Measurements (STREAM) on 29 July 1994 (Str ¨om et al., 1999). Convection was ini- tiated as cool air from the Atlantic Ocean was advected towards Western Europe af- ter several weeks of stagnant weather with clear skies, high temperatures and weak winds. During the preceding high-pressure period, a build-up of high boundary layer concentrations ofaerosol particles, CO and O 3 had occurred. Several smaller groups
The SOWC model tracked two 6-D variables for aerosol/cloud properties which introduce considerable com- putational burden for model simulations when compared to standard WRF/Chem modelsimulation (with prescribed aerosol concentration). The computational cost of the SOWC model, which is proportional to the extra information that is tracked, is approximately 25 times greater than the stan- dard WRF/Chem 3.1.1 simulation with prescribed aerosols (chem_opt = 0) or approximately 5 times greater than the standard WRF/Chem 3.1.1 simulation with any chemistry option (/ = 0) in the current study. SOWC model simula- tions started at 00:00 UTC on 9 January (7 days prior to the start of the thick fog event) with 4-D data assimilation (FDDA), which nudges model fields in domain 1 to analy- sis including the u and v components of horizontal winds, water vapor mixing ratio and temperature above the PBL height in all simulations. This approach provides a realis- tic heterogeneous aerosol distribution and low-level temper- ature and moisture fields at the start of the thick fog simu- lation. Observations from surface stations and NARR data were used for nudging during this aerosol spin-up period. Between 00:00 UTC on 16 January and 00:00 UTC on 19 January, the SOWC model integrated without FDDA (3-day free run) during which time the effects of the different model
Abstract. In this study, the Weather Research and Forecast- ing model was used to simulate the diurnal variation in sum- mer precipitation over the Tibetan Plateau (TP) at a cloud- resolving scale. Compared with the TRMM, precipitation data shows that the model can well simulate the diurnal rain- fall cycle with an overall late-afternoon maximum precipi- tation in the central TP and a nighttime maximum in the southern edge. The simulated diurnal variations in regional circulation and thermodynamics are in good correspondence with the precipitation diurnal cycles in the central and south- ern edge of TP, respectively. A possible mechanism responsi- ble for the nocturnal precipitation maximum in the southern edge has been proposed, indicating the importance of the TP in regulating the regional circulation and precipitation.
second. Based on empirical relationships, Price and Rind (1992) derived an exponent of k=4.54, which is somewhat less than the model study based estimate of at least 6 by Baker et al. (1995). Pickering et al. (1998), who among other convective systems studied a squall line during TOGA COARE, used different values for different model simulated storms in order to obtain results comparable to observed flash rates. For their 2-D TOGA COARE simulation, they in- creased the exponent from 4.54 to 5.3. α is an empirical scaling factor which is adjusted to improve the agreement with available flash rate observations. For a mid-latitude su- percell storm Fehr et al. (2004) applied a scaling factor of 0.26. Here, sensitivity studies using various exponents and scaling factors were conducted (Table 1, Sect. 3).
