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Lazaros Oreopoulos
,
Nayeong Cho
,
Dongmin Lee
,
Matthew Lebsock
, and
Zhibo Zhang

Abstract

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed-phase clouds when blended at ∼200 m vertical and ∼2 km horizontal scales. Upon segmenting the dataset to individual “scenes,” mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud-top pressure–cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 W m−2 for the cloud radiative effect in the shortwave part of the spectrum.

Significance Statement

The purpose of the paper is to assess the realism of relatively simple ways of producing fine-scale cloud variability in global models from coarsely resolved cloud properties. The assessment is achieved via comparisons to observed cloud fields where the fine-scale variability is known in both the horizontal and vertical directions. Our results show that while the generators have considerable skill, they still suffer from consistent deficiencies that need to be addressed with further development guided by appropriate observations.

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Daeho Jin
,
Lazaros Oreopoulos
,
Dongmin Lee
,
Jackson Tan
, and
Nayeong Cho

Abstract

To better understand cloud–precipitation relationships, we extend the concept of cloud regimes developed from two-dimensional joint histograms of cloud optical thickness and cloud-top pressure from MODIS to include precipitation information. Taking advantage of the high-resolution IMERG precipitation dataset, we derive cloud–precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1° grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud–precipitation regimes (CPRs) and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud–precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies for which characterization of the diurnal cycle is essential.

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Matthew Lebsock
,
Hanii Takahashi
,
Richard Roy
,
Marcin J. Kurowski
, and
Lazaros Oreopoulos

Abstract

An algorithm that derives the nonprecipitating cloud liquid water path W cld from CloudSat using a surface reference technique (SRT) is presented. The uncertainty characteristics of the SRT are evaluated. It is demonstrated that an accurate analytical formulation for the pixel-scale precision can be derived. The average precision of the SRT is estimated to be 34 g m−2 at the individual pixel scale; however, precision systematically decreases from around 30 to 40 g m−2 as cloud fraction varies from 0% to 100%. The retrievals of clear-sky W cld have a mean bias of 0.9 g m−2. Output from a large-eddy simulation coupled to a radar simulator shows that an additional bias of −8% may result from nonuniformity within the footprint of cloudy pixels. The retrieval yield for the SRT, measured relative to all warm clouds over ocean between 60°N and 60°S latitude is 43%. The SRT W cld is compared with one estimate of W cld from the Moderate Resolution Imaging Spectroradiometer (MODIS) using an adiabatic cloud profile and an effective radius derived from 3.7-μm reflectance. A strong correlation between the mean MODIS W cld and SRT W cld is found across diverse cloud regimes, but with biases in the mean W cld that are cloud-regime dependent. Overall, the mean bias of the SRT relative to MODIS is −13.1 g m−2. Systematic underestimates of W cld by the SRT resulting from nonuniform beamfilling cannot be ruled out as an explanation for the retrieval bias.

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Daeho Jin
,
Ryan J. Kramer
,
Lazaros Oreopoulos
, and
Dongmin Lee

Abstract

Twenty years of satellite-based cloud and radiation observations allow us to examine the observed cloud radiative effect (CRE) feedback (i.e., CRE change per unit change in global mean surface temperature). By employing a decomposition method to separate the contribution of “internal changes” and “relative-frequency-of-occurrence (RFO) changes” of distinct cloud regime (CR) groups, notable seasonal contrasts of CRE feedback characteristics emerge. Boreal winter CRE feedback is dominated by the positive shortwave CRE (SWCRE) feedback of oceanic low-thick clouds, due to their decreasing RFO as temperature rises. This signal is most likely due to El Niño–Southern Oscillation (ENSO) activity. When ENSO signals are excluded, boreal winter CRE feedback becomes qualitatively similar to the boreal summer feedback, where several CR groups contribute to the total CRE feedback more evenly. Most CR groups’ CRE feedbacks largely come from changing RFO (e.g., the predominant transition from oceanic cumulus to broken clouds and more occurrences of higher convective clouds with warming temperature). At the same time, low-thick and broken clouds experience optical thinning and decreasing cloud fraction, and these features are more prominent in boreal summer than winter. Overall, the seasonally asymmetric patterns of CRE feedback, primarily due to ENSO, introduce complexity in assessments of CRE feedback.

