Daytime Top-of-the-Atmosphere Cirrus Cloud Radiative Forcing Properties at Singapore
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Radiative transfer calculations are performed for polar stratospheric clouds (PSCs) using newly acquired PSC properties and polar atmospheric data. PSC radiative effects depend strongly on upwelling thermal radiation and vary from infrared heating over warm polar surfaces, such as oceans, to cooling over cold surfaces, such as the Antarctic plateau. Heating and cooling rates of nitric acid PSCs are smaller than + or - 0.1 K/day. Rates for optically thicker ice PSCs vary from 1.0 to -0.2 K/day, those for orographically forced ice PSCs even from 3.0 to -0.5 K/day. Frequently observed optically thick cirrus decks near the tropopause provide a very cold radiative surface. These clouds not only act to prevent heating and enhance cooling in ice PSCs to -0.5 K/day and orographic ice PSCs to 2 K/day, but such cirrus cloud decks also cool the entire stratosphere by up to -0.5 K/day over warm surfaces, even in the absence of PSCs.
Visible radiation reflection, transmission and inside intensities of terrestrial clouds calculated by Monte Carlo program utilizing scattering phase function
The effect of radiative heating and cooling by clouds on the available potential energy (APE) is theoretically discussed. It is shown that the cloud radiative contribution to the generation of APE is determined by the net cloud radiative heating and the efficiency factor, which is a function of the temperature distribution of the atmosphere. Results are presented for low and middle cloud effects for three atmospheric layers. Cloud radiative heating is found to be a single function of cloud optical thickness for all classes designed in terms of cloud top heights and optical thickness. Low clouds at low latitudes destroy APE an midclouds generate APE. A concept is developed to relate the cloud radiative heating to cloud heights and optical depths. Cloud-generated radiative heating is computed for January zonal mean conditions for low and midclouds. For both cases, the strongest influence is found in the low troposphere, with marked differences in signs and magnitudes. At extratropical latitudes, both cloud classes generate net radiative cooling. In the tropics, the effect of low cloud changes from net cooling to the net heating as the optical thickness increases, and midclouds cause net heating. A mechanism is described whereby this dependence produces a strong positive feedback effect on the development of SST anomalies in the tropical oceans.
A one-year’s worth of near-global marine shallow single-layer cloud top radiative cooling (CTRC) is derived from a radiative transfer model with inputs from the satellite cloud retrievals and reanalysis sounding. The mean cloud top radiative flux divergence is 61 Wm -2 , decomposed into the longwave and shortwave components of 73 and -11 W m -2 , respectively. Equatorward of 30°N/S, the CTRC is largely a reflection of free-atmospheric specific humidity distribution: a dry atmosphere enhances CTRC by reducing downward thermal radiation. Consequently, the cooling minimizes in the “wet” tropics and maximizes in the “dry” eastern subtropics. Poleward of 30°N/S, the CTRC decreases slightly due to the colder clouds that emit less effectively. Furthermore, the CTRC exhibits distinctive seasonal cycles with stronger cooling in the winter and has amplitudes of order 10–20 Wm -2 in stratocumulus-rich regions. The datasets were used to train a machine-learning model that substantially speeds up the retrieval.
Discussion of the first results of extensive recent aircraft measurements, in the short wavelengths, of the reflection and absorption properties of clouds and their impact on radiation budget studies. From the data obtained, the albedo of typical cloud types was determined integrally and spectrally. Some preliminary results are also offered for short-wave absorption and the apparent transmission of clouds. The effect of the atmospheric pathlength between sensor and target, exhibited through molecular scattering and aerosol scattering and absorption, is included in the data analysis. Results are also presented on vertical (short-wave) radiative flux profiles, within specific wavelength intervals, from near sea level to altitudes of about 40,000 ft over relatively short time intervals.
