Abstract
China is several decades into large-scale afforestation programs to help address significant ecological and environmental degradation, with further afforestation planned for the future. However, the biophysical impact of afforestation on local surface temperature remains poorly understood, particularly in midlatitude regions where the importance of the radiative effect driven by albedo and the nonradiative effect driven by energy partitioning is uncertain. To examine this issue, we investigated the local impact of afforestation by comparing adjacent forest and open land pixels using satellite observations between 2001 and 2012. We attributed local surface temperature change between adjacent forest and open land to radiative and nonradiative effects over China based on the Intrinsic Biophysical Mechanism (IBM) method. Our results reveal that forest causes warming of 0.238C (±0.218C) through the radiative effect and cooling of -0.748C (±0.508C) through the nonradiative effect on local surface temperature compared with open land. The nonradiative effect explains about 79% (±16%) of local surface temperature change between adjacent forest and open land. The contribution of the nonradiative effect varies with forest and open land types. The largest cooling is achieved by replacing grasslands or rain-fed croplands with evergreen tree types. Conversely, converting irrigated croplands to deciduous broadleaf forest leads to warming. This provides new guidance on afforestation strategies, including how these should be informed by local conditions to avoid amplifying climate-related warming.
| Original language | English |
|---|---|
| Pages (from-to) | 4445-4471 |
| Number of pages | 27 |
| Journal | Journal of Climate |
| Volume | 32 |
| Issue number | 14 |
| DOIs | |
| Publication status | Published - 2019 |
Bibliographical note
Funding Information:This research is supported by the National Key Research and Development Program of China (2017YFA0603803), the Natural Science Foundation of China (41775075, 41475063), and the Jiangsu Collaborative Innovation Center for Climate Change. This work is also supported by the Australian Research Council (ARC) via the ARC Centre of Excellence for Climate Extremes (CE170100023). J.G. gratefully acknowledges financial support from China Scholarship Council. M.D.K. acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023) and the New South Wales Research Attraction and Acceleration Program. We thank Dr. FengGao of ARS, USDA, Beltsville, Maryland, USA, who provided the climatological albedo look-up maps. We also appreciate three anonymous reviewers for their constructive and valuable suggestions for the great improvements to the manuscript. The MODIS land cover type (MCD12C1 and MCD12Q1), land surface temperature (MOD11C3 and MYD11C3), and albedo (MCD43C3) products are retrieved from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. The evapotranspiration (MOD16A) product is downloaded from http://files.ntsg.umt.edu/data/NTSG_ Products/MOD16/. The area equipped for irrigation is from Historical Irrigation Data, https://mygeohub.org/publications/8/2. SRTM30 DEM data are available from the U.S. Geological Survey, https://dds.cr.usgs.gov/srtm/version2_1/SRTM30/. The ITPCAS meteorological forcing dataset is developed by the Data Assimilation and Modeling Center for Tibetan Multi-spheres, Institute of Tibetan Plateau Research, ChineseAcademy of Sciences, http://westdc.westgis.ac.cn/. The GLASS albedo andLAI product is fromGlobal Land Cover Facility, http://glcf.umd.edu/.
Funding Information:
Acknowledgments. This research is supported by the National Key Research and Development Program of China (2017YFA0603803), the Natural Science Foundation of China (41775075, 41475063), and the Jiangsu Collaborative Innovation Center for Climate Change. This work is also supported by the Australian Research Council (ARC) via the ARC Centre of Excellence for Climate Extremes (CE170100023). J.G. gratefully acknowledges financial support from China Scholarship Council. M.D.K. acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023) and the New South Wales Research Attraction and Acceleration Program. We thank Dr. Feng Gao of ARS, USDA, Beltsville, Maryland, USA, who provided the climatological albedo look-up maps. We also appreciate three anonymous reviewers for their constructive and valuable suggestions for the great improvements to the manuscript. The MODIS land cover type (MCD12C1 and MCD12Q1), land surface temperature (MOD11C3 and MYD11C3), and albedo (MCD43C3) products are retrieved from https:// lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. The evapotranspiration (MOD16A) product is downloaded from http://files.ntsg.umt.edu/data/NTSG_ Products/MOD16/. The area equipped for irrigation is from Historical Irrigation Data, https://mygeohub.org/ publications/8/2. SRTM30 DEM data are available from the U.S. Geological Survey, https://dds.cr.usgs.gov/srtm/ version2_1/SRTM30/. The ITPCAS meteorological forcing dataset is developed by the Data Assimilation and Modeling Center for Tibetan Multi-spheres, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, http://westdc.westgis.ac.cn/. The GLASS albedo and LAI product is from Global Land Cover Facility, http:// glcf.umd.edu/.
Publisher Copyright:
© 2019 American Meteorological Society.
Keywords
- Atmosphere-land interaction
