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Interactions between aerosol particles and clouds contribute a great deal of uncertainty to the scientific community's understanding of anthropogenic climate forcing. Aerosol particles serve as the nucleation sites for cloud droplets, establishing a direct linkage between anthropogenic particulate emissions and clouds in the climate system. To resolve this linkage, the community has developed parameterizations of aerosol activation which can be used in global climate models to interactively predict cloud droplet number concentrations (CDNCs). However, different activation schemes can exhibit different sensitivities to aerosol perturbations in different meteorological or pollution regimes. To assess the impact these different sensitivities have on climate forcing, we have coupled three different core activation schemes and variants with the CESM-MARC (two-Moment, Multi-Modal, Mixing-state-resolving Aerosol model for Research of Climate (MARC) coupled with the National Center for Atmospheric Research's (NCAR) Community Earth System Model (CESM; version 1.2)). Although the model produces a reasonable present-day CDNC climatology when compared with observations regardless of the scheme used, ΔCDNCs between the present and preindustrial era regionally increase by over 100 % in zonal mean when using the most sensitive parameterization. These differences in activation sensitivity may lead to a different evolution of the model meteorology, and ultimately to a spread of over 0.8 W m−2 in global average shortwave indirect effect (AIE) diagnosed from the model, a range which is as large as the inter-model spread from the AeroCom intercomparison. Model-derived AIE strongly scales with the simulated preindustrial CDNC burden, and those models with the greatest preindustrial CDNC tend to have the smallest AIE, regardless of their ΔCDNC. This suggests that present-day evaluations of aerosol-climate models may not provide useful constraints on the magnitude of the AIE, which will arise from differences in model estimates of the preindustrial aerosol and cloud climatology.

The phase partitioning of cloud mass between liquid and ice in mixed phase clouds and its dependence on ambient ice nuclei (IN) concentrations and ice habit parameterizations is explored in this paper. Single‐layered and multilayered cloud systems observed during the Mixed‐Phase Arctic Cloud Experiment were simulated with a cloud‐resolving model. The model used a two‐moment (mass and number concentration) microphysical scheme with ice crystal habit parameterized by mass and fall speed relationships and IN prediction scheme that accounts for depletion of IN through nucleation scavenging. The mixed phase cloud simulations show a strong sensitivity to the ambient deposition/condensation freezing IN concentrations, which is similar to some prior studies. This sensitivity depends on the mass, fall speed, and capacitance relationships used to parameterize crystal habit and vapor growth: Mass and fall speed relationships associated with compact, high‐density crystals produce clouds with a weaker sensitivity to ambient IN concentrations, whereas more extreme crystal shapes (e.g., dendrites) produce clouds with strong IN concentration sensitivity. This sensitivity also affects the number of liquid layers (from 1 to 5) predicted for the multilayer case. The strength of the vapor growth rates and the crystal fall speeds appear to be of roughly equal importance for determining the strength of mixed phase cloud sensitivity to ice concentrations.

We quantify the effective radiative forcing (ERF) of anthropogenic aerosols modelled by the aerosol–climate model CAM5.3-MARC-ARG. CAM5.3-MARC-ARG is a new configuration of the Community Atmosphere Model version 5.3 (CAM5.3) in which the default aerosol module has been replaced by the two-Moment, Multi-Modal, Mixing-state-resolving Aerosol model for Research of Climate (MARC). CAM5.3-MARC-ARG uses the ARG aerosol-activation scheme, consistent with the default configuration of CAM5.3. We compute differences between simulations using year-1850 aerosol emissions and simulations using year-2000 aerosol emissions in order to assess the radiative effects of anthropogenic aerosols. We compare the aerosol lifetimes, aerosol column burdens, cloud properties, and radiative effects produced by CAM5.3-MARC-ARG with those produced by the default configuration of CAM5.3, which uses the modal aerosol module with three log-normal modes (MAM3), and a configuration using the modal aerosol module with seven log-normal modes (MAM7). Compared with MAM3 and MAM7, we find that MARC produces stronger cooling via the direct radiative effect, the shortwave cloud radiative effect, and the surface albedo radiative effect; similarly, MARC produces stronger warming via the longwave cloud radiative effect. Overall, MARC produces a global mean net ERF of -1.79±0.03 W m−2, which is stronger than the global mean net ERF of -1.57±0.04 W m−2 produced by MAM3 and -1.53±0.04 W m−2 produced by MAM7. The regional distribution of ERF also differs between MARC and MAM3, largely due to differences in the regional distribution of the shortwave cloud radiative effect. We conclude that the specific representation of aerosols in global climate models, including aerosol mixing state, has important implications for climate modelling.