Abstract. This study assesses the ability of the recent chem- istry version (v3.3) of the Weather Research and Forecast- ing (WRF-Chem) model to simulate boundary layer struc- ture, aerosols, stratocumulus clouds, and energy fluxes over the Southeast Pacific Ocean. Measurements from the VA- MOS Ocean-Cloud-Atmosphere-Land Study Regional Ex- periment (VOCALS-REx) and satellite retrievals (i.e., prod- ucts from the MODerate resolution Imaging Spectrora- diometer (MODIS), Clouds and Earth’s Radiant Energy Sys- tem (CERES), and GOES-10) are used for this assessment. The Morrison double-moment microphysics scheme is newly coupled with interactive aerosols in the model. The 31- day (15 October–16 November 2008) WRF-Chem simula- tion with aerosol-cloud interactions (AERO hereafter) is also compared to a simulation (MET hereafter) with fixed cloud droplet number concentrations in the microphysics scheme and simplified cloud and aerosol treatments in the radiation scheme. The well-simulated aerosol quantities (aerosol num- ber, mass composition and optical properties), and the inclu- sion of full aerosol-cloud couplings lead to significant im- provements in many features of the simulated stratocumulus clouds: cloud optical properties and microphysical proper- ties such as cloud top effective radius, cloud water path, and cloud optical thickness. In addition to accounting for the aerosol direct and semi-direct effects, these improvements feed back to the simulationof boundary-layer characteris- tics and energy budgets. Particularly, inclusion of interac-
The model results indicate a strong link between the sim- ulation ofcloud processing and of CDNC. The in-cloud pro- duced sulfate modifies the size and solubility of particles, es- pecially of the Aitken mode. In our simulations in-cloud sul- fate formation in activated Aitken particles acts as an impor- tant source of accumulation mode particles and cloud con- densation nuclei, either directly by chemical growth due to sulfate production inside cloud drops or indirectly through particle coagulation after the cloud event. The magnitude of this source depends on the produced sulfate amount and how it is distributed over the activated modes. Our study shows that many parameters that play a role incloud formation also affect this distribution. This includes some of the initial as- sumptions applied in our study, such as the neglect ofexplicit ammonia chemistry which changes cloud water acidity, and of the autoconversion process which affects the liquid water associated with the separate modes. The assumptions and uncertainties may have a significant influence on the simu- lated CDNC. Therefore, through its effect on CDNC cloud processing may also influence precipitation formation (Al- brecht, 1989; Roelofs and Jongen, 2004).
Abstract. Simulations are conducted with a cloud-resolving numerical model to examine the transformation of a weak incipient mid-level cyclonic vortex into a tropical cyclone. Results demonstrate that two distinct pathways are possible and that development along a particular pathway is sensi- tive to modelphysics and initial conditions. One pathway involves a steady increase of the surface winds to tropical cyclone strength as the radius of maximum winds gradually decreases. A notable feature of this evolution is the creation of small-scale lower tropospheric cyclonic vorticity anoma- lies by deep convective towers and subsequent merger and convergence by the low-level secondary circulation. The sec- ond pathway also begins with a strengthening low-level cir- culation, but eventually a significantly stronger mid-level cir- culation develops. Cyclogenesis occurs subsequently when a small-scale surface concentrated vortex forms abruptly near the center of the larger-scale circulation. The small-scale vor- tex is warm core throughout the troposphere and results in a fall in local surface pressure of a few millibars. It usually de- velops rapidly, undergoing a modest growth to form a small tropical cyclone. Many of the simulated systems approach or reach tropical cyclone strength prior to development of a prominent mid-level vortex so that the subsequent formation of a strong small-scale surface concentrated vortex in these cases could be considered intensification rather than genesis. Experiments are performed to investigate the dependence on the inclusion of the ice phase, radiation, the size and strength of the incipient mid-level vortex, the amount of moisture present in the initial vortex, and the sea surface tem- perature. Notably, as the sea surface temperature is raised, the likelihood of development along the second pathway is in-
