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Climatic Change

, Volume 142, Issue 1–2, pp 53–66 | Cite as

Solar radiation management and ecosystem functional responses

  • Akihiko Ito
Article

Abstract

Geoengineering such as solar radiation management (SRM) can be an emergent option to avoid devastating climatic warming, but its ramifications are barely understood. The perturbation of the Earth’s energy balance, atmospheric dynamics, and hydrological cycling may exert unexpected influences on natural and human systems. In this study, I evaluate the impacts of SRM deployment on terrestrial ecosystem functions using a process-based ecosystem model (the Vegetation Integrative Simulator for Trace gases, VISIT) driven by the climate projections by multiple climate models. In the SRM-oriented climate projections, massive injection of sulphate aerosols into the stratosphere lead to increased scattering of solar radiation and delayed anthropogenic climate warming. The VISIT simulations show that canopy light absorption and gross primary production are enhanced in subtropics in spite of the slight decrease of total incident solar radiation. The retarded temperature rise during the deployment period leads to lower respiration, and consequently, an additional net terrestrial ecosystem carbon uptake by about 20%. After the SRM termination, however, along with the temperature rise, this carbon is released rapidly to the atmosphere. As a result of altered precipitation and radiation budget, simulated runoff discharge is suppressed mainly in the tropics. These SRM-induced influences on terrestrial ecosystems occurr heterogeneously over the land surface and differed among the ecosystem functions. These responses of terrestrial functions should be taken into account when discussing the costs and benefits of geoengineering.

Keywords

Gross Primary Production Climate Projection Sulphate Aerosol Solar Radiation Management Community Atmosphere Model Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This study was conducted as a part of Integrated Climate Assessment—Risks, Uncertainties and Society (ICA-RUS), funded by the Environmental Research Fund of the Ministry of Environment, Japan, and it was also supported in part by a KAKENHI grant (no. 26281014) from the Japan Society for the Promotion of Science. The CMIP5 and GeoMIP model outputs were obtained from the Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, a node of the Earth System Grid Federation.

Supplementary material

10584_2017_1930_MOESM1_ESM.pdf (6 mb)
ESM 1 (PDF 6177 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.National Institute for Environmental StudiesTsukubaJapan
  2. 2.Japan Agency for Marine-Earth Science and TechnologyYokohamaJapan

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