From Error Identification to Targeted Observations: The Role of 3D Soil Moisture Errors in Improving Subseasonal Heatwave Onset Predictions

Improving subseasonal predictions of heatwave (HW) onset is crucial for early warning systems. While soil moisture (SM) is recognized as a key initial land surface condition, the impact of its three-dimensional (3D) error structure on HW onset prediction uncertainty, and strategies to mitigate this uncertainty, remain insufficiently explored.

This two-part study addresses these gaps, focusing on predictions with a three-week lead time for eight HW onsets over the Middle and Lower Reaches of the Yangtze River (MLYR) region. First, the Conditional Nonlinear Optimal Perturbation (CNOP) method was employed to identify the 3D-structured initial SM errors that maximize uncertainty in subseasonal HW onset predictions. Results show that these structured CNOP-type errors, characterized primarily by negative anomalies with coherent vertical patterns, intensify HW magnitude and advance onset timing. They exert greater impact than spatial random errors by altering surface energy partitioning: reducing latent heat and enhancing sensible heat primarily through vegetation-related processes, while also modulating net longwave radiation via the Stefan-Boltzmann law. Further experiments revealed the importance of deep-layer SM errors and nonlinear synergistic effects across soil layers.

Building on this, the second part evaluates whether targeted observations of initial SM in CNOP-identified sensitive areas (SAs) can enhance prediction skill. Observing System Simulation Experiments (OSSEs) for eight HW events demonstrate that initializing with more realistic SM over SAs consistently outperforms improvements over non-sensitive areas. This targeted approach improves predictions for an average of 86% of ensemble members per case and reduces the mean error in area-averaged maximum temperature during HW onset by 43%. The improvement is attributed to more accurate initial SM conditions, leading to a better representation of surface heat fluxes.

Collectively, these studies systematically highlight the error structure of initial SM field as a key source of subseasonal HW prediction uncertainty and demonstrate the practical potential of CNOP-based targeted observation strategies to improve HW onset predictions.