The concept of cloud radiative forcing (CRF) is commonly applied to quantify the impact of clouds on the surface radiative energy budget (REB). In
the Arctic, specific radiative interactions between ...microphysical and macrophysical properties of clouds and the surface strongly modify the warming
or cooling effect of clouds, complicating the estimate of CRF obtained from observations or models. Clouds tend to increase the broadband surface
albedo over snow or sea ice surfaces compared to cloud-free conditions. However, this effect is not adequately considered in the derivation of CRF
in the Arctic so far. Therefore, we have quantified the effects caused by surface-albedo–cloud interactions over highly reflective snow or sea ice
surfaces on the CRF using radiative transfer simulations and below-cloud airborne observations above the heterogeneous springtime marginal sea ice
zone (MIZ) during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. The impact of a modified surface
albedo in the presence of clouds, as compared to cloud-free conditions, and its dependence on cloud optical thickness is found to be relevant for
the estimation of the shortwave CRF. A method is proposed to consider this surface albedo effect on CRF estimates by continuously retrieving the cloud-free
surface albedo from observations under cloudy conditions, using an available snow and ice albedo parameterization. Using ACLOUD data
reveals that the estimated average shortwave cooling by clouds almost doubles over snow- and ice-covered surfaces (−62 W m−2 instead of
−32 W m−2), if surface-albedo–cloud interactions are considered. As a result, the observed total (shortwave plus longwave) CRF shifted
from a warming effect to an almost neutral one. Concerning the seasonal cycle of the surface albedo, it is demonstrated that this effect enhances
shortwave cooling in periods when snow dominates the surface and potentially weakens the cooling by optically thin clouds during the summertime
melting season. These findings suggest that the surface-albedo–cloud interaction should be considered in global climate models and in long-term
studies to obtain a realistic estimate of the shortwave CRF to quantify the role of clouds in Arctic amplification.
The cloud droplet number concentration (Nd) 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 Nd, but it can be inferred from retrievals of cloud optical depth (τc) cloud droplet effective radius (re) 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. Nd uncertainty is dominated by errors in re, and therefore, improvements in re retrievals would greatly improve the quality of the Nd retrievals. Recommendations are made for how this might be achieved. Some existing Nd data sets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative Nd 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.
Plain Language Summary
Clouds have a very large influence on weather and climate. It is thus a prime task for satellite‐ and ground‐based observations to measure clouds. For satellites and many other instruments this is done by remote sensing—radiation is measured, and knowledge about clouds is inferred. Liquid water clouds consist of numerous droplets of order of 10 μm in size. A key quantity that describes clouds is the number of droplets in a given volume or the droplet number concentration. However, satellite observations of droplet number concentration are only emerging, and the quality of these observations is poorly known. This review fulfills two tasks, namely, (1) to quantify how uncertain the current way to observe droplet number concentrations from satellite is and (2) to propose ways toward better approaches. It is concluded that the current way to obtain cloud droplet number concentration works for homogeneous stratus and stratocumulus clouds, with, however, a substantial error of around 50%. For cumulus clouds the observations are substantially worse. New avenues that are proposed for a better estimate of cloud droplet concentration exploit instruments that emit light (lidar) or microwaves (radar), and measure the reflected signal, or explore the polarization of light induced by clouds.
Key Points
Satellite cloud droplet concentration uncertainties of 78% for pixel‐level retrievals and 54% for 1 by 1 degree retrievals are estimated
The effective radius retrieval is the most important aspect for improvement, and more in situ evaluation is needed
Potential improvements using passive and active satellite, and ground‐based instruments are discussed
Clouds play a potentially important role in Arctic climate change but are poorly represented in current atmospheric models across scales. To
improve the representation of Arctic clouds in models, it ...is necessary to compare models to observations to consequently reduce this
uncertainty. This study compares aircraft observations from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD)
campaign around Svalbard, Norway, in May–June 2017 and simulations using the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather
prediction (NWP) setup at 1.2 km horizontal resolution. By comparing measurements of solar and terrestrial irradiances during ACLOUD
flights to the respective properties in ICON, we showed that the model systematically overestimates the transmissivity of the mostly liquid clouds
during the campaign. This model bias is traced back to the way cloud condensation nuclei (CCN) get activated into cloud droplets in the two-moment
bulk microphysical scheme used in this study. This process is parameterized as a function of grid-scale vertical velocity in the microphysical scheme
used, but in-cloud turbulence cannot be sufficiently resolved at 1.2 km horizontal resolution in Arctic clouds. By parameterizing
subgrid-scale vertical motion as a function of turbulent kinetic energy, we are able to achieve a more realistic CCN activation into cloud
droplets. Additionally, we showed that by scaling the presently used CCN activation profile, the hydrometeor number concentration could be modified
to be in better agreement with ACLOUD observations in our revised CCN activation parameterization. This consequently results in an improved
representation of cloud optical properties in our ICON simulations.
