4.8 Article

On the relationship between aerosol model uncertainty and radiative forcing uncertainty

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1507050113

Keywords

aerosol-cloud interaction; uncertainty analysis; model constraint

Funding

  1. Natural Environment Research Council (NERC) Grant AEROS [NE/G006172/1]
  2. NERC Grant GASSP [NE/J024252/1]
  3. Royal Society
  4. N8 consortium
  5. Engineering and Physical Sciences Research Council [EP/K000225/1]
  6. JASMIN facility via Centre for Environmental Data Analysis - NERC
  7. UK Space Agency
  8. Engineering and Physical Sciences Research Council [EP/K000209/1, EP/K000225/1] Funding Source: researchfish
  9. Natural Environment Research Council [NE/G006172/1, NE/J024252/1] Funding Source: researchfish
  10. EPSRC [EP/K000225/1, EP/K000209/1] Funding Source: UKRI
  11. NERC [NE/J024252/1, NE/G006172/1] Funding Source: UKRI

Ask authors/readers for more resources

The largest uncertainty in the historical radiative forcing of climate is caused by the interaction of aerosols with clouds. Historical forcing is not a directly measurable quantity, so reliable assessments depend on the development of global models of aerosols and clouds that are well constrained by observations. However, there has been no systematic assessment of how reduction in the uncertainty of global aerosol models will feed through to the uncertainty in the predicted forcing. We use a global model perturbed parameter ensemble to show that tight observational constraint of aerosol concentrations in the model has a relatively small effect on the aerosol-related uncertainty in the calculated forcing between preindustrial and present-day periods. One factor is the low sensitivity of present-day aerosol to natural emissions that determine the preindustrial aerosol state. However, the major cause of the weak constraint is that the full uncertainty space of the model generates a large number of model variants that are equally acceptable compared to present-day aerosol observations. The narrow range of aerosol concentrations in the observationally constrained model gives the impression of low aerosol model uncertainty. However, these multiple equifinal models predict a wide range of forcings. To make progress, we need to develop a much deeper understanding of model uncertainty and ways to use observations to constrain it. Equifinality in the aerosol model means that tuning of a small number of model processes to achieve model-observation agreement could give a misleading impression of model robustness.

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