Model structure was configured to combine modules in- cluded in contemporary WRF-Chem public release code that best represent known aerosol, cloud, and MBL processes and their couplings. Wherever possible, the most complete repre- sentations of complex physical and chemical processes were chosen. This application requires a boundary layer closure scheme that can make use of (and maintain numerical stabil- ity at) high vertical resolution, and can accurately represent the diurnal evolution of the MBL at low wind speeds. Mellor- Yamada type schemes have generally exhibited good cloud representation under these conditions (Otkin and Greenwald, 2008; Zhu et al., 2010; Rahn and Garreaud, 2010). The MYNN level 2.5 scheme (Nakanishi and Niino, 2004) was chosen since it performed well in prior applications at this resolution over Chile (Saide et al., 2011). The Lin micro- physics scheme (Chapman et al., 2009) and Goddard short wave radiation (Chou et al., 1998; Fast et al., 2006) were chosen to support aerosol direct, indirect, and semi-direct feedbacks to meteorology. Activation of aerosols from the interstitial to the cloudborne “attachment state” (Ghan and Easter, 2006) is based on a maximum supersaturation deter- mined from a Gaussian spectrum of updraft velocities and the internally mixed aerosol properties within each aerosol size bin (Abdul-Razzak and Ghan, 2002). The updraft ve- locity distribution is centered in the model vertical wind component plus the subgrid vertical velocity diagnosed from vertical diffusivity. No cumulus scheme was used follow- ing the recommendation of Q. Yang et al. (2011). The RRTM longwave radiation scheme (Mlawer et al., 1997) was used. Gases and aerosols were simulated using the CBMZ gas-phase chemical mechanism (Zaveri et al., 1999; Fast et al., 2006) with dimethyl sulfide (DMS) reactions cou- pled to the 8-bin sectional MOSAIC (Zaveri et al., 2008) aerosol module. Seawater DMS concentration was set to 2.8 nM, following the VOCA Modeling Experiment Specifi- cation (http://www.atmos.washington.edu/ ∼ mwyant/vocals/
mate change) associated with the aerosol indirect effect (AIE). This is because thin clouds cover 28% of the globe as shown by the International Satellite Cloud Climatol- ogy Project (ISCCP). Also, Turner et al. (2007) show that the surface and the top of the atmosphere (TOA) longwave and shortwave radiative fluxes are very sensitive to small changes in the cloud LWP when the LWP is less than ∼50 g m −2 (see Fig. SB1 in
Although recent efforts advanced the prediction of SOA formation, partitioning-based SOA models remain incom- plete and inaccurate. For example, the physical state (Zuend and Seinfeld, 2012) and water content of aerosols as well as complex aerosol-phase chemistry via the interaction be- tween organic and inorganic compounds all influence SOA formation and likely require treatment by a multiphase SOA model. It has been known that aerosol-phase chemistry can significantly increase SOA mass, forming nonvolatile high molecular weight (MW) oligomers. Many studies have re- cently reported that SOA formation is accelerated by acid- catalyzed reactions (e.g., hydration, polymerization, forma- tion of hemiacetal/acetal/trioxane, aldol condensation, and cationic rearrangement) (Jang and Kamens, 2001; Garland et al., 2006) in the presence of inorganic aerosol for both the oxidation products of biogenic (Czoschke et al., 2003; Iinuma et al., 2004; Kleindienst et al., 2006; Surratt et al., 2007) and aromatic hydrocarbons (Cao and Jang, 2007, 2010). However, there are noted discrepancies among field studies on the effect ofaerosol acidity on SOA production (Zhang et al., 2007; Peltier et al., 2007). When humidity is high, the effect of acid-catalyzed reactions on SOA yields becomes indistinct since the reactions of organic compounds in the aqueous phase can considerably contribute to SOA for- mation (Czoschke and Jang, 2006). Furthermore, it also has been found that a new class of products, organosulfates (OS), can be formed through the aerosol-phase reaction of organic species with sulfate, bisulfate (Liggio et al., 2005; Betterton and Hoffmann, 1987) or their radicals (Galloway et al., 2009; Olson et al., 2011; Darer et al., 2011). As OS forms, the ef- fect of acid-catalyzed reactions on SOA yields becomes less apparent due to the consumption of sulfuric acid (SA) and the reduction of the amount of water inaerosol.
