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Inferred Net Aerosol Forcing Based on Historical Climate Changes: a Review

Abstract

Purpose of Review

This review summarizes the inverse methods used to estimate the net aerosol forcing inferred from the historical climate change records for the Earth.

Recent Findings

The available methods are similar in design while differing in their assumptions. Primary differences are (a) the complexity of the earth system model used for forward simulations of the historical period (~ 1850 to the present), (b) the uncertainty sampling methodology, and (c) the representation of internal climate variability in the statistical approach. All methods, in some fashion, include the net aerosol radiative forcing as a residual forcing that is scaled to find simulations that match the observed records of surface air and deep ocean temperatures. Inverse methods also require sampling the model response uncertainty in the equilibrium climate sensitivity and the transient climate response (i.e., the delay due to mixing heat into the deep ocean), and therefore, a joint probability distribution is estimated that includes uncertainty across multiple components.

Summary

The resulting estimates of the net aerosol forcing and its uncertainty are, by construction, necessarily linked to the earth system model, its response characteristics, and the estimates of the internal chaotic variability. Summary results indicate that the net aerosol forcing during the late twentieth century was − 0.77 Wm−2 with a 5–95% range of − 1.15 to − 0.31 Wm−2 based on 19 results from simple- to full-complexity climate system models.

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Notes

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    We note that ECBMs and EMICs are already an emulator of the true climate system, and thus, a statistical emulator is similar to these. The ability to capture the critical dynamics and feedbacks by the ECBMs and EMICs can set them apart from simple statistical emulators. As statistical models become more complicated, their computational efficiency will also eventually limit their abilities.

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Acknowledgments

The author would like to thank A.G. Libardoni and A.P. Sokolov for their comments, O. Boucher for his patience and helpful review suggestions, and the two anonymous reviewers.

Funding

This work was supported in part by the Office of Science (BER), the U.S. Department of Energy Grant No. DE-FG02-94ER61937, and the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507.

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Correspondence to Chris E. Forest.

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Appendix. Uncertainty Sampling Strategies for EMICs

Appendix. Uncertainty Sampling Strategies for EMICs

Because the MIT IGSM [57] is the only model to include similar atmospheric physics to full AOGCMs, it is significantly different from the other models in its representation of the feedbacks and processes in the model equations. The IGSM is based on the GISS Model II atmospheric general circulation model [16] and solves the zonal mean atmospheric geophysical fluid dynamics equations, which includes zonal mean representations of radiative transfer, convection, clouds, land surface fluxes, sea-ice, snow-cover, and others. While having a more complex atmospheric component, in contrast with both the UVIC and BERN2.5 models, the model does not account for changes in ocean circulation that can occur on multi-century timescales related to glacial and interglacial climate states.

For this review, there are several differences from the other models that are relevant for comparing results with other estimates of net aerosol forcing. By including the relevant atmospheric physics components, the MIT IGSM does not specify the radiative forcings, but the model is forced directly by the concentrations of the long-lived and short-lived greenhouse gases and other climate forcing agents [59]. Thus, both the radiative forcing and the feedbacks are modeled explicitly rather than being specified as done in the ECBMs as well as in the UVIC and BERN 2.5 EMICs. A similar issue is that the IGSM does not specify the equilibrium climate sensitivity (Seq) to CO2 concentrations. Instead, the model uses a temperature-dependent adjustment to the cloud fraction used in the radiative transfer equations, which, in turn, changes the model’s “cloud feedbacks” while the other feedbacks (e.g., water vapor, lapse rate, Planck, and albedo feedbacks) are left to the model physics. As such, the net feedback, often called λ, corresponding to Seq, can be varied by the cloud feedback term in the IGSM.

In all models, the rate of ocean heat uptake is typically altered by changing an ocean thermal diffusion coefficient within the model and, for a given Seq, this will also adjust the transient climate response (TCR) estimated as the global mean surface temperature change from equilibrium at the time of doubling for a 1% increasing CO2 concentration scenario. Overall, these adjustments (for Seq, TCR, and the net aerosol forcing) allow for changes in the net forcing and the net response that are required in the inverse methods as discussed in this paper. For the IGSM, the thermal diffusion coefficient is for diffusion of heat anomalies. For UVIC and BERN 2.5, the models vary an explicit thermal diffusion equation within the GFD equations. For the energy balance models with an upwelling-diffusion ocean component, the vertical diffusivity of heat is specified and downward heat flux is compensated by an advective upwelling term.

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Forest, C.E. Inferred Net Aerosol Forcing Based on Historical Climate Changes: a Review. Curr Clim Change Rep 4, 11–22 (2018). https://doi.org/10.1007/s40641-018-0085-2

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Keywords

  • Anthropogenic radiative forcing
  • Net aerosol radiative forcing
  • Inverse methods
  • Observed historical climate change
  • Intermediate complexity earth system models
  • Internal climate variability
  • Joint probability distributions