• Chen, Y., K. Yang, W. Tang, J. Qin, and L. Zhao, 2012: Parameterizing soil organic carbon’s impacts on soil porosity and thermal parameters for eastern Tibet grasslands. Sci. China Earth Sci., 55, 10011011, https://doi.org/10.1007/s11430-012-4433-0.

    • Search Google Scholar
    • Export Citation
  • Cui, T., C. Li, and F. Tian, 2021: Evaluation of temperature and precipitation simulations in CMIP6 models over the Tibetan Plateau. Earth Space Sci., 8, e2020EA001620, https://doi.org/10.1029/2020EA001620.

  • Dai, Y., N. Wei, H. Yuan, S. Zhang, W. Shangguan, S. Liu, X. Lu, and Y. Xin, 2019a: Evaluation of soil thermal conductivity schemes for use in land surface modelling. J. Adv. Model. Earth Syst., 11, 34543473, https://doi.org/10.1029/2019MS001723.

    • Search Google Scholar
    • Export Citation
  • Dai, Y., and Coauthors, 2019b: A global high-resolution data set of soil hydraulic and thermal properties for land surface modeling. J. Adv. Model. Earth Syst., 11, 29963023, https://doi.org/10.1029/2019MS001784.

    • Search Google Scholar
    • Export Citation
  • Del Grosso, S., D. Ojima, W. Parton, A. Mosier, G. Peterson, and D. Schimel, 2002: Simulated effects of dryland cropping intensification on soil organic matter and greenhouse gas exchanges using the DAYCENT ecosystem model. Environ. Pollut., 116 (Suppl. 1), S75S83, https://doi.org/10.1016/S0269-7491(01)00260-3.

    • Search Google Scholar
    • Export Citation
  • Farouki, O. T., 1981: The thermal properties of soils in cold regions. Cold Reg. Sci. Technol., 5, 6775, https://doi.org/10.1016/0165-232X(81)90041-0.

    • Search Google Scholar
    • Export Citation
  • Ganeshi, N. G., M. Mujumdar, R. Krishnan, and M. Goswami, 2020: Understanding the linkage between soil moisture variability and temperature extremes over the Indian region. J. Hydrol., 589, 125183, https://doi.org/10.1016/j.jhydrol.2020.125183.

    • Search Google Scholar
    • Export Citation
  • Gao, Y., K. Li, F. Chen, Y. Jiang, and C. Lu, 2015a: Assessing and improving Noah-MP land model simulations for the central Tibetan Plateau. J. Geophys. Res. Atmos., 120, 92589278, https://doi.org/10.1002/2015JD023404.

    • Search Google Scholar
    • Export Citation
  • Gao, Y., J. Xu, and D. Chen, 2015b: Evaluation of WRF mesoscale climate simulations over the Tibetan Plateau during 1979–2011. J. Climate, 28, 28232841, https://doi.org/10.1175/JCLI-D-14-00300.1.

    • Search Google Scholar
    • Export Citation
  • Guo, Z., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7, 611625, https://doi.org/10.1175/JHM511.1.

    • Search Google Scholar
    • Export Citation
  • He, J., K. Yang, W. Tang, H. Lu, J. Qin, Y. Chen, and X. Li, 2020: The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, https://doi.org/10.1126/science.1100217.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590610, https://doi.org/10.1175/JHM510.1.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and A. G. Slater, 2008: Incorporating organic soil into a global climate model. Climate Dyn., 30, 145160, https://doi.org/10.1007/s00382-007-0278-1.

    • Search Google Scholar
    • Export Citation
  • Letts, M. G., N. T. Roulet, N. T. Comer, M. R. Skarupa, and D. L. Verseghy, 2000: Parametrization of peatland hydraulic properties for the Canadian land surface scheme. Atmos.–Ocean, 38, 141160, https://doi.org/10.1080/07055900.2000.9649643.

    • Search Google Scholar
    • Export Citation
  • Li, D., P. Tian, H. Luo, T. Hu, B. Dong, Y. Cui, S. Khan, and Y. Luo, 2020: Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau. Sci. Total Environ., 742, 140570, https://doi.org/10.1016/j.scitotenv.2020.140570.

