Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone

Taeho Kim; Wenbo Zhou; Vinh Ngoc Tran; Liujing Zhang; Jingfeng Wang; Modi Zhu; Aleksey Y. Sheshukov; Tianqi Zhang; Desheng Liu; Valeriy S. Mazepa; Alexandr A. Sokolov; Victor V. Valdayskikh; Valeriy Y. Ivanov

doi:10.1016/j.agrformet.2025.110814

Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone

Taeho Kim, Wenbo Zhou, Vinh Ngoc Tran, Liujing Zhang, Jingfeng Wang, Modi Zhu, Aleksey Y. Sheshukov, Tianqi Zhang, Desheng Liu, Valeriy S. Mazepa, Alexandr A. Sokolov, Victor V. Valdayskikh, Valeriy Y. Ivanov

Earth Systems Modeling

Research output: Contribution to journal › Article › peer-review

Abstract

Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone.

Original language	English
Article number	110814
Journal	Agricultural and Forest Meteorology
Volume	374
DOIs	https://doi.org/10.1016/j.agrformet.2025.110814
State	Published - Nov 15 2025

Funding

This research is sponsored by the National Science Foundation (NSF) Office of Polar Programs grants 1725654 (University of Michigan), 1724868 (Kansas State University), 1724633 (Georgia Tech), and 1724786 (Ohio State University), respectively. The NSF Navigating the New Arctic Program Track-I grants 2126792, 2126793, 2126797, 2126798 to the same co-authors facilitated this work. V. Ivanov and V. Mazepa acknowledge the support from project RUB1-7032-EK-11 funded by the U.S. Civilian Research & Development Foundation . V. Mazepa acknowledges the partial support from grant RFBR-19-05-00756 from the Russian Foundation for Basic Research . V. Valdayskikh was supported by the state task of the Ministry of Education and Science of the Russian Federation , project no. FEUZ 2023-0019 . The team is highly grateful for the design of field monitoring stations as well as the relentless maintenance efforts to Yuriy Trubnikov. The field support by Vyacheslav Osokin, Grigoriy Popov, and Andrey Baryshnikov is acknowledged. The field data that support the findings of this study can be downloaded below links and be available from the corresponding author upon request. In addition, the final reconstructed dataset for each plot has been uploaded in the repository on Zenodo (https://doi.org/10.5281/zenodo.15083964). Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J.: ENGAYU-FOREST1: Hydrometeorological, subsurface, and snow data in Western Siberia near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2k649v2x, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: ENGAYU-TREES1: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a23775w97, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: ENGAYU-TREES2: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2tq5rg04, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: ENGAYU-TUNDRA1: Hydrometeorological, subsurface, and snow data in Western Siberia near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2m61br3n, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: RAIIZ-TREES1: Hydrometeorological, subsurface, and snow data in Western Siberia near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2fj29f20, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: RAIIZ-TUNDRA1: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2610vt4d, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: LPTEG-TREES1: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2nz80r6h, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: LPTEG-TREES2: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://doi.org/10.18739/A2J960B6W, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: LPTEG-TUNDRA1: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2xg9fc5h, 2023. Ivanov, V. Sheshukov, A. Zhou, W. Zhu, M. and Wang, J: LPTEG-TUNDRA2: Hydrometeorological, Subsurface, and Snow Data in Western Siberia Near Salekhard of Yamal-Nenets Autonomous District, Russia, 2018–2021, NSF Arctic Data Center, https://dx.doi.org/10.18739/a2sq8qk0w, 2023. This research is sponsored by the National Science Foundation (NSF) Office of Polar Programs grants 1725654 (University of Michigan), 1724868 (Kansas State University), 1724633 (Georgia Tech), and 1724786 (Ohio State University), respectively. The NSF Navigating the New Arctic Program Track-I grants 2126792, 2126793, 2126797, 2126798 to the same co-authors facilitated this work. V. Ivanov and V. Mazepa acknowledge the support from project RUB1-7032-EK-11 funded by the U.S. Civilian Research & Development Foundation. V. Mazepa acknowledges the partial support from grant RFBR-19-05-00756 from the Russian Foundation for Basic Research. V. Valdayskikh was supported by the state task of the Ministry of Education and Science of the Russian Federation, project no. FEUZ 2023-0019. The team is highly grateful for the design of field monitoring stations as well as the relentless maintenance efforts to Yuriy Trubnikov. The field support by Vyacheslav Osokin, Grigoriy Popov, and Andrey Baryshnikov is acknowledged.

Keywords

Arctic
Data reconstruction
Forest-tundra ecotone
Net radiation
Polar urals
Precipitation effects

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10.1016/j.agrformet.2025.110814

Cite this

Kim, T., Zhou, W., Tran, V. N., Zhang, L., Wang, J., Zhu, M., Sheshukov, A. Y., Zhang, T., Liu, D., Mazepa, V. S., Sokolov, A. A., Valdayskikh, V. V., & Ivanov, V. Y. (2025). Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone. Agricultural and Forest Meteorology, 374, Article 110814. https://doi.org/10.1016/j.agrformet.2025.110814

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title = "Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone",

abstract = "Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone.",

keywords = "Arctic, Data reconstruction, Forest-tundra ecotone, Net radiation, Polar urals, Precipitation effects",

author = "Taeho Kim and Wenbo Zhou and Tran, \{Vinh Ngoc\} and Liujing Zhang and Jingfeng Wang and Modi Zhu and Sheshukov, \{Aleksey Y.\} and Tianqi Zhang and Desheng Liu and Mazepa, \{Valeriy S.\} and Sokolov, \{Alexandr A.\} and Valdayskikh, \{Victor V.\} and Ivanov, \{Valeriy Y.\}",

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year = "2025",

month = nov,

day = "15",

doi = "10.1016/j.agrformet.2025.110814",

language = "English",

volume = "374",

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issn = "0168-1923",

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TY - JOUR

T1 - Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone

AU - Kim, Taeho

AU - Zhou, Wenbo

AU - Tran, Vinh Ngoc

AU - Zhang, Liujing

AU - Wang, Jingfeng

AU - Zhu, Modi

AU - Sheshukov, Aleksey Y.

AU - Zhang, Tianqi

AU - Liu, Desheng

AU - Mazepa, Valeriy S.

AU - Sokolov, Alexandr A.

AU - Valdayskikh, Victor V.

AU - Ivanov, Valeriy Y.

PY - 2025/11/15

Y1 - 2025/11/15

N2 - Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone.

AB - Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone.

KW - Arctic

KW - Data reconstruction

KW - Forest-tundra ecotone

KW - Net radiation

KW - Polar urals

KW - Precipitation effects

UR - https://www.scopus.com/pages/publications/105014258264

U2 - 10.1016/j.agrformet.2025.110814

DO - 10.1016/j.agrformet.2025.110814

M3 - Article

AN - SCOPUS:105014258264

SN - 0168-1923

VL - 374

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

M1 - 110814

ER -

Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone

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