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. Key Points Constrained LES of mixed‐phase Arctic clouds from 11 models are analyzed Ice water path differences are attributed to assumed ice size distributions Bulk schemes with gamma size distributions agree better with bin schemes

Observations of long-lived mixed-phase Arctic boundary layer clouds on 7 May 1998 during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE)/Surface Heat Budget of the Arctic Ocean (SHEBA) campaign provide a unique opportunity to test understanding of cloud ice formation. Under the microphysically simple conditions observed (apparently negligible ice aggregation, sublimation, and multiplication), the only expected source of new ice crystals is activation of heterogeneous ice nuclei (IN) and the only sink is sedimentation. Large-eddy simulations with size-resolved microphysics are initialized with IN number concentration NIN measured above cloud top, but details of IN activation behavior are unknown. If activated rapidly (in deposition, condensation, or immersion modes), as commonly assumed, IN are depleted from the well-mixed boundary layer within minutes. Quasi-equilibrium ice number concentration Ni is then limited to a small fraction of overlying NIN that is determined by the cloud-top entrainment rate we divided by the number-weighted ice fall speed at the surface υf. Because wc < 1 cm s−1 and υf > 10 cm s−1, Ni/NIN ≪ 1. Such conditions may be common for this cloud type, which has implications for modeling IN diagnostically, interpreting measurements, and quantifying sensitivity to increasing NIN (when we/υf < 1, entrainment rate limitations serve to buffer cloud system response). To reproduce observed ice crystal size distributions and cloud radar reflectivities with rapidly consumed IN in this case, the measured above-cloud NIN must be multiplied by approximately 30. However, results are sensitive to assumed ice crystal properties not constrained by measurements. In addition, simulations do not reproduce the pronounced mesoscale heterogeneity in radar reflectivity that is observed.

Mixed-phase stratus clouds are ubiquitous in the Arctic and play an important role in climate in this region. However, climate and regional models have generally proven unsuccessful at simulating Arctic cloudiness, particularly during the colder months. Specifically, models tend to underpredict the amount of liquid water in mixed-phase clouds. The Mixed-Phase Arctic Cloud Experiments (M-PACE), conducted from late September through October 2004 in the vicinity of the Department of Energy’s Atmospheric Radiation Measurement (ARM) North Slope of Alaska field site, focused on characterizing low-level Arctic stratus clouds. Ice nuclei (IN) measurements were made using a continuous-flow ice thermal diffusion chamber aboard the University of North Dakota’s Citation II aircraft. These measurements indicated IN concentrations that were significantly lower than those used in many models. Using the Regional Atmospheric Modeling System (RAMS), we show that these low IN concentrations, as well as inadequate parameterizations of the depletion of IN through nucleation scavenging, may be partially responsible for the poor model predictions. Moreover, we show that this can lead to errors in the modeled surface radiative energy budget of 10–100 W m−2. Finally, using the measured IN concentrations as input to RAMS and comparing to a mixed-phase cloud observed during M-PACE, we show excellent agreement between modeled and observed liquid water content and net infrared surface flux.

Arctic clouds are often mixed-phase, such that the radiative properties of the clouds are a strong function of the relative amounts of cloud liquid and ice. Modeling studies have shown that the poorly understood ice phase processes are the regulators of the liquid water fraction. However, evaluating the fidelity of the model ice parameterizations has proven to be a difficult task. This study evaluates results of different ice microphysics representations in a cloud resolving model (CRM) using cloud radar measurements. An algorithm is presented to generate realistic ice crystals and their aggregates from which radar backscattering cross sections may be calculated using a generalized solution for a cluster of spheres. The aggregate is composed of a collection of ice crystals, each of which is constructed from a cluster of tiny ice spheres. Each aggregate satisfies the constraints set by the component crystal type and the mass-dimensional relationship used in the cloud resolving model, but is free to adjust its aspect ratio. This model for calculating radar backscattering is compared to two spherical and two spheroidal (bulk model) representations for ice hydrometeors. It was found that a refined model for representing the ice hydrometeors, both pristine crystals and their aggregates, is required in order to obtain good comparisons between the CRM calculations and the radar measurements. The addition of the radar-CRM comparisons to CRM-in situ measurements comparisons allowed conclusions about the appropriateness of different CRM ice microphysics parameterizations. Copyright 2011 by the American Geophysical Union.

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 1 − 2 × 10−5 g kg−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.

Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 µg m−3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.

Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). Wefocus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.