标题： 利用昨日气候预测未来：人工智能天气和气候模型中的温度偏差 显示英文标题

标题： Forecasting the Future with Yesterday's Climate: Temperature Bias in AI Weather and Climate Models

摘要： 基于人工智能的气候和天气模型迅速获得了流行，提供了速度更快且技能可以与甚至超过传统动力模型的预测。 尽管取得了成功，这些模型面临一个关键挑战：仅使用历史数据进行训练时预测未来气候。 在本研究中，我们通过分析AI天气和气候模型中的北半球冬季陆地温度偏差来研究这个问题。 我们检查了两个天气模型，FourCastNet V2 Small（FourCastNet）和Pangu Weather（Pangu），评估它们对2020-2025年的预测以及Ai2 Climate Emulator版本2（ACE2）对1996-2010年的预测。 这些时间段超出了各自模型的训练集，并且比它们大部分训练数据要新得多，使我们能够评估模型在新的即更现代条件下的泛化能力。 我们发现所有三个模型都产生了冷偏差的平均温度，类似于它们所预测时期之前15-20年的气候。 在某些地区，如美国东部，预测类似于比所预测时期早20-30年的气候。 进一步分析显示，FourCastNet和Pangu的冷偏差在最热的预测温度中最强，表明它们对现代极端高温事件的训练接触有限。 相比之下，ACE2的偏差分布更为均匀，但在气候变化最显著的地区、季节和温度分布部分最大。 这些发现强调了仅使用历史数据训练人工智能模型的挑战，并突出了在将它们应用于未来气候预测时需要考虑此类偏差的必要性。 显示英文摘要

摘要： AI-based climate and weather models have rapidly gained popularity, providing faster forecasts with skill that can match or even surpass that of traditional dynamical models. Despite this success, these models face a key challenge: predicting future climates while being trained only with historical data. In this study, we investigate this issue by analyzing boreal winter land temperature biases in AI weather and climate models. We examine two weather models, FourCastNet V2 Small (FourCastNet) and Pangu Weather (Pangu), evaluating their predictions for 2020-2025 and Ai2 Climate Emulator version 2 (ACE2) for 1996-2010. These time periods lie outside of the respective models' training sets and are significantly more recent than the bulk of their training data, allowing us to assess how well the models generalize to new, i.e. more modern, conditions. We find that all three models produce cold-biased mean temperatures, resembling climates from 15-20 years earlier than the period they are predicting. In some regions, like the Eastern U.S., the predictions resemble climates from as much as 20-30 years earlier. Further analysis shows that FourCastNet's and Pangu's cold bias is strongest in the hottest predicted temperatures, indicating limited training exposure to modern extreme heat events. In contrast, ACE2's bias is more evenly distributed but largest in regions, seasons, and parts of the temperature distribution where climate change has been most pronounced. These findings underscore the challenge of training AI models exclusively on historical data and highlight the need to account for such biases when applying them to future climate prediction.