Climatic Change

, Volume 131, Issue 4, pp 691–703 | Cite as

Accounting for radiative forcing from albedo change in future global land-use scenarios

  • Andrew D. Jones
  • Katherine V. Calvin
  • William D. Collins
  • James Edmonds


We demonstrate the effectiveness of a new method for quantifying radiative forcing from land use and land cover change (LULCC) within an integrated assessment model, the Global Change Assessment Model (GCAM). The method relies on geographically differentiated estimates of radiative forcing from albedo change associated with major land cover transitions derived from the Community Earth System Model. We find that conversion of 1 km2 of woody vegetation (forest and shrublands) to non-woody vegetation (crops and grassland) yields between 0 and −0.71 nW/m2 of globally averaged radiative forcing determined by the vegetation characteristics, snow dynamics, and atmospheric radiation environment characteristic within each of 151 regions we consider globally. Across a set of scenarios designed to span a range of potential future LULCC, we find LULCC forcing ranging from −0.06 to −0.29 W/m2 by 2070 depending on assumptions regarding future crop yield growth and whether climate policy favors afforestation or bioenergy crops. Inclusion of this previously uncounted forcing in the policy targets driving future climate mitigation efforts leads to changes in fossil fuel emissions on the order of 1.5 PgC/yr by 2070 for a climate forcing limit of 4.5 Wm−2, corresponding to a 12–67 % change in fossil fuel emissions depending on the scenario. Scenarios with significant afforestation must compensate for albedo-induced warming through additional emissions reductions, and scenarios with significant deforestation need not mitigate as aggressively due to albedo-induced cooling. In all scenarios considered, inclusion of albedo forcing in policy targets increases forest and shrub cover globally.


Land Cover Change Woody Vegetation Carbon Price Earth System Model Bioenergy Crop 
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.



This research was supported by the Office of Science of the U.S. Department of Energy as part of the Improving the Representations of Human-Earth System Interactions Project. This work used the Community Earth System Model, CESM and the Global Change Assessment Model, GCAM. The National Science Foundation and the Office of Science of the U.S. Department of Energy support the CESM project. The authors acknowledge long-term support for GCAM development from the Integrated Assessment Research Program in the Office of Science of the U.S. Department of Energy. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-0 5CH11231. Battelle Memorial Institute operates the Pacific Northwest National Laboratory for DOE under contract DE-AC06-76RLO 1830. Lawrence Berkeley National Laboratory is supported by the U.S. Department of Energy under Contract No. DE-AC02-0 5CH11231.


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Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2015

Authors and Affiliations

  • Andrew D. Jones
    • 1
  • Katherine V. Calvin
    • 2
  • William D. Collins
    • 1
    • 3
  • James Edmonds
    • 2
  1. 1.Lawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.Pacific Northwest National Laboratory’s Joint Global Change Research InstituteCollege ParkUSA
  3. 3.University of California Department of Earth and Planetary Science, BerkeleyBerkeleyUSA

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