Climate classification plays a vital role in agricultural planning, hydrological studies, and climate science. One of the most widely used systems for classifying global climate zones is the Köppen-Trewartha (KT) classification. However, the KT classification is fundamentally deterministic, offering discrete labels to spatial locations without accounting for uncertainties in classification. In this paper, we provide a framework for probabilistic modeling of climatic zones. We implement a feedforward artificial neural network (ANN) for classification, allowing for efficient, uncertainty-aware categorization of climatic regions, thereby offering a more nuanced understanding of transitional climate zones compared to traditional deterministic methods. We apply this method to the Sahara Desert region over the 30-year period of 1960 - 1989, using data at more than 400,000 space-time locations from the first 11 years to train our model. We assess the model's short- and long-term classification capabilities to evaluate its stability and accuracy over time. We also compare the probabilistic classification from our model with the traditional KT classification. In addition, we use fluctuation analysis methods to highlight the temporal evolution of climatic zones across the Sahara region and identify areas undergoing significant flux of probabilities of their climate classes, providing insights into broader trends in desertification.

翻译：气候分类在农业规划、水文研究和气候科学中具有重要作用。最广泛使用的全球气候区划分系统之一是柯本-特雷瓦萨（Köppen-Trewartha, KT）分类法。然而，KT分类本质上属于确定性方法，仅能为空间位置提供离散标签，而无法解释分类中的不确定性。本文提出了一种气候区概率建模框架，采用前馈人工神经网络（ANN）进行分类，实现了对气候区域的高效、不确定性感知分类，从而相较于传统确定性方法，对过渡性气候带提供了更细致的理解。我们将该方法应用于撒哈拉沙漠地区1960-1989年30年期间的研究，利用前11年超过40万个时空位置的数据训练模型。通过评估模型的短期与长期分类能力，我们检验了其随时间变化的稳定性与准确性，并将模型得到的概率分类与传统KT分类进行了对比。此外，我们采用波动分析方法揭示了撒哈拉地区气候区的时序演变特征，识别出气候类别概率发生显著通量变化的区域，从而为荒漠化宏观趋势研究提供了新见解。