CMIP6 evaluation and projection of terrestrial ecosystem over Asia

doi:10.12006/j.issn.1673-1719.2022.045

Abstract

Abstract:

Based on the simulations of 9 CMIP6 models and observation data, the performance of the CMIP6 models in simulating the terrestrial leaf area index (LAI), gross primary productivity (GPP) and net primary productivity (NPP) over Asia is evaluated in this paper. The evaluation results indicate that the CMIP6 models can reasonably reproduce the temporal and spatial characteristics of LAI, GPP and NPP over Asia. Moreover, the multi-model ensemble mean (MME) simulation outperforms the individual models. The spatial correlation coefficient between the MME simulated and observed LAI, GPP and NPP are 0.90, 0.81 and 0.89, respectively, and the root-mean-square error of the MME simulation with respect to the observation is around 0.5. On this basis, the changes of LAI, GPP and NPP over Asia under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios are further projected. In general, the MME projections show an increasing trend by the end of the 21st century for the LAI, GPP and NPP over Asia, with larger increases under high emission scenario than under low emission scenario and larger increases over the mid-high latitudes than over the low latitudes of Asia. Regionally averaged, the largest increases in LAI, GPP and NPP by the end of the 21st century are expected in North Asia, where the LAI, GPP and NPP are projected to increase by 68%, 106% and 90% under SSP5-8.5 and by 23%, 29% and 26% under SSP1-2.6, respectively. The smallest changes are anticipated in Southeast Asia, where the projected increases in LAI, GPP and NPP are 15%, 34% and 39% under SSP5-8.5 and 3%, 10% and 11% under SSP1-2.6, respectively. These results signify a greening of Asian ecosystem and a strengthening of carbon sequestration in the context of future global warming.

Key words: Asia, Terrestrial ecosystem, CMIP6, Evaluation, Projection

SUN Xiao-Ling, XIE Wen-Xin, ZHOU Bo-Tao. CMIP6 evaluation and projection of terrestrial ecosystem over Asia[J]. Climate Change Research, 2023, 19(1): 49-62.

Figures/Tables 10

Table 1 Basic information of 9 CMIP6 models used

Fig. 1 Time series of observed and multi-model ensemble (MME) simulated percentage anomalies of annual leaf area index (LAI), gross primary productivity (GPP) during 1985-2014 and annual net primary productivity (NPP) during 1982-2011 in North Asia (a-c),Central Asia (d?f), East Asia (g-i), West Asia (j-l), South Asia (m-o), and Southeast Asia (p-r)

Fig. 2 Climatological distribution of observed (a-c) and MME simulated (d-f) annual mean LAI, GPP during 1995-2014 and NPP during 1992-2011 in Asia

Fig. 3 Taylor diagrams of annual mean LAI (a), GPP (b) and NPP (c) in Asia

Fig. 4 Temporal changes in percentage anomalies of annual LAI under three scenarios in Asia. (Time series are smoothed with a 20-year running mean filter and shadings represent the ranges of two standard deviations of model simulations)

Fig. 5 Spatial distribution of the MME projected percentage anomalies of annual LAI during 2021-2040, 2041-2060 and 2081-2100 under three scenarios in Asia. (Significant changes are dotted)

