. This is often termed the source separation problem and we utilize novel statistical methods to solve for this and for dimensional reduction to allow the problem to be computationally tractable. We use the Integrated Nested Laplace Approximation (INLA) framework to approximate the observation layer and for the inference itself due to its accuracy and computational speed. The BHM has the potential to address a wider class of spatio-temporal inference problems and here we introduce the model structure (named 4D-modeller) and apply it to new classes of problem to extend its versatility. We apply it to COVID-19 transmittability in England and hydrology uncertainties related to hydropower reservoirs in Norway: problems that span social and physical sciences.</p><p><strong>How to cite:</strong> Aiken, J. M., Yin, X., Royston, S., Ziegler, Y., and Bamber, J. L.: From Sea Level Rise to COVID-19: Extending a Bayesian Hierarchical Model to unfamiliar problems with the 4D-Modeller framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1680, https://doi.org/10.5194/egusphere-egu23-1680, 2023.</p&gt