Articles | Volume 14, issue 1
https://doi.org/10.5194/esd-14-1-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-1-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2023
Research article |
| 09 Jan 2023
| 09 Jan 2023
Global and northern-high-latitude net ecosystem production in the 21st century from CMIP6 experiments
Department of Forest and Wildlife Ecology, University of
Wisconsin-Madison, Madison, WI, USA
now at: Atmospheric Sciences and Global Change Division, Pacific
Northwest National Laboratory, Richland, WA, USA
Dalei Hao
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Yelu Zeng
Department of Forest and Wildlife Ecology, University of
Wisconsin-Madison, Madison, WI, USA
Xuesong Zhang
USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD
20705-2350, USA
Department of Forest and Wildlife Ecology, University of
Wisconsin-Madison, Madison, WI, USA
Related authors
Xiaofan Yang, Yu Chen, Han Qiu, Virgílio A. Bento, Hongquan Song, Wei Shui, Jingyu Zeng, and Qianfeng Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2560, https://doi.org/10.5194/egusphere-2025-2560, 2025
Short summary
Short summary
The future of global evaporation under climate change remains uncertain. The current ET model relies primarily on high emission CMIP5 scenarios and does not fully represent the enhanced vegetation-climate interaction in CMIP6 low emission scenarios. Updated models using output of four CMIP6 GCMs under four SSPs show that ET projections will become increasingly dependent on emissions scenarios.
Jie Niu, Mengyu Xie, Yanqun Lu, Liwei Sun, Na Liu, Han Qiu, Dongdong Liu, Chuanhao Wu, and Pan Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3221, https://doi.org/10.5194/egusphere-2024-3221, 2024
Short summary
Short summary
Our results reveal the effectiveness of probabilistic forecasting models in analyzing the uncertainty of marine NPP estimates. Both the Bayesian and neural network models demonstrate superior capabilities in capturing the dynamic trends and uncertainties inherent in NPP data, with the neural network model demonstrating superior accuracy and reliability. Furthermore, we successfully applied these models to forecast NPP in specific ocean regions, highlighting the interannual variability of NPP.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Xiaofan Yang, Yu Chen, Han Qiu, Virgílio A. Bento, Hongquan Song, Wei Shui, Jingyu Zeng, and Qianfeng Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2560, https://doi.org/10.5194/egusphere-2025-2560, 2025
Short summary
Short summary
The future of global evaporation under climate change remains uncertain. The current ET model relies primarily on high emission CMIP5 scenarios and does not fully represent the enhanced vegetation-climate interaction in CMIP6 low emission scenarios. Updated models using output of four CMIP6 GCMs under four SSPs show that ET projections will become increasingly dependent on emissions scenarios.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Jie Niu, Mengyu Xie, Yanqun Lu, Liwei Sun, Na Liu, Han Qiu, Dongdong Liu, Chuanhao Wu, and Pan Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3221, https://doi.org/10.5194/egusphere-2024-3221, 2024
Short summary
Short summary
Our results reveal the effectiveness of probabilistic forecasting models in analyzing the uncertainty of marine NPP estimates. Both the Bayesian and neural network models demonstrate superior capabilities in capturing the dynamic trends and uncertainties inherent in NPP data, with the neural network model demonstrating superior accuracy and reliability. Furthermore, we successfully applied these models to forecast NPP in specific ocean regions, highlighting the interannual variability of NPP.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
Short summary
Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
Short summary
Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-187, https://doi.org/10.5194/hess-2022-187, 2022
Manuscript not accepted for further review
Short summary
Short summary
Watershed modeling is important to protect water resources. However, errors are involved in watershed modeling. To reduce errors, remotely sensed evapotranspiration data are widely used. However, the use of remotely sensed evapotranspiration data still includes errors. This study applied two remotely sensed data (evapotranspiration and leaf area index) into watershed modeling to reduce errors. The results showed advancement of watershed modeling by two remotely sensed data.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
Short summary
Short summary
Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Qing Zhu, Jianguang Wen, Qing Xiao, and Min Chen
Earth Syst. Sci. Data, 12, 2209–2221, https://doi.org/10.5194/essd-12-2209-2020, https://doi.org/10.5194/essd-12-2209-2020, 2020
Short summary
Short summary
We adopted machine-learning models to generate the first global land products of SW–PAR based on DSCOVR/EPIC data. Our products are consistent with ground-based observations, capture the spatiotemporal patterns well and accurately track substantial diurnal, monthly and seasonal variations in SW–PAR. Our products provide a valuable alternative for solar photovoltaic applications and can be used to improve our understanding of the diurnal cycles of terrestrial water, carbon and energy fluxes.
