A global survey of cloud overlap based on CALIPSO and CloudSat measurements

Li, J.; Huang, Jianping; Stamnes, Knut; Lv, Q.; Jin, Hongchun

doi:10.5194/acp-15-519-2015

Cited by 133 publications

(117 citation statements)

References 65 publications

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“…Active remote sensing indicates that random overlap is a reasonable assumption outside of regions of deep convection and landmasses (Li et al 2015;McCoy et al 2014). To calculate LCC we use the 8-day-averaged MODIS cloud-top pressure (CTP) histogram provided in the Aqua collection 6 dataset filtered for sensor zenith angles of less than 328, which reduces bias in cloud fraction retrieval (Maddux et al 2010).…”

Section: ) Modismentioning

confidence: 99%

The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim

et al. 2017

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Decreases in subtropical low cloud cover (LCC) occur in climate model simulations of global warming. In this study 8-day-averaged observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) spanning 2002-14 are combined with European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis to compute the dependence of the observed variability of LCC on various predictor variables. Large-scale thermodynamic and dynamic predictors of LCC are selected based on insight from large-eddy simulations (LESs) and observational analysis. It is found that increased estimated inversion strength (EIS) is associated with increased LCC. Drying of the free troposphere is associated with decreased LCC. Decreased LCC accompanies subsidence in regions of relatively low EIS; the opposite is found in regions of high EIS. Finally, it is found that increasing sea surface temperature (SST) leads to a decrease in LCC. These results are in keeping with previous studies of monthly and annual data. Based upon the observed response of LCC to natural variability of the control parameters, the change in LCC is estimated for an idealized warming scenario where SST increases by 1 K and EIS increases by 0.2 K. For this change in EIS and SST the LCC is inferred to decrease by 0.5%-2.7% when the regression models are trained on data observed between 408S and 408N and by 1.1%-1.4% when trained on data from trade cumulusdominated regions. When the data used to train the regression model are restricted to stratocumulusdominated regions the change in LCC is highly uncertain and varies between 21.6% and 11.4%, depending on the stratocumulus-dominated region used to train the regression model.

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Section: ) Modismentioning

confidence: 99%

The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim

et al. 2017

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“…This is a reasonable approximation in marine stratocumulus decks, where the typical length scale of variability in Liquid Water Path can be 10-30 km (Wood and Hartmann, 2006), but will be violated in many low level cloud cases such as at the edges of the 25 stratocumulus-trade cumulus transition. In addition, multi-layered clouds are ubiquitous (Li et al, 2015), although for overlying cirrus it may be possible to identify and flag many of these cases based on the inferred distribution of photon path lengths from A-band measurements (Min et al, 2004).…”

Section: Reconstructed 15mentioning

confidence: 99%

Information content of OCO-2 oxygen A-band channels for retrieving marine liquid cloud properties

Richardson¹,

Stephens²

2017

Preprint

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Abstract. An information content analysis is used to select channels for a marine liquid cloud retrieval using the high-spectralresolution oxygen A-band instrument on NASA's Orbiting Carbon Observatory-2 (OCO-2). Desired retrieval properties are cloud optical depth, cloud pressure thickness and cloud-top pressure and the optimal channels depend on the atmospheric state, cloud properties and position within the OCO-2 swath. Based on information content criteria we select a micro-window of 75 10 of the 853 functioning OCO-2 channels spanning 763.5-764.6 nm and perform a series of synthetic retrievals with perturbed initial conditions. We estimate posterior errors from the sample standard deviations and obtain ±0.75 in optical depth, ±12.9 hPa in both cloud-top pressure and cloud pressure thickness, although removing the 10 % of samples with the highest χ 2 reduces posterior error in cloud-top pressure to ±2.9 hPa and cloud pressure thickness to ±2.5 hPa. The application of this retrieval to real OCO-2 measurements is briefly discussed, along with limitations and the greatest caution is urged regarding 15 the assumption of a single homogeneous cloud layer, which is often, but not always, a reasonable approximation for marine boundary layer clouds.

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“…These observations have been used to describe global cloud spatial distributions and their temporal changes (Li et al, 2015;Naud et al, 2015). However, space-based low-cloud observations are limited by radar ground clutter and strong attenuation of lidar signals, especially by liquid and mixedphase clouds (Marchand et al, 2008;Blanchard et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Cloud vertical distribution from combined surface and space radar–lidar observations at two Arctic atmospheric observatories

Liu¹,

Shupe

Wang

et al. 2017

Atmos. Chem. Phys.

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Abstract. Detailed and accurate vertical distributions of cloud properties (such as cloud fraction, cloud phase, and cloud water content) and their changes are essential to accurately calculate the surface radiative flux and to depict the mean climate state. Surface and space-based active sensors including radar and lidar are ideal to provide this information because of their superior capability to detect clouds and retrieve cloud microphysical properties. In this study, we compare the annual cycles of cloud property vertical distributions from space-based active sensors and surface-based active sensors at two Arctic atmospheric observatories, Barrow and Eureka. Based on the comparisons, we identify the sensors' respective strengths and limitations, and develop a blended cloud property vertical distribution by combining both sets of observations. Results show that surface-based observations offer a more complete cloud property vertical distribution from the surface up to 11 km above mean sea level (a.m.s.l.) with limitations in the middle and high altitudes; the annual mean total cloud fraction from space-based observations shows 25-40 % fewer clouds below 0.5 km than from surface-based observations, and space-based observations also show much fewer ice clouds and mixed-phase clouds, and slightly more liquid clouds, from the surface to 1 km. In general, space-based observations show comparable cloud fractions between 1 and 2 km a.m.s.l., and larger cloud fractions above 2 km a.m.s.l. than from surface-based observations. A blended product combines the strengths of both products to provide a more reliable annual cycle of cloud property vertical distributions from the surface to 11 km a.m.s.l. This information can be valuable for deriving an accurate surface radiative budget in the Arctic and for cloud parameterization evaluation in weather and climate models. Cloud annual cycles show similar evolutions in total cloud fraction and ice cloud fraction, and lower liquidcontaining cloud fraction at Eureka than at Barrow; the differences can be attributed to the generally colder and drier conditions at Eureka relative to Barrow.

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A global survey of cloud overlap based on CALIPSO and CloudSat measurements

Cited by 133 publications

References 65 publications

The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim

The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim

Information content of OCO-2 oxygen A-band channels for retrieving marine liquid cloud properties

Cloud vertical distribution from combined surface and space radar–lidar observations at two Arctic atmospheric observatories

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