The Nonradiative Effect Dominates Local Surface Temperature Change Caused by Afforestation in China

Jun Ge Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China, and Australian Research Council Centre of Excellence for Climate Extremes and Climate Change Research Centre, University of New South Wales, Sydney, Australia

Search for other papers by Jun Ge in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-8876-1650
,
Weidong Guo Institute for Climate and Global Change Research, School of Atmospheric Sciences, and Joint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China

Search for other papers by Weidong Guo in
Current site
Google Scholar
PubMed
Close
,
Andrew J. Pitman Australian Research Council Centre of Excellence for Climate Extremes and Climate Change Research Centre, University of New South Wales, Sydney, Australia

Search for other papers by Andrew J. Pitman in
Current site
Google Scholar
PubMed
Close
,
Martin G. De Kauwe Australian Research Council Centre of Excellence for Climate Extremes and Climate Change Research Centre, University of New South Wales, Sydney, Australia

Search for other papers by Martin G. De Kauwe in
Current site
Google Scholar
PubMed
Close
,
Xuelong Chen Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Xuelong Chen in
Current site
Google Scholar
PubMed
Close
, and
Congbin Fu Institute for Climate and Global Change Research, School of Atmospheric Sciences, and Joint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China

Search for other papers by Congbin Fu in
Current site
Google Scholar
PubMed
Close
Restricted access

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 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) 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.

Additional affiliation: Chinese Academy of Sciences Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jun Ge, junge@smail.nju.edu.cn

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 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) 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.

Additional affiliation: Chinese Academy of Sciences Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jun Ge, junge@smail.nju.edu.cn
Save
  • Alkama, R., and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600604, https://doi.org/10.1126/science.aac8083.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, R. G., and Coauthors, 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174182, https://doi.org/10.1890/090179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arora, V. K., and A. Montenegro, 2011: Small temperature benefits provided by realistic afforestation efforts. Nat. Geosci., 4, 514518, https://doi.org/10.1038/ngeo1182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avila, F. B., A. J. Pitman, M. G. Donat, L. V. Alexander, and G. Abramowitz, 2012: Climate model simulated changes in temperature extremes due to land cover change. J. Geophys. Res., 117, D04108, https://doi.org/10.1029/2011JD016382.

    • Search Google Scholar
    • Export Citation
  • Bala, G., K. Caldeira, M. Wickett, T. J. Phillips, D. B. Lobell, C. Delire, and A. Mirin, 2007: Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. USA, 104, 65506555, https://doi.org/10.1073/pnas.0608998104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisier, J. P., and Coauthors, 2012: Attributing the impacts of land-cover changes in temperate regions on surface temperature and heat fluxes to specific causes: Results from the first LUCID set of simulations. J. Geophys. Res., 117, D12116, https://doi.org/10.1029/2011JD017106.