Cited articles
Belshe, E. F., Schuur, E. A. G., Bolker, B. M., and Bracho, R.:
Incorporating spatial heterogeneity created by permafrost thaw into a
landscape carbon estimate, J. Geophys. Res.-Biogeo.,
117, G01026, https://doi.org/10.1029/2011JG001836, 2012.
Berner, L. T., Massey, R., Jantz, P., Forbes, B. C., Macias-Fauria, M.,
Myers-Smith, I., Kumpula, T., Gauthier, G., Andreu-Hayles, L., Gaglioti, B.
V., Burns, P., Zetterberg, P., D'Arrigo, R., and Goetz, S. J.: Summer
warming explains widespread but not uniform greening in the Arctic tundra
biome, Nat. Commun., 11, 4621,
https://doi.org/10.1038/s41467-020-18479-5, 2020.
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8250, 2019.
Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., and Vargas, R.:
Globally rising soil heterotrophic respiration over recent decades, Nature,
560, 80–83, https://doi.org/10.1038/s41586-018-0358-x, 2018.
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.:
IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5262, 2019.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., Lavergne, C. de, Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé,
C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, L., E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N.,
Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S.,
Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul,
L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P.,
Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P.,
Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle,
M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and
Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv.
Model. Earth Sy., 12, e2019MS002010,
https://doi.org/10.1029/2019MS002010, 2020.
Bradford, M. A., Wieder, W. R., Bonan, G. B., Fierer, N., Raymond, P. A.,
and Crowther, T. W.: Managing uncertainty in soil carbon feedbacks to
climate change, Nat. Clim. Change, 6, 751–758,
https://doi.org/10.1038/nclimate3071, 2016.
Campbell, J. E., Berry, J. A., Seibt, U., Smith, S. J., Montzka, S. A.,
Launois, T., Belviso, S., Bopp, L., and Laine, M.: Large historical growth
in global terrestrial gross primary production, Nature, 544, 84–87,
https://doi.org/10.1038/nature22030, 2017.
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S.,
Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.:
Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled
Model, J. Adv. Model. Earth Sy., 11, 185–209,
https://doi.org/10.1029/2018MS001369, 2019.
Collins, M., Knutti, R., Gutowski, W., Brooks, H., Shindell, D., and Webb,
R.: Long-term Climate Change: Projections, Commitments and Irreversibility,
Geological and Atmospheric Sciences Reports, in: Climate change 2013-The physical science basis: Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, 1029–1136, ISBN 9781107057991, 2013.
Crowther, T. W., Todd-Brown, K. E. O., Rowe, C. W., Wieder, W. R., Carey, J.