Fig. 6 Same as Fig. 4, but for GPP

Fig. 7 Same as Fig. 4, but for NPP

Fig. 8 Same as Fig. 5, but for GPP

Fig. 9 Same as Fig. 5, but for NPP

References 49

[1]	Friedlingstein P, O′Sullivan M, Jones M, et al. Global carbon budget 2020[J]. Earth System Science Data, 2020, 12 (4): 3269-3340
[2]	Piao S L, Wang X H, Park T, et al. Characteristics, drivers and feedbacks of global greening[J]. Nature Reviews Earth and Environment, 2020, 1 (1): 14-27
[3]	Reichstein M, Bahn M, Ciais P, et al. Climate extremes and the carbon cycle[J]. Nature, 2013, 500 (7462): 287-295
[4]	IPCC. Climate change 2014: synthesis report[M]. Cambridge: Cambridge University Press, 2014
[5]	朴世龙, 张新平, 陈安平, 等. 极端气候事件对陆地生态系统碳循环的影响[J]. 中国科学: 地球科学, 2019, 49 (9): 1321-1334.
	Piao S L, Zhang X P, Chen A P, et al. The impacts of climate extremes on the terrestrial carbon cycle: a review[J]. Science China: Earth Sciences, 2019, 49 (9): 1321-1334 (in Chinese)
[6]	IPCC. Climate change 2021: the physical science basis[M]. Cambridge: Cambridge University Press, 2021
[7]	翟盘茂, 周佰铨, 陈阳, 等. 气候变化科学方面的几个最新认知[J]. 气候变化研究进展, 2021, 17 (6): 629-635.
	Zhai P M, Zhou B Q, Chen Y, et al. Several new understandings in the climate change science[J]. Climate Change Research, 2021, 17 (6): 629-635 (in Chinese)
[8]	周波涛. 全球气候变暖: 浅谈AR5到AR6的认知进展[J]. 大气科学学报, 2021, 44 (5): 667-671.
	Zhou B T. Global warming: scientific progress from AR5 to AR6[J]. Transactions of Atmospheric Sciences, 2021, 44 (5): 667-671 (in Chinese)
[9]	周波涛, 钱进. IPCC AR6报告解读: 极端天气气候事件变化[J]. 气候变化研究进展, 2021, 17 (6): 713-718.
	Zhou B T, Qian J. Changes of weather and climate extremes in the IPCC AR6[J]. Climate Change Research, 2021, 17 (6): 713-718 (in Chinese)
[10]	Fang H L, Baret F, Plummer S, et al. An overview of global leaf area index (LAI): methods, products, validation, and applications[J]. Reviews of Geophysics, 2019, 57 (3): 739-799
[11]	方精云, 柯金虎, 唐志尧, 等. 生物生产力的“4P”概念、估算及其相互关系[J]. 植物生态学报, 2001, 25 (4): 414-419.
	Fang J Y, Ke J H, Tang Z Y, et al. Implications and estimations of four terrestrial productivity parameters[J]. Chinese Journal of Plant Ecology, 2001, 25 (4): 414-419 (in Chinese)
[12]	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
[13]	Mahowald N, Lo F, Zheng Y, et al. Leaf area index in Earth System Models: evaluation and projections[J]. Earth System Dynamics Discussions, 2015, 6 (1): 761-818
[14]	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
[15]	Zeng Z Z, Zhu Z C, Lian X, et al. Responses of land evapotranspiration to Earth’s greening in CMIP5 Earth System Models[J]. Environmental Research Letters, 2016, 11 (10): 104006
[16]	Park H, Jeong S. Leaf area index in Earth System Models: how the key variable of vegetation seasonality works in climate projections[J]. Environmental Research Letters, 2021, 16 (3): 034027
[17]	Wittig V E, Ainsworth E A, Naidu S L, et al. Quantifying the impact of current and future tropospheric ozone on tree biomass, growth, physiology and biochemistry: a quantitative meta-analysis[J]. Global Change Biology, 2009, 15 (2): 396-424
[18]	Song X, Wang D Y, Li F, et al. Evaluating the performance of CMIP6 Earth System Models in simulating global vegetation structure and distribution[J]. Advances in Climate Change Research, 2021, 12 (4): 584-595
[19]	Wang S P, Duan J C, Xu G P, et al. Effects of warming and grazing on soil N availability, species composition, and ANPP in an alpine meadow[J]. Ecology, 2012, 93 (11): 2365-2376 pmid: 23236908
[20]	Yu M, Wang G L, Parr D, et al. Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs[J]. Climatic Change, 2014, 127 (2): 257-271
[21]	Schlund M, Eyring V, Camps-Valls G, et al. Constraining uncertainty in projected gross primary production with machine learning[J]. Journal of Geophysical Research: Biogeosciences, 2020, 125 (11): e2019JG005619
[22]	施红霞, 王澄海. 21世纪北半球中高纬度净初级生产力(NPP)变化及其与气候因子之间的关系[J]. 冰川冻土, 2015, 37 (2): 327-335.
	Shi H X, Wang C H. Change of NPP at the mid-high latitude of Northern Hemisphere in the 21st century and its relation with climate factors[J]. Journal of Glaciology and Geocryology, 2015, 37 (2): 327-335 (in Chinese)
[23]	朱再春, 刘永稳, 刘祯, 等. CMIP5模式对未来升温情景下全球陆地生态系统净初级生产力变化的预估[J]. 气候变化研究进展, 2018, 14 (1): 31-39.
	Zhu Z C, Liu Y W, Liu Z, et al. Projection of changes in terrestrial ecosystem net primary productivity under future global warming scenarios based on CMIP5 models[J]. Climate Change Research, 2018, 14 (1): 31-39 (in Chinese)