    • Search Google Scholar
    • Export Citation
  • Lin, C., D. Chen, K. Yang, and T. Ou, 2018: Impact of model resolution on simulating the water vapor transport through the central Himalayas: Implication for models’ wet bias over the Tibetan Plateau. Climate Dyn., 51, 31953207, https://doi.org/10.1007/s00382-018-4074-x.

    • Search Google Scholar
    • Export Citation
  • Lin, C., and Coauthors, 2021: Summer afternoon precipitation associated with wind convergence near the Himalayan glacier fronts. Atmos. Res., 259, 105658, https://doi.org/10.1016/j.atmosres.2021.105658.

  • Lin, Y., W. Dong, M. Zhang, Y. Xie, W. Xue, J. Huang, and Y. Luo, 2017: Causes of model dry and warm bias over central U.S. and impact on climate projections. Nat. Commun., 8, 881, https://doi.org/10.1038/s41467-017-01040-2.

    • Search Google Scholar
    • Export Citation
  • Mahmood, R., and Coauthors, 2014: Land cover changes and their biogeophysical effects on climate. Int. J. Climatol., 34, 929953, https://doi.org/10.1002/joc.3736.

    • Search Google Scholar
    • Export Citation
  • Meng, X., and Coauthors, 2018: Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model. Environ. Res. Lett., 13, 044028, https://doi.org/10.1088/1748-9326/aab44a.

    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Search Google Scholar
    • Export Citation
  • Parton, W. J., M. Hartman, D. Ojima, and D. Schimel, 1998: DAYCENT and its land surface submodel: Description and testing. Global Planet. Change, 19, 3548, https://doi.org/10.1016/S0921-8181(98)00040-X.

    • Search Google Scholar
    • Export Citation
  • Qiu, J., 2008: China: The third pole. Nature, 454, 393396, https://doi.org/10.1038/454393a.

  • Ran, Y., X. Li, and G. Cheng, 2018: Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau. Cryosphere, 12, 595608, https://doi.org/10.5194/tc-12-595-2018.

    • Search Google Scholar
    • Export Citation
  • Ran, Y., and Coauthors, 2021: Mapping the permafrost stability on the Tibetan Plateau for 2005–2015. Sci. China Earth Sci., 64, 6279, https://doi.org/10.1007/s11430-020-9685-3.

    • Search Google Scholar
    • Export Citation
  • Sakaguchi, K., and X. Zeng, 2009: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5). J. Geophys. Res., 114, D01107, https://doi.org/10.1029/2008JD010834.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., Jr., C. D. Peters-Lidard, and S. V. Kumar, 2011: Diagnosing the sensitivity of local land–atmosphere coupling via the soil moisture–boundary layer interaction. J. Hydrometeor., 12, 766786, https://doi.org/10.1175/JHM-D-10-05014.1.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., M. D. Heiser, F. G. Hall, S. B. Verma, R. L. Desjardins, P. M. Schuepp, and J. I. MacPherson, 1997: The impact of using area-averaged land surface properties-topography, vegetation condition, soil wetness-in calculations of intermediate scale (approximately 10 km2) surface-atmosphere heat and moisture fluxes. J. Hydrol., 190, 269301, https://doi.org/10.1016/S0022-1694(96)03130-7.

    • Search Google Scholar
    • Export Citation
  • Shangguan, W., Y. Dai, Q. Duan, B. Liu, and H. Yuan, 2014: A global soil data set for earth system modeling. J. Adv. Model. Earth Syst., 6, 249263, https://doi.org/10.1002/2013MS000293.

    • Search Google Scholar
    • Export Citation
  • Su, F., X. Duan, D. Chen, Z. Hao, and L. Cuo, 2013: Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau. J. Climate, 26, 31873208, https://doi.org/10.1175/JCLI-D-12-00321.1.

    • Search Google Scholar
    • Export Citation
  • Sun, J., Y. Chen, K. Yang, H. Lu, L. Zhao, and D. Zheng, 2021: Influence of organic matter on soil hydrothermal processes in the Tibetan Plateau: Observation and parameterization. J. Hydrometeor., 22, 26592674, https://doi.org/10.1175/JHM-D-21-0059.1.

