Cumulus convection and the terrestrial water-vapor distribution

Cumulus convection plays a significant role in determining the structure of the terrestrial water vapor field. Cumulus convection acts directly on the moisture field by condensing and precipitating water vapor and by redistributing water vapor through cumulus induced eddy circulations. The mechanisms by which cumulus convection influences the terrestrial water vapor distribution is outlined. Calculations using a theory due to Kuo is used to illustrate the mechanisms by which cumulus convection works. Understanding of these processes greatly aids the ability of researchers to interpret the seasonal and spatial distribution of atmospheric water vapor by providing information on the nature of sources and sinks and the global circulation

A scheme for parameterizing cirrus cloud ice water content in general circulation models

Clouds strongly influence th earth's energy budget. They control th amount of solar radiative energy absorbed by the climate system, partitioning the energy between the atmosphere and the earth's surface. They also control the loss of energy to space by their effect on thermal emission. Cirrus and altostratus are the most frequent cloud types, having an annual average global coverage of 35 and 40 percent, respectively. Cirrus is composed almost entirely of ice crystals and the same is frequently true of the upper portions of altostratus since they are often formed by the thickening of cirrostratus and by the spreading of the middle or upper portions of thunderstorms. Thus, since ice clouds cover such a large portion of the earth's surface, they almost certainly have an important effect on climate. With this recognition, researchers developing climate models are seeking largely unavailable methods for specifying the conditions for ice cloud formation, and quantifying the spatial distribution of ice water content, IWC, a necessary step in deriving their radiative characteristics since radiative properties are apparently related to IWC. A method is developed for specifying IWC in climate models, based on theory and measurements in cirrus during FIRE and other experiments

Tropical Intraseasonal Variability in Version 3 of the GFDL Atmosphere Model

Tropical intraseasonal variability is examined in version 3 of the Geophysical Fluid Dynamics Laboratory Atmosphere Model (AM3). In contrast to its predecessor AM2, AM3 uses a new treatment of deep and shallow cumulus convection and mesoscale clouds. The AM3 cumulus parameterization is a mass-flux-based scheme but also, unlike that in AM2, incorporates subgrid-scale vertical velocities; these play a key role in cumulus microphysical processes. The AM3 convection scheme allows multiphase water substance produced in deep cumuli to be transported directly into mesoscale clouds, which strongly influence large-scale moisture and radiation fields. The authors examine four AM3 simulations using a control model and three versions with different modifications to the deep convection scheme. In the control AM3, using a convective closure based on CAPE relaxation, both MJO and Kelvin waves are weak relative to those in observations. By modifying the convective closure and trigger assumptions to inhibit deep cumuli, AM3 produces reasonable intraseasonal variability but a degraded mean state. MJO-like disturbances in the modified AM3 propagate eastward at roughly the observed speed in the Indian Ocean but up to 2 times the observed speed in the west Pacific Ocean. Distinct differences in intraseasonal convective organization and propagation exist among the modified AM3 versions. Differences in vertical diabatic heating profiles associated with the MJO are also found. The two AM3 versions with the strongest intraseasonal signals have a more prominent “bottom heavy” heating profile leading the disturbance center and “top heavy” heating profile following the disturbance. The more realistic heating structures are associated with an improved depiction of moisture convergence and intraseasonal convective organization in AM3

Transport of radon-222 and methyl iodide by deep convection in the GFDL Global Atmospheric Model AM2

Transport of radon-222 and methyl iodide by deep convection is analyzed in the Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model 2 (AM2) using two parameterizations for deep convection. One of these parameterizations represents deep convection as an ensemble of entraining plumes; the other represents deep convection as an ensemble of entraining plumes with associated mesoscale updrafts and downdrafts. Although precipitation patterns are generally similar in AM2 with both parameterizations, the deep convective mass fluxes are more than three times larger in the middle- to upper troposphere for the parameterization consisting only of entraining plumes, but do not extend across the tropopause, unlike the parameterization including mesoscale circulations. The differences in mass fluxes result mainly from a different partitioning between convective and stratiform precipitation; the parameterization including mesoscale circulations detrains considerably more water vapor in the middle troposphere and is associated with more stratiform rain. The distributions of both radon-222 and methyl iodide reflect the different mass fluxes. Relative to observations (limited by infrequent spatial and temporal sampling), AM2 tends to simulate lower concentrations of radon-222 and methyl iodide in the planetary boundary layer, producing a negative model bias through much of the troposphere, with both cumulus parameterizations. The shapes of the observed profiles suggest that the larger deep convective mass fluxes and associated transport in the parameterization lacking a mesoscale component are less realistic

Practice and Philosophy of Climate Model Tuning Across Six U.S. Modeling Centers

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean-atmosphere climatemodels regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major U.S. climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present day radiative imbalance vs. the implied balance in the pre-industrial as a target

Are atmospheric updrafts a key to unlocking climate forcing and sensitivity?

