No abstract

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.

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.

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. Plain Language Summary Cloud longwave scattering has never been deemed as a necessity in climate models. Out of all climate models in the IPCC fifth and sixth assessments, only three modeling centers have longwave scattering included in their models. Our study explained why the traditional wisdom of neglecting longwave scattering breaks down for the simulation of high-latitude climate in the fully coupled models. We showed the critical importance of atmosphere-surface radiative coupling for correctly assessing the role of cloud longwave scattering in the model simulation of climate mean state as well as climate changes, an issue overlooked by all previous studies. We argued that the cloud longwave scattering is a necessity in climate models, not an option.

No abstract

No abstract

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.

fine spectral resolution microwave sounders now 25 viable as replacements to the current operational 26 instruments. This study demonstrates that retrievals 27 of temperature and moisture soundings can be 28 improved by as much as 50% when 60-80 29 appropriately chosen pseudo-channels are employed. 30 While the current simulations were limited to cloud 31 free oceans, perhaps even greater benefits can be 32 realized over land and cloud conditions where 33 additional channels can help constrain the surface and 34 clouds. The study also demonstrated the advantages 35 of hyperspectral sensors as a way to detect Radio 36 Frequency Interference (RFI) in the few Kelvin 37 range, as well as its ability to improve intercalibration 38 efforts due to its ability to match frequency response 39 functions of target sensors. 40 41 42 43 Temperature and humidity soundings form the 44 bedrock of modern data assimilation due to their 45 ability to directly constrain the atmospheric state 46 variables. The primary sources of global sounding 47 data are satellite infrared (IR) and passive microwave 48 sensors, although radio occultation [1] has also had 49 success in constraining numerical models. While IR 50 sensors dominated the very early instrument suites, 51 and have very good vertical resolution, they are 52 limited to cloud-free scenes. To overcome this 53 limitation, microwave sensors were deployed. While 54 their weighting functions and subsequent vertical 55 resolution are much broader than their IR 56 counterparts, their ability to penetrate clouds and 57 work in all weather conditions make them perhaps 58 the most useful instruments for constraining 59 Numerical Weather Prediction (NWP) models (e.g. 60 [2]).