    • Search Google Scholar
    • Export Citation
  • Bonan, G. B., 2008: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320, 14441449, https://doi.org/10.1126/science.1155121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bright, R. M., K. Zhao, R. B. Jackson, and F. Cherubini, 2015: Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities. Global Change Biol., 21, 32463266, https://doi.org/10.1111/gcb.12951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bright, R. M., E. Davin, T. O’Halloran, J. Pongratz, K. Zhao, and A. Cescatti, 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nat. Climate Change, 7, 296302, https://doi.org/10.1038/nclimate3250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, B. A., and Coauthors, 2018: China’s response to a national land-system sustainability emergency. Nature, 559, 193204, https://doi.org/10.1038/s41586-018-0280-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burakowski, E., A. Tawfik, A. Ouimette, L. Lepine, K. Novick, S. Ollinger, C. Zarzycki, and G. Bonan, 2018: The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the eastern United States. Agric. For. Meteor., 249, 367376, https://doi.org/10.1016/j.agrformet.2017.11.030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Canadell, J. G., and M. R. Raupach, 2008: Managing forests for climate change mitigation. Science, 320, 14561457, https://doi.org/10.1126/science.1155458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, S., L. Chen, D. Shankman, C. Wang, X. Wang, and H. Zhang, 2011: Excessive reliance on afforestation in China’s arid and semi-arid regions: Lessons in ecological restoration. Earth-Sci. Rev., 104, 240245, https://doi.org/10.1016/j.earscirev.2010.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C., and Coauthors, 2019: China and India lead in greening of the world through land-use management. Nature Sustainability, 2, 122129, https://doi.org/10.1038/s41893-019-0220-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, L., and P. A. Dirmeyer, 2016: Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modeling. Environ. Res. Lett., 11, 034002, https://doi.org/10.1088/1748-9326/11/3/034002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., Z. Su, Y. Ma, J. Cleverly, and M. Liddell, 2017: An accurate estimate of monthly mean land surface temperatures from MODIS clear-sky retrievals. J. Hydrometeor., 18, 28272847, https://doi.org/10.1175/JHM-D-17-0009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., K. Wang, Y. Lin, W. Shi, Y. Song, and X. He, 2015: Balancing green and grain trade. Nat. Geosci., 8, 739741, https://doi.org/10.1038/ngeo2544.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davin, E. L., and N. de Noblet-Ducoudré, 2010: Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. J. Climate, 23, 97112, https://doi.org/10.1175/2009JCLI3102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Devaraju, N., N. de Noblet-Ducoudré, B. Quesada, and G. Bala, 2018: Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J. Climate, 31, 38113829, https://doi.org/10.1175/JCLI-D-17-0563.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duveiller, G., J. Hooker, and A. Cescatti, 2018: The mark of vegetation change on Earth’s surface energy balance. Nat. Commun., 9, 679, https://doi.org/10.1038/s41467-017-02810-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, X., and Coauthors, 2016: Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Climate Change, 6, 10191022, https://doi.org/10.1038/nclimate3092.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K. L., A. Berg, P. Gentine, J. P. Krasting, B. R. Lintner, S. Malyshev, J. A. Santanello, and E. Shevliakova, 2017: The impact of anthropogenic land use and land cover change on regional climate extremes. Nat. Commun., 8, 989, https://doi.org/10.1038/s41467-017-01038-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. M. Huang, 2010: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168182, https://doi.org/10.1016/j.rse.2009.08.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, B., S. Wang, Y. Liu, J. Liu, W. Liang, and C. Miao, 2017: Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu. Rev. Earth Planet. Sci., 45, 223243, https://doi.org/10.1146/annurev-earth-063016-020552.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, F., T. He, Z. Wang, B. Ghimire, Y. Shuai, J. Masek, C. Schaaf, and C. Williams, 2014: Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products. J. Appl. Remote Sens., 8, 083532, https://doi.org/10.1117/1.JRS.8.083532.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghimire, B., C. A. Williams, J. Masek, F. Gao, Z. Wang, C. Schaaf, and T. He, 2014: Global albedo change and radiative cooling from anthropogenic land cover change, 1700 to 2005 based on MODIS, land use harmonization, radiative kernels, and reanalysis. Geophys. Res. Lett., 41, 90879096, https://doi.org/10.1002/2014GL061671.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, W., X. Wang, J. Sun, A. Ding, and J. Zou, 2016: Comparison of land–atmosphere interaction at different surface types in the mid- to lower reaches of the Yangtze River valley. Atmos. Chem. Phys., 16, 98759890, https://doi.org/10.5194/acp-16-9875-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, D. K., and G. A. Riggs, 2015: MODIS/Terra Snow Cover Monthly L3 Global 0.05 Deg CMG, Version 6 [MOD10CM and MYD10CM]. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 15 March 2017, https://doi.org/10.5067/MODIS/MOD10CM.006.