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many stateof- science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations that do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity

The Community Climate System Model version 4

Author Posting. © American Meteorological Society, 2011. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 24 (2011): 4973–4991, doi:10.1175/2011JCLI4083.1.The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.National Science Foundation, which sponsors NCAR and the CCSM Project. The project is also sponsored by the U.S. Department of Energy (DOE). Thanks are also due to the many other software engineers and scientists who worked on developing CCSM4, and to the Computational and Information Systems Laboratory at NCAR, which provided the computing resources through the Climate Simulation Laboratory. Hunke was supported within theClimate, Ocean and Sea Ice Modeling project at Los Alamos National Laboratory, which is funded by the Biological and Environmental Research division of the DOE Office of Science. The Los Alamos National Laboratory is operated by theDOENationalNuclear Security Administration under Contract DE-AC52-06NA25396. Raschwas supported by theDOEOffice of Science, Earth System Modeling Program, which is part of the DOE Climate Change Research Program. The Pacific Northwest National Laboratory is operated forDOEbyBattelle Memorial Institute under Contract DE-AC06-76RLO 1830. Worley was supported by the Climate Change Research Division of the Office of Biological and Environmental Research and by the Office ofAdvanced Scientific Computing Research, both in the DOE Office of Science, under Contract DE-AC05-00OR22725 with UT-Batelle, LLC

The dependence of aerosol effects on clouds and precipitation on cloud‐system organization, shear and stability

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95546/1/jgrd14296.pd

Aerosol indirect effects

Aerosol indirect effects continue to constitute one of the most important uncertainties for anthropogenic climate perturbations. Within the international AEROCOM initiative, the representation of aerosol-cloud-radiation interactions in ten different general circulation models (GCMs) is evaluated using three satellite datasets. The focus is on stratiform liquid water clouds since most GCMs do not include ice nucleation effects, and none of the model explicitly parameterises aerosol effects on convective clouds. We compute statistical relationships between aerosol optical depth (tau a) and various cloud and radiation quantities in a manner that is consistent between the models and the satellite data. cloud droplet number concentration (N d) compares relatively well to the satellite data at least over the ocean. The relationship between (tau a) and liquid water path is simulated much too strongly by the models. This suggests that the implementation of the second aerosol indirect effect mainly in terms of an autoconversion parameterisation has to be revisited in the GCMs. A positive relationship between total cloud fraction (fcld) and tau a as found in the satellite data is simulated by the majority of the models, albeit less strongly than that in the satellite data in most of them. In a discussion of the hypotheses proposed in the literature to explain the satellite-derived strong fcld–tau a relationship, our results indicate that none can be identified as a unique explanation. Relationships similar to the ones found in satellite data between tau a and cloud top temperature or outgoing long-wave radiation (OLR) are simulated by only a few GCMs. The GCMs that simulate a negative OLR - tau a relationship show a strong positive correlation between tau a and fcld. The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of tau a, and parameterisation assumptions such as a lower bound on Nd. Nevertheless, the strengths of the statistical relationships are good predictors for the aerosol forcings in the models. An estimate of the total short-wave aerosol forcing inferred from the combination of these predictors for the modelled forcings with the satellite-derived statistical relationships yields a global annual mean value of −1.5±0.5Wm−2. In an alternative approach, the radiative flux perturbation due to anthropogenic aerosols can be broken down into a component over the cloud-free portion of the globe (approximately the aerosol direct effect) and a component over the cloudy portion of the globe (approximately the aerosol indirect effect). An estimate obtained by scaling these simulated clearand cloudy-sky forcings with estimates of anthropogenic tau a and satellite-retrieved Nd–tau a regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2Wm−2 and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5Wm−2, with a total estimate of −1.2±0.4Wm−2