Aerosol Indirect Effects on Cirrus Clouds Based on Global Aircraft Observations

Cirrus clouds have large climate impacts, yet aerosol indirect effects on cirrus microphysical properties remain highly uncertain. There is a lack of observational analysis on thermodynamic, dynamical, and aerosol indirect effects simultaneously, which limits the quantification of each effect. Using seven National Science Foundation aircraft campaigns, impacts of temperature, relative humidity, vertical velocity, and aerosols are individually quantified. Nonmonotonic correlations of ice water content, ice crystal number concentration (Ni), and mean diameter (Di) with respect to aerosol number concentrations (Na) are consistently seen at various conditions. Positive correlations become significant when Na \u3e 500 nm (Na500) and \u3e100 nm (Na100) are 3 and 10 times higher than average, respectively. While Na500 are more effective at temperatures closer to −40 °C with small vertical velocity fluctuations and are less sensitive to ice supersaturation, Na100 are more effective at colder temperatures with higher updraft and higher ice supersaturation, indicating heterogeneous and homogeneous nucleation mechanisms, respectively

Examination of aerosol indirect effects during cirrus cloud evolution

Aerosols affect cirrus formation and evolution, yet quantification of these effects remain difficult based on in situ observations due to the complexity of nucleation mechanisms and large variabilities in ice microphysical properties. This work employed a method to distinguish five evolution phases of cirrus clouds based on in situ aircraft-based observations from seven U.S. National Science Foundation (NSF) and five NASA flight campaigns. Both homogeneous and heterogeneous nucleation were captured in the 1 Hz aircraft observations, inferred from the distributions of relative humidity in the nucleation phase. Using linear regressions to quantify the correlations between cirrus microphysical properties and aerosol number concentrations, we found that ice water content (IWC) and ice crystal number concentration (Ni) show strong positive correlations with larger aerosols (\u3e500 nm) in the nucleation phase, indicating strong contributions of heterogeneous nucleation when ice crystals first start to nucleate. For the later growth phase, IWC and Ni show similar positive correlations with larger and smaller (i.e., \u3e100 nm) aerosols, possibly due to fewer remaining ice-nucleating particles in the later growth phase that allows more homogeneous nucleation to occur. Both 200m and 100 km observations were compared with the nudged simulations from the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 6 (CAM6). Simulated aerosol indirect effects are weaker than the observations for both larger and smaller aerosols for in situ cirrus, while the simulated aerosol indirect effects are closer to observations in convective cirrus. The results also indicate that simulations overestimate homogeneous freezing, underestimate heterogeneous nucleation and underestimate the continuous formation and growth of ice crystals as cirrus clouds evolve. Observations show positive correlations of IWC, Ni and ice crystal mean diameter (Di) with respect to Na in both the Northern and Southern Hemisphere (NH and SH), while the simulations show negative correlations in the SH. The observations also show higher increases of IWC and Ni in the SH under the same increase of Na than those shown in the NH, indicating higher sensitivity of cirrus microphysical properties to increases of Na in the SH than the NH. The simulations underestimate IWC by a factor of 3 30 in the early/later growth phase, indicating that the low bias of simulated IWC was due to insufficient continuous ice particle formation and growth. Such a hypothesis is consistent with the model biases of lower frequencies of ice supersaturation and lower vertical velocity standard deviation in the early/later growth phases. Overall, these findings show that aircraft observations can capture both heterogeneous and homogeneous nucleation, and their contributions vary as cirrus clouds evolve. Future model development is also recommended to evaluate and improve the representation of water vapor and vertical velocity on the sub-grid scale to resolve the insufficient ice particle formation and growth after the initial nucleation event

Comparisons of cirrus cloud microphysical properties between polluted and pristine air

In this work, we analyze high-resolution (~200 m), in-situ observations from two global flight campaigns: 1) the HIAPER Pole-to-Pole Observations (HIPPO) global campaign in 2009-2011 funded by the US National Science Foundation (NSF), and 2) the Interhemispheric Differences In Cirrus Properties from Anthropogenic Emissions (INCA) campaign in 2000 funded by the European Union and participating research institutions. The HIPPO campaign observations were obtained over the North America continent and the central Pacific Ocean from 87ºN to 67ºS. During the INCA campaign, cirrus clouds were sampled with optical particle counters in the size range of about 1 to 800 m at midlatitudes, mainly over the Pacific west of Punta Arenas and over the North Atlantic west of Great Britain. We find that as CO concentration increases, the cirrus clouds tend to have smaller ice crystals (HIPPO data based on Fast-2DC ice probe > 87.5 µm), higher Nc of small particles (INCA data based on ice crystals > 3 µm measured by FSSP instrument), and slightly lower Nc for larger particles (HIPPO data Fast-2DC > 87.5 µm and INCA 2DC data for particles > 100 µm). These three features are consistent with each other, indicating that when there is stronger signature of anthropogenic emission, ice crystals would be more numerous and smaller

