Predictions of a simple cloud model for water vapor cloud albedo feedback on Venus

Abstract By comparing the response of clouds and water vapor to ENSO forcing in nature with that in Atmospheric Model Intercomparison Project (AMIP) simulations by some leading climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed the following two common biases in the models: 1) an underestimate of the strength of the negative cloud albedo feedback and 2) an overestimate of the positive feedback from the greenhouse effect of water vapor. Extending the same analysis to the fully coupled simulations of these models as well as other Intergovernmental Panel on Climate Change (IPCC) coupled models, it is found that these two biases persist. Relative to the earlier estimates from AMIP simulations, the overestimate of the positive feedback from water vapor is alleviated somewhat for most of the coupled simulations. Improvements in the simulation of the cloud albedo feedback are only found in the models whose AMIP runs suggest either a positive or nearly positive cloud albedo feedback. The strength of the negative cloud albedo feedback in all other models is found to be substantially weaker than that estimated from the corresponding AMIP simulations. Consequently, although additional models are found to have a cloud albedo feedback in their AMIP simulations that is as strong as in the observations, all coupled simulations analyzed in this study have a weaker negative feedback from the cloud albedo and therefore a weaker negative feedback from the net surface heating than that indicated in observations. The weakening in the cloud albedo feedback is apparently linked to a reduced response of deep convection over the equatorial Pacific, which is in turn linked to the excessive cold tongue in the mean climate of these models. The results highlight that the feedbacks of water vapor and clouds—the cloud albedo feedback in particular—may depend on the mean intensity of the hydrological cycle. Whether the intermodel variations in the feedback from cloud albedo (water vapor) in the ENSO variability are correlated with the intermodel variations of the feedback from cloud albedo (water vapor) in global warming has also been examined. While a weak positive correlation between the intermodel variations in the feedback of water vapor during ENSO and the intermodel variations in the water vapor feedback during global warming was found, there is no significant correlation found between the intermodel variations in the cloud albedo feedback during ENSO and the intermodel variations in the cloud albedo feedback during global warming. The results suggest that the two common biases revealed in the simulated ENSO variability may not necessarily be carried over to the simulated global warming. These biases, however, highlight the continuing difficulty that models have in simulating accurately the feedbacks of water vapor and clouds on a time scale of the observations available.

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The effects of aerosols on precipitation and dimensions of subtropical clouds: a sensitivity study using a numerical cloud model

Atmospheric Chemistry and Physics ◽

10.5194/acp-6-67-2006 ◽

2006 ◽

Vol 6 (1) ◽

pp. 67-80 ◽

Cited By ~ 149

Author(s):

A. Teller ◽

Z. Levin

Keyword(s):

Water Vapor ◽

Climate Models ◽

Cloud Model ◽

Cloud Condensation Nuclei ◽

Sensitivity Study ◽

Meteorological Conditions ◽

Dust Particles ◽

Noticeable Effect ◽

Convective Clouds ◽

Ice Nuclei

Abstract. Numerical experiments were carried out using the Tel-Aviv University 2-D cloud model to investigate the effects of increased concentrations of Cloud Condensation Nuclei (CCN), giant CCN (GCCN) and Ice Nuclei (IN) on the development of precipitation and cloud structure in mixed-phase sub-tropical convective clouds. In order to differentiate between the contribution of the aerosols and the meteorology, all simulations were conducted with the same meteorological conditions. The results show that under the same meteorological conditions, polluted clouds (with high CCN concentrations) produce less precipitation than clean clouds (with low CCN concentrations), the initiation of precipitation is delayed and the lifetimes of the clouds are longer. GCCN enhance the total precipitation on the ground in polluted clouds but they have no noticeable effect on cleaner clouds. The increased rainfall due to GCCN is mainly a result of the increased graupel mass in the cloud, but it only partially offsets the decrease in rainfall due to pollution (increased CCN). The addition of more effective IN, such as mineral dust particles, reduces the total amount of precipitation on the ground. This reduction is more pronounced in clean clouds than in polluted ones. Polluted clouds reach higher altitudes and are wider than clean clouds and both produce wider clouds (anvils) when more IN are introduced. Since under the same vertical sounding the polluted clouds produce less rain, more water vapor is left aloft after the rain stops. In our simulations about 3.5 times more water evaporates after the rain stops from the polluted cloud as compared to the clean cloud. The implication is that much more water vapor is transported from lower levels to the mid troposphere under polluted conditions, something that should be considered in climate models.

