Ben Shipway

Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO

Twelve large-eddy simulations, with a wide range of microphysical representations, are compared to each other and to independent measurements. The measurements and the initial and forcing data for the simulations are taken from the undisturbed period of the Rain in Cumulus over the Ocean (RICO) field study. A regional downscaling of meteorological analyses is performed so as to provide forcing data consistent with the measurements. The ensemble average of the simulations plausibly reproduces many features of the observed clouds, including the vertical structure of cloud fraction, profiles of cloud and rain water, and to a lesser degree the population density of rain drops. The simulations do show considerable departures from one another in the representation of the cloud microphysical structure and the ensuant surface precipitation rates, increasingly so for the more simplified microphysical models. There is a robust tendency for simulations that develop rain to produce a shallower, somewhat more stable cloud layer. Relations between cloud cover and precipitation are ambiguous.

A comparison of TWP-ICE observational data with cloud-resolving model results

Observations made during the TWP-ICE campaign are used to drive and evaluate thirteen cloud-resolving model simulations with periodic lateral boundary conditions. The simulations employ 2D and 3D dynamics, one- and two-moment microphysics, several variations on large-scale forcing, and the use of observationally derived aerosol properties to prognose droplet numbers. When domain means are averaged over a 6-day active monsoon period, all simulations reproduce observed surface precipitation rate but not its structural distribution. Simulated fractional areas covered by convective and stratiform rain are uncorrelated with one another, and are both variably overpredicted by up to a factor of {approx}2. Stratiform area fractions are strongly anticorrelated with outgoing longwave radiation (OLR) but are negligibly correlated with ice water path (IWP), indicating that ice spatial distribution controls OLR more than mean IWP. Overpredictions of OLR tend to be accompanied by underpredictions of reflected shortwave radiation (RSR). When there are two simulations differing only in microphysics scheme or large-scale forcing, the one with smaller stratiform area tends to exhibit greater OLR and lesser RSR by similar amounts. After {approx}10 days, simulations reach a suppressed monsoon period with a wide range of mean precipitable water vapor, attributable in part to varying overprediction of cloud-modulated radiativemorexa0» flux divergence compared with observationally derived values. Differences across the simulation ensemble arise from multiple sources, including dynamics, microphysics, and radiation treatments. Close agreement of spatial and temporal averages with observations may not be expected, but the wide spreads of predicted stratiform fraction and anticorrelated OLR indicate a need for more rigorous observation-based evaluation of the underlying micro- and macrophysical properties of convective and stratiform structures.«xa0less

Droplet nucleation: Physically‐based parameterizations and comparative evaluation

[1]xa0One of the greatest sources of uncertainty in simulations of climate and climate change is the influence of aerosols on the optical properties of clouds. The root of this influence is the droplet nucleation process, which involves the spontaneous growth of aerosol into cloud droplets at cloud edges, during the early stages of cloud formation, and in some cases within the interior of mature clouds. Numerical models of droplet nucleation represent much of the complexity of the process, but at a computational cost that limits their application to simulations of hours or days. Physically-based parameterizations of droplet nucleation are designed to quickly estimate the number nucleated as a function of the primary controlling parameters: the aerosol number size distribution, hygroscopicity and cooling rate. Here we compare and contrast the key assumptions used in developing each of the most popular parameterizations and compare their performances under a variety of conditions. We find that the more complex parameterizations perform well under a wider variety of nucleation conditions, but all parameterizations perform well under the most common conditions. We then discuss the various applications of the parameterizations to cloud-resolving, regional and global models to study aerosol effects on clouds at a wide range of spatial and temporal scales. We compare estimates of anthropogenic aerosol indirect effects using two different parameterizations applied to the same global climate model, and find that the estimates of indirect effects differ by only 10%. We conclude with a summary of the outstanding challenges remaining for further development and application.

Evaluation of Cloud-Resolving Model Intercomparison Simulations Using TWP-ICE Observations: Precipitation and Cloud Structure