Effects of thermodynamics, dynamics and aerosols on cirrus clouds based on in situ observations and NCAR CAM6

Cirrus cloud radiative effects are largely affected by ice microphysical properties, including ice water content (IWC), ice crystal number concentration (Ni) and mean diameter (Di). These characteristics vary significantly due to thermodynamic, dynamical and aerosol conditions. In this work, a global-scale observation dataset is used to examine regional variations of cirrus cloud microphysical properties, as well as several key controlling factors, i.e., temperature, relative humidity with respect to ice (RHi), vertical velocity (w) and aerosol number concentrations (Na). Results are compared with simulations from the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 6 (CAM6). Observed and simulated ice mass and number concentrations are constrained to 62:5 um to reduce potential uncertainty from shattered ice in data collection. The differences between simulations and observations are found to vary with latitude and temperature. Comparing with averaged observations at 100 km horizontal scale, simulations are found to underestimate (overestimate) IWC by a factor of 3-10 in the Northern (Southern) Hemisphere. Simulated Ni is overestimated in most regions except the Northern Hemisphere midlatitudes. Simulated Di is underestimated by a factor of 2, especially for warmer conditions (50 to 40 C), possibly due to misrepresentation of ice particle growth/sedimentation. For RHi effects, the frequency and magnitude of ice supersaturation are underestimated in simulations for clear-sky conditions. The simulated IWC and Ni show bimodal distributions with maximum values at 100% and 80% RHi, differing from the unimodal distributions that peak at 100% in the observations. For w effects, both observations and simulations show variances of w (w) decreasing from the tropics to polar regions, but simulations show much higher w for the in-cloud condition than the clear-sky condition. Compared with observations, simulations show weaker aerosol indirect effects with a smaller increase of IWC and Di at higher Na. These findings provide an observation-based guideline for improving simulated ice microphysical properties and their relationships with key controlling factors at various geographical locations

Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica

A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka-band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12-h increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in-cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid-containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), that is, high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid-containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. This diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein

The University of Washington Ice-Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

Mixed-phase Southern Ocean clouds are challenging to simulate, and their representation in climate models is an important control on climate sensitivity. In particular, the amount of supercooled water and frozen mass that they contain in the present climate is a predictor of their planetary feedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased the amount of in situ data available from mixed-phase Southern Ocean clouds useful for model evaluation. Bulk measurements distinguishing liquid and ice water content are not available from SOCRATES, so single-particle phase classifications from the Two-Dimensional Stereo (2D-S) probe are invaluable for quantifying mixed-phase cloud properties. Motivated by the presence of large biases in existing phase discrimination algorithms, we develop a novel technique for single-particle phase classification of binary 2D-S images using a random forest algorithm, which we refer to as the University of Washington Ice-Liquid Discriminator (UWILD). UWILD uses 14 parameters computed from binary image data, as well as particle inter-arrival time, to predict phase. We use liquid-only and ice-dominated time periods within the SOCRATES dataset as training and testing data. This novel approach to model training avoids major pitfalls associated with using manually labeled data, including reduced model generalizability and high labor costs. We find that UWILD is well calibrated and has an overall accuracy of 95 % compared to 72 % and 79 % for two existing phase classification algorithms that we compare it with. UWILD improves classifications of small ice crystals and large liquid drops in particular and has more flexibility than the other algorithms to identify both liquid-dominated and ice-dominated regions within the SOCRATES dataset. UWILD misclassifies a small percentage of large liquid drops as ice. Such misclassified particles are typically associated with model confidence below 75 % and can easily be filtered out of the dataset. UWILD phase classifications show that particles with area-equivalent diameter (Deq) \u3c 0.17 mm are mostly liquid at all temperatures sampled, down to -40 °. Larger particles (Deq\u3e0.17 mm) are predominantly frozen at all temperatures below 0 °. Between 0 and 5 °, there are roughly equal numbers of frozen and liquid mid-sized particles (0.170.33 mm) are mostly frozen. We also use UWILD\u27s phase classifications to estimate sub-1 Hz phase heterogeneity, and we show examples of meter-scale cloud phase heterogeneity in the SOCRATES dataset