C., Machmuller, M. B., Snoek, B. L., Fang, S., Zhou, G., Allison, S. D.,
Blair, J. M., Bridgham, S. D., Burton, A. J., Carrillo, Y., Reich, P. B.,
Clark, J. S., Classen, A. T., Dijkstra, F. A., Elberling, B., Emmett, B. A.,
Estiarte, M., Frey, S. D., Guo, J., Harte, J., Jiang, L., Johnson, B. R.,
Kröel-Dulay, G., Larsen, K. S., Laudon, H., Lavallee, J. M., Luo, Y.,
Lupascu, M., Ma, L. N., Marhan, S., Michelsen, A., Mohan, J., Niu, S.,
Pendall, E., Peñuelas, J., Pfeifer-Meister, L., Poll, C., Reinsch, S.,
Reynolds, L. L., Schmidt, I. K., Sistla, S., Sokol, N. W., Templer, P. H.,
Treseder, K. K., Welker, J. M., and Bradford, M. A.: Quantifying global soil
carbon losses in response to warming, Nature, 540, 104–108,
https://doi.org/10.1038/nature20150, 2016.
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10100, 2019.
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1,
2013.
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4876,
2019.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Falge, E., Baldocchi, D., Tenhunen, J., Aubinet, M., Bakwin, P., Berbigier,
P., Bernhofer, C., Burba, G., Clement, R., Davis, K. J., Elbers, J. A.,
Goldstein, A. H., Grelle, A., Granier, A., Guðmundsson, J., Hollinger,
D., Kowalski, A. S., Katul, G., Law, B. E., Malhi, Y., Meyers, T., Monson,
R. K., Munger, J. W., Oechel, W., Paw U, K. T., Pilegaard, K., Rannik,
Ü., Rebmann, C., Suyker, A., Valentini, R., Wilson, K., and Wofsy, S.:
Seasonality of ecosystem respiration and gross primary production as derived
from FLUXNET measurements, Agr. Forest Meteorol., 113, 53–74,
https://doi.org/10.1016/S0168-1923(02)00102-8, 2002.
Fisher, J. B., Sikka, M., Oechel, W. C., Huntzinger, D. N., Melton, J. R., Koven, C. D., Ahlström, A., Arain, M. A., Baker, I., Chen, J. M., Ciais, P., Davidson, C., Dietze, M., El-Masri, B., Hayes, D., Huntingford, C., Jain, A. K., Levy, P. E., Lomas, M. R., Poulter, B., Price, D., Sahoo, A. K., Schaefer, K., Tian, H., Tomelleri, E., Verbeeck, H., Viovy, N., Wania, R., Zeng, N., and Miller, C. E.: Carbon cycle uncertainty in the Alaskan Arctic, Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, 2014.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections
due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/JCLI-D-12-00579.1, 2014.
Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K.,
Marsh, D. R., Tilmes, S., Vitt, F., Bardeen, C. G., McInerny, J., Liu,
H.-L., Solomon, S. C., Polvani, L. M., Emmons, L. K., Lamarque, J.-F.,
Richter, J. H., Glanville, A. S., Bacmeister, J. T., Phillips, A. S., Neale,
R. B., Simpson, I. R., DuVivier, A. K., Hodzic, A., and Randel, W. J.: The
Whole Atmosphere Community Climate Model Version 6 (WACCM6), J.
Geophys. Res.-Atmos., 124, 12380–12403,
https://doi.org/10.1029/2019JD030943, 2019.
Hu, F. S., Higuera, P. E., Duffy, P., Chipman, M. L., Rocha, A. V., Young,
A. M., Kelly, R., and Dietze, M. C.: Arctic tundra fires: natural
variability and responses to climate change, Frontiers in Ecology and the
Environment, 13, 369–377, https://doi.org/10.1890/150063, 2015.
Ito, A., Hajima, T., Lawrence, D. M., Brovkin, V., Delire, C., Guenet, B.,
Jones, C. D., Malyshev, S., Materia, S., McDermid, S. P., Peano, D.,
Pongratz, J., Robertson, E., Shevliakova, E., Vuichard, N., Wårlind, D., Wiltshire, A., and Ziehn, T.: Soil carbon sequestration
simulated in CMIP6-LUMIP models: implications for climatic mitigation,
Environ. Res. Lett., 15, 124061, https://doi.org/10.1088/1748-9326/abc912,
2020.