[24]	Zhao Q, Zhu Z C, Zeng H, et al. Future greening of the Earth may not be as large as previously predicted[J]. Agricultural and Forest Meteorology, 2020, 292-293: 108111
[25]	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
[26]	Eyring V, Bock L, Lauer A, et al. Earth System Model Evaluation Tool (ESMValTool) v2.0: an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth System Models in CMIP[J]. Geoscientific Model Development, 2020, 13 (7): 3383-3438
[27]	於琍, 朴世龙. IPCC第五次评估报告对碳循环及其他生物地球化学循环的最新认识[J]. 气候变化研究进展, 2014, 10 (1): 33-36.
	Yu L, Piao S L. Key scientific points on carbon and other biogeochemical cycles from the IPCC Fifth Assessment Report[J]. Climate Change Research, 2014, 10 (1): 33-36 (in Chinese)
[28]	O′Neill, B C, Tebaldi C, van Vuuren D P, et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6[J]. Geoscientific Model Development, 2016, 9: 3461-3482
[29]	Riahi K, van Vuuren D P, Kriegler E, et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview[J]. Global Environmental Change, 2017, 42: 153-168
[30]	Li W P, Zhang Y W, Shi X L, et al. Development of land surface model BCC_AVIM2.0 and its preliminary performance in LS3MIP/CMIP6[J]. Journal of Meteorological Research, 2019, 33 (5): 851-869
[31]	Swart N C, Cole J N S, Kharin V V, et al. The Canadian Earth System Model version 5 (CanESM5.0.3)[J]. Geoscientific Model Development, 2019, 12 (11): 4823-4873 doi: 10.5194/gmd-12-4823-2019
[32]	Wyser K, van Noije T, Yang S, et al. On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6[J]. Geoscientific Model Development, 2020, 13 (8): 3465-3474
[33]	Dunne J P, Horowitz L W, Adcroft A J, et al. The GFDL Earth System Model version 4.1 (GFDL-ESM 4.1): overall coupled model description and simulation characteristics[J]. Journal of Advances in Modeling Earth Systems, 2020, 12 (11): e2019MS002015
[34]	Volodin E M. Atmosphere-ocean general circulation model with the carbon cycle[J]. Izvestiya, Atmospheric and Oceanic Physics, 2007, 43 (3): 266-280
[35]	Volodin E M, Mortikov E V, Kostrykin S V, et al. Simulation of the modern climate using the INM-CM48 climate model[J]. Russian Journal of Numerical Analysis and Mathematical Modelling, 2018, 33 (6): 367-374
[36]	Lurton T, Balkanski Y, Bastrikov V, et al. Implementation of the CMIP6 forcing data in the IPSL-CM6A-LR model[J]. Journal of Advances in Modeling Earth Systems, 2020, 12 (4): e2019MS001940
[37]	Mauritsen T, Bader J, Becker T, et al. Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO₂[J]. Journal of Advances in Modeling Earth Systems, 2019, 11 (4): 998-1038 doi: 10.1029/2018MS001400 pmid: 32742553
[38]	Zhu Z C, Bi J, Pan Y Z, et al. Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011[J]. Remote Sensing, 2013, 5 (2): 927-948
[39]	Wang S H, Zhang Y G, Ju W M, et al. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data[J]. Science of The Total Environment, 2021, 755: 142569
[40]	Kolby S W, Reed S C, Cleveland C C, et al. Large divergence of satellite and Earth System Model estimates of global terrestrial CO₂ fertilization[J]. Nature Climate Change, 2016, 6 (3): 306-310
[41]	IPCC. Climate change 2013: the physical science basis[M]. Cambridge: Cambridge University Press, 2013
[42]	Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research: Atmospheres, 2001, 106 (D7): 7183-7192
[43]	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 (in Chinese)
[44]	Peng X B, Yu M, Chen H S. Projected changes in terrestrial vegetation and carbon fluxes under 1.5℃ and 2.0℃ global warming[J]. Atmosphere, 2022, 13 (1): 42
[45]	Jeong S. Autumn greening in a warming climate[J]. Nature Climate Change, 2020, 10 (8): 712-713
[46]	Slater A G, Lawrence D M. Diagnosing present and future permafrost from climate models[J]. Journal of Climate, 2013, 26 (15): 5608-5623
[47]	Chen J M, Ju W, Ciais P, et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink[J]. Nature Communications, 2019, 10 (1): 4259 doi: 10.1038/s41467-019-12257-8 pmid: 31534135
[48]	Park H, Jeong S, Ho C, et al. Nonlinear response of vegetation green-up to local temperature variations in temperate and boreal forests in the Northern Hemisphere remote sensing of environment[J]. Remote Sensing of Environment, 2015, 165: 100-108
[49]	Berg A, Sheffield J. Evapotranspiration partitioning in CMIP5 models: uncertainties and future projections[J]. Journal of Climate, 2019, 32 (10): 2653-2671