Observations and regional climate modeling (RCM) studies demonstrate that global climate models (GCMs) are unreliable for predicting changes in extreme precipitation. Yet RCM climate change simulations are subject to boundary conditions provided by GCMs and do not interact with large-scale dynamical feedbacks that may be critical to the overall regional response. Limitations of both global and regional modeling approaches contribute significant uncertainty to future rainfall projections. Progress requires a modeling framework capable of capturing the observed regional-scale variability of rainfall intensity without sacrificing planetary scales. Here the United States summer rainfall response to quadrupled CO2 climate change is investigated using conventional (CAM) and superparameterized (SPCAM) versions of the NCAR Community Atmosphere Model. The superparameterization approach, in which cloud-resolving model arrays are embedded in GCM grid columns, improves rainfall statistics and convective variability in global simulations. A set of 5 year time-slice simulations, with prescribed sea surface temperature and sea ice boundary conditions harvested from preindustrial and abrupt four times CO2 coupled Community Earth System Model (CESM/CAM) simulations, are compared for CAM and SPCAM. The two models produce very different changes in mean precipitation patterns, which develop from differences in large-scale circulation anomalies associated with the planetary-scale response to warming. CAM shows a small decrease in overall rainfall intensity, with an increased contribution from the weaker parameterized convection and a decrease from large-scale precipitation. SPCAM has the opposite response, a significant shift in rainfall occurrence toward higher precipitation rates including more intense propagating Central United States mesoscale convective systems in a four times CO2 climate. Key Points Large-scale dynamics are critical to regional rainfall climate change responses Superparameterization captures expected increases in rain and storm intensity Extreme rain may be decoupled from key climate change drivers in standard GCMs

Mesoscale convective systems (MCSs) can bring up to 60% of summer rainfall to the central United States but are not simulated by most global climate models. In this study, a new empirical orthogonal function based index is developed to isolate the MCS activity, similar to that developed by Wheeler and Hendon (2004) for the Madden-Julian Oscillation. The index is applied to compactly compare three conventional- and super-parameterized (SP) versions (3.0, 3.5, and 5.0) of the National Center for Atmospheric Research Community Atmosphere Model (CAM). Results show that nocturnal, eastward propagating convection is a robust effect of super-parameterization but is sensitive to its specific implementation. MCS composites based on the index show that in SP-CAM3.5, convective MCS anomalies are unrealistically large scale and concentrated, while surface precipitation is too weak. These aspects of the MCS signal are improved in the latest version (SP-CAM5.0), which uses high-order microphysics. Key Points A new EOF based index compactly evaluates the mid-latitude MCS signal in GCMs Central US summer MCS physics is a robust effect of cloud super-parameterization The MCS signal is most realistic in SP-CAM5.0 that uses high-order microphysics ©2013. American Geophysical Union. All Rights Reserved.

Frost flowers are clusters of highly saline ice crystals growing on newly formed sea ice or frozen lakes. Based on observations of particles derived from frost flowers in the Arctic, we formulate an observation-based parameterization of salt aerosol source function from frost flowers. The particle flux from frost flowers in winter has the order of 106 m-2 s-1 at the wind speed of 10 m s-1, but the source flux is highly localized to new sea ice regions and strongly dependent on wind speed. We have implemented this parameterization into the regional Weather Research and Forecasting model with Chemistry initialized for two wintertime scenarios. The addition of sea salt aerosol emissions from frost flowers increases averaged sea salt aerosol mass and number concentration and subsequent cloud droplet number. This change of cloud droplet number concentration increases downward longwave cloud radiative forcing through enhanced cloud optical depth and emissivity. The magnitude of this forcing of sea salt aerosols from frost flowers on clouds and radiation, however, contributes negligibly to surface warming in Barrow, Alaska, in the wintertime scenarios studied here. Key Points We evaluate a parameterization of salt aerosol from frost flowers in WRF-Chem The modeled salt explains half of the observed submicron salt aerosol Longwave cloud forcing increases but does not add to Arctic surface warming ©2013. American Geophysical Union. All Rights Reserved.

We describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof-of-concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5-day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short- and long-time means, the 5-day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction. Key Points Bayesian blending of camera and radar data to form a cloud fraction pdfFeasibility study of a method on probabilistic assessment of cloud fractionsBeta distribution as a model for cloud fractions ©2013. American Geophysical Union. All Rights Reserved.

An aerosol-enabled globally driven regional modeling system has been developed by coupling the National Center for Atmospheric Research's Community Atmosphere Model version 5 (CAM5) with the Weather Research and Forecasting model with chemistry (WRF-Chem). In this modeling system, aerosol-enabled CAM5, a state-of-the-art global climate model is downscaled to provide coherent meteorological and chemical boundary conditions for regional WRF-Chem simulations. Aerosol particle emissions originating outside the WRF-Chem domain can be a potentially important nonlocal aerosol source. As a test case, the potential impacts of nonlocal forest fire aerosols on regional precipitation and radiation were investigated over the northeastern United States during the summer of 2004. During this period, forest fires in Alaska and western Canada lofted aerosol particles into the midtroposphere, which were advected across the United States. WRF-Chem simulations that included nonlocal biomass burning aerosols had domain-mean aerosol optical depths that were nearly three times higher than those without, which reduced peak downwelling domain-mean shortwave radiation at the surface by ~25 W m-2. In this classic twin experiment design, adding nonlocal fire plume led to near-surface cooling and changes in cloud vertical distribution, while variations in domain-mean cloud liquid water path were negligible. The higher aerosol concentrations in the simulation with the fire plume resulted in a ~10% reduction in domain-mean precipitation coincident with an ~8% decrease in domain-mean CAPE. A suite of simulations was also conducted to explore sensitivities of meteorological feedbacks to the ratio of black carbon to total plume aerosols, as well as to overall plume concentrations. Results from this ensemble revealed that plume-induced near-surface cooling and CAPE reduction occur in a wide range of conditions. The response of moist convection was very complex because of strong thermodynamic internal variability. Key Points Nonlocal fire emissions resulted in ~10% precipitation reduction Nonlocal fire emissions reduced peak surface shortwave radiation at by 25 W m-2 An aerosol-enabled globally driven regional modeling system is developed ©2014. American Geophysical Union. All Rights Reserved.