Jeong, S.-J., Bloom, A. A., Schimel, D., Sweeney, C., Parazoo, N. C.,
Medvigy, D., Schaepman-Strub, G., Zheng, C., Schwalm, C. R., Huntzinger, D.
N., Michalak, A. M., and Miller, C. E.: Accelerating rates of Arctic carbon
cycling revealed by long-term atmospheric CO2 measurements, Science
Advances, 4, eaao1167, https://doi.org/10.1126/sciadv.aao1167, 2018.
Koven, C. D., Ringeval, B., Friedlingstein, P., Ciais, P., Cadule, P.,
Khvorostyanov, D., Krinner, G., and Tarnocai, C.: Permafrost carbon-climate
feedbacks accelerate global warming, P. Natl. Acad. Sci. USA, 108, 14769–14774,
https://doi.org/10.1073/pnas.1103910108, 2011.
Lara, M. J., Nitze, I., Grosse, G., Martin, P., and McGuire, A. D.: Reduced
arctic tundra productivity linked with landform and climate change
interactions, Sci. Rep.-UK, 8, 2345, https://doi.org/10.1038/s41598-018-20692-8,
2018.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., Kampenhout, L. van, Kennedy, D.,
Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J.,
Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A.,
Badger, A. M., Bisht, G., Broeke, M. van den, Brunke, M. A., Burns, S. P.,
Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B.,
Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox,
R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey,
A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M.,
Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin,
M. V., and Zeng, X.: The Community Land Model Version 5: Description of New
Features, Benchmarking, and Impact of Forcing Uncertainty, J.
Adv. Model. Earth Sy., 11, 4245–4287,
https://doi.org/10.1029/2018MS001583, 2019.
Liang, J., Xia, J., Shi, Z., Jiang, L., Ma, S., Lu, X., Mauritz, M., Natali,
S. M., Pegoraro, E., Penton, C. R., Plaza, C., Salmon, V. G., Celis, G.,
Cole, J. R., Konstantinidis, K. T., Tiedje, J. M., Zhou, J., Schuur, E. A.
G., and Luo, Y.: Biotic responses buffer warming-induced soil organic carbon
loss in Arctic tundra, Glob. Change Biol., 24, 4946–4959,
https://doi.org/10.1111/gcb.14325, 2018.
Lovato, T. and Peano, D.: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMI, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3887, 2020.
Lu, M., Zhou, X., Yang, Q., Li, H., Luo, Y., Fang, C., Chen, J., Yang, X.,
and Li, B.: Responses of ecosystem carbon cycle to experimental warming: a
meta-analysis, Ecology, 94, 726–738, https://doi.org/10.1890/12-0279.1,
2013.
Luo, Y. and Schuur, E. A. G.: Model parameterization to represent processes
at unresolved scales and changing properties of evolving systems, Glob.
Change Biol., 26, 1109–1117, https://doi.org/10.1111/gcb.14939, 2020.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F.,
Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.,
McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra,
C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., Groenigen, K.
J. van, Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J., Xu, X.,
Xu, X., and Zhou, T.: Toward more realistic projections of soil carbon
dynamics by Earth system models, Global Biogeochem. Cy., 30, 40–56,
https://doi.org/10.1002/2015GB005239, 2016.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H.,
Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T.,
Jimenéz-de-la-Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S.,
Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke,
J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B.,
Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira,
S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J.,
Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider,
T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D.,
Stein, L., Stemmler, I., Stevens, B., Storch, J.-S. von, Tian, F., Voigt,
A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner,
E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2)
and Its Response to Increasing CO2, J. Adv. Model. Earth
Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019.
McGuire, A. D., Anderson, L. G., Christensen, T. R., Dallimore, S., Guo, L.,
Hayes, D. J., Heimann, M., Lorenson, T. D., Macdonald, R. W., and Roulet,
N.: Sensitivity of the carbon cycle in the Arctic to climate change,
Ecol. Monogr., 79, 523–555, https://doi.org/10.1890/08-2025.1,
2009.
