Marine Cloud Brightening Program: Studies of Aerosol-Cloud Interactions

Studies of Aerosol-Cloud Interactions: modeling and field science

Scientists estimate that aerosols from human emissions are offsetting 0.5°C of global warming from greenhouse gases, but their cooling effect could be as little as 0.2°C or as much as 1.0°C (Forster et al., 2021).  This large uncertainty is long-standing in climate science and one of the greatest near-term uncertainties in climate change. 

The large uncertainty in the present-day effects of aerosols on clouds and climate, as well as uncertainty in the potential effectiveness of marine cloud brightening (MCB), stem from many of the same knowledge gaps. It is well-established that adding small (<1 μm diameter) aerosols to clean, low-altitude clouds increases the number of droplets in a cloud (Martin et al., 1994). This effectively increases the cloud water surface area; for a cloud with a given amount of liquid, this increases the cloud reflectivity (also called “albedo”), a process referred to as the Twomey effect (Twomey, 1974). 

For a given set of meteorological and aerosol conditions, the Twomey effect is relatively straightforward to assess (Wood, 2021), but despite this, representations of this process in models are not very accurate (Simpson et al. 2014), especially for aerosol concentrations and size distributions likely to be relevant for MCB (Connolly et al., 2014; Wood 2021). Droplet formation on aerosols in the atmosphere remains especially challenging to represent in climate models (e.g., Silva et al., 2021).  

In addition, the transition from a cloud with fewer, larger droplets to a cloud with more, smaller droplets can drive other changes that also affect cloud reflectivity (albedo). The magnitude and direction of these adjustments are sensitive to the meteorological and aerosol conditions. They can either offset or enhance the Twomey effect by decreasing or increasing, respectively, gross cloud properties that affect albedo – specifically, the total amount of water in the cloud (measured as the cloud “liquid water path”, LWP) and/or the cloud lifetime and therefore the fraction of the planet covered by cloud (cloud fraction, or CF) (e.g., Ackerman et al., 2004; Wood 2007; Xue et al., 2008; Wang et al., 2011; Chun et al., 2023; Zhang et al., 2022). 

Much of the uncertainty in our understanding of these processes stems from a combination of it being difficult to quantify how aerosols are affecting clouds through observations, and because of persistent uncertainties in models simulating these effects. 

Simulating cloud-aerosol effects

Global model simulations of how clouds respond to aerosols is often inconsistent with what is seen in observations (Malavelle et al., 2017) and in higher resolution modeling studies that are capable of representing the detailed processes driving these responses. This is important because these global models are used to simulate how aerosol effects on clouds are contributing to climate change today, and because these models are used to simulate how intentional marine cloud brightening would affect climate impacts and risks in the future, if MCB were ever implemented.

Higher resolution models that cover smaller geographic areas (for example, a 50 km x 50 km region, rather than over the whole globe), are run for shorter periods (days rather than years to  centuries), and that directly simulate many of the factors driving cloud evolution that have to be approximated in global models (for example, air motions at very small scales) do a better job at simulating these cloud responses than do global models. 

Simulations with cloud-resolving models are of enormous utility for a range of insights, including:

  • Studying which atmospheric properties and processes drive changes in clouds
  • The expected range of cloud responses to aerosol perturbations under different background meteorological and aerosol conditions
  • The temporal and spatial scales and magnitude of changes in different cloud and atmospheric processes, which is of utility for planning observational studies of these changes

The role for field studies of aerosol-cloud interactions

Even the higher-resolution “cloud-resolving” models require some approximations, because they do not resolve the smallest scales (<~5 m) of air motions or the very large-scale (>25 km) circulations that also affect clouds. Computational limitations also often dictate that simulations made with these models do not include all model capabilities, so components or aspects of the model that are deemed less essential to the goals of the simulation aren’t employed in the simulations. More generally, as with any simulations their accuracy can only be quantified through testing against real-world observations.

Observational studies can then be used to test the fidelity of the models in simulating processes, their roles in driving cloud changes, and how clouds respond to aerosol changes over a smaller sub-set of conditions. Observations can also be designed to test specific model processes, and field studies can reveal processes that may be missing from models. Iterative, joint analysis of the model simulations and observations can be used to not just test but also improve the higher-resolution, smaller scale models. 

Passive observations of clouds and aerosols are very valuable – and are a speciality of this team. But it has been difficult for scientists to characterize cloud-aerosol processes and reduce uncertainty in their effects because natural and anthropogenic (pollution) aerosols generally occur within a mix of substances and a range of conditions such that specific effects are very hard to untangle. Including the possibility of studies that control for particle type, size, volume and meteorological conditions could support significant improvement in characterizing these processes. Small-scale controlled release studies of particles into the marine atmosphere and clouds add a valuable new source of information for improving models and reducing cloud-aerosol uncertainty.  

