This review assesses harmful algal bloom (HAB) modeling in the context of climate change, examining modeling methodologies that are currently being used, approaches for representing climate processes, and time scales of HAB model projections. Statistical models are most commonly used for near-term HAB forecasting and resource management, but statistical models are not well suited for longer-term projections as forcing conditions diverge from past observations. Process-based models are more complex, difficult to parameterize, and require extensive calibration, but can mechanistically project HAB response under changing forcing conditions. Nevertheless, process-based models remain prone to failure if key processes emerge with climate change that were not identified in model development based on historical observations. We review recent studies on modeling HABs and their response to climate change, and the various statistical and process-based approaches used to link global climate model projections and potential HAB response. We also make several recommendations for how the field can move forward: 1) use process-based models to explicitly represent key physical and biological factors in HAB development, including evaluating HAB response to climate change in the context of the broader ecosystem; 2) quantify and convey model uncertainty using ensemble approaches and scenario planning; 3) use robust approaches to downscale global climate model results to the coastal regions that are most impacted by HABs; and 4) evaluate HAB models with long-term observations, which are critical for assessing long-term trends associated with climate change and far too limited in extent.

这篇综述评估了气候变化背景下的有害藻华（HAB）建模，研究了当前正在使用的建模方法，代表气候过程的方法以及HAB模型预测的时间尺度。统计模型最常用于近期HAB预测和资源管理，但是由于强迫条件与过去的观察结果有所不同，因此统计模型不太适合长期预测。基于过程的模型更复杂，难以参数化并且需要广泛的校准，但是可以在变化的强迫条件下以机械方式预测HAB响应。但是，如果基于历史观测的模型开发中未发现气候变化的关键过程出现，则基于过程的模型仍然容易失败。我们回顾了最近关于建模HAB及其对气候变化的响应的研究，以及用于将全球气候模型预测与潜在HAB响应联系起来的各种基于统计和基于过程的方法。我们还为该领域的发展提出了一些建议：1）使用基于过程的模型来明确表示HAB开发中的关键物理和生物学因素，包括在更广泛的生态系统背景下评估HAB对气候变化的响应；2）使用集成方法和场景规划来量化和传达模型不确定性；3）使用健壮的方法将全球气候模型的结果缩减到受HAB影响最大的沿海地区；和4）通过长期观察评估HAB模型，