Ice and Supercooled Liquid Water Distributions Over the Southern Ocean Based on In Situ Observations and Climate Model Simulations

Three climate models are evaluated using in situ airborne observations from the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) campaign. The evaluation targets cloud phases, microphysical properties, thermodynamic conditions, and aerosol indirect effects from −40°C to 0°C. Compared with 580-s averaged observations (i.e., 100 km horizontal scale), the Community Atmosphere Model version 6 (CAM6) shows the most similar result for cloud phase frequency distribution and allows more liquid-containing clouds below −10°C compared with its predecessor—CAM5. The Energy Exascale Earth System Model (E3SM) underestimates (overestimates) ice phase frequencies below (above) −20°C. CAM6 and E3SM show liquid and ice water contents (i.e., LWC and IWC) similar to observations from −25°C to 0°C, but higher LWC and lower IWC than observations at lower temperatures. Simulated in-cloud RH shows higher minimum values than observations, possibly restricting ice growth during sedimentation. As number concentrations of aerosols larger than 500 nm (Na500) increase, observations show increases of LWC, IWC, liquid, and ice number concentrations (Nliq, Nice). Number concentrations of aerosols larger than 100 nm (Na100) only show positive correlations with LWC and Nliq. From −20°C to 0°C, higher aerosol number concentrations are correlated with lower glaciation ratio and higher cloud fraction. From −40°C to −20°C, large aerosols show positive correlations with glaciation ratio. CAM6 shows small increases of LWC and Nliq with Na500 and Na100. E3SM shows small increases of Nice with Na500. Overall, CAM6 and E3SM underestimate aerosol indirect effects on ice crystals and supercooled liquid droplets over the Southern Ocean

Clarifying the Dominant Sources and Mechanisms of Cirrus Cloud Formation

Formation of cirrus clouds depends on the availability of ice nuclei to begin condensation of atmospheric water vapor. Although it is known that only a small fraction of atmospheric aerosols are efficient ice nuclei, the critical ingredients that make those aerosols so effective have not been established. We have determined in situ the composition of the residual particles within cirrus crystals after the ice was sublimated. Our results demonstrate that mineral dust and metallic particles are the dominant source of residual particles, whereas sulfate and organic particles are underrepresented, and elemental carbon and biological materials are essentially absent. Further, composition analysis combined with relative humidity measurements suggests that heterogeneous freezing was the dominant formation mechanism of these clouds.National Science Foundation (U.S.) (NSF AGS-0840732)National Science Foundation (U.S.) (NSF grant AGS-1036275)United States. National Aeronautics and Space Administration (NASA Earth and Space Science Graduate Fellowship)United States. National Aeronautics and Space Administration (NASA Radiation Sciences Program award number NNX07AL11G)United States. National Aeronautics and Space Administration (NASA Radiation Sciences Program award number NNX08AH57G)United States. National Aeronautics and Space Administration (NASA Earth Science Division Atmospheric Composition program award number NNH11AQ58UI

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

Sex differences in 3- to 5-year-old children's motor competence : a pooled cross-sectional analysis of 6241 children

There is some, albeit inconsistent, evidence supporting sex differences in preschoolers' motor competence (MC), with these observations not uniform when analyzed by age, and cultural groups. Thus, this study examined sex differences across ages in 3- to 5-year-old children's MC. A cross-country pooled sample of 6241 children aged 3–5 years (49.6% girls) was assessed for MC using the Test of Gross Motor Development—2nd/3rd edition, and children were categorized into groups of age in months. Multiple linear regression models and predictive margins were calculated to explore how sex and age in months affect scores of MC (i.e., locomotor and ball skills), with adjustments for country and BMI. The Chow's Test was used to test for the presence of a structural break in the data. Significant differences in favor of girls were seen at 57–59 and 66–68 months of age for locomotor skills; boys performed better in ball skills in all age periods, except for 42–44 and 45–47 months of age. The higher marginal effects were observed for the period between 45–47 and 48–50 months for locomotor skills (F = 30.21; and F = 25.90 for girls and boys, respectively), and ball skills (F = 19.01; and F = 42.11 for girls and boys, respectively). A significantly positive break point was seen at 45–47 months, highlighting the age interval where children's MC drastically improved. The identification of this breakpoint provides an evidence-based metric for when we might expect MC to rapidly increase, and an indicator of early delay when change does not occur at that age