McKane, R. B., Rastetter, E. B., Shaver, G. R., Nadelhoffer, K. J., Giblin,
A. E., Laundre, J. A., and Chapin, F. S.: Climatic Effects on Tundra Carbon
Storage Inferred from Experimental Data and a Model, Ecology, 78,
1170–1187, https://doi.org/10.1890/0012-9658(1997)078[1170:CEOTCS]2.0.CO;2,
1997.
Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F., and Rogers,
B. M.: Expansion of high-latitude deciduous forests driven by interactions
between climate warming and fire, Nat. Plants, 5, 952–958,
https://doi.org/10.1038/s41477-019-0495-8, 2019.
Myers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H.
E., Assmann, J. J., John, C., Andreu-Hayles, L., Angers-Blondin, S., Beck,
P. S. A., Berner, L. T., Bhatt, U. S., Bjorkman, A. D., Blok, D., Bryn, A.,
Christiansen, C. T., Cornelissen, J. H. C., Cunliffe, A. M., Elmendorf, S.
C., Forbes, B. C., Goetz, S. J., Hollister, R. D., de Jong, R., Loranty, M.
M., Macias-Fauria, M., Maseyk, K., Normand, S., Olofsson, J., Parker, T. C.,
Parmentier, F.-J. W., Post, E., Schaepman-Strub, G., Stordal, F., Sullivan,
P. F., Thomas, H. J. D., Tømmervik, H., Treharne, R., Tweedie, C. E.,
Walker, D. A., Wilmking, M., and Wipf, S.: Complexity revealed in the
greening of the Arctic, Nat. Clim. Change, 10, 106–117,
https://doi.org/10.1038/s41558-019-0688-1, 2020.
Naidu, D. G. T. and Bagchi, S.: Greening of the earth does not compensate
for rising soil heterotrophic respiration under climate change, Glob.
Change Biol., 27, 2029–2038, https://doi.org/10.1111/gcb.15531, 2021.
Natali, S. M., Schuur, E. A. G., Webb, E. E., Pries, C. E. H., and Crummer, K. G.: Permafrost degradation stimulates carbon loss from experimentally warmed tundra, Ecology, 95, 602–608, https://doi.org/10.1890/13-0602.1, 2014.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Petrie, R., Denvil, S., Ames, S., Levavasseur, G., Fiore, S., Allen, C., Antonio, F., Berger, K., Bretonnière, P.-A., Cinquini, L., Dart, E., Dwarakanath, P., Druken, K., Evans, B., Franchistéguy, L., Gardoll, S., Gerbier, E., Greenslade, M., Hassell, D., Iwi, A., Juckes, M., Kindermann, S., Lacinski, L., Mirto, M., Nasser, A. B., Nassisi, P., Nienhouse, E., Nikonov, S., Nuzzo, A., Richards, C., Ridzwan, S., Rixen, M., Serradell, K., Snow, K., Stephens, A., Stockhause, M., Vahlenkamp, H., and Wagner, R.: Coordinating an operational data distribution network for CMIP6 data, Geosci. Model Dev., 14, 629–644, https://doi.org/10.5194/gmd-14-629-2021, 2021.
Phoenix, G. K. and Bjerke, J. W.: Arctic browning: extreme events and trends
reversing arctic greening, Glob. Change Biol., 22, 2960–2962,
https://doi.org/10.1111/gcb.13261, 2016.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L.,
Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change:
Current progresses and challenges, Glob. Change Biol., 25, 1922–1940,
https://doi.org/10.1111/gcb.14619, 2019.
Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W.,
Chen, A., Ciais, P., Tømmervik, H., Nemani, R. R., and Myneni, R. B.:
Characteristics, drivers and feedbacks of global greening, Nature Reviews
Earth & Environment, 1, 14–27,
https://doi.org/10.1038/s43017-019-0001-x, 2020.
