Reply on RC1

Abstract

In this study, a novel ensemble regression model was developed for hypoxic area (HA) forecast in the Louisiana–Texas (LaTex) Shelf. The ensemble model combines a zero-inflated Poisson generalized linear model (GLM) and a quasi-Poisson generalized additive model (GAM) and considers predictors with hydrodynamic and biochemical features. Both models were trained and calibrated using the daily hindcast (2007–2020) by a three-dimensional coupled hydrodynamic–biogeochemical model embedded in the Reginal Ocean Modeling System (ROMS). A promising HA forecast is provided by the ensemble model with a low RMSE (3,204 km2), a high R2 (0.8005), and a precise performance in capturing hypoxic area peaks in the summers. To test its robustness, the model was further applied to a global forecast model and produces HA prediction from 2019 to 2020 with the adjusted predictors from the HYbrid Coordinate Ocean Model (HYCOM). Predicted HA shows a high agreement with the ROMS hindcast time series (RMSE = 4,571 km2, R2 = 0.8178). Our model can also predict the magnitude and onsets of summer HA peaks in both 2019 and 2020 with high accuracy. To the best of our knowledge, this ensemble model is by far the first one providing fast and accurate daily HA predictions for the LaTex Shelf while considering both hydrodynamic and biochemical effects. This study demonstrates that it is feasible to perform regional ocean HA prediction using global ocean forecast.