资源环境科技发展态势分析平台

Unlike coupled global climate models (CGCMs) that run in a stand-alone mode, nested regional climate models (RCMs) are driven by either a CGCM or a reanalysis dataset. This feature makes high correlations between the RCM simulation and its driver possible. When the driving dataset is a reanalysis, time correlations between RCM output and observations are also common and to be expected. In certain situations time correlation between driver and driven RCM is of particular interest and techniques have been developed to increase it (e.g. large-scale spectral nudging). For such cases, a question that remains open is whether aggregating in time increases the correlation between RCM output and observations. That is, although the RCM may be unable to reproduce a given daily event, whether it will still be able to satisfactorily simulate an anomaly on a monthly or annual basis. This is a preconception that the authors of this work and others in the community have held, perhaps as a natural extension of the properties of upscaling or aggregating other statistics such as the mean squared error. Here we explore analytically four particular cases that help us partially answer this question. In addition, we use observations datasets and RCM-simulated data to illustrate our findings. Results indicate that time upscaling does not necessarily increase time correlations, and that those interested in achieving high monthly or annual time correlations between RCM output and observations may have to do so by increasing correlation as much as possible at the shortest time scale. This may indicate that even when only concerned with time correlations at large temporal scale, large-scale spectral nudging acting at the time-step level may have to be used.