    • Crossref
    • Export Citation
  • Hua, W., and Coauthors, 2017: Observational quantification of climatic and human influences on vegetation greening in China. Remote Sens., 9, 425, https://doi.org/10.3390/rs9050425.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, R. B., and Coauthors, 2008: Protecting climate with forests. Environ. Res. Lett., 3, 044006, https://doi.org/10.1088/1748-9326/3/4/044006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jian, S., C. Zhao, S. Fang, and K. Yu, 2015: Effects of different vegetation restoration on soil water storage and water balance in the Chinese Loess Plateau. Agric. For. Meteor., 206, 8596, https://doi.org/10.1016/j.agrformet.2015.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juang, J. Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick, 2007: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett., 34, L21408, https://doi.org/10.1029/2007GL031296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, X., and Coauthors, 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384387, https://doi.org/10.1038/nature10588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., and Coauthors, 2008: Cryospheric change in China. Global Planet. Change, 62, 210218, https://doi.org/10.1016/j.gloplacha.2008.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., H. Chen, J. Wei, W. Hua, S. Sun, H. Ma, X. Li, and J. Li, 2018: Inconsistent responses of hot extremes to historical land use and cover change among the selected CMIP5 models. J. Geophys. Res. Atmos., 123, 34973512, https://doi.org/10.1002/2017JD028161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Y., M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, and S. Li, 2015: Local cooling and warming effects of forests based on satellite observations. Nat. Commun., 6, 6603, https://doi.org/10.1038/ncomms7603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, S., and Q. Liu, 2012: Global Land Surface Products: Albedo Product Data Collection (1985–2010). Beijing Normal University, accessed 15 March 2018, https://doi.org/10.6050/glass863.3001.db.

    • Crossref
    • Export Citation
  • Liang, S., and Z. Xiao, 2012: Global Land Surface Products: Leaf Area Index Product Data Collection (1985–2010). Beijing Normal University, accessed 15 March 2018, https://doi.org/10.6050/glass863.3004.db.

    • Crossref
    • Export Citation
  • Liu, J., S. Li, Z. Ouyang, C. Tam, and X. Chen, 2008: Ecological and socioeconomic effects of China’s policies for ecosystem services. Proc. Natl. Acad. Sci. USA, 105, 94779482, https://doi.org/10.1073/pnas.0706436105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., X. Zhu, S. Li, Y. Liu, and Y. Pan, 2015: Changes in growing season vegetation and their associated driving forces in China during 2001–2012. Remote Sens., 7, 15 51715 535, https://doi.org/10.3390/rs71115517.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luyssaert, S., and Coauthors, 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Climate Change, 4, 389393, https://doi.org/10.1038/nclimate2196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, D., M. Notaro, Z. Liu, G. Chen, and Y. Liu, 2013: Simulated impacts of afforestation in East China monsoon region as modulated by ocean variability. Climate Dyn., 41, 24392450, https://doi.org/10.1007/s00382-012-1592-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, W., G. Jia, and A. Zhang, 2017: Multiple satellite-based analysis reveals complex climate effects of temperate forests and related energy budget. J. Geophys. Res. Atmos., 122, 38063820, https://doi.org/10.1002/2016JD026278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meier, R., and Coauthors, 2018: Evaluating and improving the Community Land Model’s sensitivity to land cover. Biogeosciences, 15, 47314757, https://doi.org/10.5194/bg-15-4731-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1975: Principles. Vol. 1, Vegetation and the Atmosphere, Academic Press, 278 pp.

