Accounting for Topographic Effects on Snow Cover Fraction and Surface Albedo Simulations Over the Tibetan Plateau in Winter

Miao, Xin; Guo, Weidong; Qiu, Bo; Lu, Sha; Xue, Yongkang; Sun, Shao

doi:10.1029/2022ms003035

Cited by 10 publications

(13 citation statements)

References 74 publications

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“…To this end, an orographic scaling factor is applied to modify the default SCF scheme following the study of Miao et al. (2022). This scaling factor is derived from Douville et al.…”

Section: Model Development and Experimental Designmentioning

confidence: 99%

“…There are still uncertainties in terms of the correction based on δ z in Equation . For example, SD might be more directly related to δ z , the calibration of this scheme is based on coarse resolution data (Douville et al., 1995), the introduction of an upper limit (200 m) is based on 0.1° offline simulations (Miao et al., 2022), and the index of the orographic scaling factor may not be a square root metric. Thus, some key parameters can be further calibrated for the TP region.…”

Section: Model Development and Experimental Designmentioning

confidence: 99%

“…It is widely acknowledged that the TP region has complex and steep terrain and thus the orographic variability should be considered in SCF parameterizations. To this end, an orographic scaling factor is applied to modify the default SCF scheme following the study of Miao et al (2022). This scaling factor is derived from Douville et al (1995), as expressed in Equation 2.…”

Section: Parameterization Of the Snow Cover Fractionmentioning

confidence: 99%

“…Jiang et al (2020) improved the simulation of the SCF by optimizing physics-scheme options and SCF schemes based on the Noah land surface model (LSM) with multiparameterization options (Noah-MP), and demonstrated that the latter is more important. Miao et al (2022) reduced biases in the simulated SCF and surface albedo in the Simplified Simple Biosphere Model by accounting for topographic effects in SCF schemes. W. L. Wang et al (2020) reduced the biases in snow depth (SD) in Noah-MP model by introducing a new shallow fresh snow albedo scheme based on the Snow and Ice Aerosol Radiation SNICAR radiation transfer model (Flanner & Zender, 2006).…”

mentioning

confidence: 99%

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Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

Zhou,

Ding,

Yang

et al. 2023

J Adv Model Earth Syst

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Most climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region.

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“…To this end, an orographic scaling factor is applied to modify the default SCF scheme following the study of Miao et al. (2022). This scaling factor is derived from Douville et al.…”

Section: Model Development and Experimental Designmentioning

confidence: 99%

Section: Model Development and Experimental Designmentioning

confidence: 99%

Section: Parameterization Of the Snow Cover Fractionmentioning

confidence: 99%

mentioning

confidence: 99%

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Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

Zhou,

Ding,

Yang

et al. 2023

J Adv Model Earth Syst

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show abstract

“…We resampled the original data to generate the elevation data with a 0.05 • spatial resolution and another one with a 0.01 • spatial resolution to calculate the topographic standard deviation. The topographic standard deviation of each 0.05 • grid cell is the standard deviation of the DEM data of the 25 grid cells with a 0.01 • spatial resolution, which characterizes the variance of subgrid-scale orography and represents the complexity of terrain [46,47]. A larger topographic standard deviation indicates a more complex terrain.…”

Section: Land Cover and Digital Elevation Datamentioning

confidence: 99%

Divergent response of crops and natural vegetation to the record-breaking extreme precipitation event in 2020 modulated by topography

Chen

Qiu

Guo

et al. 2023

Environ. Res. Lett.

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Extreme precipitation events have posed a threat to global terrestrial ecosystems in recent decades. However, the response of terrestrial ecosystems to extreme precipitation in areas with various vegetation types and complex topography remains unclear. Here, we used satellite-based solar-induced chlorophyll fluorescence (SIF) measurements, a direct proxy of photosynthetic activity, to assess the response of vegetation to the record-breaking extreme precipitation event during the East Asia monsoon season in eastern China in 2020. Our results demonstrate that vegetation was adaptable to moderate increases in precipitation, but photosynthetic activity was significantly inhibited by exposure to extreme precipitation because of insufficient PAR and waterlogging. The responses of vegetation photosynthesis to extreme precipitation were regulated by both vegetation type and topography. Crops in the lowland areas in eastern China were severely damaged due to their higher vulnerability and exposure to extreme precipitation. The topography-induced redistribution of precipitation accounts for the modulation of vegetation response to extreme precipitation. Our research highlights the urgent need for effective management and adaptive measures of croplands under the elevated risk of extreme precipitation in the future.

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Parameterizations of Snow Cover, Snow Albedo and Snow Density in Land Surface Models: A Comparative Review

Lee,

Gim,

Park

2023

Asia-Pac J Atmos Sci

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Snow plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. While snow plays a crucial role as a boundary condition in meteorological applications and serves as a vital water resource in certain regions, the acquisition of its observational data poses significant challenges. An effective alternative lies in utilizing simulation data generated by Land Surface Models (LSMs), which accurately calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. In this regard, the synthetic intercomparisons of the snow physics in the LSMs can give insight for further improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the earth system models or operational forecasting systems. We examined single-layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), the University of Torino land surface Process Interaction model in Atmosphere (UTOPIA), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). Through the comparison analysis, we emphasized that inclusion of geomorphic and vegetation-related variables such as elevation, slope, time-varying roughness length, and vegetation indexes as well as optimized parameters for specific regions, in the snow-related physical processes, are crucial for further improvement of the LSMs.

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Accounting for Topographic Effects on Snow Cover Fraction and Surface Albedo Simulations Over the Tibetan Plateau in Winter

Cited by 10 publications

References 74 publications

Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

Divergent response of crops and natural vegetation to the record-breaking extreme precipitation event in 2020 modulated by topography

Parameterizations of Snow Cover, Snow Albedo and Snow Density in Land Surface Models: A Comparative Review

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