Qiu, H.: CMIP6-carbonflux, Zenodo [code], https://doi.org/10.5281/zenodo.4768532, 2021.
Qian, H., Joseph, R., and Zeng, N.: Enhanced terrestrial carbon uptake in
the Northern High Latitudes in the 21st century from the Coupled Carbon
Cycle Climate Model Intercomparison Project model projections, Glob. Change
Biol., 16, 641–656, https://doi.org/10.1111/j.1365-2486.2009.01989.x,
2010.
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen,
O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed
nearly four times faster than the globe since 1979, Commun. Earth Environ., 3,
1–10, https://doi.org/10.1038/s43247-022-00498-3, 2022.
Reick, C. H., Raddatz, T., Brovkin, V., and Gayler, V.: Representation of
natural and anthropogenic land cover change in MPI-ESM, J. Adv. Model. Earth Sy., 5, 459–482, https://doi.org/10.1002/jame.20022,
2013.
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W.,
Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao,
S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da
Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: An overview, Global
Environ. Chang., 42, 153–168,
https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Furze, M. E.,
Seyednasrollah, B., Krassovski, M. B., Latimer, J. M., Nettles, W. R.,
Heiderman, R. R., Warren, J. M., and Hanson, P. J.: Ecosystem warming
extends vegetation activity but heightens vulnerability to cold
temperatures, Nature, 560, 368–371,
https://doi.org/10.1038/s41586-018-0399-1, 2018.
Schaefer, K., Zhang, T., Bruhwiler, L., and Barrett, A. P.: Amount and timing of permafrost carbon release in response to climate warming, Tellus B, 63, 165–180, https://doi.org/10.1111/j.1600-0889.2011.00527.x, 2011.
Schuur, E. A. G. and Abbott, B.: High risk of permafrost thaw, Nature, 480,
32–33, https://doi.org/10.1038/480032a, 2011.
Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J.
W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M.,
Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M.
R., Treat, C. C., and Vonk, J. E.: Climate change and the permafrost carbon
feedback, Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015.
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8248, 2019.
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.
Shao, P., Zeng, X., Sakaguchi, K., Monson, R. K., and Zeng, X.: Terrestrial
Carbon Cycle: Climate Relations in Eight CMIP5 Earth System Models, J.
Climate, 26, 8744–8764, https://doi.org/10.1175/JCLI-D-12-00831.1, 2013.
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., and Janssens,
I.: Sensitivity of decomposition rates of soil organic matter with respect
to simultaneous changes in temperature and moisture, J. Adv.
Model. Earth Sy., 7, 335–356, https://doi.org/10.1002/2014MS000358,
2015.
Sistla, S. A., Moore, J. C., Simpson, R. T., Gough, L., Shaver, G. R., and
Schimel, J. P.: Long-term warming restructures Arctic tundra without
changing net soil carbon storage, Nature, 497, 615–618,
https://doi.org/10.1038/nature12129, 2013.
Song, X., Wang, D.-Y., Li, F., and Zeng, X.-D.: Evaluating the performance
of CMIP6 Earth system models in simulating global vegetation structure and
distribution, Advances in Climate Change Research, 12, 584–595,
https://doi.org/10.1016/j.accre.2021.06.008, 2021.
Spafford, L. and MacDougall, A. H.: Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review, Geosci. Model Dev., 14, 5863–5889, https://doi.org/10.5194/gmd-14-5863-2021, 2021.
Stouffer, R. J., Eyring, V., Meehl, G. A., Bony, S., Senior, C., Stevens,
B., and Taylor, K. E.: CMIP5 Scientific Gaps and Recommendations for CMIP6,
B. Am. Meteorol. Soc., 98, 95–105,
https://doi.org/10.1175/BAMS-D-15-00013.1, 2017.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019a.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3683, 2019b.
