Climate data for adaptation and vulnerability assessments — southwest

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

《适应性与脆弱性评估气候数据集——西南部》（ClimAVA-SW）提供了经偏差校正的、以约4公里空间分辨率的每日气候数据，数据来源于17个CMIP6全球气候模型（GCM），包含三种不同的气候变量（降水、最高气温和最低气温），以及三种不同的共享社会经济路径（SSP245、SSP370和SSP585）。历史数据回溯至1981年1月1日至2014年12月31日，未来情景覆盖2015年1月1日至2100年12月31日。ClimAVA-SW数据集覆盖了美国西南部六个州的地理政治边界：加利福尼亚州、内华达州、亚利桑那州、新墨西哥州、犹他州和科罗拉多州，以及流入这些州的流域。采用空间模式交互降尺度（SPID）方法，ClimAVA通过机器学习模型确保了高质量的降尺度。这些模型捕捉了全球环流模型（GCM）分辨率下的空间模式与参考数据（PRISM 4K）中得到的精细分辨率像素值之间的关系。针对每个像素，训练了一个随机森林模型，将更精细的参考数据作为预测变量，并将参考数据空间重采样（较粗分辨率）的九个像素作为预测变量。然后，利用这些模型对偏差校正的GCM数据进行降尺度。该方法的结果已证明能够保持气候现实性，并极大地再现极端事件。