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Dongmin Lee
,
Lazaros Oreopoulos
,
George J. Huffman
,
William B. Rossow
, and
In-Sik Kang

Abstract

The authors examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35°S–35°N) for a 10-yr period. The main precipitation dataset used is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis operational product 3B42 dataset, but Global Precipitation Climatology Project daily data are also used for comparison. It is found that the most convectively active ISCCP weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropics; yet, even this weather state appears to not precipitate about half the time, although this may be to some extent an artifact of detection and spatiotemporal matching limitations of the precipitation dataset. WS1 exhibits a modest annual cycle of the domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states appear to be stronger when occurring before or after WS1. The precipitation rates of the various weather states are different between ocean and land, with WS1 producing higher daytime rates on average over ocean than land, likely because of the larger size and more persistent nature of oceanic WS1s. The results of this study, in addition to advancing the understanding of tropical hydrology, can serve as higher-order diagnostics for evaluating the realism of tropical precipitation distributions in large-scale models.

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Eli J. Mlawer
,
Michael J. Iacono
,
Robert Pincus
,
Howard W. Barker
,
Lazaros Oreopoulos
, and
David L. Mitchell
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Bingqi Yi
,
Ping Yang
,
Bryan A. Baum
,
Tristan L'Ecuyer
,
Lazaros Oreopoulos
,
Eli J. Mlawer
,
Andrew J. Heymsfield
, and
Kuo-Nan Liou

Abstract

Ice clouds influence the climate system by changing the radiation budget and large-scale circulation. Therefore, climate models need to have an accurate representation of ice clouds and their radiative effects. In this paper, new broadband parameterizations for ice cloud bulk scattering properties are developed for severely roughened ice particles. The parameterizations are based on a general habit mixture that includes nine habits (droxtals, hollow/solid columns, plates, solid/hollow bullet rosettes, aggregate of solid columns, and small/large aggregates of plates). The scattering properties for these individual habits incorporate recent advances in light-scattering computations. The influence of ice particle surface roughness on the ice cloud radiative effect is determined through simulations with the Fu–Liou and the GCM version of the Rapid Radiative Transfer Model (RRTMG) codes and the National Center for Atmospheric Research Community Atmosphere Model (CAM, version 5.1). The differences in shortwave (SW) and longwave (LW) radiative effect at both the top of the atmosphere and the surface are determined for smooth and severely roughened ice particles. While the influence of particle roughening on the single-scattering properties is negligible in the LW, the results indicate that ice crystal roughness can change the SW forcing locally by more than 10 W m−2 over a range of effective diameters. The global-averaged SW cloud radiative effect due to ice particle surface roughness is estimated to be roughly 1–2 W m−2. The CAM results indicate that ice particle roughening can result in a large regional SW radiative effect and a small but nonnegligible increase in the global LW cloud radiative effect.

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Xianglei Huang
,
Xiuhong Chen
,
Gerald L. Potter
,
Lazaros Oreopoulos
,
Jason N. S. Cole
,
Dongmin Lee
, and
Norman G. Loeb

