Cloud water interception (CWI) is not captured by conventional rain gauges and not well characterized, but could have ecohydrological significance in systems such as tropical montane cloud forests. Quantifying CWI is necessary to assess the impacts of climate and land cover changes in places such as Hawai‘i. CWI can be estimated from wind speed, cloud liquid water content (LWC), and vegetation characteristics with an empirical model. Cloud microphysics sensors measure LWC accurately, but are expensive and often designed only for use on aircraft. LWC can be estimated by fog gauges, but poorly constrained catch efficiency and spurious rain catch can cause large errors. Visibility is related to LWC, but the relationship is non-linear and depends on the (usually unknown) drop size distribution. This study is part of a project aimed at mapping CWI across the Hawaiian Islands. Earlier analyses found disagreement between LWC estimated from fog gauge and visibility observations at the project field sites. In this study, we experimented with a novel in situ observation platform and cross disciplinary collaboration to compare cloud microphysics observations with those commonly used in cloud forest studies. Field missions took place from April to July 2018 at the summit of Mt. Ka‘ala (1,200 m) on O‘ahu Island. We built a pickup truck-mounted mobile weather station that can be assembled in the field, with weather-sensitive processing modules inside the cab. A total of 10 instruments were deployed: Phase Doppler Interferometer, Cloud Droplet Probe, fog gauge, visibility sensor, rain gauge, wind monitor, camera, water isotope sampler, UAV atmospheric sensor, and Aerosol Spectrometer. A nearby long-term station provides climate and canopy water balance data. Analyses found a strong relationship between visibility and LWC in dense fog. The fog gauge showed weak correlations due to coarse resolution and false rain catch, but had a reasonable catch efficiency. The start of fog catch lagged compared to the nearby station possibly due to screen surface wetting. Concurrent with other analyses, one goal is to calibrate the fog gauge and visibility sensor for long-term LWC monitoring. The mobile platform was effective for short-term deployment of airborne sensors. We hope to repeat the experiment in the future on O‘ahu and other islands.

The southeast Atlantic Ocean provides an excellent natural laboratory to study smoke-cloud interactions, a large driver of uncertainty in climate projections. The value of studying this in particular region is largely attributable to two factors---the expansive, bright, semi-permanent stratocumulus cloud deck and the fact that southern Africa is the largest source of biomass-burning aerosols in the world. We study this region using the WRF-Chem model with CAM5 aerosols and in situ observations from the ORACLES, LASIC, and CLARIFY field campaigns, all of which overlapped in August 2017. Across these campaigns, we compare aerosol, cloud, and thermodynamic variables to quantify model performance and expand upon observational findings of aerosol-cloud effects. Specifically, our approach is to analyze aerosol and cloud properties along flight tracks, picking out uniform legs within tropospheric smoke plumes and in the boundary layer. This unique approach allows us to sample the high spatiotemporal variability that can get lost to large-scale averaging. It also allows process-level comparison of local cloud responses to aerosol conditions, and measure model performance in those same processes. Along with better quantifying model predictive power, we find and justify updates to model parameters and processes to better emulate observations, notably aerosol size parameters. Preliminary results suggest that WRF-CAM5 is activating a smaller percentage of aerosols into cloud droplets than shown in observations, which could lead to biased modeling of aerosol indirect radiative effects on a larger scale. We explore this effect further with CCN activation tendency, updraft, particle sizing, and composition analysis, as well as broader dynamics like entrainment and removal rates. Comparing the model with similar instrument suites across multiple colocated campaigns also allows us to quantify instrument uncertainty in ways that a focus on a single campaign cannot and gives further context to the model performance.

Biomass-burning emissions impact almost all of the radiative forcing terms considered by the IPCC, yet little is known about smoke aerosol aging in nature beyond a few days. The marine southeast Atlantic free-troposphere is a natural testbed for examining aging through photolysis/oxidation of African continental fire emissions advected westward. In-situ measurements primarily from September, 2016 indicate highly-oxidized aerosol with minimal primary source signatures after 4-9 model-predicted days since emission. Aerosol loses approximately one-half of its organic aerosol over the ocean. The organic aerosol to black carbon mass ratios decrease from 14 to 10, significantly lower than many model predictions. This mass loss, combined with stability in black carbon, supports an observed 20% increase in solar absorptivity. The decreased single scattering albedos, reaching 0.83 at 9 days, arguably represent the lowest values measured globally. The relationship of the aerosol properties to model-derived time since emission suggests a useful new modeling constraint.

Aerosol-cloud interactions are both uncertain and important in global and regional climate models, and especially in the southeast Atlantic Ocean. This uncertainty in the region is largely due to two correlated factors---the expansive, bright, semi-permanent stratocumulus cloud deck and the fact that southern Africa is the largest source of biomass-burning aerosols in the world. We study this region using the WRF-Chem model with CAM5 aerosols and in situ observations from the ORACLES and LASIC field campaigns in August-October of 2016 through 2018. We compare aerosol and cloud properties to measure and improve model performance and expand upon observational findings of aerosol-cloud effects. Relevant comparison variables include aerosol number concentration, mean particle diameter and spread, CCN activation tendency, hygroscopicity, and cloud droplet number concentrations. Specifically, our approach is to analyze colocated model data along flight tracks to resolve aerosol-cloud interactions. Within and between single-day flights, there is high spatiotemporal variability that can get lost to large-scale averaging analyses. We have found that CCN is substantially under-represented in the model compared to observations. For a given aerosol number concentration, size, supersaturation and hygroscopicity, the model will consider fewer particles as CCN than observations indicate. We plan to explore this result further, diagnosing the model-observation differences more consistently and updating the model with more physically accurate values of aerosol size, concentration, or hygroscopicity based on observations. We will also intercompare multiple instrument platforms involved with the ORACLES and LASIC campaigns. With improved small-scale aerosol-cloud interactions, this work also shows promise to substantially improve that representation in climate models.