Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.

The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included. <em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H_s) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em> <em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H_s, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em> <em>In addition, the coefficients of the matrix, C_n, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em> <em>Soon, the results of the peak period (T_p) and mean incoming wave direction (ϑ_m) and the coefficients of the multivariate VAR model will also be included.</em>本文摘选自题为《用于非平稳随机模拟的安达卢西亚沿岸波浪气候参数化特征分析》的扩展摘要，作者为Manuel Cobos、Pedro Magaña、Pedro Otiñar与Asunción Baquerizo，本数据集收录于第39届国际水利与环境工程学会（International Association of Hydraulic Engineering and Research, IAHR）世界大会论文集。本数据集的处理后数据源自PIMA Adapta Costas科研项目（Ramírez等，2019），具体为2026-2045年与2081-2100年的海洋气候预估数据。海洋气候数据包含多项信息，其中包括针对典型浓度路径8.5（Representative Concentration Pathway 8.5，RCP 8.5）情景下EUR-11区域的多组全球气候模式-区域气候模式（Global Climate Model-Regional Climate Model, GCM-RCM）预估结果所提取的有效波高（significant wave height, H_s）时间序列。针对大西洋沿岸海域，研究采用了ACCE、CMCC、CNRM、GFDL、HADG、IPSL、MIRO共7组0.1度网格分辨率的GCM-RCM组合模式；针对地中海沿岸海域，则采用了CNRM、HADG、IPSL、MIRO、MEDC、MPIE、ESM2、EART共8组1/11度网格分辨率的GCM-RCM组合模式。本研究共分析了210个观测点位：大西洋沿岸海域54个，地中海沿岸海域156个（见图1）。数据已通过经验分位数映射法（Empirical Quantile Mapping，Déqué等，2007；Michelangeli等，2009）完成偏差校正。当前已公开有效波高数据，以及基于向量自回归模型（Vector Autoregression, VAR(q)）构建的某时刻波高值与历史时刻波高值间的依赖关系信息。针对每个观测点位，研究采用了Lira-Loarca等（2021）提出的分析方法，并使用了Cobos等（2022a）中描述的配套软件。具体而言，针对每一组GCM-RCM组合模式（下文记为模式n，其中n=1,…,N；大西洋数据集对应N=7，地中海数据集对应N=8），研究以年作为气候的最大周期，采用分段分布形式对H_s的非平稳边缘分布进行拟合：主体部分采用对数正态模型，上下尾部则采用广义帕累托分布（generalized Pareto distribution），该拟合方案参考了Solari与Losada（2011）的研究。非平稳性通过将分布参数与区间公共端点的分位数分解为截断三角级数展开形式得以实现。此外，研究还针对q最大为92小时的VAR(q)模型，估计了其系数矩阵C_n。数据集的多模式集合特征可通过复合分布与加权平均系数矩阵得到。后续还将补充谱峰周期（peak period, T_p）、平均入射波浪方向（mean incoming wave direction, ϑ_m）的计算结果，以及多变量VAR模型的系数信息。