Cloud Heterogeneity in the Marine Midlatitudes: Dependence on Large‐Scale Meteorology and Implications for General Circulation Models

Loveridge, Jesse; Davies, Roger

doi:10.1029/2018jd029826

Cited by 6 publications

(7 citation statements)

References 112 publications

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“…Climatological characteristics of cloud phases provided in this study can serve as a benchmark to improve the performance of climate models in validating their simulations of 15 cloud phases and their vertical overlap, their spatial heterogeneity and spectral signatures. Particularly, spatial heterogeneity, a direct measured variable from satellite that reveals subpixel variability of different cloud phases, is not only able to track the MJO and ENSO but is also useful to evaluate how well GCMs capture subpixel clouds (e.g., Loveridge and Davies, 2019).…”

Section: Discussionmentioning

confidence: 99%

Cloud Phase Characteristics Over Southeast Asia from A-Train Satellite Observations

Hong¹,

Girolamo²

2019

Preprint

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Abstract. This study examines the climatological characteristics of cloud phase over Southeast Asia (SEA) based on A-Train satellite observations. Using the combined CloudSat-CALIPSO (CC) data, five main cloud groups are investigated: ice-only, ice-above-liquid, liquid-only, ice-above-mixed and mixed-only clouds that have annual mean frequencies of 27.8 %, 21.9 %, 16.9 %, 7.4 % and 6.2 %, respectively. Liquid-only clouds tend to occur in relatively cold, dry and stable environments. The other four cloud groups appear more frequently in relatively warm, humid and unstable conditions and their seasonal distributions move with large-scale climate systems: Asian monsoon and ITCZ. Liquid clouds are found to be highly inhomogeneous based on the heterogeneity index (Hσ) from Aqua MODIS, while ice-only and mixed-only clouds are often very smooth. Ice-above-liquid clouds are less inhomogeneous than liquid-only clouds due to the thin overlying ice clouds. Undetected subpixel liquid clouds enlarge clear sky Hσ, leading to some inhomogeneous clear sky pixels, which otherwise should be smooth. The reflectance at 0.645 (R0.645) and brightness temperature at 11 μm (BT11) of ice-only, liquid-only and ice-above-liquid clouds show peak frequencies near that of clear sky (R0.645 ~ 0.02, BT11 ~ 294 K), implying that a large population of thin or subpixel clouds over SEA are difficult to see by MODIS. The CC data also show abundant thin clouds over SEA, which indicates 78.3 % ice-only and 89.6 % ice-above-liquid clouds with ice τ

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Section: Discussionmentioning

confidence: 99%

Cloud Phase Characteristics Over Southeast Asia from A-Train Satellite Observations

Hong¹,

Girolamo²

2019

Preprint

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“…To characterize heterogeneity, we start with a cloud-only heterogeneity index, H σ , at a resolution of (2.2 km) 2 . As described in Loveridge and Davies (2019), H σ is the ratio of the standard deviation to the mean of radiances (usually at nadir view) over (2.2 km) 2 regions of overcast cloud (interior cloud pixels). This index is then averaged, wherever it exists, to produce the domain heterogeneity, as used in previous studies (Loveridge & Davies, 2019).…”

Section: Domain Propertiesmentioning

confidence: 99%

“…A third reason may be the treatment of cloud heterogeneity, which is known to cause albedo biases in general (Harshvardhan & Randall, 1985). Loveridge and Davies (2019) recently noted the existence of both cloud fraction and heterogeneity biases in the UK Met Office Global Atmosphere 7.0 (Walters et al., 2019) that affected marine midlatitudes.…”

Section: Introductionmentioning

confidence: 99%

“…The cloud types are obtained similarly to Wood and Hartmann (2006), but we obtain a measure of heterogeneity directly from the radiance measurements without recourse to a radiative transfer inversion. Neither do we reconsider the meteorological factors important in creating the different cloud types or heterogeneity, as was done by Loveridge and Davies (2019). Our data set is extensive over a wide range of oceanic latitudes, including all boundary layer clouds, but excluding scenes with higher cloud or sea ice.…”

Section: Introductionmentioning

confidence: 99%

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On the Factors That Determine Boundary Layer Albedo

