대기 (Atmosphere)

  • 제34권4호
  • /
  • Pages.359-369
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  • 2024
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  • 1598-3560(pISSN)
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  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
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직접관측자료를 이용한 동아시아 토양수분 재분석자료 성능 진단

Evaluation of Soil Moisture Reanalysis Datasets over East Asia Using In-situ Measurements

  • 이보라 (부경대학교 지구환경시스템과학부 환경대기과학전공) ;
  • 서은교 (부경대학교 지구환경시스템과학부 환경대기과학전공)
  • Bora Lee (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Eunkyo Seo (Department of Environmental Atmospheric Sciences, Pukyong National University)
  • 투고 : 2024.08.28
  • 심사 : 2024.08.30
  • 발행 : 2024.11.30
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초록

This study evaluates the performance of various soil moisture reanalysis datasets over the East Asian region to identify the most suitable product for climate and hydrological studies. The analysis includes land reanalysis products generated by the Noah, VIC, and Catchment land surface models (LSMs), driven by GLDAS2.0 near-surface atmospheric forcing, alongside MERRA2 and ERA5-land datasets. The 62 in-situ soil moisture measurements observed from 1980 to 2014 are used to validate the modeled data across the entire study period, while 58 of these measurements are used for the May to September (MJJAS) period. Results indicate that, when driven by the same atmospheric forcing, the Noah and Catchment models outperform VIC, and MERRA2 shows lower errors compared to ERA5-land. Seasonal soil moisture variability, primarily driven by the East Asian monsoon, peaks in September, with MERRA2 providing the most realistic simulation of seasonal phase and amplitude. Daily soil moisture variations are better captured by MERRA2 and ERA5-land than by GLDAS2.0-based products. Overall, MERRA2 emerges as the most reliable reanalysis dataset for evaluating both the climatological mean and variability of soil moisture in East Asia. Additionally, multi-model mean analysis reveals a long-term trend of drying soil moisture and enhanced land-atmosphere coupling in northern East Asia.

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과제정보

이 논문은 국립부경대학교 자율창의학술연구비(2022년)에 의하여 연구되었음.

참고문헌

  1. Benson, D. O., and P. A. Dirmeyer, 2023: The soil Moisture-Surface flux relationship as a factor for extreme heat predictability in subseasonal to seasonal forecasts. J. Climate, 36, 6375-6392, doi:10.1175/JCLID-22-0447.1.
  2. Chen, F., and Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251-7268, doi:10.1029/95JD02165.
  3. Dirmeyer, P. A., 2003: The role of the land surface background state in climate predictability. J. Hydrometeor, 4, 599-610, doi:10.1175/1525-7541(2003)004<0599:TROTLS>2.0.CO;2.
  4. Dirmeyer, P. A., 2011: The terrestrial segment of soil moisture-climate coupling: SOIL MOISTURE-CLIMATE COUPLING. Geophys. Res. Lett., 38, L16702, doi:10.1029/2011GL048268.
  5. Dirmeyer, P. A., and Coauthors, 2018: Verification of Land-Atmosphere coupling in forecast models, reanalyses, and land surface models using flux site observations. J. Hydrometeor., 19, 375-392, doi:10.1175/JHM-D-17-0152.1.
  6. Dirmeyer, P. A., X. Gao, M. Zhao, Z. Guo, T. Oki, and N. Hanasaki, 2006: GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87, 1381-1397, doi:10.1175/BAMS-87-10-1381.
  7. Dirmeyer, P. A., G. Balsamo, E. M. Blyth, R. Morrison, and H. M. Cooper, 2021: Land-Atmosphere interactions exacerbated the drought and heatwave over northern Europe during summer 2018. AGU Advances, 2, e2020AV000283, doi:10.1029/2020AV000283.
  8. Dorigo, W., and Coauthors, 2021: The international soil moisture network: serving earth system science for over a decade. Hydrol. Earth Syst. Sci., 25, 5749-5804, doi:10.5194/hess-25-5749-2021.
  9. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the national centers for environmental prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.
  10. Gelaro, R., and Coauthors, 2017: The Modern-Era retrospective analysis for research and applications, Version 2 (MERRA-2). J. Climate, 30, 5419-5454, doi:10.1175/JCLI-D-16-0758.1.
  11. Guo, Z., and P. A. Dirmeyer, 2006: Evaluation of GSWP-2 soil moisture simulations, Part I: Inter-model comparison. J. Geophys. Res., 111, D22S02, doi:10.1029/2006JD007233.
  12. Guo, Z., P. A. Dirmeyer, Z.-Z. Hu, X. Gao, and M. Zhao, 2006: Evaluation of GSWP-2 soil moisture simulations. Part 2: Sensitivity to external meteorological forcing. J. Geophys. Res., 111, D22S03, doi:10.1029/2006JD007845.
  13. Hsu, H., P. A. Dirmeyer, and E. Seo, 2024: Exploring the mechanisms of the soil moisture-air temperature hypersensitive coupling regime. Water Resour. Res., 60, e2023WR036490, doi:10.1029/2023WR036490.
  14. Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res., 105, 24809-24822, doi:10.1029/2000JD900327.
  15. Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14415-14428, doi:10.1029/94JD00483.
  16. Munoz-Sabater, J., and Coauthors, 2021: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data, 13, 4349-4383, doi:10.5194/essd-13-4349-2021.
  17. Richter, J. H., and Coauthors, 2024: Quantifying sources of subseasonal prediction skill in CESM2. npj Clim. Atmos. Sci., 7, 59, doi:10.1038/s41612-024-00595-4.
  18. Rodell, M., and Coauthors, 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381-394, doi:10.1175/BAMS-85-3-381.
  19. Santanello, J. A., and Coauthors, 2018: Land-atmosphere interactions: The LoCo perspective. Bull. Amer. Meteor. Soc., 99, 1253-1272, doi:10.1175/BAMS-D17-0001.1.
  20. Seo, E., and P. A. Dirmeyer, 2022a: Improving the ESA CCI daily soil moisture time series with physically-based land surface model datasets using a Fourier time-filtering method. J. Hydrometeor., 23, 473-489, doi:10.1175/JHM-D-21-0120.1.
  21. Seo, E., and P. A. Dirmeyer, 2022b: Understanding the diurnal cycle of land-atmosphere interactions from flux site observations. Hydrol. Earth Syst. Sci., 26, 5411-5429, doi:10.5194/hess-26-5411-2022.
  22. Seo, E., and Coauthors, 2019: Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events. Clim. Dyn., 52, 1695-1709, doi:10.1007/s00382-018-4221-4.
  23. Seo, E., M.-I. Lee, S. D. Schubert, R. D. Koster, and H.-S. Kang, 2020: Investigation of the 2016 Eurasia heat wave as an event of the recent warming. Environ. Res. Lett., 15, 114018, doi:10.1088/1748-9326/abbbae.
  24. Seo, E., P. A. Dirmeyer, M. Barlage, H. Wei, and M. Ek, 2024: Evaluation of land-atmosphere coupling processes and climatological bias in the UFS global coupled model. J. Hydrometeor., 25, 161-175, doi:10.1175/JHM-D-23-0097.1.
  25. Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-Year High-Resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 3088-3111, doi:10.1175/JCLI3790.1.
  26. Tak, S., E. Seo, P. A. Dirmeyer, and M.-I. Lee, 2024: The role of soil moisture-temperature coupling for the 2018 Northern European heatwave in a subseasonal forecast. Wea. Climate Extremes, 44, 100670, doi:10.1016/j.wace.2024.100670.