Abstract
The California Current System (CCS) sustains economically valuable fisheries and is particularly vulnerable to ocean acidification, due to its natural upwelling of carbon-enriched waters that generate corrosive conditions for local ecosystems. Here we use a novel suite of retrospective, initialized ensemble forecasts with an Earth system model (ESM) to predict the evolution of surface pH anomalies in the CCS. We show that the forecast system skillfully predicts observed surface pH variations a year in advance over a naive forecasting method, with the potential for skillful prediction up to five years in advance. Skillful predictions of surface pH are mainly derived from the initialization of dissolved inorganic carbon anomalies that are subsequently transported into the CCS. Our results demonstrate the potential for ESMs to provide skillful predictions of ocean acidification on large scales in the CCS. Initialized ESMs could also provide boundary conditions to improve high-resolution regional forecasting systems.
Highlights
The California Current System (CCS) sustains economically valuable fisheries and is vulnerable to ocean acidification, due to its natural upwelling of carbon-enriched waters that generate corrosive conditions for local ecosystems
We use the phrase potential predictability when referring to correlations between CESM-DPLE and the reconstruction
We use the phrase predictive skill when comparing CESM-DPLE to observations; skill demonstrates our ability to predict the true evolution of the real world with CESM-DPLE
Summary
The California Current System (CCS) sustains economically valuable fisheries and is vulnerable to ocean acidification, due to its natural upwelling of carbon-enriched waters that generate corrosive conditions for local ecosystems. Ensemble simulations of initialized ESMs provide the most powerful approach currently available for improving upon decadal persistence forecasts Their coupling of global physical models of the atmosphere, ocean, cryosphere, and land with the carbon cycle, terrestrial and marine ecosystems, atmospheric chemistry, and natural and human disturbances allows one to deeply investigate how interactions between the physical climate system and biosphere lead to predictability in a complex system such as the CCS20. These predictions have the potential to improve upon persistence forecasts, pushing the horizon of forecasting ecosystem stressors past a single season or year. Predictability in surface pH results mainly from the initialization of dissolved inorganic carbon (DIC) anomalies, which are subsequently advected into the CCS, modifying local pH conditions
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