The new BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) tethered balloon system is introduced. It combines a set of instruments to measure turbulent and radiative parameters ...and energy fluxes. BELUGA enables collocated measurements either at a constant altitude or as vertical profiles up to 1.5 km in height. In particular, the instrument payload of BELUGA comprises three modular instrument packages for high-resolution meteorological, wind vector and broadband radiation measurements. Collocated data acquisition allows for estimates of the driving parameters in the energy balance at various heights. Heating rates and net irradiances can be related to turbulent fluxes and local turbulence parameters such as dissipation rates. In this paper the technical setup, the instrument performance, and the measurement strategy of BELUGA are explained. Furthermore, the high vertical resolution due to the slow ascent speed is highlighted as a major advantage of tethered balloon-borne observations. Three illustrative case studies of the first application of BELUGA in the Arctic atmospheric boundary layer are presented. As a first example, measurements of a single-layer stratocumulus are discussed. They show a pronounced cloud top radiative cooling of up to 6 K h−1. To put this into context, a second case elaborates respective measurements with BELUGA in a cloudless situation. In a third example, a multilayer stratocumulus was probed, revealing reduced turbulence and negligible cloud top radiative cooling for the lower cloud layer. In all three cases the net radiative fluxes are much higher than turbulent fluxes. Altogether, BELUGA has proven its robust performance in cloudy conditions of the Arctic atmospheric boundary layer.
According to the hypothesis of aerosol invigoration, the higher concentration of aerosols in polluted air intensifies storms. A leading theory for explaining such a relationship is warm‐phase ...invigoration, in which cloudy updrafts that are more polluted more readily condense water vapor onto liquid drops, thereby releasing latent heat faster, leading to higher buoyancies and higher updraft speeds. For this mechanism to work, water‐vapor supersaturations well in excess of 1% must be typical of relatively unpolluted cloudy updrafts. Here, the supersaturation is calculated from in situ observations of warm‐phase cloudy updrafts over the Amazon. Instead of values well in excess of 1%, the typical values are found to be around 0.2%. These observations imply that cleaner preindustrial air might have generated supersaturations around 1%, but those are still too low for warm‐phase invigoration to have any practically significant impact on cloud buoyancy and updraft speeds.
Plain Language Summary
An actively debated hypothesis is that air pollution from human activities regularly makes storms around the world more intense, with intensity measured, for example, by the speed of storm updrafts. There are three main proposed mechanisms for how aerosols might invigorate storms, one of which is the so‐called “warm‐phase invigoration mechanism.” In this mechanism, extra particles of air pollution (aerosols) make storm updrafts more buoyant and, therefore, faster, with “warm‐phase” referring to the lower altitudes where clouds are composed of liquid water (as opposed to ice). But this mechanism requires the water vapor in cloud updrafts to be far out of equilibrium, as measured by the supersaturation (the amount by which the relative humidity exceeds 100%). We show that observed supersaturations in storm clouds over the Amazon are much too low for this mechanism to operate. In other words, the warm‐phase mechanism is unlikely to have any practically significant effect on storm intensity.
Key Points
Warm‐phase invigoration of clouds by air pollution requires supersaturations well in excess of 1% in relatively unpolluted cloudy updrafts
Observed warm‐phase clouds convecting over the Amazon have typical supersaturations around 0.2%
Observed supersaturations are too low for air pollution to have any practical impact on updraft speeds via warm‐phase invigoration
Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically ...influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization.
The polarization state of electromagnetic radiation scattered by atmospheric particles such as aerosols, cloud droplets, or ice crystals contains much more information about the optical and ...microphysical properties than the total intensity alone. For this reason an increasing number of polarimetric observations are performed from space, from the ground and from aircraft. Polarized radiative transfer models are required to interpret and analyse these measurements and to develop retrieval algorithms exploiting polarimetric observations. In the last years a large number of new codes have been developed, mostly for specific applications. Benchmark results are available for specific cases, but not for more sophisticated scenarios including polarized surface reflection and multi-layer atmospheres. The International Polarized Radiative Transfer (IPRT) working group of the International Radiation Commission (IRC) has initiated a model intercomparison project in order to fill this gap. This paper presents the results of the first phase A of the IPRT project which includes ten test cases, from simple setups with only one layer and Rayleigh scattering to rather sophisticated setups with a cloud embedded in a standard atmosphere above an ocean surface. All scenarios in the first phase A of the intercomparison project are for a one-dimensional plane–parallel model geometry. The commonly established benchmark results are available at the IPRT website (http://www.meteo.physik.uni-muenchen.de/iprt).
•Intercomparison of polarized radiative transfer codes.•Simple and complex cases with multiple scattering in clouds and surface reflection.•Comparison of various methodologies (Monte Carlo, Discrete Ordinate, etc.)•Large benchmark dataset for model developers.
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