The Biggest Unknown in the Climate Forecast

The effect of aerosol particles on cloud properties and Earth's radiation budget is the single most uncertain component of global radiative forcing estimates. We do not know, to within a factor of 2–3, how much aerosols have offset greenhouse gas warming since preindustrial times. This uncertainty directly limits our ability to constrain climate sensitivity — the key parameter for projecting future warming. Specific unknowns include how different aerosol species (organosulfates, black carbon, mineral dust) modify cloud droplet number, size, and lifetime; how microphysical processes feed back on large-scale atmospheric circulation; and how aerosol above cloud contributes to total radiative forcing.

If aerosol cooling has been strong, climate sensitivity is high and we face more warming as air pollution is cleaned up. If aerosol cooling has been weak, sensitivity is lower but past warming is harder to explain. IPCC AR6 estimated aerosol effective radiative forcing at -1.1 W/m² (range: -1.7 to -0.4), a spread that translates to nearly 1°C of uncertainty in equilibrium warming projections. As developing nations clean up particulate air pollution — a public health imperative — the aerosol cooling "mask" will be removed, potentially accelerating warming. Getting this number right is essential for every climate policy decision from national emissions targets to global carbon budgets.

Satellite observations can see cloud tops and column-integrated aerosol but cannot simultaneously profile aerosol properties and cloud dynamics and microphysics at the same location — the lack of concurrent multi-parameter measurements is the core obstacle. Climate models use widely varying cloud microphysics parameterizations, producing a large spread of results with no convergence across model generations. Traditional field observations have blended coupled and decoupled cloud regimes, biasing estimates of aerosol indirect radiative forcing. Deep convective clouds and associated anvils and cirrus are particularly poorly represented in models but critically important for the energy and water cycle. Aerosol absorption properties (single scattering albedo, vertical distribution) remain highly uncertain because in-situ measurements are sparse and satellite retrievals are ambiguous.

Concurrent aircraft and surface-based measurement programs that simultaneously profile aerosol properties and cloud microphysical and dynamic properties across multiple scales in representative climate regimes. Closure studies that rigorously link observations to model parameterizations. AI/ML approaches to extract more information from combined satellite, aircraft, and ground-based data streams (supported by NSF's CAIG program). Better laboratory characterization and field measurement of organosulfate and other understudied aerosol species. Process-level models that resolve cloud-aerosol interactions at scales below current climate model grid cells.

A student team could analyze publicly available data from ARM (Atmospheric Radiation Measurement) sites to quantify how cloud droplet number concentration varies with aerosol loading across different meteorological regimes, testing whether simple parameterizations capture the observed relationships. Alternatively, a team could build a low-cost aerosol optical measurement instrument (nephelometer or aethalometer) and compare measurements in different local environments. Relevant skills: atmospheric science, remote sensing, data analysis, optics, machine learning.