[1]Yao Hongbin,Wen Zhongming,Zhang Tianyou,et al.Spatiotemporal Pattern of GPP of Grassland Ecosystem in Northern China Based on CMIP6[J].Research of Soil and Water Conservation,2024,31(04):266-274.[doi:10.13869/j.cnki.rswc.2024.04.017]
Copy

Spatiotemporal Pattern of GPP of Grassland Ecosystem in Northern China Based on CMIP6

References:
[1]Urban M C. Accelerating extinction risk from climate change[J]. Science, 2015,348(6234):571-573.
[2]Malhi G S, Kaur M, Kaushik P. Impact of climate change on agriculture and its mitigation strategies:A review[J]. Sustainability, 2021,13(3):1318.
[3]Scurlock J M O, Hall D O. The global carbon sink:A grassland perspective[J]. Global Change Biology, 1998,4(2):229-233.
[4]白永飞,赵玉金,王扬,等.中国北方草地生态系统服务评估和功能区划助力生态安全屏障建设[J].中国科学院院刊,2020,35(6):675-689.
Bai Y F, Zhao Y J, Wang Y, et al. Assessment of ecosystem services and ecological regionalization of grasslands support establishment of ecological security barriers in northern China[J]. Bulletin of Chinese Academy of Sciences, 2020,35(6):675-689.
[5]李紫晶,高翠萍,王忠武,等.中国草地固碳减排研究现状及其建议[J].草业学报,2023,32(2):191-200.
Li Z J, Gao C P, Wang Z W, et al. Research status and suggestions for grassland carbon sequestration and emission reduction in China[J]. Acta Prataculturae Sinica, 2023,32(2):191-200.
[6]Moss R H, Edmonds J A, Hibbard K A, et al. The next generation of scenarios for climate change research and assessment[J]. Nature, 2010,463:747-756.
[7]Jian J S, Bailey V, Dorheim K, et al. Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle[J]. Nature Communications, 2022,13(1):1733.
[8]Beer C, Reichstein M, Tomelleri E, et al. Terrestrial gross carbon dioxide uptake:Global distribution and covariation with climate[J]. Science, 2010,329(5993):834-838.
[9]Li W, Ciais P, Wang Y L, et al. Recent changes in global photosynthesis and terrestrial ecosystem respiration constrained from multiple observations[J]. Geophysical Research Letters, 2018,45(2):1058-1068.
[10]Yuan W P, Liu S G, Yu G R, et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data[J]. Remote Sensing of Environment, 2010,114(7):1416-1431.
[11]Mystakidis S, Davin E L, Gruber N, et al. Constraining future terrestrial carbon cycle projections using observation-based water and carbon flux estimates[J]. Global Change Biology, 2016,22(6):2198-2215.
[12]Ma M N, Yuan W P, Dong J, et al. Large-scale estimates of gross primary production on the Qinghai-Tibet Plateau based on remote sensing data[J]. International Journal of Digital Earth, 2018,11(11):1166-1183.
[13]Eyring V, Bony S, Meehl G A, et al. Overview of the Coupled Model Intercomparison Project Phase 6(CMIP6)experimental design and organization[J]. Geoscientific Model Development, 2016,9(5):1937-1958.
[14]Tian C G, Yue X, Zhou H, et al. Projections of changes in ecosystem productivity under 1.5 ℃ and 2 ℃ global warming[J]. Global and Planetary Change, 2021,205:103588.
[15]李伯新,姜超,孙建新.CMIP6模式对中国西南部地区植被碳利用率模拟能力综合评估[J].植物生态学报,2023,47(9):1211-1224.
Li B X, Jiang C, Sun J X. Comprehensive assessment of vegetation carbon use efficiency in southwestern China simulated by CMIP6 models[J]. Chinese Journal of Plant Ecology, 2023,47(9):1211-1224.
[16]Wang Z Q, Fu Z H, Liu B, et al. Northward migration of the East Asian summer monsoon northern boundary during the twenty-first century[J]. Scientific Reports, 2022,12(1):10066.
[17]Yuan W, Wu S Y, Hou S G, et al. Projecting future vegetation change for Northeast China using CMIP6 model[J]. Remote Sensing, 2021,13(17):3531.
[18]孙晓玲,谢文欣,周波涛.CMIP6模式对亚洲陆地生态系统的模拟评估与预估[J].气候变化研究进展,2023,19(1):49-62.
Sun X L, Xie W X, Zhou B T. CMIP6 evaluation and projection of terrestrial ecosystem over Asia[J]. Climate Change Research, 2023,19(1):49-62.
[19]黄禄丰,朱再春,黄萌田,等.基于CMIP6模式优化集合平均预估21世纪全球陆地生态系统总初级生产力变化[J].气候变化研究进展,2021,17(5):514-524.
Huang L F, Zhu Z C, Huang M T, et al. Projection of gross primary productivity change of global terrestrial ecosystem in the 21st century based on optimal ensemble averaging of CMIP6 models[J]. Climate Change Research, 2021,17(5):514-524.
[20]Zhang M Y, He H L, Zhang L, et al. Increased forest coverage will induce more carbon fixation in vegetation than in soil during 2015—2060 in China based on CMIP6[J]. Environmental Research Letters, 2022,17(10):105002.
[21]张新时.中华人民共和国植被图(1:1 000 000)[M].武汉:地质出版社,2007.
Zhang S X. Vegetation Map of the People's Republic of China(1:1 000 000)[M]. Wuhan:Geology Press, 2007.
[22]胡一阳,徐影,李金建,等.CMIP6不同分辨率全球气候模式对中国降水模拟能力评估[J].气候变化研究进展,2021,17(6):730-743.
Hu Y Y, Xu Y, Li J J, et al. Evaluation on the performance of CMIP6 global climate models with different horizontal resolution in simulating the precipitation over China[J]. Climate Change Research, 2021,17(6):730-743.
[23]Su F G, Duan X L, Chen D L, et al. Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau[J]. Journal of Climate, 2013,26(10):3187-3208.
[24]Lei X N, Xu C C, Liu F, et al. Evaluation of CMIP6 models and multi-model ensemble for extreme precipitation over arid central Asia[J]. Remote Sensing, 2023,15(9):2376.
[25]Anav A, Friedlingstein P, Kidston M, et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models[J]. Journal of Climate, 2013,26(18):6801-6843.
[26]Kim D, Lee M I, Jeong S J, et al. Intercomparison of terrestrial carbon fluxes and carbon use efficiency simulated by CMIP5 earth system models[J]. Asia-Pacific Journal of Atmospheric Sciences, 2018,54(2):145-163.
[27]Zhang J, Wu T W, Li L, et al. Constraint on regional land surface air temperature projections in CMIP6 multi-model ensemble[J]. NPJ Climate and Atmospheric Science, 2023,6(1):85.
[28]Working Group Ⅱ of IPCC. Climate Change 2022:Impacts, Adaptation and Vulnerability[R]. WMO, UNEP, 2022.
Similar References:

Memo

-

Last Update: 2024-06-30

Online:5806       Total Traffic Statistics:27356834

Website Copyright: Research of Soil and Water Conservation Shaanxi ICP No.11014090-10
Tel: 029-87012705 Address: Editorial Department of Research of Soil and Water Conservation, No. 26, Xinong Road, Yangling, Shaanxi Postcode: 712100