Dataset Identified

Hindcast Gulf of Mexico Sea Level Anomalies Using ECCO Sensitivities and Seasonal Prediction Models: 1-12 Month Predictions

Identified On: Apr 18 2023 19:21 UTC
UDI: H5.x993.000:0003
Project Information

Funded By:
National Academies of Sciences Gulf Research Program

Funding Cycle:
Healthy Ecosystems 5

Research Group:
Coherent dynamic sea-level changes along the Gulf Coast: physics, seasonal predictions, and long-term projections

Point of Contact

Andrew Delman
University of California Los Angeles
andrewdelman@g.ucla.edu

Abstract:

This dataset uses the hybrid dynamical prediction approach (Frederikse et al., 2022) to generate hindcast predictions of sea level anomalies at lead times of up to 12 months. The inputs used to generate the predictions are adjoint sensitivities from the Estimating the Circulation and Climate of the Ocean (ECCO) state estimate, and surface fluxes from ECCO and a range of seasonal prediction models. Observations from co-located tide gauges and satellite altimetry are also included for easy validation of the hindcast predictions.