der to estimate the maximum supersaturation in a cloud layer for cloud droplet activation (e.g. Lohmann et al., 1999). This approach aims to provide a single, suitable vertical velocity value for the climate model grid cell, which is reminiscent of the typical small-scale variability of the turbulent vertical motions and is the method used in the ECHAM model. An- other popular approach is to use a probability density func- tion (PDF) to describe the subgrid variation of vertical ve- locity, where the grid-mean number of activated droplets is obtained by integration over the PDF (Chuang et al., 1997; Ghan et al., 1997; Storelvmo et al., 2006; Golaz et al., 2011). Tonttila et al. (2013) developed a more elaborate approach, using a PDF in the footsteps of Ghan et al. (1997) to ex- tend the stochastic subcolumn framework of Räisänen et al. (2004). Instead of integrating over the PDF for a grid-mean cloud droplet number concentration (CDNC), random verti- cal velocity samples were drawn from the PDF. This enabled the calculation of CDNC individually in each cloudy sub- column, yielding an explicit representation of the variability ofcloud structure and the distribution of the microphysical properties inside the climate model grid cells. The cloudy subcolumns can be directly used in the radiation calculations by the use of the Monte Carlo Independent Column Approx- imation method (MCICA; Pincus et al., 2003). This is a sig- nificant advantage, as now the entire chain of processes from formation ofcloud droplets to radiative transfer can be con- sidered consistently using the same subgrid framework. In addition, it provides an innovative approach for estimating the aerosol indirect effects, which is the main topic of this paper.
Interactions between CDNC and supersaturation and those between rain evaporation and cloud-base instability change with varying aerosols. This controls the LWP responses to aerosol changes in the thin, non-precipitating stratiform clouds as shown in the comparison between the CSRM run and the CSRM×2 run and as also reported in Lee et al. (2009). The role of autoconversion and collection pro- cesses and thus sedimentation in the LWP is negligible when spectral information in the size distribution is considered. However, the GCM is not capable of simulating those inter- actions and the spectral information, indicating that the GCM parameterization of shallow clouds is too limited and unable to simulate the changing aerosol-cloud interactions and their effects on thin stratocumulus clouds. Considering that thin stratocumulus clouds cover 28% of the globe and that a sig- nificant portion of these clouds has no surface precipitation (Turner et al., 2007), this limitation can be a considerable set- back for the prediction of the responses of clouds to aerosol increases. Hence, microphysics parameterizations, able to predict particle mass and number, and thereby, surface area, coupled with a prediction of supersaturation, need to be im- plemented into climate models for a correct assessment ofaerosol effects on thin stratocumulus clouds. Also, those pa- rameterizations should be able to take into account the spec- tral information as well as rain evaporation and its effects on the instability around cloud base.
Abstract. This report addresses the effects of pollution on the development of precipitation in clean (“pristine”) and polluted (“hazy”) environments in the Eastern Mediterranean by using the Integrated Community Limited Area Modeling System (ICLAMS) (an extended version of the Regional At- mospheric Modeling System, RAMS). The use of this model allows one to investigate the interactions of the aerosols with cloud development. The simulations show that the on- set of precipitation in hazy clouds is delayed compared to pristine conditions. Adding small concentrations of GCCN to polluted clouds promotes early-stage rain. The addition of GCCN to pristine clouds has no effect on precipitation amounts. Topography was found to be more important for the distribution of precipitation than aerosol properties. In- creasing by 15% the concentration of hygroscopic dust par- ticles for a case study over the Eastern Mediterranean re- sulted in more vigorous convection and more intense up- drafts. The clouds that were formed extended about three kilometers higher, delaying the initiation of precipitation by one hour. Prognostic treatment of the aerosol concentrations in the explicitcloud droplet nucleation scheme of the model, improved the model performance for the twenty-four hour accumulated precipitation. The spatial distribution and the amounts of precipitation were found to vary greatly between the different aerosol scenarios. These results indicate the large uncertainty that remains and the need for more accu- rate description ofaerosol feedbacks in atmospheric models and climate change predictions.
