Drought-busting events in the Colorado River Basin, such as the “Miracle May” of 2015 that greatly alleviated an unprecedented water shortage, have been observed for more than a century. But while such events are much prayed for in times of drought, they have not been well researched or even characterized. In this study, conducted in collaboration with water managers from across the basin, we propose a definition for “miracle events” that reflects real-world, actionable relevance. The resulting characterization offers a framework by which to quantify the frequency and strength of extreme dry-to-wet springtime transitions. While limited by uncertainties in model simulations and the myriad hydrological futures these simulations seek to project, and thus requiring cautious interpretation, this study finds that such transitions may become less frequent and less powerful under climate warming.

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.

Nitrate aerosol plays an important role in affecting regional air quality as well as Earth’s climate. However, it is not well represented or even neglected in many global climate models. In this study, we couple the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) module with the four-mode version of the Modal Aerosol Module (MAM4) in DOE’s Energy Exascale Earth System Model version 2 (E3SMv2) to treat nitrate aerosol and its radiative effects. We find that nitrate aerosol simulated by E3SMv2-MAM4-MOSAIC is sensitive to the treatment of gaseous HNO3 transfer to/from interstitial particles related to accommodation coefficients of HNO3 (αHNO3) on dust and non-dust particles. We compare three different treatments of HNO3 transfer: 1) a treatment (MTC_SLOW) that uses a low αHNO3 in the mass transfer coefficient (MTC) calculation; 2) a dust-weighted MTC treatment (MTC_WGT) that uses a high αHNO3 on non-dust particles; and 3) a dust-weighted MTC treatment that also splits coarse mode aerosols into the coarse dust and sea salt sub-modes in MOSAIC (MTC_SPLC). MTC_WGT and MTC_SPLC increase the global annual mean (2005-2014) nitrate burden from 0.096 (MTC_SLOW) to 0.237 and 0.185 Tg N, respectively, mostly in the coarse mode. They also produce stronger nitrate direct radiative forcing (–0.048 and –0.051 W m–2, respectively) and indirect forcing (–0.33 and –0.35 W m–2, respectively) than MTC_SLOW (–0.021 and –0.24 W m–2). All three treatments overestimate nitrate surface concentrations compared with ground-based observations. MTC_WGT and MTC_SPLC improve the vertical profiles of nitrate concentrations against aircraft measurements below 400 hPa.

River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model’s spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model’s spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationships-based river network representation method for spatially distributed hydrologic models. This novel method uses (1) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (2) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (1) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (2) it is mesh-independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

This study aims at improving understanding of the environments supporting summer MCS initiation in the U.S. Great Plains. A self-organizing map analysis is conducted to identify four types of summer MCS initiation environments during 2004-2017: Type-1 and Type-2 feature favorable large-scale environments, Type-3 has favorable lower-level and surface conditions but unfavorable upper-level circulation, while Type-4 features the most unfavorable large-scale environments. Despite the unfavorable large-scale environment, convection-centered composites reveal the presence of favorable sub-synoptic scale environments for MCS initiation in Type-3 and Type-4. All four types of MCS initiation environments delineate a clear eastward propagating feature in many meteorological fields, such as potential vorticity, surface pressure and equivalent potential temperature, upstream up to 25 west of and ~36 hours before MCS initiation. While the propagating environments and local, non-propagating low-level moisture are important to MCS initiation at the foothill of the Rocky Mountains, MCS initiation in the Great Plains is supported by the coupled dynamical and moisture anomalies, both associated with eastward propagating waves. Hence, the MCSs initiated at the plains can produce more rainfall than those initiated at the foothill due to more abundant moisture supply. By tracking MCSs and mid-tropospheric perturbations (MPs), a unique type of sub-synoptic disturbances with Rocky Mountains origin, it is shown that ~30% of MPs is associated with MCS initiation, mostly in Type-4. Although MPs are related to a small fraction of MCS initiation, MCSs that are associated with MPs tend to produce more rainfall in a larger area with a stronger convective intensity.

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.