Periodic Reporting for period 1 - CLOSeR (Contribution of Land water stOrage to Sea-level Rise)
Reporting period: 2019-07-01 to 2021-06-30
Sea-level rise is one of the most dreadful consequences of future climate change. To estimate the associated risk and prepare ourselves better for its implications, we need to identify the spatiotemporal characteristics of the contributors to sea level change. Out of all the contributors, land water storage change is the most debated one because studies so far do not even agree on its sign. In the framework of CLOSeR (Contribution of Land water stOrage to Sea-level Rise), I will estimate the spatiotemporal characteristic of contribution from land water storage to sea-level rise between 2002 and 2020, in a robust statistical framework called BHM (Bayesian Hierarchical Modelling) with the help of: 1) multi-mission satellite altimetry data over oceans 2) a new data-driven leakage corrected GRACE product 3) steric information from ARGO floats and 4) a novel data-driven GIA model. I will develop a data-driven leakage corrected GRACE product for global applications, develop a BHM to solve for land water storage contribution while closing the sea-level budget, and identify the regions that contribute significantly to sea-level rise. The approach is unique, global in scale, and will address a multi-disciplinary problem comprehensively.
I have been able to process latest GRACE gravity field products and have developed tools to map the response of sea level to temporal changes in the gravity field of the Earth, commonly known as Sea Level Fingerprints. They represent the spatial distribution of sea level rise due to mass redistribution. We can clearly see that ice mas loss in Greenland and East Antarctica leads to a drop in relative sea level near these regions, while rest of the oceans is observing a rise in relative sea surface heights (see the attached figure).
will estimate the spatiotemporal characteristic of contribution from land water storage to sea-level rise between 2002 and 2020, in a robust statistical framework called BHM (Bayesian Hierarchical Modelling) with the help of: 1) multi-mission satellite altimetry data over oceans 2) a new data-driven leakage corrected GRACE product 3) steric information from ARGO floats and 4) a novel data-driven GIA model.