Rampal

Davies

2020

JGR Atmospheres

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This study investigates the factors that control marine boundary layer cloud albedo measured by the Multiangle Imaging SpectroRadiometer (MISR) over domains of (200 km)2. We use three key metrics to investigate domain albedo: cloud fraction, cloud heterogeneity, and cloud morphology. Cloud heterogeneity is quantified at the domain level with a unified heterogeneity index. Cloud morphology is determined from a cloud classification algorithm using an Artificial Neural Network (ANN) to classify each domain into one of four categories: (i) closed‐cell Mesoscale Cellular Convection (MCC); (ii) open‐cell MCC; (iii) disorganized MCC; and (iv) No MCC. These different types of MCC are usefully defined as low clouds of different morphologies. Classifications from the ANN are also combined with the satellite observations of MISR to develop relationships between cloud morphology, domain albedo, cloud fraction, and cloud heterogeneity. Cloud morphology is found to play an essential role in modulating these relationships. The cloud fraction‐albedo relationships are found to be directly a function of cloud morphology. Relationships between domain albedo and cloud heterogeneity are also found to be a function of MCC type. Our results indicate that the albedo has a strong dependence on cloud morphology and cloud heterogeneity. Understanding both the physical properties and the meteorological controls on MCC has important implications for understanding low cloud behavior and improving their representation in General Circulation Models.

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“…The radar on CloudSat and the lidar on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) have offered unprecedented opportunities to explore cloud vertical details globally (Stephens et al, 2002;Winker et al, 2003). Using the combined CloudSat-CALIPSO (CC) observations, the vertical and horizonal structures of global hydrometeors have been examined in Mace et al (2009). More details of cloud phase characteristics including their macrophysical properties, such as cloud amount, heights, and water mass, and microphysical properties, such as effective radius (R e ) and ice and liquid water content (IWC, LWC), have also been examined in many studies (Eliasson et al, 2011;Hong and Liu, 2015;Hu et al, 2010;Yoshida et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Cloud phase characteristics over Southeast Asia from A-Train satellite observations

Hong

Girolamo

2020

Atmos. Chem. Phys.

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Abstract. This study examines the climatology of cloud phase over Southeast Asia (SEA) based on A-Train satellite observations. Using the combined CloudSat–CALIPSO (CC) data, five main cloud groups are investigated: ice-only, ice-above-liquid, liquid-only, ice-above-mixed, and mixed-only clouds that have annual mean frequencies of 28.6 %, 20.1 %, 16.0 %, 9.3 %, and 6.7 %, respectively. Liquid-only clouds tend to occur in relatively cold, dry, and stable lower troposphere. The other four cloud groups appear more frequently in relatively warm, humid, and unstable conditions, and their seasonal distributions move with the Asian monsoon and the Intertropical Convergence Zone (ITCZ). Liquid clouds are found to be highly inhomogeneous based on the heterogeneity index (Hσ) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), while ice-only and mixed-only clouds are often very smooth. Ice-above-liquid clouds are more heterogeneous than ice-only clouds owing to ice clouds being optically thin. We demonstrate that the distribution of clear-sky Hσ has a long tail towards heterogeneous values that are caused by undetected subpixel cloud within both CC and MODIS datasets. The reflectance at 0.645 µm (R0.645) and brightness temperature at 11 µm (BT11) of CC ice-only, liquid-only, and ice-above-liquid clouds show peak frequencies near that of clear sky (R0.645∼0.02; BT11∼294 K), which explains why up to 30 % of these CC cloud groups are classified as clear by MODIS. In contrast, mixed-only clouds are thick (average top ∼13 km), bright (average R0.645∼0.6), and cold (average BT11 ∼234 K). Cloud phase comparison between CC and MODIS reveals only modest agreement, with the best agreement (73 %) occurring between CC ice-above-mixed and MODIS ice clouds. The intraseasonal and interannual behaviors of the all-sky Hσ and spectral signatures follow that of cloud phase and vary with the Madden–Julian oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) phases.

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Cloud Heterogeneity in the Marine Midlatitudes: Dependence on Large‐Scale Meteorology and Implications for General Circulation Models

Cited by 6 publications

References 112 publications

Cloud Phase Characteristics Over Southeast Asia from A-Train Satellite Observations

Cloud Phase Characteristics Over Southeast Asia from A-Train Satellite Observations

On the Factors That Determine Boundary Layer Albedo

Cloud phase characteristics over Southeast Asia from A-Train satellite observations

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