Comparing the near-surface wind speeds obtained from the most recent global circulation model (GCM) simulations to well-known benchmark datasets like the European Centre for Medium-Range Weather Forecasts reanalysis Version 5 (ERA5) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), is necessary to make a critical assessment. Using 28 Coupled Model Intercomparison Project Phase 6 (CMIP6)-based monthly surface wind predictions, the multi-model ensemble (MME) approach in this study generates these predictions using random forest (RF) and multiple linear regression (MLR) methods over seven geographical regions in Türkiye with varying topographic complexity between 1980 and 2014, along with an offshore region. Benchmark datasets, station observations, and individual GCM predictions are used to compare the performances of MME predictions. The analysis showed that individual and the simple mean of GCM simulations are highly biased in spatial and temporal wind means. On the other hand, the MMEs formed by using groups of GCMs have significant skill for representing temporal variability in wind speed as well as for producing annual climatology and anomaly range for topographically complex regions. In MME predictions, the correlation improvements are 38–45% for RF and 22–34% for MLR. Moreover, the effect of the model group with dynamic vegetation growth on improvement remains only marginal.

将最新全球环流模型 (GCM) 模拟获得的近地表风速与欧洲中期天气预报中心再分析版本 5 (ERA5) 和现代研究回顾分析等著名基准数据集进行比较和应用程序版本 2 (MERRA2) 是进行关键评估所必需的。本研究中的多模式集合 (MME) 方法使用 28 个基于耦合模型比对项目第 6 阶段 (CMIP6) 的每月地面风预测，使用随机森林 (RF) 和多元线性回归 (MLR) 方法在七个地理区域生成这些预测1980 年至 2014 年间，土耳其境内地形复杂程度各异的地区以及近海地区。基准数据集、站点观测和单独的 GCM 预测用于比较 MME 预测的性能。分析表明，GCM模拟的个体和简单平均值在时空风平均值上存在很大偏差。另一方面，通过使用 GCM 组形成的 MME 具有表示风速时间变化以及生成地形复杂区域的年度气候和异常范围的显着技能。在 MME 预测中，RF 的相关性改进为 38-45%，MLR 的相关性改进为 22-34%。此外，具有动态植被生长的模型组对改善的影响仍然很小。