30 October 2015

Do Responses to Different Anthropogenic Forcings Add Linearly in Climate Models?


Many Detection and Attribution studies assume that different drivers of radiative forcing changes are additive. This implies that the sum of responses to different forcingsis statistically indistinguishable from the response to the sum of forcings.  As models become more complex, this assumption must be tested.


We calculated trends in global, annual-mean temperature and precipitation of varying lengths and times from CCSM4 and GISS single-forcing (SF) ensembles (aerosols; Land Use; Ozone; natural forcings; GHG), and compared their sum to trends calculated from historical (HIST) ensembles.

  • The temperature trends are generally additive but nonlinearities appear in precipitation trends in GISS with interactive chemistry.
  • This arise from nonlinear interactions between chemical species captured in HISTbut absent in SF runs.Specifically, ozone depletion is lower in HIST run, with major consequences for global mean precipitation.
  • The single-forcing runs help us to attribute phenomena in the real world.  If the climate response to multiple forcings is not additive, this may complicate certain D&A studies. 
  • The single-forcing experiements were not a priority in CMIP5. Our results should encourage modeling groups to perform these experiments.
Kate Marvel
2015.  "Do Responses to Different Anthropogenic Forcings Add Linearly in Climate Models?"  Environmental Research Letters 10(10): 104010.

Climate modeling at GISS is supported by the NASA Modeling, Analysis and Prediction program and resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. CB was supported by the United States Department of Energy's Office of Science through her Early Career Research Program award, and under the auspices of LLNL under Contract DE-AC52-07NA27344. The (LLNL/Columbia/GISS) multi-institution collaboration has been partially motivated by the DOE's Regional and Climate Modeling (RGCM) Program through the funding opportunity number DOE-FOA-0001036.