The Tropical Warm Pool - International Cloud Experiment (TWP-ICE) provided high quality model forcing and observational datasets through which detailed model and observational intercomparisons could be performed. In this first of a two part study, precipitation and cloud structures within nine cloud-resolving model simulations are compared with scanning radar reflectivity and satellite infrared brightness temperature observations during an active monsoon period from 19 to 25 January 2006. Most simulations slightly overestimate volumetric convective rainfall. Overestimation of simulated convective area by 50% or more in several simulations is somewhat offset by underestimation of mean convective rain rates. Stratiform volumetric rainfall is underestimated by 13% to 53% despite overestimation of stratiform area by up to 65% because stratiform rain rates in every simulation are much lower than observed. Although simulations match the peaked convective radar reflectivity distribution at low levels, they do not reproduce the peaked distributions observed above the melting level. Simulated radar reflectivity aloft in convective regions is too high in most simulations. 29 In stratiform regions, there is a large spread in model results with none resembling 30 observed distributions. Above the melting level, observed radar reflectivity decreases 31 more gradually with height than simulated radar reflectivity. A fewmorexa0» simulations produce 32 unrealistically uniform and cold 10.8-μm infrared brightness temperatures, but several 33 simulations produce distributions close to observed. Assumed ice particle size 34 distributions appear to play a larger role than ice water contents in producing incorrect 35 simulated radar reflectivity distributions aloft despite substantial differences in mean 36 graupel and snow water contents across models. 37«xa0less

Intercomparison of cloud model simulations of Arctic mixed‐phase boundary layer clouds observed during SHEBA/FIRE‐ACE

An intercomparison of six cloud-resolving and large-eddy simulation models is presented. This case study is based on observations of a persistent mixed-phase boundary layer cloud gathered on 7 May, 1998 from the Surface Heat Budget of Arctic Ocean (SHEBA) and First ISCCP Regional Experiment - Arctic Cloud Experiment (FIRE-ACE). Ice nucleation is constrained in the simulations in a way that holds the ice crystal concentration approximately fixed, with two sets of sensitivity runs in addition to the baseline simulations utilizing different specified ice nucleus (IN) concentrations. All of the baseline and sensitivity simulations group into two distinct quasi-steady states associated with either persistent mixed-phase clouds or all-ice clouds after the first few hours of integration, implying the existence of multiple states for this case. These two states are associated with distinctly different microphysical, thermodynamic, and radiative characteristics. Most but not all of the models produce a persistent mixed-phase cloud qualitatively similar to observations using the baseline IN/crystal concentration, while small increases in the IN/crystal concentration generally lead to rapid glaciation and conversion to the all-ice state. Budget analysis indicates that larger ice deposition rates associated with increased IN/crystal concentrations have a limited direct impact on dissipation of liquid in these simulations. However, the impact of increased ice deposition is greatly enhanced by several interaction pathways that lead to an increased surface precipitation flux, weaker cloud top radiative cooling and cloud dynamics, and reduced vertical mixing, promoting rapid glaciation of the mixed-phase cloud for deposition rates in the cloud layer greater than about 122610 –5 gk g –1 s –1 for this case. These results indicate the critical importance of precipitation-radiative-dynamical interactions in simulating cloud phase, which have been neglected in previous fixed-dynamical parcel studies of the cloud phase parameter space. Large sensitivity to the IN/crystal concentration also suggests the need for improved understanding of ice nucleation and its parameterization in models.

Intercomparison of large‐eddy simulations of Arctic mixed‐phase clouds: Importance of ice size distribution assumptions

Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP), in agreement with earlier studies. In contrast to previous intercomparison studies, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSDs) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case. Sensitivity tests indicate LWP and IWP are much closer to the bin model simulations when a modified shape factor which is similar to that predicted by bin model simulation is used in bulk scheme. These results demonstrate the importance of representation of ice PSD in determining the partitioning of liquid and ice and the longevity of mixed-phase clouds.

Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP‐ICE observations: 1. Deep convective updraft properties

Ten 3-D cloud-resolving model simulations and four 3-D limited area model simulations of an intense mesoscale convective system observed on 23–24 January 2006 during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) are compared with each other and with observed radar reflectivity fields and dual-Doppler retrievals of vertical wind speeds in an attempt to explain published results showing a high bias in simulated convective radar reflectivity aloft. This high-bias results from ice water content being large, which is a product of large, strong convective updrafts, although hydrometeor size distribution assumptions modulate the size of this bias. Making snow mass more realistically proportional to D2 rather than D3 eliminates unrealistically large snow reflectivities over 40 dBZ in some simulations. Graupel, unlike snow, produces high biased reflectivity in all simulations, which is partly a result of parameterized microphysics but also partly a result of overly intense simulated updrafts. Peak vertical velocities in deep convective updrafts are greater than dual-Doppler-retrieved values, especially in the upper troposphere. Freezing of liquid condensate, often rain, lofted above the freezing level in simulated updraft cores greatly contributes to these excessive upper tropospheric vertical velocities. The strongest simulated updraft cores are nearly undiluted, with some of the strongest showing supercell characteristics during the multicellular (presquall) stage of the event. Decreasing horizontal grid spacing from 900 to 100 m slightly weakens deep updraft vertical velocity and moderately decreases the amount of condensate aloft but not enough to match observational retrievals. Therefore, overly intense simulated updrafts may additionally be a product of unrealistic interactions between convective dynamics, parameterized microphysics, and large-scale model forcing that promote different convective strengths than observed.

