Seasonal forecast of Arctic sea ice concentration is key to mitigate the negative impact and assess potential opportunities posed by the rapid decline of sea ice coverage. Seasonal prediction systems based on climate models often show systematic biases and complex spatio-temporal errors that grow with the forecasts. Consequently, operational predictions are routinely bias corrected and calibrated using retrospective forecasts. For predictions of Arctic sea ice concentration, error corrections are mainly based on one-to-one post-processing methods including climatological mean or linear regression correction and, more recently, machine learning. Such deterministic adjustments are confined at best to the limited number of costly-to-run ensemble members of the raw forecast. However, decision-making requires proper quantification of uncertainty and likelihood of events, particularly of extremes. We introduce a probabilistic error correction framework based on a conditional Variational Autoencoder model to map the conditional distribution of observations given the biased model prediction. This method naturally allows for generating large ensembles of adjusted forecasts. We evaluate our model using deterministic and probabilistic metrics and show that the adjusted forecasts are better calibrated, closer to the observational distribution, and have smaller errors than climatological mean adjusted forecasts.

翻译：北极海冰浓度的季节性预测对于缓解海冰覆盖快速减少带来的负面影响及评估其潜在机遇至关重要。基于气候模式的季节性预测系统常存在系统性偏差和复杂的时空误差，且误差随预测时效增长而扩大。因此，业务化预测通常需利用历史回报数据进行偏差订正与校准。针对北极海冰浓度预测，误差校正主要基于一对一的后处理方法，包括气候平均订正、线性回归订正以及近年兴起的机器学习方法。此类确定性调整方法至多只能覆盖原始预测中数量有限且计算成本高昂的集合成员。然而，决策制定需要合理量化不确定性及事件（特别是极端事件）的发生概率。本文提出一种基于条件变分自编码器模型的概率误差校正框架，用于构建在给定偏差模式预测条件下观测数据的条件分布。该方法可自然生成大量经调整的预测集合。通过确定性及概率性评估指标验证，本研究表明：经校正的预测较气候平均订正预测具有更好的校准性、更接近观测分布且误差更小。