Kang et al. (2026) Impacts of full-scale turbulence intermittency on land-atmosphere interactions in the hinterland of the Taklimakan Desert

Identification

• Journal: Atmospheric Research
• Year: 2026
• Date: 2026-01-16
• Authors: Peixuan Kang, Yan Ren, Hongsheng Zhang, Wei Wei, Yue Xu, Ali Mamtimin, Yu Wang, Meiqi Song, Jiening Liang, Lei Zhang, Jianping Huang
• DOI: 10.1016/j.atmosres.2026.108782

Research Groups

• Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China
• Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
• Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
• State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, China Meteorological Administration (CMA), Beijing, China
• Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
• National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi, China
• Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi, China

Short Summary

This study investigates the impact of full-scale turbulence intermittency on land-atmosphere interactions in the Taklimakan Desert using summer observations. It develops a spectra-based algorithm to classify intermittency regimes and proposes empirical correction schemes that significantly improve estimates of turbulent statistics and reduce counter-gradient transport.

Objective

• To quantitatively investigate the impact of sub-mesoscale motions and turbulence intermittency on land-atmosphere exchange in the Taklimakan Desert within a unified theoretical and methodological framework.
• To develop an automatic classification algorithm for turbulence intermittency regimes (no intermittency, large-scale intermittency, small-scale intermittency, and full-scale intermittency).
• To quantify the occurrence frequency of each regime and its associated wind speed, wind shear, and stability conditions.
• To establish empirical relationships linking intermittency strength to wind speed and wind shear.
• To propose regime-dependent correction relationships for land-atmosphere exchange quantities, with implications for observational post-processing and potential model parameterizations.

Study Configuration

• Spatial Scale: Tazhong meteorological station (39°00′N, 83°40′E) in the hinterland of the Taklimakan Desert, China. Measurements were taken at heights of 0.5 m, 1 m, 2 m, 4 m, and 10 m for the meteorological tower, and approximately 3 m for the eddy-covariance system.
• Temporal Scale: 1 to 31 July 2023 (intensive summer observation campaign). Turbulence data were processed with a 30-minute averaging interval.

Methodology and Data

• Models used:
  • Spectra-based automatic classification algorithm for turbulence intermittency regimes.
  • SMT (Separation and Reconstruction of Sub-mesoscale and Turbulent Motions) algorithm, based on the Hilbert-Huang transform, used to calculate the Local Intermittency Strength of Turbulence (LIST) index.
  • EddyPro software (v 6.2.1) for raw turbulence data pre-processing.
• Data sources:
  • Field observation campaign at Tazhong meteorological station.
  • 10 m meteorological tower equipped with temperature and humidity sensors (HMP45C, Vaisala Co.) and wind speed and direction sensors (Wind Observer II-65, Gill Instruments Ltd.).
  • Independent eddy-covariance system mounted at approximately 3 m, consisting of a three-dimensional sonic anemometer and an open-path gas analyzer (IRGASON, Campbell Scientific Inc.), sampling at 10 Hz.

Main Results

• A spectra-based automatic classification algorithm was developed, categorizing turbulence into four regimes: Regime 1 (no intermittency), Regime 2 (large-scale intermittency), Regime 3 (small-scale intermittency), and Regime 4 (full-scale intermittency).
• Regimes 2 and 4, corresponding to large-scale and full-scale intermittency, occur most frequently, with Regime 2 dominating for horizontal wind components (e.g., 78.9% for u) and Regime 4 being more prevalent for scalars (e.g., 47.8% for CO2).
• Large-scale intermittency (Regime 2) can occur under various wind speed and stability conditions but is stronger under nocturnal weak-wind stable conditions.
• Full-scale intermittency (Regime 4) predominantly occurs under nocturnal weak winds or near-neutral conditions with relatively strong winds during day-night transitions.
• Empirical power-law relationships were derived for Regimes 2 and 4, linking large-scale (LIST) and small-scale (IF) intermittency strengths to wind speed and wind shear. For example, LIST generally increases with wind speed and wind shear, while IF decreases.
• Sub-mesoscale motions lead to a general overestimation of standard deviations, turbulent fluxes, and turbulent kinetic energy (TKE) when using original data in Regimes 2 and 4.
• The impact of intermittency is strongest for momentum and water vapor transport (e.g., 82% overestimation for vʹwʹ in Regime 2, 90% overestimation for σq in Regime 4), and less pronounced for heat transport.
• For most quantities (σu, σv, σθ, σCO2, −uʹwʹ, vʹwʹ, wʹθʹ, wʹCO2ʹ, TKE), the overestimation biases are larger in Regime 2 than in Regime 4, indicating a stronger influence of sub-mesoscale motions under large-scale intermittency.
• Proposed regime-dependent empirical correction schemes effectively reduce the scatter in the relationship between turbulence intensity and mean wind speed and decrease the fractions of counter-gradient momentum and sensible heat transport, thereby improving the physical consistency of flux estimates.

Contributions

• Developed a novel spectra-based automatic classification algorithm to objectively identify and quantify large-scale, small-scale, and full-scale turbulence intermittency within a unified framework.
• Provided quantitative characterization of intermittency regimes, their occurrence frequencies, and associated meteorological conditions over the Taklimakan Desert, a region with strong thermal forcing.
• Established empirical relationships between intermittency strength (LIST, IF) and routinely available meteorological variables (wind speed, wind shear), facilitating easier diagnosis of intermittency.
• Proposed regime-dependent empirical correction schemes for turbulent statistics (standard deviations, fluxes, TKE), significantly improving the accuracy and physical consistency of land-atmosphere exchange estimates in desert regions.
• Deepened understanding of turbulent intermittency over the Taklimakan Desert, with important implications for improving parameterizations in weather and climate prediction models.

Funding

• National Natural Science Foundation of China (42475184, 42305071, 42375185)
• Beijing Science and Technology Program Project (Z241100009124014)
• Longyuan Youth Talent Project

Citation

@article{Kang2026Impacts,
  author = {Kang, Peixuan and Ren, Yan and Zhang, Hongsheng and Wei, Wei and Xu, Yue and Mamtimin, Ali and Wang, Yu and Song, Meiqi and Liang, Jiening and Zhang, Lei and Huang, Jianping},
  title = {Impacts of full-scale turbulence intermittency on land-atmosphere interactions in the hinterland of the Taklimakan Desert},
  journal = {Atmospheric Research},
  year = {2026},
  doi = {10.1016/j.atmosres.2026.108782},
  url = {https://doi.org/10.1016/j.atmosres.2026.108782}
}