Droplet Nucleation: Physically-Based Parameterization and Validation

[1]xa0One of the greatest sources of uncertainty in simulations of climate and climate change is the influence of aerosols on the optical properties of clouds. The root of this influence is the droplet nucleation process, which involves the spontaneous growth of aerosol into cloud droplets at cloud edges, during the early stages of cloud formation, and in some cases within the interior of mature clouds. Numerical models of droplet nucleation represent much of the complexity of the process, but at a computational cost that limits their application to simulations of hours or days. Physically-based parameterizations of droplet nucleation are designed to quickly estimate the number nucleated as a function of the primary controlling parameters: the aerosol number size distribution, hygroscopicity and cooling rate. Here we compare and contrast the key assumptions used in developing each of the most popular parameterizations and compare their performances under a variety of conditions. We find that the more complex parameterizations perform well under a wider variety of nucleation conditions, but all parameterizations perform well under the most common conditions. We then discuss the various applications of the parameterizations to cloud-resolving, regional and global models to study aerosol effects on clouds at a wide range of spatial and temporal scales. We compare estimates of anthropogenic aerosol indirect effects using two different parameterizations applied to the same global climate model, and find that the estimates of indirect effects differ by only 10%. We conclude with a summary of the outstanding challenges remaining for further development and application.

Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 2. Precipitation microphysics

Ten 3-D cloud-resolving model (CRM) simulations and four 3-D limited area model (LAM) simulations of an intense mesoscale convective system observed on 23-24 January 2006 during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) are compared with each other and with observations and retrievals from a scanning polarimetric radar, colocated UHF and VHF vertical profilers, and a Joss-Waldvogel disdrometer in an attempt to explain a low bias in simulated stratiform rainfall. Despite different forcing methodologies, similar precipitation microphysics errors appear in CRMs and LAMs with differences that depend on the details of the bulk microphysics scheme used. One-moment schemes produce too many small raindrops, which biases Doppler velocities low, but produces rainwater contents (RWCs) that are similar to observed. Two-moment rain schemes with a gamma shape parameter (mu) of 0 produce excessive size sorting, which leads to larger Doppler velocities than those produced in one-moment schemes but lower RWCs. Two-moment schemes also produce a convective median volume diameter distribution that is too broad relative to observations and, thus, may have issues balancing raindrop formation, collision-coalescence, and raindrop breakup. Assuming a mu of 2.5 rather than 0 for the raindrop size distribution improves one-moment scheme biases, and allowing mu to have values greater than 0 may improve excessive size sorting in two-moment schemes. Underpredicted stratiform rain rates are associated with underpredicted ice water contents at the melting level rather than excessive rain evaporation, in turn likely associated with convective detrainment that is too high in the troposphere and mesoscale circulations that are too weak. A limited domain size also prevents a large, well-developed stratiform region like the one observed from developing in CRMs, although LAMs also fail to produce such a region.

Processes Controlling Tropical Tropopause Temperature and Stratospheric Water Vapor in Climate Models

A warm bias in tropical tropopause temperature is found in the Met Office Unified Model (MetUM), in common with most models from phase 5 of CMIP (CMIP5). Key dynamical, microphysical, and radiative processes influencing the tropical tropopause temperature and lower-stratospheric water vapor concentrations in climate models are investigated using the MetUM. A series of sensitivity experiments are run to separate the effects of vertical advection, ice optical and microphysical properties, convection, cirrus clouds, and atmospheric composition on simulated tropopause temperature and lower-stratospheric water vapor concentrations in the tropics. The numerical accuracy of the vertical advection, determined in the MetUM by the choice of interpolation and conservation schemes used, is found to be particularly important. Microphysical and radiative processes are found to influence stratospheric water vapor both through modifying the tropical tropopause temperature and through modifying upper-tropospheric water vapor concentrations, allowing more water vapor to be advected into the stratosphere. The representation of any of the processes discussed can act to significantly reduce biases in tropical tropopause temperature and stratospheric water vapor in a physical way, thereby improving climate simulations.