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REVIEW ARTICLE Cloud Feedbacks in the Climate System: A Critical Review

TLDR
A review of cloud-climate feedbacks can be found in this paper, where it is argued that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet's hydrological cycle to climate radiative forcings.
Abstract
This paper offers a critical review of the topic of cloud–climate feedbacks and exposes some of the underlying reasons for the inherent lack of understanding of these feedbacks and why progress might be expected on this important climate problem in the coming decade. Although many processes and related parameters come under the influence of clouds, it is argued that atmospheric processes fundamentally govern the cloud feedbacks via the relationship between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. It is also shown how perturbations to the atmospheric radiation budget that are induced by cloud changes in response to climate forcing dictate the eventual response of the global-mean hydrological cycle of the climate model to climate forcing. This suggests that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet’s hydrological cycle to climate radiative forcings. The paper provides a brief overview of the effects of clouds on the radiation budget of the earth– atmosphere system and a review of cloud feedbacks as they have been defined in simple systems, one being a system in radiative–convective equilibrium (RCE) and others relating to simple feedback ideas that regulate tropical SSTs. The systems perspective is reviewed as it has served as the basis for most feedback analyses. What emerges is the importance of being clear about the definition of the system. It is shown how different assumptions about the system produce very different conclusions about the magnitude and sign of feedbacks. Much more diligence is called for in terms of defining the system and justifying assumptions. In principle, there is also neither any theoretical basis to justify the system that defines feedbacks in terms of global–time-mean changes in surface temperature nor is there any compelling empirical evidence to do so. The lack of maturity of feedback analysis methods also suggests that progress in understanding climate feedback will require development of alternative methods of analysis. It has been argued that, in view of the complex nature of the climate system, and the cumbersome problems encountered in diagnosing feedbacks, understanding cloud feedback will be gleaned neither from observations nor proved from simple theoretical argument alone. The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations. Models provide the tool for diagnosing processes and quantifying feedbacks while observations provide the essential test of the model’s credibility in representing these processes. While GCM climate and NWP models represent the most complete description of all the interactions between the processes that presumably establish the main cloud feedbacks, the weak link in the use of these models lies in the cloud parameterization imbedded in them. Aspects of these parameterizations remain worrisome, containing levels of empiricism and assumptions that are hard to evaluate with current global observations. Clearly observationally based methods for evaluating cloud parameterizations are an important element in the road map to progress. Although progress in understanding the cloud feedback problem has been slow and confused by past analysis, there are legitimate reasons outlined in the paper that give hope for real progress in the future.

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Measurement of the Earth Radiation Budget at the Top of the Atmosphere—A Review

Steven Dewitte, +1 more
- 07 Nov 2017 - 
TL;DR: The link between long-term changes of the Outgoing Longwave Radiation, the strengthening of El Nino in the period 1985–1997 and the strengtheningof La Nina in theperiod 2000–2009 is highlighted.
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Observational and Model Estimates of Cloud Amount Feedback over the Indian and Pacific Oceans

TL;DR: In this paper, the authors examined cloud amount in three independent ship-based and satellite-based observational datasets, and models from phase 5 of the Coupled Model Intercomparison Project (CMIP5).
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Improvement and further development in CESM/CAM5: gas-phase chemistry and inorganic aerosol treatments

Abstract: . Gas-phase chemistry and subsequent gas-to-particle conversion processes such as new particle formation, condensation, and thermodynamic partitioning have large impacts on air quality, climate, and public health through influencing the amounts and distributions of gaseous precursors and secondary aerosols. Their roles in global air quality and climate are examined in this work using the Community Earth System Model version 1.0.5 (CESM1.0.5) with the Community Atmosphere Model version 5.1 (CAM5.1) (referred to as CESM1.0.5/CAM5.1). CAM5.1 includes a simple chemistry that is coupled with a 7-mode prognostic Modal Aerosol Model (MAM7). MAM7 includes classical homogenous nucleation (binary and ternary) and activation nucleation (empirical first-order power law) parameterizations, and a highly simplified inorganic aerosol thermodynamics treatment that only simulates particulate-phase sulfate and ammonium. In this work, a new gas-phase chemistry mechanism based on the 2005 Carbon Bond Mechanism for Global Extension (CB05_GE) and several advanced inorganic aerosol treatments for condensation of volatile species, ion-mediated nucleation (IMN), and explicit inorganic aerosol thermodynamics for sulfate, ammonium, nitrate, sodium, and chloride have been incorporated into CESM/CAM5.1-MAM7. Compared to the simple gas-phase chemistry, CB05_GE can predict many more gaseous species, and thus could improve model performance for PM2.5, PM10, PM components, and some PM gaseous precursors such as SO2 and NH3 in several regions as well as aerosol optical depth (AOD) and cloud properties (e.g., cloud fraction (CF), cloud droplet number concentration (CDNC), and shortwave cloud forcing, SWCF) on the global scale. The modified condensation and aqueous-phase chemistry could further improve the prediction of additional variables such as HNO3, NO2, and O3 in some regions, and new particle formation rate (J) and AOD on the global scale. IMN can improve the prediction of secondary PM2.5 components, PM2.5, and PM10 over Europe as well as AOD and CDNC on the global scale. The explicit inorganic aerosol thermodynamics using the ISORROPIA II model improves the prediction of all major PM2.5 components and their gaseous precursors in some regions as well as downwelling shortwave radiation, SWCF, and cloud condensation nuclei at a supersaturation of 0.5% on the global scale. For simulations of 2001–2005 with all the modified and new treatments, the improved model predicts that on global average, SWCF increases by 2.7 W m−2, reducing the normalized mean bias (NMB) of SWCF from −5.4 to 1.2%. Uncertainties in emissions can largely explain the inaccurate prediction of precursor gases (e.g., SO2, NH3, and NO) and primary aerosols (e.g., black carbon and primary organic matter). Additional factors leading to the discrepancies between model predictions and observations include assumptions associated with equilibrium partitioning for fine particles assumed in ISORROPIA II, irreversible gas/particle mass transfer treatment for coarse particles, uncertainties in model treatments such as dust emissions, secondary organic aerosol formation, multi-phase chemistry, cloud microphysics, aerosol–cloud interaction, dry and wet deposition, and model parameters (e.g., accommodation coefficients and prefactors of the nucleation power law) as well as uncertainties in model configuration such as the use of a coarse-grid resolution.
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SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation

TL;DR: A novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation and achieves better results than traditional methods do, which is validated by extensive experiments.
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The Effects of Scattering Angle and Cumulus Cloud Geometry on Satellite Retrievals of Cloud Droplet Effective Radius

TL;DR: The effect of scattering angle on Moderate Resolution Imaging Spectroradiometer retrievals of cloud drop effective radius is studied using ensembles of cumulus clouds with varying sun-satellite scattering geometries, and recommendations are provided for reducing the 3-D cloud effects using current satellite retrieval algorithms.
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