Analytic Parameterization of Longwave Optical Properties of Bulk Vegetation Layer Permitting Non‐Zero Leaf Reflectivity and Its Implementation in CLM5

Abstract For modern land surface models (LSMs) representing a singular bulk vegetation layer, the longwave optical properties (i.e., emissivity, reflectivity, and transmittivity) of vegetation layer are derived with a simplified constraint of assuming zero leaf reflectivity. This constraint is necessary, for instance, to the Beer–Lambert (B–L) law to establish a relationship between the optical properties and leaf area index. However, the simplified constraint leads to an overestimation of land surface emissivity in the vegetated regions. In this study, we introduce a new scheme considering realistic leaf reflectivity values rather than assuming zero. This new scheme is based on the relationship derived from the B–L law, but it is statistically augmented to consider the effects of leaf reflections. It is designed to emulate a multi‐vegetation‐layer numerical model known as the Norman model, which is capable of numerical calculations of multi‐reflections among leaves. This new method consists of only a couple of simple equations; despite its simplicity, it very closely mimics the Norman model; The discrepancy of the results between the new method and the Norman model is less than measurement uncertainties for any combination of input parameters. When the new scheme is implemented in the Community Land Model version 5 (CLM5), the land surface emissivity values are simulated much more consistently with global measurements, resulting in significant alterations of land surface energy budget. The enhanced realism through our new scheme is poised to contribute to more accurate numerical weather and climate simulations.

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Publication Details

Journal
Journal of Advances in Modeling Earth Systems
Published
2024-09-01
DOI
https://doi.org/10.1029/2023ms003957
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.92

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article

Analytic Parameterization of Longwave Optical Properties of Bulk Vegetation Layer Permitting Non‐Zero Leaf Reflectivity and Its Implementation in CLM5

Seon Ki Park, Hyeon‐Ju Gim
2 citations
Journal of Advances in Modeling Earth Systems
Remote Sensing in Agriculture
0.92
article

Analytic Parameterization of Longwave Optical Properties of Bulk Vegetation Layer Permitting Non‐Zero Leaf Reflectivity and Its Implementation in CLM5

Seon Ki Park, Hyeon‐Ju Gim
article en
2 citations

Abstract

Abstract For modern land surface models (LSMs) representing a singular bulk vegetation layer, the longwave optical properties (i.e., emissivity, reflectivity, and transmittivity) of vegetation layer are derived with a simplified constraint of assuming zero leaf reflectivity. This constraint is necessary, for instance, to the Beer–Lambert (B–L) law to establish a relationship between the optical properties and leaf area index. However, the simplified constraint leads to an overestimation of land surface emissivity in the vegetated regions. In this study, we introduce a new scheme considering realistic leaf reflectivity values rather than assuming zero. This new scheme is based on the relationship derived from the B–L law, but it is statistically augmented to consider the effects of leaf reflections. It is designed to emulate a multi‐vegetation‐layer numerical model known as the Norman model, which is capable of numerical calculations of multi‐reflections among leaves. This new method consists of only a couple of simple equations; despite its simplicity, it very closely mimics the Norman model; The discrepancy of the results between the new method and the Norman model is less than measurement uncertainties for any combination of input parameters. When the new scheme is implemented in the Community Land Model version 5 (CLM5), the land surface emissivity values are simulated much more consistently with global measurements, resulting in significant alterations of land surface energy budget. The enhanced realism through our new scheme is poised to contribute to more accurate numerical weather and climate simulations.

Journal of Advances in Modeling Earth SystemsVol. 16(9)
Ewha Womans University (KR), Korea Institute of Atmospheric Prediction Systems (KR)
Korea Meteorological Administration, National Research Foundation of Korea
Openalex Percentile: Top 23%
Remote Sensing in Agriculture
0.92
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