Spatial Unevenness of Ground–Atmosphere Heat Sources over the Southern Tibetan Plateau and Its Relationship with Convection over the Philippine Sea

This study assesses the suitability of ERA5 reanalysis data for representing heat sources over the southern Tibetan Plateau (STP). The results show strong agreement between ERA5 sensible heat flux (SH), atmospheric heat source (AH), and independent observations (R2 > 0.998, RMSE < 0.82 W·m−2). Heat fluxes over the STP exhibit pronounced spatiotemporal heterogeneity. In summer, surface latent heat flux (SLHF) displays a distinct east–west gradient, with values of 140–180 W·m−2 in the east and lower values in the west, while surface sensible heat flux (SSHF) remains uniformly low (40–80 W·m−2). In spring, SSHF shows a reversed west–east pattern, with a high-value center of 100–140 W·m−2. The SH peak precedes the latent heat peak by approximately 1 h and coincides with an increase of approximately 25 W·m−2 in mid-to-upper atmospheric heating, confirming the dominant role of SH in vertical thermal development. The atmospheric heat source (Q_atm) leads the Indian monsoon onset by 7–10 days. When Q_atm exceeds 110 W·m−2, the probability of monsoon onset increases from 35% to 78%. A coupled altitude–moisture column forms between the Bay of Bengal “wet tongue” and enhanced STP heating, with a phase lag of approximately 12 h. On intra-seasonal timescales (10–20 days), STP heat source variability and Philippine Sea convection exhibit a clear ~12-day lagged teleconnection, characterized by a “heat source enhancement–convection intensification” dipole pattern. These results provide quantitative dynamic–thermodynamic indicators for sub-seasonal monsoon prediction.

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Journal
Atmosphere
Published
2026-02-12
DOI
https://doi.org/10.3390/atmos17020196
Primary Topic
Climate variability and models
Type
article
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article

Spatial Unevenness of Ground–Atmosphere Heat Sources over the Southern Tibetan Plateau and Its Relationship with Convection over the Philippine Sea

Jiajia Gao, Zhitong Yu, Junbo Wang, Hongshi Tang et al.
Atmosphere
Climate variability and models
article

Spatial Unevenness of Ground–Atmosphere Heat Sources over the Southern Tibetan Plateau and Its Relationship with Convection over the Philippine Sea

Jiajia Gao, Zhitong Yu, Junbo Wang, Hongshi Tang, Ba Sang, Pingcuo Sangdan
article en

Abstract

This study assesses the suitability of ERA5 reanalysis data for representing heat sources over the southern Tibetan Plateau (STP). The results show strong agreement between ERA5 sensible heat flux (SH), atmospheric heat source (AH), and independent observations (R2 > 0.998, RMSE < 0.82 W·m−2). Heat fluxes over the STP exhibit pronounced spatiotemporal heterogeneity. In summer, surface latent heat flux (SLHF) displays a distinct east–west gradient, with values of 140–180 W·m−2 in the east and lower values in the west, while surface sensible heat flux (SSHF) remains uniformly low (40–80 W·m−2). In spring, SSHF shows a reversed west–east pattern, with a high-value center of 100–140 W·m−2. The SH peak precedes the latent heat peak by approximately 1 h and coincides with an increase of approximately 25 W·m−2 in mid-to-upper atmospheric heating, confirming the dominant role of SH in vertical thermal development. The atmospheric heat source (Q_atm) leads the Indian monsoon onset by 7–10 days. When Q_atm exceeds 110 W·m−2, the probability of monsoon onset increases from 35% to 78%. A coupled altitude–moisture column forms between the Bay of Bengal “wet tongue” and enhanced STP heating, with a phase lag of approximately 12 h. On intra-seasonal timescales (10–20 days), STP heat source variability and Philippine Sea convection exhibit a clear ~12-day lagged teleconnection, characterized by a “heat source enhancement–convection intensification” dipole pattern. These results provide quantitative dynamic–thermodynamic indicators for sub-seasonal monsoon prediction.

AtmosphereVol. 17(2)
Hebei Meteorological Bureau (CN), Climate Centre (NL), Yunnan Institute of Environmental Sciences (CN)
Life below water
Openalex Percentile: Top 69%
Climate variability and models
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