Abstract

Longwave (LW) spectral flux and cloud radiative effect (CRE) are important for understanding the earth’s radiation budget and cloud–radiation interaction. Here, the authors extend their previous algorithms to collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) observations over the entire globe and show that the algorithms yield consistently good performances for measurements over both land and ocean. As a result, the authors are able to derive spectral flux and CRE at 10-cm−1 intervals over the entire LW spectrum from all currently available collocated AIRS and CERES observations. Using this multiyear dataset, they delineate the climatology of spectral CRE, including the far IR, over the entire globe as well as in different climate zones. Furthermore, the authors define two quantities, IR-effective cloud-top height (CTHeff) and cloud amount (CAeff), based on the monthly-mean spectral (or band by band) CRE. Comparisons with cloud fields retrieved by the CERES–Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm indicate that, under many circumstances, the CTHeff and CAeff can be related to the physical retrievals of CTH and CA and thus can enhance understandings of model deficiencies in LW radiation budgets and cloud fields. Using simulations from the GFDL global atmosphere model, version 2 (AM2); NASA’s Goddard Earth Observing System, version 5 (GEOS-5); and Environment Canada’s Canadian Centre for Climate Modelling and Analysis (CCCma) Fourth Generation Canadian Atmospheric General Circulation Model (CanAM4) as case studies, the authors further demonstrate the merits of the CTHeff and CAeff concepts in providing insights on global climate model evaluations that cannot be obtained solely from broadband LW flux and CRE comparisons.

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Xianglei Huang
,
Jason N. S. Cole
,
Fei He
,
Gerald L. Potter
,
Lazaros Oreopoulos
,
Dongmin Lee
,
Max Suarez
, and
Norman G. Loeb

Abstract

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM’s radiation code is uniquely valuable for GCM evaluation because 1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and 2) the fractional contribution of each band to the LW broadband CRE (f CRE) is sensitive to cloud-top height but largely insensitive to cloud fraction, thereby presenting a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band-by-band quantities from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations. A study is presented here that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs—the GFDL Atmospheric Model version 2 (GFDL AM2), NASA Goddard Earth Observing System version 5 (GEOS-5), and the fourth-generation AGCM of the Canadian Centre for Climate Modelling and Analysis (CCCma CanAM4)—forced by observed SST. The models agree with observation on the annual-mean LW broadband CRE over the tropical oceans within ±1 W m−2. However, the differences among these three GCMs in some bands can be as large as or even larger than ±1 W m−2. Observed seasonal cycles of f CRE in major bands are shown to be consistent with the seasonal cycle of cloud-top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of f CRE agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of f CRE highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

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Robert F. Cahalan
,
Lazaros Oreopoulos
,
Alexander Marshak
,
K. Franklin Evans
,
Anthony B. Davis
,
Robert Pincus
,
Ken H. Yetzer
,
Bernhard Mayer
,
Roger Davies
,
Thomas P. Ackerman
,
Howard W. Barker
,
Eugene E. Clothiaux
,
Robert G. Ellingson
,
Michael J. Garay
,
Evgueni Kassianov
,
Stefan Kinne
,
Andreas Macke
,
William O'hirok
,
Philip T. Partain
,
Sergei M. Prigarin
,
Alexei N. Rublev
,
Graeme L. Stephens
,
Frederic Szczap
,
Ezra E. Takara
,
Tamas Várnai
,
Guoyong Wen
, and
Tatiana B. Zhuravleva

The interaction of clouds with solar and terrestrial radiation is one of the most important topics of climate research. In recent years it has been recognized that only a full three-dimensional (3D) treatment of this interaction can provide answers to many climate and remote sensing problems, leading to the worldwide development of numerous 3D radiative transfer (RT) codes. The international Intercomparison of 3D Radiation Codes (I3RC), described in this paper, sprung from the natural need to compare the performance of these 3D RT codes used in a variety of current scientific work in the atmospheric sciences. I3RC supports intercomparison and development of both exact and approximate 3D methods in its effort to 1) understand and document the errors/limits of 3D algorithms and their sources; 2) provide “baseline” cases for future code development for 3D radiation; 3) promote sharing and production of 3D radiative tools; 4) derive guidelines for 3D radiative tool selection; and 5) improve atmospheric science education in 3D RT. Results from the two completed phases of I3RC have been presented in two workshops and are expected to guide improvements in both remote sensing and radiative energy budget calculations in cloudy atmospheres.

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