Climate statistics is of course a very broad field, along with the many connections and impacts for yet other areas, with a history as long as mankind has been recording temperatures, describing drastic weather events, etc. The important work of Klaus Hasselmann, with crucial contributions to the field, along with various other connected strands of work,is being reviewed and discussed in other chapters. The aim of the present chapter is to point to a few statistical methodology themes of relevance for and joint interest with climate statistics. These themes, presented from a statistical methods perspective, include (i) more careful modelling and model selection strategies for meteorological type time series; (ii) methods for prediction, not only for future values of a time series, but for assessing when a trend might be crossing a barrier, along with relevant measures of uncertainty for these; (iii) climatic influence on marine biology; (iv) monitoring processes to assess whether and then to what extent models and their parameters have stayed reasonably constant over time; (v) combination of outputs from different information sources; and (vi) analysing probabilities and their uncertainties related to extreme events.

翻译：气候统计学无疑是一个极为广阔的领域，其与众多其他学科的联系与影响同样深远，其历史可追溯至人类开始记录温度、描述极端天气事件等活动的早期。克劳斯·哈塞尔曼在该领域作出关键贡献的重要工作，以及其他相关研究脉络，已在其他章节得到评述。本章旨在指出若干与气候统计学相关且具有共同研究价值的统计方法论主题。这些从统计方法视角出发的主题包括：（i）针对气象类时间序列的更精细建模与模型选择策略；（ii）预测方法，不仅针对时间序列的未来值，还包括评估趋势何时可能跨越临界阈值，以及相关的不确定性度量；（iii）气候对海洋生物学的影响；（iv）通过监测过程评估模型及其参数是否以及多大程度上随时间保持相对稳定；（v）整合多源信息的输出结果；（vi）分析与极端事件相关的概率及其不确定性。