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Abstract Climate interventions like Marine Cloud Brightening have gained attention for their potential to protect vulnerable marine ecosystems from the worst impacts of climate change. Early modeling studies raised concerns about potential harmful global side effects stemming from regional interventions. Here we propose a modeling framework to evaluate these risks based on using maximal deployment scenarios in a global climate model to identify potential pathways of concern, combined with more realistic large intervention levels. We demonstrate this framework by modeling a cooling intervention over the Great Barrier Reef using the Community Earth System Model. We identify potential impacts on tropical convection that could produce remote impacts, and show that limiting intervention duration to deployment in the key season largely eliminates these risks. Overall we illustrate that the local ecological goals can be achieved at a level of cooling well below what poses a risk of significant remote effects. 

Abstract Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design. 

Abstract Recent research suggests atmospheric cloud radiative effect (ACRE) acts as an important feedback mechanism for enhancing the development of convective self‐aggregation in idealized numerical simulations. Here, we seek observational relationships between longwave (LW) ACRE and the spatial organization of mesoscale convective systems (MCSs) in the tropics. Three convective organization metrics that are positively correlated with the area of MCS, that is, convective organization potential, the area fraction of precipitating MCS, and the precipitation fraction of MCS, are used to indicate the degree of convective organization. Our results show that the contrast in the LW ACRE inside and outside an MCS is consistent across different MCS precipitation intensities throughout the life cycle of an MCS, typically 90–100 W/m², and provides important positive feedback to the circulation of the given MCS. However, the LW ACRE inside and outside an MCS as well as their difference are not strongly related to the degree of organization, suggesting that the LW cloud radiative feedback may be supportive of MCS formation and maintenance without necessarily being a dominant factor for spatial organization of MCSs. The domain average vertical velocity does tend to be related to the measures of convective organization, suggesting that factors that favor large‐scale low‐level convergence may exert a leading effect in creating an environment favorable for mesoscale organization of deep convection. 

Abstract Orographically‐locked diurnal convection involves interactions between local circulation and the thermodynamic environment of convection. Here, the relationships of convective updraft structures over orographic precipitation hotspots and their upstream environment in the TaiwanVVM large‐eddy simulations are analyzed for the occurrence of the orographic locking features. Strong convective updraft columns within heavily precipitating, organized systems exhibit a mass flux profile gradually increasing with height through a deep lower‐tropospheric inflow layer. Enhanced convective development is associated with higher upstream moist static energy (MSE) transport through this deep‐inflow layer via local circulation, augmenting the rain rate by 36% in precipitation hotspots. The simulations provide practical guidance for targeted observations within the most common deep‐inflow path. Preliminary field measurements support the presence of high MSE transport within the deep‐inflow layer when organized convection occurs at the hotspot. Orographically‐locked convection facilitate both modeling and field campaign design to examine the general properties of active deep convection. 

Abstract Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.