Benchmarking and lightweight correction of daily near-surface air temperature products over the Qinghai-Tibetan Plateau

Abstract

Reliable near–surface air temperature (Ta) over the Qinghai-Tibetan Plateau is critical for energy balance closure, cryosphere assessment, and land–atmosphere modeling, yet complex relief and sparse gauges challenge gridded fields. We build a daily, lineage-spanning benchmark across 20 research-grade stations to evaluate five representative datasets—global land reanalysis, observation-conditioned regional fusions, and recent kilometer-scale, observation-based interpolations—under a unified collocation. Performance is stratified by eco-climatic zone (HI/HII), ground-thermal state (permafrost/seasonally frozen), and 500-m elevation bands. Observation-conditioned fields outperform the purely model-driven ERA5-Land reanalysis at the daily scale; the reanalysis shows a pervasive cold bias, strongest in HII winter. Elevation composites indicate rising R² and shrinking MAE from 3.0 to 4.5 km, with winter weakest seasonally. A lightweight two-stage correction—regime-aware height normalization (γ) followed by quantile mapping—cuts MAE by 0.5–2.0 °C and removes height-dependent cold bias. A unitless usability matrix translates multi-metric skill into scene-specific guidance by season–zone–elevation, offering an operational pathway to select and minimally correct TP Ta forcings transferable to other high-relief regions.