The current wealth of spaceborne passive and active measurements from ultraviolet to the infrared wavelengths provides an unprecedented opportunity to construct ice cloud bulk optical property models that lead to consistent ice cloud property retrievals across multiple sensors and platforms. To infer the microphysical and radiative properties of ice clouds from these satellite measurements, the general approach is to assume an ice cloud optical property model that implicitly assumes the habit (shape) and size distributions of the ice particles in these clouds. The assumption is that this ice optical property model will be adequate for global retrievals. In this review paper, we first summarize the key optical properties of individual particles and then the bulk radiative properties of their ensemble, followed by a review of the ice cloud models developed for application to satellite remote sensing. We illustrate that the random orientation condition assumed for ice particles is arguably justified for passive remote sensing applications based on radiometric measurements. The focus of the present discussion is on the ice models used by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and Earth’s Radiant Energy System (CERES) science teams. In addition, we briefly review the ice cloud models adopted by the Polarization and Directionality of the Earth’s Reflectance (POLDER) and the Himawari-8 Advanced Himawari Imager (AHI) for ice cloud retrievals. We find that both the MODIS Collection 6 ice model and the CERES two-habit model result in spectrally consistent retrievals.

General circulation models (GCMs) are extensively used to estimate the influence of clouds on the global energy budget and other aspects of climate. Because radiative transfer computations involved in GCMs are costly, it is typical to consider only absorption but not scattering by clouds in longwave (LW) spectral bands. In this study, the flux and heating rate biases due to neglecting the scattering of LW radiation by clouds are quantified by using advanced cloud optical property models, and satellite data from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Clouds and the Earth's Radiant Energy System (CERES), and Moderate Resolution Imaging Spectrometer (MODIS) merged products (CCCM). From the products, information about the atmosphere and clouds (microphysical and buck optical properties, and top and base heights) is used to simulate fluxes and heating rates. One‐year global simulations for 2010 show that the LW scattering decreases top‐of‐atmosphere (TOA) upward flux and increases surface downward flux by 2.6 and 1.2 W/m², respectively, or approximately 10% and 5% of the TOA and surface LW cloud radiative effect, respectively. Regional TOA upward flux biases are as much as 5% of global averaged outgoing longwave radiation (OLR). LW scattering causes approximately 0.018 K/d cooling at the tropopause and about 0.028 K/d heating at the surface. Furthermore, over 40% of the total OLR bias for ice clouds is observed in 350–500 cm⁻¹. Overall, the radiative effects associated with neglecting LW scattering are comparable to the counterpart due to doubling atmospheric CO₂ under clear‐sky conditions.

Most general circulation models (GCMs) neglect cloud longwave scattering in pursuit of computational efficiency. This study implements the 2‐/4‐stream (2/4S) method, a relatively fast cloud longwave scattering treatment, in Community Atmospheric Model version 5 (CAM5) to analyze the impact of cloud longwave scattering on the large‐scale circulation. Two 45‐years‐long integrations are performed with prescribed sea surface temperature (SST). In the experiment run, cloud longwave scattering is included using the 2/4S method; in the control run, clouds only absorb in the longwave. The results show that cloud longwave scattering acts to enhance the cloud longwave (greenhouse) effect by reducing outgoing longwave radiation (OLR) and enhancing downward longwave irradiance at the surface. The OLR reduction is most significant over the tropics, where surface temperatures and cloud elevations are high. The surface downward irradiance increase is most significant over polar areas and the Tibetan Plateau, where cloud elevations are low and the air below clouds is dry. Inclusion of cloud longwave scattering enhances the Walker circulation, suggestive of the importance of diabatic radiative heating in the tropical circulation. Inclusion of cloud longwave scattering also appears to shift the eddy‐driven jet poleward in the austral summer in the Southern Hemisphere, suggesting that the cloud longwave effect plays a role in shaping the jet position. Persistent equatorward jet biases in GCMs may be reduced if cloud longwave scattering is considered.

Most climate models neglect cloud longwave (LW) scattering because scattering is considered negligible compared to strong LW absorption by clouds and greenhouse gases. While this rationale is valid for simulating extrapolar regions, it is questionable for the polar regions, where the atmosphere is dry and hence has weak absorption, and ice clouds that have strong scattering capability frequently occur. Using the slab‐ocean Community Earth System Model, we show that ice cloud LW scattering can warm winter surface air temperature by 0.8–1.8 K in the Arctic and 1.3–1.9 K in the Antarctic, while this warming becomes much weaker in polar summer. Such scattering effect cannot be correctly assessed when sea surface temperature and sea ice are prescribed as this effect is manifested through a surface‐atmosphere coupling. Cloud LW scattering is a necessity for the correct simulation of polar climate and surface radiation budget, especially in the winter.

The Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) demonstrated the capability of CubeSat satellites to provide high-quality, stable microwave signals for estimating water vapor, clouds, and precipitation from space. Unlike the operational NOAA and MetOp series satellites, which combine microwave and hyperspectral infrared sensors on the same platforms to optimize retrievals, CubeSat radiometers such as TEMPEST do not carry additional sensors. In such cases, the high-temporal- and spatial-resolution and multi-channel measurements from the Advanced Baseline Imager (ABI) on the next-generation series of Geostationary Operational Environmental Satellites (GOES-R) are ideal for assisting these smaller, stand-alone radiometers. Based on sensitivity tests, the water vapor retrievals from TEMPEST are improved by adding water-vapor-sounding, window, and CO₂ channels at 6.2, 6.9, 7.3, 8.4, 10.3, 11.2, 12.3, and 13.3 µm from ABI, which help to increase the vertical resolution of soundings and reduce retrieval errors. Adding three ABI water-vapor-sounding channels, under clear-sky conditions, retrieval biases and root mean square errors improve by approximately 10 %, while under cloudy skies, biases remain unchanged, but root mean square errors still decrease by 5 %; meanwhile, retrieval biases and root mean square errors are substantially reduced by adding more information from eight ABI bands in both clear and cloudy skies. Humidity soundings are also validated using coastal radiosonde data from the Integrated Global Radiosonde Archive (IGRA) from 2019 to 2020. When ABI indicates clear skies, water vapor retrievals improve somewhat by decreasing the overall bias in the microwave-only estimate by roughly 10 %, although layer root mean square errors remain roughly unchanged at 1 g kg⁻¹ when three or eight ABI channels are added. When ABI indicates cloudy conditions, there is little change in the results. The small number of matched radiosondes may limit the observed improvement.