Evaluation of the subseasonal forecast skill of surface soil moisture in the S2S database

Based on the reforecasts from five models of the Subseasonal to Seasonal (S2S) Prediction project, the S2S prediction skill of surface soil moisture (SM) over East Asia during May–September is evaluated against ERA-Interim. Results show that good prediction skill of SM is generally 5–10 forecast days prior over southern and northeastern China in the majority of models. Over the Tibetan Plateau and northwestern China, only the ECMWF model has good prediction skill 20 days in advance. Generally, better prediction skill tends to appear over wet regions rather than dry regions. In terms of the seasonal variation of SM prediction skill, some differences are noticed among the models, but most of them show good prediction skill during September. Furthermore, the significant positive correlation between the prediction skill of SM and ENSO index indicates modulation by ENSO of the S2S prediction of SM. When there is an El Niño (a La Niña) event, the SM prediction skill over eastern China tends to be high (low). Through evaluation of the S2S prediction skill of SM in these models, it is found that the prediction skill of SM is lower than that of most atmospheric variables in S2S forecasts. Therefore, more attention needs to be given to the S2S forecasting of land processes.

Abstract licence: CC BY-NC-ND

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