How Does Land Cover and Its Heterogeneity Length Scales Affect the Formation of Summertime Shallow Cumulus Clouds in Observations From the US Southern Great Plains?

Tian, Jingjing; Zhang, Yunyan; Klein, Stephen A.; Öktem, R.; Wang, Likun

doi:10.1029/2021gl097070

Cited by 11 publications

(4 citation statements)

References 86 publications

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“…This assessment of different cloud regimes underscores the significance of cloud coupling in analyzing cloud-land interactions. The initiation of convection in coupled cumulus is closely tied to surface processes on a sub-grid scale (Tian et al, 2022). As these cloud regimes respond to climate change, misrepresentation of these cloud dynamics within climate models could lead to uncertainties in predictions of climate sensitivity, as posited by Schneider et al (2019).…”

Section: Discussionmentioning

confidence: 99%

Observation and Reanalysis Derived Relationships Between Cloud and Land Surface Fluxes Across Cumulus and Stratiform Coupling Over the Southern Great Plains

Su,

Li,

Zhang

et al. 2024

Geophysical Research Letters

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Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions.

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Section: Discussionmentioning

confidence: 99%

Observation and Reanalysis Derived Relationships Between Cloud and Land Surface Fluxes Across Cumulus and Stratiform Coupling Over the Southern Great Plains

Su,

Li,

Zhang

et al. 2024

Geophysical Research Letters

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“…Numerous studies have been dedicated to investigating the dynamics of boundary layer clouds, highlighting the pivotal role of land surface in modulating cloud formation and affecting the spatial and temporal distribution of low clouds (Rieck et al, 2014;Lee et al, 2019;Fast et al, 2019b;Tang et al, 2018;Tang et al, 2019;Tao et al, 2019;Tian et al, 2022;Qian et al, 2023). Despite considerable advancements in observations and modeling capabilities, simulating the boundary layer clouds remains a significant challenge, largely due to the complex feedback mechanisms between land surface fluxes, PBL processes, and cloud microphysics (Miao et al, 2019;Lareau et al, 2018;Lu et al, 2011;Fast et al, 2019a;Wang et al, 2014;Yang et al, 2018;Nogherotto et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Simulations of Boundary Layer Cloud over the US Southern Great Plains

Su,

Zhang

2024

Preprint

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Abstract. This study developed a deep learning model to simulate the complex dynamics of boundary layer clouds (BLCs) over the US Southern Great Plains. Using over twenty years of extensive observations from the Atmospheric Radiation Measurement program for training and validation, the model diagnoses the BLCs from the perspective of cloud-land coupling. Morning meteorological profiles set as the initial conditions and then identifying triggers for BLCs formation from surface meteorology. The deep learning model offer accurate simulation of the convection initiation and cloud base of BLCs. In comparison with reanalysis data (i.e., ERA-5 and MERRA-2), it provides a notable improvement in the vertical structure of low clouds from a climatological perspective. The deep learning model can serve as the cloud parameterization and extend to analyzing stratiform and cumulus clouds within reanalysis frameworks, offering insights into improving the simulation of BLCs. By quantifying biases due to various meteorological factors and parameterizations, this deep learning-driven approach bridges the observational-modeling divide. Surface humidity and parameterization emerge as key limiting factors to affect the representation of BLCs in the reanalysis data. This deep learning approach holds promise for improving the convection parameterization and advancing model diagnostics in weather forecasting and climate modelling.

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“…One of the recurring challenges in modeling (and understanding) land‐atmosphere interactions is the role of meter to kilometer scale land surface spatial heterogeneity (Tian et al., 2022). Land surface features (e.g., soils and land cover) are highly heterogeneous in space, varying on scales ranging from millimeters to hundreds of kilometers (Blöschl & Sivapalan, 1995).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Land‐Surface Effects on TKE and Cloud Formation: Statistical Insights From LES Cases

Simon,

Bragg,

Chaney

2024

JGR Atmospheres

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This manuscript investigates the impact of land‐surface heterogeneity on atmospheric processes by comparing 92 large‐eddy simulation cases over the Southern Great Plains, leveraging high‐resolution spatially heterogeneous and homogeneous land‐surface fields. Utilizing the HydroBlocks land‐surface model for detailed surface data and the Weather Research and Forecasting model for atmospheric simulations, this study emphasizes the significant role land‐surface details play in atmospheric dynamics, particularly in cloud formation and boundary‐layer development. The analysis focuses on the comparison of turbulent kinetic energy and liquid water path between heterogeneous and homogeneous surface conditions, revealing a strong correlation between surface heterogeneity and enhanced atmospheric activity. Furthermore, the study underscores that the most influential land‐surface characteristics on the atmosphere are encapsulated within the largest spatial scales, suggesting a potential simplification for incorporating sub‐grid scale land‐surface features into global models. The findings advocate for a more formal coupling between the sub‐grid land and atmosphere in Earth system models to improve the accuracy of weather and climate predictions, particularly for processes such as cloud formation and boundary‐layer dynamics that are sensitive to surface conditions. This work lays foundational insights for future parameterization schemes in global models, highlighting the importance of land‐surface details on atmospheric modeling.

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How Does Land Cover and Its Heterogeneity Length Scales Affect the Formation of Summertime Shallow Cumulus Clouds in Observations From the US Southern Great Plains?

Cited by 11 publications

References 86 publications

Observation and Reanalysis Derived Relationships Between Cloud and Land Surface Fluxes Across Cumulus and Stratiform Coupling Over the Southern Great Plains

Observation and Reanalysis Derived Relationships Between Cloud and Land Surface Fluxes Across Cumulus and Stratiform Coupling Over the Southern Great Plains

Deep Learning Driven Simulations of Boundary Layer Cloud over the US Southern Great Plains

Heterogeneous Land‐Surface Effects on TKE and Cloud Formation: Statistical Insights From LES Cases

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