Radiation transport through particle clouds plays a major role in many engineering applications. Here in this paper, we study this problem by means of solving the radiative transport equation (RTE) on a Eulerian continuous domain. The particle clouds are generated through direct numerical simulation of Navier-Stokes equations coupled with Lagrangian particle transport at different Stokes numbers. We confirm the earlier observation noted in the literature that the solution to the RTE on Eulerian mesh diverges when the Eulerian mesh size is of the order of the particle diameter. This observation is often called the homogenization error stemming from relegation of number density onto the Eulerian domain. In order to circumvent this divergence problem, we propose a filtering strategy that spreads the information of the particles to the neighboring cells in a way that the representation of the particles remains the same even when the mesh size is smaller than that of the particle size. We show through our simulations that our filtering strategy solves the issue of homogenization error for the cases where the Beer-Bouger law is valid.
Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
CloudSat and CALIPSO cloud and aerosol information is convolved with CERES and MODIS cloud and radiation data to produce a merged 3-dimensional cloud and radiation dataset.
Here, we present 123 cloud-resolving simulations to study how temperatures of anvil clouds and radiative tropopause (RT) change with surface warming. Our simulation results show that the RT warms at approximately the same rate as anvil clouds. This relationship persists across a variety of modeling choices, including surface temperature, greenhouse gas concentration, and the representation of radiative transfer. We further show that the shifting ozone profile associated with climate warming may give rise to a fixed RT temperature as well as a fixed anvil temperature. This result points to the importance of faithful treatment of ozone in simulating clouds and climate change; the robust anvil–RT relationship may also provide alternative ways to understand what controls anvil temperature.
The generation of zonal available potential energy (APE) by cloud radiative heating is discussed. The APE concept was mathematically formulated by Lorenz (1955) as a measure of the maximum amount of total potential energy that is available for conversion by adiabatic processes to kinetic energy. The rate of change of APE is the rate of the generation of APE minus the rate of conversion between potential and kinetic energy. By radiative transfer calculations, a mean cloud-generated radiative heating for a well defined set of cloud classes is derived as a function of cloud optical thickness. The formulation is suitable for using a general cloud parameter data set and has the advantage of taking into account nonlinearities between the microphysical and macrophysical cloud properties and the related radiation field.
Cloud and surface radiative properties and their effects on the earth and surface radiation budgets are obtained based on global daily visible and IR radiance measurements. The magnitude of cloud property variations and their effects on radiation increase strongly with decreasing space/time scales. Cloud properties are systematically different between land and ocean, with ocean having larger cloud cover with somewhat larger optical thicknesses and lower cloud top altitudes. Although cloud variations appear to be the primary cause of regional radiation budget variability at 5-30 daytime scales, the effects of their seasonal variations at larger spatial scales are less important than the changes associated with changes in solar declination and atmospheric/surface temperatures. The largest seasonal variations in radiation occur in the 30-60 deg latitude band in each hemisphere. Although clouds have a net cooling effect on the global, annual mean radiation balance at both the top of the atmosphere and the surface, their net effect on regional, seasonal balances is much more varied.
Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops <3 km) and precipitation using ground-based ceilometer, depolarization lidar and vertically-pointing W-band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016 to March 2017. During MICRE, low clouds occurred ~65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold-topped (temperatures ≤0°C). These were thicker and had higher bases on average than warm-topped clouds. 83%–88% of cold-topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150–250 m below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small-particle (low-radar-reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below-cloud, while large-particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold-topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below-cloud precipitation with radar reflectivity factors below about -10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice.
Increases in aerosol concentration are well known to influence the microphysical processes and radiative properties of clouds. By reducing droplet size, an increase in aerosol can lessen collision efficiency and increase liquid water path (LWP) in precipitating clouds or enhance evaporation rate and decrease LWP in non-precipitating clouds. We utilize large eddy simulations to further investigate these aerosol indirect effects in Arctic mixed-phase clouds and find, in agreement with previous studies, precipitating clouds to experience an increase in LWP and non-precipitating clouds a decrease in LWP. Most importantly however, our results reveal a different explanation for why such an LWP decrease occurs in decoupled, non-precipitating clouds. We find enhanced evaporation near cloud top to be driven primarily by a strengthening of maximum radiative cooling rate with aerosol concentration which drives stronger entrainment, an effect that holds true even in clouds that are optically thick.