Vegetation-cloud feedbacks to future vegetation changes in the Arctic regions
This study investigates future changes in the Arctic region and vegetation-cloud feedbacks simulated using the National Center for Atmospheric Research Community Atmosphere Model Version 3 coupled with a mixed layer ocean model. Impacts of future greening of the Arctic region are tested using altered surface boundary conditions for hypothetical vegetation distributions: (1) grasslands poleward of 60°N replaced by boreal forests and (2) both grasslands and shrubs replaced by boreal forests. Surface energy budget analysis reveals that future greening induces a considerable surface warming effect locally and warming is largely driven by an increase in short wave radiation. Both upward and downward shortwave radiation contribute to positive surface warming: upward shortwave radiation decreases mainly due to the decreased surface albedo (a darker surface) and downward shortwave radiation increases due to reduced cloud cover. The contribution of downward shortwave radiation at surface due to cloud cover reduction is larger than the contribution from surface albedo alone. The increased roughness length also transported surface fluxes to upper layer more efficiently and induce more heating and dry lower atmosphere. A relatively smaller increase in water vapor compared to the large increase in low-level air temperature in the simulation reduces relative humidity and results in reduced cloud cover. Therefore, vegetation-cloud feedbacks induced from land cover change significantly amplify Arctic warming. In addition to previously suggested feedback mechanisms, we propose that the vegetation-cloud feedback should be considered as one of major components that will give rise to an additional positive feedback to Arctic amplification.
KeywordsArctic greening CAM3 Albedo Roughness Vegetation-cloud feedback
This study was supported by KMIPA2015-2093 (PN17040) of the Korean government and ‘Development and Application of the Korea Polar Prediction System (KPOPS) for Climate Change and Weather Disaster (PE17130)’ project of the Korea Polar Research Institute. This study was funded by the Ministry of Oceans and Fisheries of the Republic of Korea under the government project, “Quantitative assessment for PM & BC to climate change and development of reduction technology for PM, BC from ships”. Sarah Kang was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (No. 2016R1A1A3A04005520). Su-Jong Jeong was supported by the internal research fund of the South University of Science and Technology of China.
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Conflict of interest
The authors declare that they have no conflict of interest.
- ACIA (2005) Arctic climate impact assessment. Cambridge University Press, CambridgeGoogle Scholar
- Chapin FS, Sturm M, Serreze MC, McFadden JP, Key JR, Lloyd AH, McGuire AD, Rupp TS, Lynch AH, Schimel JP, Beringer J, Chapman WL, Epstein HE, Euskirchen ES, Hinzman LD, Jia G, Ping CL, Tape KD, Thompson CDC, Walker DA, Welker JM (2005) Role of land-surface changes in Arctic summer warming. Science 310(5748):657–660CrossRefGoogle Scholar
- Collins WD, Rasch PJ, Boville BA (2004) Description of the NCAR community atmosphere model (CAM 3.0). Technical note NCAR/TN-464 + STR. National Center for Atmospheric Research, BoulderGoogle Scholar
- IPCC (2014) Climate change 2014: synthesis report. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, p 151Google Scholar
- Levis S, Bonan G, Vertenstein M, Oleson K (2004) The community land model’s dynamic global vegetation model (CLM-DGVM): technical description and user’s guide. NCAR Tech. Note TN-459 + IA. National Center for Atmospheric Research, BoulderGoogle Scholar
- Macias-Fauria M, Forbes BC, Zetterberg P, Kumpula T (2012) Eurasian Arctic greening reveals teleconnections and the potential for structurally novel ecosystems. Nat Clim Change 2(6):13–18Google Scholar
- Oleson KW, Dai Y, Bonan GB (2004) Technical description of the community land model (CLM). Technical Note NCAR/TN-461 + STR. National Center for Atmospheric Research, BoulderGoogle Scholar
- Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) (2007) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- Wilks DS (2006) Statistical methods in the atmospheric sciences. Academic, San DiegoGoogle Scholar
- Xue Y, Shukla J (1993) The influence of land surface properties on Sahel climate. Part I: Desertification. Journal of climate, 6Google Scholar