This work proposes a new general procedure to stochastically analyze multi-model multivariate wave climate time series projections at different temporal scales. For every projection, it characterizes significant wave height, peak period and mean direction by means of univariate non-stationary distributions capable of capturing cyclic climate behavior over a reference time interval duration. The temporal dependence between the values at a given sea state and previous short-term wave climate is described with a vector autoregressive model (VAR). The multi-model ensemble wave climate characterization is based on a compound distribution of the individual non-stationary distributions and a weighted averaged VAR model. The methodology is applied to bias-adjusted wave climate projections derived using WaveWatch III forced by wind field data from EURO-CORDEX models at a location close to the Mediterranean Spanish coast. Results are compared to hindcast data which shows a clear bi-seasonal behavior. Different temporal references were considered, starting with a 1-year reference period to analyze overall changes in wave climate at scales ranging from days, months and seasons with respect to historic conditions. The results show that the projected wave climate has a very different temporal behavior than hindcast data, delaying and widening/shortening the start and duration of the two main seasons and including shorter term variations. Regarding the energetic content of the sea states, the compound variable highest percentiles of the significant wave height present lower values than the hindcast (≈3−10%) during the traditionally more severe period (November–March) but higher values (≈10−35%) during the calmer months. The projected peak period presents a similar temporal pattern to the hindcast data, while the mean wave direction shows a significant change from the historical bi-modal behavior towards more likely easterly waves throughout the year. Additionally, a 10-year analysis is done to find larger temporal variabilities such as decadal variations associated with the North Atlantic Oscillation. The observed temporal variability in the yearly seasonal pattern throughout the century is addressed by analysing 20-year rolling windows in all the model projections and in the compound variable. The compound distribution shows significant temporal variabilities throughout the century with the most severe periods and more likely severe waves during summer at the end of the century.

这项工作提出了一个新的通用程序，以随机分析不同时间尺度上的多模式多元波气候时间序列投影。对于每个投影，它都通过单变量非平稳分布来表征重要的波高，峰值周期和平均方向，这些分布能够捕获在参考时间间隔持续时间内的周期性气候行为。使用向量自回归模型（VAR）描述了给定海况下的值与先前的短期波浪气候之间的时间依赖性。多模式集合波气候特征描述基于单个非平稳分布的复合分布和加权平均VAR模型。该方法适用于使用WaveWatch III进行偏差调整的海浪气候预测，WaveWatch III由来自EURO-CORDEX模型的风场数据在靠近地中海西班牙海岸的位置推算得出。将结果与后预报数据进行比较，后预报数据显示出明显的双季节行为。考虑了不同的时间参考，从一年的参考期开始，以相对于历史条件从天，月，季的尺度分析海浪气候的总体变化。结果表明，预计的浪潮气候与后预报数据的时空行为截然不同，延迟和扩大/缩短了两个主要季节的开始和持续时间，包括短期变化。关于海洋国家的能量含量，在传统上更为严重的时期（11月至3月），显着波高的复合变量最高百分位数呈现出比后播更低的值（≈3-10％），而在平静的月份呈现出更高的值（≈10-35％）。预计的高峰期呈现出与后播数据类似的时间模式，而平均波向则显示出从历史上的双峰行为向全年可能发生的东风波发生了重大变化。此外，进行了为期10年的分析，以发现较大的时间变化，例如与北大西洋涛动有关的年代际变化。通过分析所有模型预测和复合变量中的20年滚动窗口，可以解决整个世纪年度季节性模式中观察到的时间变化。