Marine cloud brightening (MCB) is the deliberate injection of aerosol particles into shallow marine clouds to increase their reflection of solar radiation and reduce the amount of energy absorbed by the climate system. From the physical science perspective, the consensus of a broad international group of scientists is that the viability of MCB will ultimately depend on whether observations and models can robustly assess the scale-up of local-to-global brightening in today's climate and identify strategies that will ensure an equitable geographical distribution of the benefits and risks associated with projected regional changes in temperature and precipitation. To address the physical science knowledge gaps required to assess the societal implications of MCB, we propose a substantial and targeted program of research-field and laboratory experiments, monitoring, and numerical modeling across a range of scales.

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Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide "opportunistic experiments"(also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.

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Clouds exert a pivotal control on the mass balance of the Greenland and Antarctic Ice Sheet and therefore their contribution to global sea level. Clouds transport moisture onto the marginal ice sheet, where steep topographic gradients force the air to rise and cool, inducing strong orographic precipitation and leaving the interior ice sheet dry (polar desert). Clouds further regulate the radiation balance at the surface and, consequently, surface melt. Depending on their frequency, phase, and structure, clouds not only mute incoming solar radiation but also enhance longwave radiation at the surface. With the advent of novel observations from space (CloudSat-CALIPSO) and in the field, we now have tools to start evaluating the representation of clouds, precipitation, and ice sheet surface radiation in climate models. Here we evaluate the Community Earth System Model version 1 (CESM1(CAM5)) to represent (1) precipitation frequency and phase, using a CALIPSO cloud simulator; (2) cloud radiative effect comparing to a CloudSat-CALIPSO based product; and (3) snowfall amounts and surface mass balance, comparing to CloudSat, in-situ observations, and regional climate model results. After discussing outstanding cloud biases in CESM1(CAM5), we present our efforts to reduce these in the recently released version 2 (CESM2). We show that clouds are considerably better represented in CESM2, leading to improvements in surface radiation, melt, and surface mass balance, although biases in precipitation phase persist. Our work demonstrates the need for high-quality, long-term observations of clouds and their effect on the ice sheet surface to enable continued climate model improvement.@en