physics. It is found that the simulated weekly cycles in sulfur dioxide, sulfate, and aerosol optical depth in both models agree reasonably well with the observed ones indicating model skill in simulating the aerosol cycle. A distinct weekly cycle incloud droplet number concentration is demonstrated in both observations and models. For other variables, such as cloud liquid water path, cloud cover, top-of-the-atmosphere ra-
The effects of warm-phase and mixed-phase orographic clouds on the aerosol population have been evaluated by sim- ulating orographic cloud formation over a 2-D double-bell- shaped topography with the regional weather forecast and climate model COSMO-Model. An explicit treatment ofin- hydrometeor aerosol mass allowing for the consideration ofaerosol processing in clouds has been implemented in the model. Aerosol scavenging processes and aerosol processing in clouds including aerosol regeneration upon evaporation or sublimation affect the aerosol population greatly. In this pa- per, different aerosol cycles have been identified. In the sim- ulated non-precipitating warm-phase cloud, aerosol mass is incorporated incloud droplets by activation scavenging and released back to the atmosphere upon cloud droplet evap- oration. In the simulated precipitating mixed-phase cloud, a significant amount ofaerosol mass can be found incloud droplets and snowflakes, but less in ice crystals. Activation and below-cloud scavenging efficiently transfer aerosol mass into the cloud droplets and snowflakes, while ice crystals are formed heterogeneously from a few cloud droplets allowing for only a limited transfer ofaerosol mass. In the mixed- phase clouds, two aerosol cycles were identified. A first cycle includes activation scavenging and cloud droplet evaporation due to the WBF process. A second cycle includes the inter- actions with snowflakes and is connected to the first cycle via the riming process which transfers aerosol from cloud droplets to snowflakes. As aerosol particles are transported together with the sedimenting hydrometeors, a vertical redis- tribution of the aerosol number and mass concentration oc- curs with aerosol mass being transported towards lower alti- tudes or even being removed from the atmosphere. Precipi- tating snow in the lower atmosphere very efficiently removes aerosol number and mass from the atmosphere.
lar analysis has also been carried out with DARDAR IWC profiles, and the patterns are highly consistent with what were found from CloudSat except that the magnitude of the difference is slightly smaller while the relative importance remains the same order of magnitude (Fig. 4). This is to be expected for IWP as CloudSat alone can detect the majority ofcloud ice. The broad consistency between CloudSat and DARDAR analysis
Martin et al. (2003) showed that the effects of aerosols on photolysis rates increased CO by 5–15 ppbv in the re- mote Northern Hemisphere (annual mean concentrations less than 140 ppbv). This increase resulted in an improved model agreement with observations, but there was a still gap be- tween the model and the observations. In our simulation with BrC, CO concentration is further increased by 0.2–1.9 ppbv in remote Northern Hemisphere regions (annual mean con- centrations less than 140 ppbv in the model). On the other hand, OH concentrations are decreased by 0–10 % in the boundary layer over the Northern Hemisphere (maximum decreases occur in regions with high BrC concentrations, shown in Fig. 6). The change of OH owing to BrC is about one-third of the OH change according to the overall aerosol effects from Martin et al. (2003). Therefore, the inclusion of BrC significantly affects tropospheric chemistry, especially for regions with heavy biomass burning and biofuel emis- sions.
timescales. Unfortunately, instruments used in satellite observations are not able to give direct information about the aerosol size distribution or CDNC, but with several assumptions it is possible to use their estimates for comparison. Bennartz et al. (2007) used two and half years of MODIS satellite data (from July 2004 to December 2005) to produce an overview of CDNC over oceans. Figure 11 shows modeled cloud top CDNC
low values were not associated with synoptic scale fronts, and they occurred within air masses that had resided at least several days over the Arctic pack ice. Further, the associated cooling was surface based, and hence not advective, while subsidence occurring during the case was not sufficient to evaporate the cloud as can be inferred from helicopter profiles obtained during the case (Fig. 4). The cooling over time occurs
Abstract. Collisionless shocks occur in various fields ofphysics. In the context of space and astrophysics they have been investigated for many decades. However, a thorough understanding of shock formation and particle acceleration is still missing. Collisionless shocks can be distinguished into electromagnetic and electrostatic shocks. Electromagnetic shocks are of importance mainly in astrophysical environments and they are mediated by the Weibel or filamentation instability. In such shocks, charged particles gain energy by diffusive shock acceleration. Electrostatic shocks are characterized by a strong electrostatic field, which leads to electron trapping. Ions are accelerated by reflection from the electrostatic potential. Shock formation and particle acceleration will be discussed in theory and simulations.