Physical science research needed to evaluate the viability and risks of marine cloud brightening.

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.

Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects.

There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100-500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately -2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.

Anthropogenic aerosol and cryosphere changes drive Earth's strong but transient clear-sky hemispheric albedo asymmetry.

A striking feature of the Earth system is that the Northern and Southern Hemispheres reflect identical amounts of sunlight. This hemispheric albedo symmetry comprises two asymmetries: The Northern Hemisphere is more reflective in clear skies, whereas the Southern Hemisphere is cloudier. Here we show that the hemispheric reflection contrast from differences in continental coverage is offset by greater reflection from the Antarctic than the Arctic, allowing the net clear-sky asymmetry to be dominated by aerosol. Climate model simulations suggest that historical anthropogenic aerosol emissions drove a large increase in the clear-sky asymmetry that would reverse in future low-emission scenarios. High-emission scenarios also show decreasing asymmetry, instead driven by declines in Northern Hemisphere ice and snow cover. Strong clear-sky hemispheric albedo asymmetry is therefore a transient feature of Earth's climate. If all-sky symmetry is maintained, compensating cloud changes would have uncertain but important implications for Earth's energy balance and hydrological cycle.

The future of Earth system prediction: Advances in model-data fusion.

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.

Opportunistic experiments to constrain aerosol effective radiative forcing.

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.

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine-learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

Aerosol-cloud-climate cooling overestimated by ship-track data.

The effect of anthropogenic aerosol on the reflectivity of stratocumulus cloud decks through changes in cloud amount is a major uncertainty in climate projections. In frequently occurring nonprecipitating stratocumulus, cloud amount can decrease through aerosol-enhanced cloud-top mixing. The climatological relevance of this effect is debated because ship exhaust only marginally reduces stratocumulus amount. By comparing detailed numerical simulations with satellite analyses, we show that ship-track studies cannot be generalized to estimate the climatological forcing of anthropogenic aerosol. The ship track-derived sensitivity of the radiative effect of nonprecipitating stratocumulus to aerosol overestimates their cooling effect by up to 200%. The offsetting warming effect of decreasing stratocumulus amount needs to be taken into account if we are to constrain the cloud-mediated radiative forcing of anthropogenic aerosol.

Realism of Lagrangian Large Eddy Simulations Driven by Reanalysis Meteorology: Tracking a Pocket of Open Cells Under a Biomass Burning Aerosol Layer.

An approach to drive Lagrangian large eddy simulation (LES) of boundary layer clouds with reanalysis data is presented and evaluated using satellite (Spinning Enhanced Visible and Infrared Imager, SEVIRI) and aircraft (Cloud-Aerosol-Radiation Interactions and Forcing, CLARIFY) measurements. The simulations follow trajectories of the boundary layer flow. They track the formation and evolution of a pocket of open cells (POC) underneath a biomass burning aerosol layer in the free troposphere. The simulations reproduce the evolution of observed stratocumulus cloud morphology, cloud optical depth, and cloud drop effective radius, and capture the timing of the cloud state transition from closed to open cells seen in the satellite imagery on the three considered trajectories. They reproduce a biomass burning aerosol layer identified by the in-situ aircraft measurements above the inversion of the POC. Entrainment of aerosol from the biomass burning layer into the POC is limited to the extent of having no impact on cloud- or boundary layer properties, in agreement with the CLARIFY observations. The two-moment bin microphysics scheme used in the simulations reproduces the in-situ cloud microphysical properties reasonably well. A two-moment bulk microphysics scheme reproduces the satellite observations in the non-precipitating closed-cell state, but overestimates liquid water path and cloud optical depth in the precipitating open-cell state due to insufficient surface precipitation. A boundary layer cold and dry bias occurring in LES can be counteracted by reducing the grid aspect ratio and by tightening the large scale wind speed nudging towards the surface.

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives.

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol-cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel-level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.