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The DMS-cloud albedo feedback effect: Greatly underestimated?

Climatic Change ◽

10.1007/bf00141380 ◽

1992 ◽

Vol 21 (4) ◽

pp. 429-433

Author(s):

Sherwood B. Idso

Keyword(s):

Feedback Effect ◽

Albedo Feedback ◽

Cloud Albedo

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Water vapor, cloud liquid water content and wind speed in tropical, extratropical and polar cyclones over the Northwest Pacific Ocean

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6351122 ◽

2012 ◽

Author(s):

L. M. Mitnik ◽

M. L. Mitnik ◽

I. A. Gurvich ◽

A. V. Vykochko ◽

M. K. Pichugin ◽

...

Keyword(s):

Water Vapor ◽

Wind Speed ◽

Liquid Water ◽

Liquid Water Content ◽

Northwest Pacific ◽

Northwest Pacific Ocean ◽

Cloud Liquid Water ◽

Vapor Cloud ◽

The Northwest Pacific Ocean ◽

Cloud Liquid Water Content

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Small lidar for remote measurements from orbit of water-vapor, cloud, and aerosol profiles

10.1117/12.578973 ◽

2005 ◽

Author(s):

Graham R. Allan ◽

Michael A. Krainak ◽

Amelia M. Gates ◽

James B. Abshire

Keyword(s):

Water Vapor ◽

Vapor Cloud ◽

Remote Measurements

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A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations

Journal of Geophysical Research Atmospheres ◽

10.1029/2001jd900085 ◽

2001 ◽

Vol 106 (D14) ◽

pp. 14887-14907 ◽

Cited By ~ 157

Author(s):

F. Aires ◽

C. Prigent ◽

W. B. Rossow ◽

M. Rothstein

Keyword(s):

Neural Network ◽

Water Vapor ◽

Network Approach ◽

Atmospheric Water ◽

Liquid Water Path ◽

Neural Network Approach ◽

Cloud Liquid Water ◽

Vapor Cloud ◽

Satellite Microwave ◽

Cloud Liquid Water Path

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water vapor cloud (US)

Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik ◽

10.1007/978-3-642-41714-6_230801 ◽

2014 ◽

pp. 1515-1515

Keyword(s):

Water Vapor ◽

Vapor Cloud

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Isotopic Fractionation in Wintertime Orographic Clouds

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0233.1 ◽

2016 ◽

Vol 33 (12) ◽

pp. 2663-2678 ◽

Cited By ~ 4

Author(s):

Douglas Lowenthal ◽

A. Gannet Hallar ◽

Ian McCubbin ◽

Robert David ◽

Randolph Borys ◽

...

Keyword(s):

Water Vapor ◽

Isotopic Composition ◽

Mesoscale Model ◽

Isotopic Fractionation ◽

Early Morning ◽

Cloud Water ◽

Mixed Phase ◽

Vapor Source ◽

Vapor Cloud ◽

Orographic Clouds

AbstractThe Isotopic Fractionation in Snow (IFRACS) study was conducted at Storm Peak Laboratory (SPL) in northwestern Colorado during the winter of 2014 to elucidate snow growth processes in mixed-phase clouds. The isotopic composition (δ18O and δD) of water vapor, cloud water, and snow in mixed-phase orographic clouds were measured simultaneously for the first time. The depletion of heavy isotopes [18O and deuterium (D)] was greatest for vapor, followed by snow, then cloud. The vapor, cloud, and snow compositions were highly correlated, suggesting similar cloud processes throughout the experiment. The isotopic composition of the water vapor was directly related to its concentration. Isotopic fractionation during condensation of vapor to cloud drops was accurately reproduced assuming equilibrium fractionation. This was not the case for snow, which grows by riming and vapor deposition. This implies stratification of vapor with altitude. The relationship between temperature at SPL and δ18O was used to show that the snow gained most of its mass within 922 m above SPL. Relatively invariant deuterium excess (d) in vapor, cloud water, and snow from day to day suggests a constant vapor source and Rayleigh fractionation during transport. The diurnal variation of vapor d reflected the differences between surface and free-tropospheric air during the afternoon and early morning hours, respectively. These observations will be used to validate simulations of snow growth using an isotope-enabled mesoscale model with explicit microphysics.

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