    • Search Google Scholar
    • Export Citation
  • Sun, S., and Coauthors, 2016: Improving soil organic carbon parameterization of land surface model for cold regions in the northeastern Tibetan Plateau, China. Ecol. Modell., 330, 115, https://doi.org/10.1016/j.ecolmodel.2016.03.014.

    • Search Google Scholar
    • Export Citation
  • Wang, X., V. Tolksdorf, M. Otto, and D. Scherer, 2020: WRF-based dynamical downscaling of ERA5 reanalysis data for high mountain Asia: Towards a new version of the High Asia Refined analysis. Int. J. Climatol., 41, 743762, https://doi.org/10.1002/joc.6686.

    • Search Google Scholar
    • Export Citation
  • Xu, X., and Q. Wu, 2021: Active layer thickness variation on the Qinghai-Tibetan Plateau: Historical and projected trends. J. Geophys. Res. Atmos., 126, e2021JD034841, https://doi.org/10.1029/2021JD034841.

  • Xue, H., Q. Jin, B. Yi, G. L. Mullendore, X. Zheng, and H. Jin, 2017: Modulation of soil initial state on WRF model performance over China. J. Geophys. Res. Atmos., 122, 11 27811 300, https://doi.org/10.1002/2017JD027023.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., F. De Sales, R. Vasic, C. R. Mechoso, A. Arakawa, and S. Prince, 2010: Global and seasonal assessment of interactions between climate and vegetation biophysical processes: A GCM study with different land–vegetation representations. J. Climate, 23, 14111433, https://doi.org/10.1175/2009JCLI3054.1.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., and Coauthors, 2021: Impact of initialized land surface temperature and snowpack on subseasonal to seasonal prediction project, phase I (LS4P-I): Organization and experimental design. Geosci. Model Dev., 14, 44654494, https://doi.org/10.5194/gmd-14-4465-2021.

    • Search Google Scholar
    • Export Citation
  • Yang, K., and J. He, 2011: China meteorological forcing dataset (1979–2015). National Tibetan Plateau Data Center, accessed 19 March 2019, https://doi.org/10.3972/westdc.002.2014.db.

  • Yang, K., T. Koike, B. Ye, and L. Bastidas, 2005: Inverse analysis of the role of soil vertical heterogeneity in controlling surface soil state and energy partition. J. Geophys. Res., 110, D08101, https://doi.org/10.1029/2004JD005500.

    • Search Google Scholar
    • Export Citation
  • Yang, K., and Coauthors, 2013: A multiscale soil moisture and freeze-thaw monitoring network on the Third Pole. Bull. Amer. Meteor. Soc., 94, 19071916, https://doi.org/10.1175/BAMS-D-12-00203.1.

    • Search Google Scholar
    • Export Citation
  • Yao, T., and Coauthors, 2019: Recent Third Pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: Multidisciplinary approach with observations, modeling, and analysis. Bull. Amer. Meteor. Soc., 100, 423444, https://doi.org/10.1175/BAMS-D-17-0057.1.

    • Search Google Scholar
    • Export Citation
  • Yu, E., J. Sun, H. Chen, and W. Xiang, 2015: Evaluation of a high-resolution historical simulation over China: Climatology and extremes. Climate Dyn., 45, 20132031, https://doi.org/10.1007/s00382-014-2452-6.

    • Search Google Scholar
    • Export Citation
  • Yue, S., K. Yang, H. Lu, X. Zhou, D. Chen, and W. Guo, 2021: Representation of stony surface-atmosphere interactions in WRF reduces cold and wet biases for the southern Tibetan Plateau. J. Geophys. Res. Atmos., 126, e2021JD035291, https://doi.org/10.1029/2021JD035291.

  • Zhang, G., Y. Chen, and J. Li, 2021a: Effects of organic soil in the Noah-MP land-surface model on simulated skin and soil temperature profiles and surface energy exchanges for China. Atmos. Res., 249, 105284, https://doi.org/10.1016/j.atmosres.2020.105284.

