Abstract
The biophysical effects of vegetation changes are important in determining future climate changes using climate model. However, compared to observations, model has biases in energy exchange between vegetation and the lower atmosphere modulated by leaf area index and albedo. In this study, land-use induced anthropogenic influences, estimated as the differences between present land-use and idealized natural vegetation, on near-surface temperature were investigated using a regional climate model. Results show that present land-use transitions over China brings a cooler summer and winter accompanied by reduced diurnal temperature ranges by 0.11 °C and 0.25 °C respectively, which are mainly determined by the overwhelming increased evaporation and latent heat flux in summer and reduced net radiation in winter. Three vegetation pairs (i.e., forest and cropland, grassland and cropland, grassland and forest) were selected using observational datasets to evaluate vegetation induced climatic impact without atmospheric feedbacks across various climatic regimes. Albedo led absorbed radiation plays a dominate role in middle to north region while both LAI and albedo are significant below 30° N for latitudinal temperature changes between cropland and forest transitions. Model results have inconsistencies with observations on temperature trends caused by vegetation pairs, indicating summer cropland and forest over southern China is the most sensitive to the atmosphere conditions and forest and grassland pair is the least. These findings demonstrate the heterogeneous biophysical effect of vegetation in different climate zones and imply that a region-oriented parameterization of vegetation types should be applied in the land surface model to reduce uncertainties in future climate prediction.
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Availability of data and material
FNL data are available upon request from the website (http://rda.ucar.edu/datasets/ds083.2/). The CRU observations can be download freely from the Climatic Research Unit at the University of East Anglia (http://www.cru.uea.ac.uk/cru/data/hrg/). CLUM land-cover data can be download freely from the Resource and Environment Science and Data Center of Chinese Academy of Sciences (https://www.resdc.cn/). The albedo and LAI dataset are download on request from Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/home). The daily meteorological observations are available from the China Meteorological Data Service Centre (http://data.cma.cn/en).
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Acknowledgements
We thank the editor and two anonymous reviewers whose insightful comments lead to a significant improvement of the manuscript. We greatly appreciate Dr. Gang Li from School of Geospatial Science and Engineering, Sun Yat-sen University for providing the computational resources to support this research. This research was funded by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University, the National Natural Science Foundation of China (Grant no. 41871029) and the National Key R&D Program of China (2019YFC1510400). Zhen Liu was supported by the Institute for Basic Science (IBS), Republic of Korea, under IBS-R028-D1. The appointment of M. Luo at Sun Yat-sen University is partially supported by the Pearl River Talent Plan of Guangdong Province, China (2017GC010634).
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Dong, N., Luo, M., Liu, Z. et al. The roles of leaf area index and albedo in vegetation induced temperature changes across China using modelling and observations. Clim Dyn 58, 2557–2573 (2022). https://doi.org/10.1007/s00382-021-06028-9
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DOI: https://doi.org/10.1007/s00382-021-06028-9