Network approach to patterns in stratocumulus clouds.

Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth's climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis's Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

Exploring the nonlinear cloud and rain equation.

Marine stratocumulus cloud decks are regarded as the reflectors of the climate system, returning back to space a significant part of the income solar radiation, thus cooling the atmosphere. Such clouds can exist in two stable modes, open and closed cells, for a wide range of environmental conditions. This emergent behavior of the system, and its sensitivity to aerosol and environmental properties, is captured by a set of nonlinear equations. Here, using linear stability analysis, we express the transition from steady to a limit-cycle state analytically, showing how it depends on the model parameters. We show that the control of the droplet concentration (N), the environmental carrying-capacity (H0), and the cloud recovery parameter (τ) can be linked by a single nondimensional parameter (µ=N/(ατH0)), suggesting that for deeper clouds the transition from open (oscillating) to closed (stable fixed point) cells will occur for higher droplet concentration (i.e., higher aerosol loading). The analytical calculations of the possible states, and how they are affected by changes in aerosol and the environmental variables, provide an enhanced understanding of the complex interactions of clouds and rain.

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system.

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

New approaches to quantifying aerosol influence on the cloud radiative effect.

The topic of cloud radiative forcing associated with the atmospheric aerosol has been the focus of intense scrutiny for decades. The enormity of the problem is reflected in the need to understand aspects such as aerosol composition, optical properties, cloud condensation, and ice nucleation potential, along with the global distribution of these properties, controlled by emissions, transport, transformation, and sinks. Equally daunting is that clouds themselves are complex, turbulent, microphysical entities and, by their very nature, ephemeral and hard to predict. Atmospheric general circulation models represent aerosol-cloud interactions at ever-increasing levels of detail, but these models lack the resolution to represent clouds and aerosol-cloud interactions adequately. There is a dearth of observational constraints on aerosol-cloud interactions. We develop a conceptual approach to systematically constrain the aerosol-cloud radiative effect in shallow clouds through a combination of routine process modeling and satellite and surface-based shortwave radiation measurements. We heed the call to merge Darwinian and Newtonian strategies by balancing microphysical detail with scaling and emergent properties of the aerosol-cloud radiation system.

Adaptive behavior of marine cellular clouds.

Shallow marine clouds appear in two formations - open cells that are weakly reflective and closed cells that are more reflective and hence more effective at cooling the climate system. Lagrangian satellite data analysis reveals that open cells oscillate, forming and disappearing with a periodicity of ~3 hours. In contrast, closed cells maintain rigid structures for periods of more than 10 hours, suggesting that self-organisation breaks the link between the lifetime and the scale of a convective entity. These dynamical states are linked to two theoretical solutions of population dynamics.

Aerosol-cloud-precipitation system as a predator-prey problem.

We show that the aerosol-cloud-precipitation system exhibits characteristics of the predator-prey problem in the field of population dynamics. Both a detailed large eddy simulation of the dynamics and microphysics of a precipitating shallow boundary layer cloud system and a simpler model built upon basic physical principles, reproduce predator-prey behavior with rain acting as the predator and cloud as the prey. The aerosol is shown to modulate the predator-prey response. Steady-state solution to the proposed model shows the known existence of bistability in cloudiness. Three regimes are identified in the time-dependent solutions: (i) the weakly precipitating regime where cloud and rain coexist in a quasi steady state; (ii) the moderately drizzling regime where limit-cycle behavior in the cloud and rain fields is produced; and (iii) the heavily precipitating clouds where collapse of the boundary layer is predicted. The manifestation of predator-prey behavior in the aerosol-cloud-precipitation system is a further example of the self-organizing properties of the system and suggests that exploiting principles of population dynamics may help reduce complex aerosol-cloud-rain interactions to a more tractable problem.

Precipitation-generated oscillations in open cellular cloud fields.

