Environmental and ecosystem models can help to guide management of changing natural systems by projecting alternative future states under a common set of scenarios. Combining contrasting models into multi-model ensembles (MMEs) can improve the skill and reliability of projections, but associated uncertainty complicates communication of outputs, affecting both the effectiveness of management decisions and, sometimes, public trust in scientific evidence itself. Effective data visualisation can play a key role in accurately communicating such complex outcomes, but we lack an evidence base to enable us to design them to be visually appealing whilst also effectively communicating accurate information. To address this, we conducted a survey to identify the most effective methods for visually communicating the outputs of an ensemble of global climate models. We measured the accuracy, confidence, and ease with which the survey participants were able to interpret 10 visualisations depicting the same set of model outputs in different ways, as well as their preferences. Dot and box plots outperformed all other visualisations, heat maps and radar plots were comparatively ineffective, while our infographic scored highly for visual appeal but lacked information necessary for accurate interpretation. We provide a set of guidelines for visually communicating the outputs of MMEs across a wide range of research areas, aimed at maximising the impact of the visualisations, whilst minimizing the potential for misinterpretations, increasing the societal impact of the models and ensuring they are well-placed to support management in the future.

环境和生态系统模型可以通过在一组通用情景下预测未来的替代状态来帮助指导不断变化的自然系统的管理。将对比模型结合到多模型集合 (MME) 中可以提高预测的技巧和可靠性，但相关的不确定性使输出的交流变得复杂，既影响管理决策的有效性，有时也会影响公众对科学证据本身的信任。有效的数据可视化可以在准确传达如此复杂的结果方面发挥关键作用，但我们缺乏证据基础来使我们能够将它们设计为具有视觉吸引力的同时还能有效地传达准确的信息。为了解决这个问题，我们进行了一项调查，以确定最有效的方法来直观地传达全球气候模型集合的输出。我们测量了调查参与者能够以不同方式解释描述同一组模型输出的 10 个可视化的准确性、信心和难易程度，以及他们的偏好。点和箱线图优于所有其他可视化，热图和雷达图相对无效，而我们的信息图在视觉吸引力方面得分很高，但缺乏准确解释所需的信息。我们提供了一套指导方针，用于在广泛的研究领域中直观地传达 MME 的输出，旨在最大限度地提高可视化的影响，同时最大限度地减少误解的可能性，