MCB Program efforts to improve understanding of aerosol-cloud interactions & the potential efficacy of marine cloud brightening

Characterizing and modeling the effects of cloud-aerosol processes are critical for both climate projections in general, and for projecting the effects of MCB (Feingold et al., 2024).

The MCB Program therefore includes efforts on:

  • conducting a comprehensive suite of high-resolution, small-scale modeling studies
  • small-scale controlled-aerosol field studies designed to inform models and to quantify key processes in the atmosphere, and
  • coordination between the modeling and field study components, in order to both optimize field study design and develop improved high-resolution models
  • leveraging the findings from these research activities to improve the representation of cloud-aerosol processes in global climate models.

This approach allows us to leverage the strengths of both the models and field observations, toward both improved fundamental understanding of atmospheric processes and more accurate models. 

The overall scientific goal of these efforts is to improve models used to simulate the aerosol-cloud effect in general and for MCB specifically, not only in the higher resolution models but also in the global-scale models used to project the climate impacts of aerosol-cloud interactions and different implementations of MCB. The latter in particular may be important for informing governance and decision-making on any large-scale experiments or activities that would significantly alter the environment or climate. 

Learn more: 

Marine Cloud Brightening Program (main page)

Small-Scale Field Studies to Quantify Key Processes & Inform Models

Generating Aerosols for Cloud-Aerosol Research

Governance, Engagement and CAARE 

Our Team, Partners and Funders

MCB Program Publications

References on this page:

Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., & Toon, O. B. (2004). The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature, 432(7020), 1014–1017.

Connolly P. J., McFiggans G. B., Wood R., & Tsiamis A. (2014). Factors determining the most efficient spray distribution for marine cloud brightening. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2031), 20140056.

Chun, J.-Y., Wood, R., Blossey, P., & Doherty, S. J. (2023). Microphysical, macrophysical, and radiative responses of subtropical marine clouds to aerosol injections. Atmospheric Chemistry and Physics, 23(2), 1345–1368.

Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., & Zhang, X. (2021). The Earth’s energy budget, climate feedbacks, and climate sensitivity. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, Ö. Yelekçi, R. Yu, & B. Zhou (Eds.), Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 923–1054). Cambridge University Press.

Malavelle, F. F., Haywood, J. M., Jones, A., Gettelman, A., Clarisse, L., Bauduin, S., Allan, R. P., Karset, I. H. H., Kristjánsson, J. E., Oreopoulos, L., Cho, N., Lee, D., Bellouin, N., Boucher, O., Grosvenor, D. P., Carslaw, K. S., Dhomse, S., Mann, G. W., Schmidt, A., … Thordarson, T. (2017). Strong constraints on aerosol–cloud interactions from volcanic eruptions. Nature, 546(7659), 485–491.

Martin, G. M., Johnson, D. W., & Spice, A. (1994). The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds. Journal of the Atmospheric Sciences, 51(13), 1823–1842.<1823:TMAPOE>2.0.CO;2

Silva, S. J., Ma, P.-L., Hardin, J. C., & Rothenberg, D. (2021). Physically regularized machine learning emulators of aerosol activation. Geoscientific Model Development, 14(5), 3067–3077.

Simpson, E., Connolly, P., & McFiggans, G. (2014). An investigation into the performance of four cloud droplet activation parameterisations. Geosci. Model Dev., 7(4), 1535–1542.

Twomey, S. (1974). Pollution and the planetary albedo. Atmospheric Environment (1967), 8(12), 1251–1256.

Wang, H., Rasch, P. J., & Feingold, G. (2011). Manipulating marine stratocumulus cloud amount and albedo: A process-modelling study of aerosol-cloud-precipitation interactions in response to injection of cloud condensation nuclei. Atmos. Chem. Phys., 11(9), 4237–4249.

Wood, R. (2007). Cancellation of Aerosol Indirect Effects in Marine Stratocumulus through Cloud Thinning. Journal of the Atmospheric Sciences, 64(7), 2657–2669.

Wood, R. (2021). Assessing the potential efficacy of marine cloud brightening for cooling Earth using a simple heuristic model. Atmospheric Chemistry and Physics, 21(19), 14507–14533.

Xue, H., Feingold, G., & Stevens, B. (2008). Aerosol Effects on Clouds, Precipitation, and the Organization of Shallow Cumulus Convection. Journal of the Atmospheric Sciences, 65(2), 392–406.

Zhang, J., Zhou, X., Goren, T., & Feingold, G. (2022). Albedo susceptibility of northeastern Pacific stratocumulus: The role of covarying meteorological conditions. Atmospheric Chemistry and Physics, 22(2), 861–880.

Professor, Atmospheric Sciences, Univ. of Washington