Abstract. Every year, from December to April, anthropogenic haze spreads over most of the North Indian Ocean, and South and Southeast Asia. The Indian Ocean Experiment (INDOEX) documented this Indo-Asian haze at scales ranging from individual particles to its contribution to the regional climate forcing. This study integrates the multiplatform observations (satellites, aircraft, ships, surface stations, and balloons) with one-and fourdimensional models to derive the regional aerosol forcing resulting from the direct, the semidirect and the two indirect effects. The haze particles consisted of several inorganic and carbonaceous species, including absorbing black carbon clusters, fly ash, and mineral dust. The most striking result was the large loading of aerosols over most of the South Asian region and the North Indian Ocean. The January to March 1999 visible optical depths were about 0.5 over most of the continent and reached values as large as 0.2 over the equatorial Indian ocean due to long-range transport. The aerosol layer extended as high as 3 km. Black carbon contributed about 14% to the fine particle mass and 11% to the visible optical depth. The single-scattering albedo estimated by several independent methods was consistently around 0.9 both inland and over the open ocean. Anthropogenic sources contributed as much as 80% (_+10%) to the aerosol loading and the optical depth. The in situ data, which clearly support the existence of the first indirect effect (increased aerosol concentration producing more cloud drops with smaller effective radii), are used to develop a composite indirect effect scheme. The Indo-Asian aerosols impact the radiative forcing through a complex set of heating (positive forcing) and cooling (negative forcing) processes. Clouds and black carbon emerge as the major players. The dominant factor, however, is the large negative forcing (-20 +_ 4 W m -t) at the surface and the comparably large atmospheric heating. Regionally, the absorbing haze decreased the surface solar radiation by an amount comparable to 50% of the total ocean heat flux and nearly doubled the lower tropospheric solar heating. We demonstrate with a general circulation model how this additional heating significantly perturbs the tropical rainfall patterns and the hydrological cycle with implications to global climate.

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The influence of aerosol concentration on the cloud-droplet size distribution is investigated in a laboratory chamber that enables turbulent cloud formation through moist convection. The experiments allow steady-state microphysics to be achieved, with aerosol input balanced by cloud-droplet growth and fallout. As aerosol concentration is increased, the cloud-droplet mean diameter decreases, as expected, but the width of the size distribution also decreases sharply. The aerosol input allows for cloud generation in the limiting regimes of fast microphysics (τc < τ t ) for high aerosol concentration, and slow microphysics (τc > τ t ) for low aerosol concentration; here, τc is the phase-relaxation time and τ t is the turbulence-correlation time. The increase in the width of the droplet size distribution for the low aerosol limit is consistent with larger variability of supersaturation due to the slow microphysical response. A stochastic differential equation for supersaturation predicts that the standard deviation of the squared droplet radius should increase linearly with a system time scale defined as τ, and the measurements are in excellent agreement with this finding. The result underscores the importance of droplet size dispersion for aerosol indirect effects: increasing aerosol concentration changes the albedo and suppresses precipitation formation not only through reduction of the mean droplet diameter but also by narrowing of the droplet size distribution due to reduced supersaturation fluctuations. Supersaturation fluctuations in the low aerosol/slow microphysics limit are likely of leading importance for precipitation formation.aerosol indirect effect | cloud-droplet size distribution | cloud-turbulence interactions T he optical properties of warm clouds depend on the droplet size distribution and its moments such as number density and effective radius, which, in turn, are influenced by the aerosol particles that act as nuclei for the formation of cloud droplets (1, 2). Thus, aerosol indirect effects are considered among the largest uncertainties in climate response to changes in radiative forcing (3). This work addresses how the aerosol number concentration affects the cloud-droplet size distribution in a turbulent environment, which is relevant to both the aerosol first and second indirect effects (albedo and lifetime effects). The lifetime effect links the development of precipitation, and thus cloud lifetime, to aerosol number concentration. The logic is that a higher aerosol concentration leads to smaller cloud droplets and narrower size distributions, and therefore suppression of the collision and coalescence of droplets, thereby increasing cloud lifetime and maintaining higher cloud liquid water content (4-7). The microphysical details of the transition from condensation growth to collision growth are not fully understood, however, and it is fair to say that the underlying mechanism of the second indirect effect is still a matter of active research (2, 8). Initiation of precipitation in warm clouds ...

A detailed understanding of interactions of aerosols, cloud droplets/ice crystals, and trace gases within the atmosphere is of prime importance for an accurate understanding of Earth’s weather and climate. One aspect that remains especially vexing is that clouds are ubiquitously turbulent, and therefore thermodynamic and compositional variables, such as water vapor supersaturation, fluctuate in space and time. With these problems in mind, a multiphase, turbulent reaction chamber—called the Π chamber because of the internal volume of 3.14 m3 with the cylindrical insert installed—has been developed. It is capable of pressures ranging from 1,000 to –60 hPa and can sustain temperatures of –55° to 55°C, thereby spanning much of the range of tropospheric clouds. To control the relative humidity in the chamber, it can be operated with a stable, unstable, or neutral temperature difference between the top and bottom surfaces, with or without expansion. A negative temperature difference induces turbulent Rayleigh–Bénard convection and associated supersaturation generation through isobaric mixing. Supporting instrumentation includes a suite of aerosol generation and characterization techniques; temperature, pressure, and humidity sensors; and a phase Doppler interferometer. Initial characterization experiments demonstrate the ability to sustain steady-state turbulent cloud conditions for times greater than 1 day, with droplet diameters typically in the range of 5–40 µm. Typical turbulence has root-mean-square velocity fluctuations on the order of 10 cm s–1 and kinetic energy dissipation rates of 1 × 10–3 W kg–1.

Diffusional growth of droplets by stochastic condensation and a resulting broadening of the size distribution has been considered as a mechanism for bridging the cloud droplet growth gap between condensation and collision–coalescence. Recent studies have shown that supersaturation fluctuations can lead to a broadening of the droplet size distribution at the condensational stage of droplet growth. However, most studies using stochastic models assume the phase relaxation time of a cloud parcel to be constant. In this paper, two questions are asked: how variability in droplet number concentration and radius influence the phase relaxation time and what effect it has on the droplet size distributions. To answer these questions, steady-state cloud conditions are created in the laboratory and digital inline holography is used to directly observe the variations in local number concentration and droplet size distribution and, thereby, the integral radius. Stochastic equations are also extended to account for fluctuations in integral radius and obtain new terms that are compared with the laboratory observations. It is found that the variability in integral radius is primarily driven by variations in the droplet number concentration and not the droplet radius. This variability does not contribute significantly to the mean droplet growth rate but does contribute significantly to the rate of increase of the size distribution width.