Soil moisture predictability on seasonal to decadal (S2D) continuum timescales over North America is examined from the Community Earth System Modeling (CESM) experiments. The effects of ocean and land initializations are disentangled using two large ensemble datasets—initialized and uninitialized experiments from the CESM. We find that soil moisture has significant predictability on S2D timescales despite limited predictability in precipitation. On sub-seasonal to seasonal timescales, precipitation variability is an order of magnitude greater than soil moisture, suggesting land surface processes, including soil moisture memory, reemergence, land–atmosphere interactions, transform a less predictable precipitation signal into a more predictable soil moisture signal.

通过社区地球系统建模（CESM）实验，研究了北美地区季节性至十年（S2D）连续时间尺度上的土壤湿度可预测性。使用两个大型整体数据集（CESM的已初始化和未初始化实验）可以解开海洋和陆地初始化的影响。我们发现，尽管降水的可预测性有限，但土壤水分在S2D时间尺度上具有显着的可预测性。在次季节到季节的时间尺度上，降水变化率比土壤湿度大一个数量级，这表明包括土壤湿度记忆，再发，土地-大气相互作用在内的陆地表面过程将难以预测的降水信号转换为更可预测的土壤湿度信号。