Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, 80309, USA.
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, 80305, USA.
Nat Commun. 2020 May 1;11(1):2166. doi: 10.1038/s41467-020-15722-x.
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.
加利福尼亚海流系统（CCS）支撑着具有经济价值的渔业，由于其富含碳的上升流海水会导致当地生态系统产生腐蚀性条件，因此特别容易受到海洋酸化的影响。在这里，我们使用一套新颖的回溯性、初始化的集合预测工具与地球系统模型（ESM）来预测 CCS 中表面 pH 异常的演变。我们表明，该预测系统能够在一年的时间内熟练地预测出观测到的表面 pH 变化，而在提前五年的时间内，该系统具有熟练预测的潜力。表面 pH 的熟练预测主要来自于溶解无机碳异常的初始化，这些异常随后被运送到 CCS 中。我们的研究结果表明，ESM 有潜力在 CCS 这样的大范围内对海洋酸化进行熟练预测。初始化 ESM 还可以提供边界条件，以改善高分辨率区域预测系统。