Tarnocai, C., Canadell, J. G., Schuur, E. a. G., Kuhry, P., Mazhitova, G.,
and Zimov, S.: Soil organic carbon pools in the northern circumpolar
permafrost region, Global Biogeochem. Cy., 23, GB2023,
https://doi.org/10.1029/2008GB003327, 2009.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93,
485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Todd-Brown, K. E. O., Randerson, J. T., Hopkins, F., Arora, V., Hajima, T., Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., and Allison, S. D.: Changes in soil organic carbon storage predicted by Earth system models during the 21st century, Biogeosciences, 11, 2341–2356, https://doi.org/10.5194/bg-11-2341-2014, 2014.
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A.,
Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Climatic Change, 109, 5,
https://doi.org/10.1007/s10584-011-0148-z, 2011.
van Vuuren, D. P., Kriegler, E., O'Neill, B. C., Ebi, K. L., Riahi, K.,
Carter, T. R., Edmonds, J., Hallegatte, S., Kram, T., Mathur, R., and
Winkler, H.: A new scenario framework for Climate Change Research: scenario
matrix architecture, Climatic Change, 122, 373–386,
https://doi.org/10.1007/s10584-013-0906-1, 2014.
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Projected land
photosynthesis constrained by changes in the seasonal cycle of atmospheric
CO2, Nature, 538, 499–501, https://doi.org/10.1038/nature19772, 2016.
Whitman, E., Parisien, M.-A., Thompson, D. K., Hall, R. J., Skakun, R. S.,
and Flannigan, M. D.: Variability and drivers of burn severity in the
northwestern Canadian boreal forest, Ecosphere, 9, e02128,
https://doi.org/10.1002/ecs2.2128, 2018.
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6690, 2019.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6, Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.
Wyser, K., Kjellström, E., Koenigk, T., Martins, H., and Döscher,
R.: Warmer climate projections in EC-Earth3-Veg: the role of changes in the
greenhouse gas concentrations from CMIP5 to CMIP6, Environ. Res. Lett., 15,
054020, https://doi.org/10.1088/1748-9326/ab81c2, 2020.
Xin, X., Wu, T., Shi, X., Zhang, F., Li, J., Chu, M., Liu, Q., Yan, J., Ma, Q., and Wei, M.: BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3028, 2019.
Yan, Y., Luo, Y., Zhou, X., and Chen, J.: Sources of variation in simulated
ecosystem carbon storage capacity from the 5th Climate Model Intercomparison
Project (CMIP5), Tellus B, 66, 22568,
https://doi.org/10.3402/tellusb.v66.22568, 2014.
Yue, K., Fornara, D. A., Yang, W., Peng, Y., Peng, C., Liu, Z., and Wu, F.:
Influence of multiple global change drivers on terrestrial carbon storage:
additive effects are common, Ecol. Lett., 20, 663–672,
https://doi.org/10.1111/ele.12767, 2017.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G.,
Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L.,
Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y.,
Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D.,
Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.:
Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795,
https://doi.org/10.1038/nclimate3004, 2016.
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O’Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4320, 2019.
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix,
M., Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth
System Model: ACCESS-ESM1.5, Journal of Southern Hemisphere Earth Systems Science, 70, 193–214,
https://doi.org/10.1071/ES19035, 2020.
Zimov, S. A., Schuur, E. A. G., and Chapin, F. S.: Permafrost and the Global
Carbon Budget, Science, 312, 1612–1613,
https://doi.org/10.1126/science.1128908, 2006.
Short summary
The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the global and the northern-high-latitude (NHL) ecosystems will continue to have positive net ecosystem production (NEP) in the next few decades under four global change scenarios but with large uncertainties. NHL ecosystems will experience faster climate warming but steadily contribute a small fraction of the global NEP. However, the relative uncertainty of NHL NEP is much larger than the global values.
The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the...
Altmetrics
Final-revised paper
Preprint