Cloud fields adopt many different patterns that can have a profound effect on the amount of sunlight reflected back to space, with important implications for the Earth's climate. These cloud patterns can be observed in satellite images of the Earth and often exhibit distinct cell-like structures associated with organized convection at scales of tens of kilometres. Recent evidence has shown that atmospheric aerosol particles-through their influence on precipitation formation-help to determine whether cloud fields take on closed (more reflective) or open (less reflective) cellular patterns. The physical mechanisms controlling the formation and evolution of these cells, however, are still poorly understood, limiting our ability to simulate realistically the effects of clouds on global reflectance. Here we use satellite imagery and numerical models to show how precipitating clouds produce an open cellular cloud pattern that oscillates between different, weakly stable states. The oscillations are a result of precipitation causing downward motion and outflow from clouds that were previously positively buoyant. The evaporating precipitation drives air down to the Earth's surface, where it diverges and collides with the outflows of neighbouring precipitating cells. These colliding outflows form surface convergence zones and new cloud formation. In turn, the newly formed clouds produce precipitation and new colliding outflow patterns that are displaced from the previous ones. As successive cycles of this kind unfold, convergence zones alternate with divergence zones and new cloud patterns emerge to replace old ones. The result is an oscillating, self-organized system with a characteristic cell size and precipitation frequency.

Untangling aerosol effects on clouds and precipitation in a buffered system.

It is thought that changes in the concentration of cloud-active aerosol can alter the precipitation efficiency of clouds, thereby changing cloud amount and, hence, the radiative forcing of the climate system. Despite decades of research, it has proved frustratingly difficult to establish climatically meaningful relationships among the aerosol, clouds and precipitation. As a result, the climatic effect of the aerosol remains controversial. We propose that the difficulty in untangling relationships among the aerosol, clouds and precipitation reflects the inadequacy of existing tools and methodologies and a failure to account for processes that buffer cloud and precipitation responses to aerosol perturbations.

On the source of organic acid aerosol layers above clouds.

During the July 2005 Marine Stratus/Stratocumulus Experiment (MASE) and the August-September 2006 Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS), the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter probed aerosols and cumulus clouds in the eastern Pacific Ocean off the coast of northern California and in southeastern Texas, respectively. An on-board particle-into-liquid sampler (PILS) quantified inorganic and organic acid species with < or = 5-min time resolution. Ubiquitous organic aerosol layers above cloud with enhanced organic acid levels were observed in both locations. The data suggest that aqueous-phase reactions to produce organic acids, mainly oxalic acid, followed by droplet evaporation is a source of elevated organic acid aerosol levels above cloud. Oxalic acid is observed to be produced more efficiently relative to sulfate as the cloud liquid water content increases, corresponding to larger and less acidic droplets. As derived from large eddy simulations of stratocumulus underthe conditions of MASE, both Lagrangian trajectory analysis and diurnal cloudtop evolution provide evidence that a significant fraction of the aerosol mass concentration above cloud can be accounted for by evaporated droplet residual particles. Methanesulfonate data suggest that entrainment of free tropospheric aerosol can also be a source of organic acids above boundary layer clouds.

Retrieval of aerosol properties from combined multiwavelength lidar and sunphotometer measurements.

Simulation studies were carried out with regard to the feasibility of using combined observations from sunphotometer (SPM) and lidar for microphysical characterization of aerosol particles, i.e., the retrieval of effective radius, volume, and surface-area concentrations. It was shown that for single, homogeneous aerosol layers, the aerosol parameters can be retrieved with an average accuracy of 30% for a wide range of particle size distributions. Based on the simulations, an instrument combination consisting of a lidar that measures particle backscattering at 355 and 1574 nm, and a SPM that measures at three to four channels in the range from 340 to 1020 nm is a promising tool for aerosol characterization. The inversion algorithm has been tested for a set of experimental data. The comparison with the particle size distribution parameters, measured with in situ instrumentation at the lidar site, showed good agreement.