  • Mu, Q., M. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 17811800, https://doi.org/10.1016/j.rse.2011.02.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pacala, S., and R. Socolow, 2004: Stabilization wedges: Solving the climate problem for the next 50 years with current technologies. Science, 305, 968972, https://doi.org/10.1126/science.1100103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, S., and Coauthors, 2014: Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci. USA, 111, 29152919, https://doi.org/10.1073/pnas.1315126111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perugini, L., L. Caporaso, S. Marconi, A. Cescatti, B. Quesada, N. de Noblet-Ducoudré, J. I. House, and A. Arneth, 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 053002, https://doi.org/10.1088/1748-9326/aa6b3f.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Piao, S., and Coauthors, 2015: Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biol., 21, 16011609, https://doi.org/10.1111/gcb.12795.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., and Coauthors, 2009: Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophys. Res. Lett., 36, L14814, https://doi.org/10.1029/2009GL039076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., and Coauthors, 2012: Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations. Earth Syst. Dyn., 3, 213231, https://doi.org/10.5194/esd-3-213-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rigden, A., and D. Li, 2017: Attribution of surface temperature anomalies induced by land use and land cover changes. Geophys. Res. Lett., 44, 68146822, https://doi.org/10.1002/2017GL073811.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotenberg, E., and D. Yakir, 2010: Contribution of semi-arid forests to the climate system. Science, 327, 451454, https://doi.org/10.1126/science.1179998.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Running, S. W., Q. Mu, M. Zhao, and A. Moreno, 2017: User’s Guide MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) NASA Earth Observing System MODIS Land Algorithm, accessed 15 March 2018, http://www.ntsg.umt.edu/project/modis/mod16.php.

  • Schultz, N. M., P. J. Lawrence, and X. Lee, 2017: Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosci., 122, 903917, https://doi.org/10.1002/2016JG003653.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siebert, S., M. Kummu, M. Porkka, P. Döll, N. Ramankutty, and B. R. Scanlon, 2015: A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci., 19, 15211545, https://doi.org/10.5194/hess-19-1521-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skinner, C. B., C. J. Poulsen, and J. S. Mankin, 2018: Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun., 9, 1094, https://doi.org/10.1038/s41467-018-03472-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teuling, A. J., and Coauthors, 2010: Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci., 3, 722727, https://doi.org/10.1038/ngeo950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van der Molen, M. K., B. J. J. M. van den Hurk, and W. Hazeleger, 2011: A dampened land use change climate response towards the tropics. Climate Dyn., 37, 20352043, https://doi.org/10.1007/s00382-011-1018-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wan, Z., 2014: New refinements and validation of the Collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ., 140, 3645, https://doi.org/10.1016/j.rse.2013.08.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., and Coauthors, 2018: Response of surface temperature to afforestation in the Kubuqi Desert, Inner Mongolia. J. Geophys. Res., 123, 948964, https://doi.org/10.1002/2017JD027522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., W. Guo, B. Qiu, Y. Liu, J. Sun, and A. Ding, 2017: Quantifying the contribution of land use change to surface temperature in the lower reaches of the Yangtze River. Atmos. Chem. Phys., 17, 49894996, https://doi.org/10.5194/acp-17-4989-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winckler, J., C. H. Reick, and J. Pongratz, 2017: Robust identification of local biogeophysical effects of land-cover change in a global climate model. J. Climate, 30, 11591176, https://doi.org/10.1175/JCLI-D-16-0067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winckler, J., and Coauthors, 2018: Different response of surface temperature and air temperature to deforestation in climate models, Earth Syst. Dyn. Discuss., https://doi.org/10.5194/esd-2018-66.

    • Crossref
    • Export Citation
  • Xiao, Z., S. Liang, J. Wang, P. Chen, X. Yin, L. Zhang, and J. Song, 2014: Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote, 52, 209223, https://doi.org/10.1109/TGRS.2013.2237780.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Z., R. Mahmood, Z. Yang, C. Fu, and H. Su, 2015: Investigating diurnal and seasonal climatic response to land use and land cover change over monsoon Asia with the Community Earth System Model. J. Geophys. Res. Atmos., 120, 11371152, https://doi.org/10.1002/2014JD022479.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, K., J. He, W. Tang, J. Qin, and C. C. K. Cheng, 2010: On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agric. For. Meteor., 150, 3846, https://doi.org/10.1016/j.agrformet.2009.08.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, M., and Coauthors, 2014: Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett., 9, 034002, https://doi.org/10.1088/1748-9326/9/3/034002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, K., and R. B. Jackson, 2014: Biophysical forcings of land-use changes from potential forestry activities in North America. Ecol. Monogr., 84, 329353, https://doi.org/10.1890/12-1705.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1559 551 30
PDF Downloads 1183 455 26