In this study, an aerosol-dependent ice nucleation scheme has been implemented in an aerosol-enabled Multiscale Modeling Framework (PNNL MMF) to study ice formation in upper troposphere cirrus clouds through both homogeneous and heterogeneous nucleation. The MMF model represents cloud scale processes by embedding a cloud-resolving model (CRM) within each vertical column of a GCM grid. By explicitly linking ice nucleation to aerosol number concentration, CRM-scale temperature, relative humidity and vertical velocity, the new MMF model simulates the persistent high ice supersaturation and low ice number concentration (10-100/L) at cirrus temperatures. The new model simulates the observed shift of the ice supersaturation PDF toward higher values at low temperatures following the homogeneous nucleation threshold. The MMF model predicts a higher frequency of midlatitude supersaturation in the Southern Hemisphere and winter hemisphere, which is consistent with previous satellite and in situ observations. It is shown that compared to a conventional GCM, the MMF is a more powerful model to simulate parameters that evolve over short time scales such as supersaturation. Sensitivity tests suggest that the simulated global distribution of ice clouds is sensitive to the ice nucleation scheme and the distribution of sulfate and dust aerosols. Simulations are also performed to test empirical parameters related to auto-conversion of ice crystals to snow. Results show that with a value of 250 μm for the critical diameter, Dcs, that distinguishes ice crystals from snow, the model can produce good agreement with the satellite-retrieved products in terms of cloud ice water path and ice water content, while the total ice water is not sensitive to the specification of Dcs value.

This study uses a century length preindustrial climate simulation by the Community Earth System Model (CESM 1.0) to explore statistical relationships between dust, clouds, and atmospheric circulation and to suggest a semidirect dynamical mechanism linking subtropical North Atlantic lower tropospheric cloud cover with North African dust transport. The length of the run allows us to account for interannual variability of North African dust emissions and transport in the model. CESM’s monthly climatology of both aerosol optical depth and surface dust concentration at Cape Verde and Barbados, respectively, agree well with available observations, as does the aerosol size distribution at Cape Verde. In addition, CESM shows strong seasonal cycles of dust burden and lower tropospheric cloud fraction, with maximum values occurring during boreal summer, when a strong correlation between these two variables exists over the subtropical North Atlantic. Calculations of Estimated Inversion Strength (EIS) and composites of EIS on high and low downstream North African dust months during boreal summer reveal that dust is likely increasing inversion strength over this region due to both solar absorption and reflection. We find no evidence for a microphysical link between dust and lower tropospheric clouds in this region. These results yield new insight over an extensive period of time into the complex relationship between North African dust and North Atlantic lower tropospheric clouds, which has previously been hindered by spatiotemporal constraints of observations. Our findings lay a framework for future analyses using different climate models and submonthly data over regions with different underlying dynamics.

This study examines multiyear climate variability associated with sea salt aerosols and their contribution to the variability of shortwave cloud forcing (SWCF) using a 150 year simulation for preindustrial conditions of the Community Earth System Model version 1.0. The results suggest that changes in sea salt and related cloud and radiative properties on interannual timescales are dominated by the El Niño-Southern Oscillation cycle. Sea salt variability on longer (interdecadal) timescales is associated with low-frequency variability in the Pacific Ocean similar to the Interdecadal Pacific Oscillation but does not show a statistically significant spectral peak. A multivariate regression suggests that sea salt aerosol variability may contribute to SWCF variability in the tropical Pacific, explaining up to 20-30% of the variance in that region. Elsewhere, there is only a small sea salt aerosol influence on SWCF through modifying cloud droplet number and liquid water path that contributes to the change of cloud effective radius and cloud optical depth (and hence cloud albedo), producing a multiyear aerosol-cloud-wind interaction.

We use three 150 year preindustrial simulations of the Community Earth System Model to quantify the impacts of El Niño-Southern Oscillation (ENSO) events on shortwave and longwave cloud radiative effects (CRESW and CRELW). Compared to recent observations from the Clouds and the Earth’s Radiant Energy System data set, the model simulation successfully reproduces larger variations of CRESW and CRELW over the tropics. The ENSO cycle is found to dominate interannual variations of cloud radiative effects. Simulated cooling (warming) effects from CRESW (CRELW) are strongest over the tropical western and central Pacific Ocean during warm ENSO events, with the largest difference between 20 and 60Wm-2, with weaker effects of 10-40Wm-2 over Indonesian regions and the subtropical Pacific Ocean. Sensitivity tests show that variations of cloud radiative effects are mainly driven by ENSO-related changes in cloud fraction. The variations in midlevel and high cloud fractions each account for approximately 20-50% of the interannual variations of CRESW over the tropics and almost all of the variations of CRELW between 60°S and 60°N. The variation of low cloud fraction contributes to most of the variations of CRESW over the midlatitude oceans. Variations in natural aerosol concentrations explained 10-30% of the variations of both CRESW and CRELW over the tropical Pacific, Indonesian regions, and the tropical Indian Ocean. Changes in natural aerosol emissions and concentrations enhance 3-5% and 1-3% of the variations of cloud radiative effects averaged over the tropics.