Memory of land surface and subsurface temperature (LST/SUBT) initial anomalies over Tibetan Plateau in different land models

This study applies three widely used land models (SSiB, CLM, and Noah-MP) coupled in a regional climate model to quantitatively assess their skill preserving the imposed ± 5 °C anomalies on initial surface subsurface temperature (LST/SUBT) generating 2-m air (T2m) over Tibetan Plateau (TP) during May–August. The memory of LST/SUBT (surface/soil memory) is defined as first time when series differences daily cross zero line simulation, with unit days. T2m (T2m same way. ensemble results indicate that simulated soil generally increases depth, which consistent based observations statistic methods. And found change rapidly depth above ~ 0.6–0.7 m vary slowly below it. have fairly long memories, mean 1.0-m longer than 60 However, they short memory, means 20 may bring big challenge use approach sub-seasonal seasonal (S2S) prediction. Comparison between shows CLM Noah-MP at deeper layers (> 0.05 m) while SSiB has T2m/surface memories near-surface ( $$\le$$ memory. As result, it difficult say optimal for application S2S T2m/surface/soil are various TP, distinct among models, different + − experiment, can be explained by both changes heat fluxes variances hydrological processes plateau.