    • Search Google Scholar
    • Export Citation
  • Zhang, G., Z. Nan, Z. Yin, and L. Zhao, 2021b: Isolating the contributions of seasonal climate warming to permafrost thermal responses over the Qinghai-Tibet Plateau. J. Geophys. Res. Atmos., 126, e2021JD035218, https://doi.org/10.1029/2021JD035218.

  • Zhao, L., and Coauthors, 2013: Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements. J. Hydrol., 482, 92104, https://doi.org/10.1016/j.jhydrol.2012.12.033.

    • Search Google Scholar
    • Export Citation
  • Zhao, L., and Coauthors, 2018: Soil organic carbon and total nitrogen pools in permafrost zones of the Qinghai-Tibetan Plateau. Sci. Rep., 8, 3656, https://doi.org/10.1038/s41598-018-22024-2.

    • Search Google Scholar
    • Export Citation
  • Zheng, D., R. van der Velde, Z. Su, X. Wang, J. Wen, M. J. Booij, A. Y. Hoekstra, and Y. Chen, 2015a: Augmentations to the Noah model physics for application to the Yellow River source area. Part I: Soil water flow. J. Hydrometeor., 16, 26592676, https://doi.org/10.1175/JHM-D-14-0198.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, D., R. van der Velde, Z. Su, X. Wang, J. Wen, M. J. Booij, A. Y. Hoekstra, and Y. Chen, 2015b: Augmentations to the Noah model physics for application to the Yellow River source area. Part II: Turbulent heat fluxes and soil heat transport. J. Hydrometeor., 16, 26772694, https://doi.org/10.1175/JHM-D-14-0199.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, G., and Coauthors, 2020: Remote sensing spatiotemporal patterns of frozen soil and the environmental controls over the Tibetan Plateau during 2002–2016. Remote Sens. Environ., 247, 111927, https://doi.org/10.1016/j.rse.2020.111927.

    • Search Google Scholar
    • Export Citation
  • Zhou, J., W. Kinzelbach, G. Cheng, W. Zhang, X. He, and B. Ye, 2013: Monitoring and modeling the influence of snow pack and organic soil on a permafrost active layer, Qinghai–Tibetan Plateau of China. Cold Reg. Sci. Technol., 90-91, 3852, https://doi.org/10.1016/j.coldregions.2013.03.003.

    • Search Google Scholar
    • Export Citation
  • Zhou, X., K. Yang, L. Ouyang, Y. Wang, Y. Jiang, X. Li, D. Chen, and A. Prein, 2021: Added value of kilometer-scale modeling over the third pole region: A CORDEX-CPTP pilot study. Climate Dyn., 57, 16731687, https://doi.org/10.1007/s00382-021-05653-8.

    • Search Google Scholar
    • Export Citation
  • Zou, D., and Coauthors, 2017: A new map of permafrost distribution on the Tibetan Plateau. Cryosphere, 11, 25272542, https://doi.org/10.5194/tc-11-2527-2017.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 358 358 128
Full Text Views 180 180 68
PDF Downloads 201 201 85

Land–Atmosphere Feedbacks Weaken the Cooling Effect of Soil Organic Matter Property toward Deep Soil on the Eastern Tibetan Plateau

Jing SunaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China

Search for other papers by Jing Sun in
Current site
Google Scholar
PubMed
Close
,
Kun YangaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China
bNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Kun Yang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-0809-2371
,
Hui LuaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China

Search for other papers by Hui Lu in
Current site
Google Scholar
PubMed
Close
,
Xu ZhoubNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Xu Zhou in
Current site
Google Scholar
PubMed
Close
,
Xin LibNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Xin Li in
Current site
Google Scholar
PubMed
Close
,
Yingying ChenbNational Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Yingying Chen in
Current site
Google Scholar
PubMed
Close
,
Weidong GuocInstitute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China
dJoint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China

Search for other papers by Weidong Guo in
Current site
Google Scholar
PubMed
Close
, and
Jonathon S. WrightaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute of Global Change Studies, Tsinghua University, Beijing, China

Search for other papers by Jonathon S. Wright in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands.

Significance Statement

The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kun Yang, yangk@tsinghua.edu.cn

Abstract

Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands.

Significance Statement

The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kun Yang, yangk@tsinghua.edu.cn
Save