A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS

Marchand, Roger; Ackerman, Thomas P.; Smyth, Mike; Rossow, William B.

doi:10.1029/2009jd013422

Cited by 175 publications

(182 citation statements)

References 76 publications

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“…The general underestimate of low-level clouds is consistent with the lack of clouds in marine stratocumulus and middle latitudes mentioned above. Differences in middle-level clouds are somewhat hard to interpret as many middle-level clouds observed by ISCCP are in fact multilayer cloud scenes of cirrus above boundary layer cloud [Marchand et al, 2010;Mace et al, 2011]. Although the ISCCP simulator is capable of reproducing this artifact [Mace et al, 2011], it will do so only if a model produces thin cirrus over boundary layer clouds.…”

Section: Improvements As a Function Of Cloud-top Pressure And Cloud Omentioning

confidence: 99%

“…Unfortunately, this quantity is problematic to define from observations: satellite estimates of total cloud amount are extremely sensitive to many observational factors including the scale and sensitivity of the fundamental observations, as well as decisions made during the aggregation to larger scales [Stubenrauch et al, 2009;Mace et al, 2009;Marchand et al, 2010;Pincus et al, 2012]. We make the comparison more robust by restricting the analysis to clouds with t exceeding some minimum threshold t min , which we set to minimize hard-to-detect and partly cloudy observations.…”

Section: Common Improvements and Failures In The Simulation Of Total mentioning

confidence: 99%

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Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

et al. 2013

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[1] The annual cycle climatology of cloud amount, cloud-top pressure, and optical thickness in two generations of climate models is compared to satellite observations to identify changes over time in the fidelity of simulated clouds. In more recent models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias. With increased amounts of clouds with lesser reflectivity, the compensating errors that permit models to simulate the time-mean radiation balance have been reduced. Errors in cloud amount as a function of height or climate regime on average show little or no improvement, although greater improvement can be found in individual models. Measuring Changes in the Simulations of Global Cloudiness Over Time[2] The simulation of clouds by climate models is a key ongoing challenge in the numerical representation of Earth's climate. Due to their large impact on Earth's radiation budget, clouds are important for determining aspects of current climate, such as surface air temperatures in many regions [Ma et al., 1996;Curry et al., 1996], the strength and variability of atmospheric circulations [Slingo and Slingo, 1988], and the magnitude of climate changes that result from perturbations in the chemical composition of the atmosphere [IPCC, 2007]. While important, the modeling of clouds is very difficult because most cloud processes happen at scales far smaller than can be resolved by climate models, and thus, their bulk effects must be represented with imperfect parameterizations.[3] Given the efforts of many scientists over several decades to understand cloud processes and improve their representation in models, it is important to ask are climate model simulations of clouds improving and, if so, by how much? Here, we analyze the ability of two generations of climate models to simulate the climatological distribution of clouds and judge fidelity by comparison to several decades of satellite observations. Because of the significant differences between the ways clouds are observed and the ways they are represented in models, we use a "satellite simulator" to increase the chances that differences between the models and observations represent actual model deficiencies. We find that significant progress in the ability of models to simulate clouds has occurred over the last decade, particularly in reducing the over-prediction of highly reflective clouds [Zhang et al., 2005].

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Section: Improvements As a Function Of Cloud-top Pressure And Cloud Omentioning

confidence: 99%

Section: Common Improvements and Failures In The Simulation Of Total mentioning

confidence: 99%

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

et al. 2013

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“…Following Rossow and Schiffer [1999] and Marchand et al [2010], the retrieval uncertainty of cloud cover in, for example, trade cumulus/broken clouds over the eastern tropical Pacific is 17.5%. For MODIS data, the uncertainty is much larger (55%), mainly due to the difficulty of adequately capturing information from inhomogeneous subpixel clouds.…”

Section: Overall Characteristicsmentioning

confidence: 99%

“…For CWP with visible/near-infrared data retrievals, Chen et al [2007] quote typical retrieval errors for MODIS and ISCCP data of 20%, and 0.025 mm for AMSR-E data from nonprecipitating clouds. Furthermore, the ISCCP low clouds should be treated cautiously because of the misidentification of middle clouds when a strong inversion layer is present [Marchand et al, 2010].…”

Section: Overall Characteristicsmentioning

confidence: 99%

Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models

Noda

Satoh

2014

Geophysical Research Letters

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This paper investigates simulation biases associated with the large-scale environments of subtropical marine stratocumulus (Sc) in present climate simulations from the Coupled Model Intercomparison Project Phase 5 models. Comparison of eight major variables that strongly control the Sc clouds, including jumps of temperature and vapor across the inversion layer, indicates that these models all have similar shortcomings, such as overestimation of sensible and latent surface fluxes. The differences among the biases of these major variables between the Sc regions were also evaluated. Of all Sc regions, the Namibian Sc region showed the largest biases. The modeled simulation skill of the annual variation of low cloud cover (LCC) increases when the correlation coefficient between LCC and lower atmospheric stability is higher. This paper reemphasizes the importance, suggested in previous studies, of an improved relationship between lower tropospheric stability and LCC if we are to better predict annual variations in subtropical LCC.

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“…In each case, the DOFs have been calculated for different values of the IWP and τ low . The altitude of cloud layers is kept constant, and they have been precisely chosen to be consistent with the known climatology in northern midlatitudes: the ice-cloud layer has been positioned between 9.0 and 11.0 km (Sassen et al, 2008) and the low liquid layer between 0.5 and 1.5 km (Marchand et al, 2010). In the triple-layer case, the mid liquid layer has been positioned between 4.0 and 5.0 km, for reasons of consistency with the case study presented in section 4.…”

Section: Theoretical Information Content Analysismentioning

confidence: 99%

A methodology for simultaneous retrieval of ice and liquid water cloud properties. Part I: Information content and case study

Sourdeval

C.‐Labonnote

Baran

et al. 2014

Quart J Royal Meteoro Soc

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This article presents a novel methodology dedicated to retrieving the optical and microphysical properties of ice and liquid water clouds simultaneously. An optimal estimation method is used to retrieve the ice water path of one ice-cloud layer and the optical thickness and droplet effective radius of up to two liquid-water cloud layers, along with rigorous uncertainties. In order to perform the retrievals, radiometric measurements in five channels ranging from the visible to the thermal infrared are utilized. The position of cloud layers is currently provided by lidar information, which narrows the retrievals to its track. In the first part of this article, theoretical information content analyses are performed under different atmospheric conditions, over an oceanic surface. This type of analysis quantifies prior to the retrievals the amount of information that should be available regarding each parameter to be retrieved and helps to identify which set of channels provides this information. It is observed that strong information can be expected for retrieving each parameter in double-layer cases, while yet stronger limitations appear in triple-layer cases. In the second part of this article, our methodology is applied to a case study. In agreement with a priori expectations, accurate retrievals of ice and liquid cloud properties are obtained. These results are later compared with the products of a single-layer retrieval method, CloudAerosol Lidar with Orthogonal Polarization (CALIOP) operational products and in situ estimates. We show that a much better consistency with the latter two is found when retrieving the properties of each layer simultaneously. However, further statistical analyses and comparisons with various operational products should be undertaken for validation of the methodology. Such results will be presented in the second part of this study.

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A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS

Cited by 175 publications

References 76 publications

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models

A methodology for simultaneous retrieval of ice and liquid water cloud properties. Part I: Information content and case study

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