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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. 

A formulation based on the anelastic approximation yields time-dependent simulations of convective updrafts, downdrafts, and other aspects of convection, such as stratiform layers, under reasonably flexible geometry assumptions. Termed anelastic convective entities (ACEs), such realizations can aid understanding of convective processes and potentially provide time-dependent building blocks for parameterization at a complexity between steady-plume models and cloud-resolving simulations. Formulation and behavior of single-ACE cases are addressed here, with multi-ACE cases in Part II. Even for cases deliberately formulated to provide a comparison to a traditional convective plume, ACE behavior differs substantially because dynamic entrainment, detrainment, and nonhydrostatic perturbation pressure are consistently included. Entrainment varies with the evolution of the entity, but behavior akin to deep-inflow effects noted in observations emerges naturally. The magnitude of the mass flux with nonlocal pressure effects consistently included is smaller than for a corresponding traditional steady-plume model. ACE solutions do not necessarily approach a steady state even with a fixed environment but can exhibit chains of rising thermals and even episodic deep convection. The inclusion of nonlocal dynamics allows a developing updraft to tunnel through layers with substantial convective inhibition (CIN). For cases of nighttime continental convection using GoAmazon soundings, this is found to greatly reduce the effect of surface-inversion CIN. The observed convective cold top is seen as an inherent property of the solution, both in a transient, rising phase and as a persistent feature in mature deep convection. 

Seeking a family of models filling the hierarchy between steady plumes and cloud-resolving simulations, Part I of this study presented a formulation termed anelastic convective entities (ACEs). The solution includes pressure-mediated nonlocal effects in both vertical and horizontal and thus yields time-dependent simulations of convective updrafts, downdrafts, and other aspects of convection even for a single column interacting with a fixed environment through dynamically determined inflow and outflow. Here, we show how a straightforward iteration of that formulation can capture interactions among entities in a variety of choices for the geometry of the interactions. Using an oceanic sounding to contrast with land cases in Part I, we first illustrate that a single ACE can exhibit ongoing time-dependent evolution depending, e.g., on choices in the parameterized turbulence. For a case in which a single ACE with a fixed environment would yield a near-steady deep-convective state, we examine the adjustment process in a multi-ACE prototype for adjustment within a climate model grid cell. This embedded ACE configuration exhibits time-dependent stratiform cloud expansion through convective outflow modified by dynamic feedbacks. The gridscale adjustment process not only includes traditional warming by large-scale descent but also captures the spread of the convective cold top. The formulation also illustrates the possibility of multihour time lag before the transition to deep convection and remote initiation by small vertical velocities in the gridcell environment. Comparing 1-, 2-, 4-, and 8-ACE instances suggests promise as a potential convective-parameterization class between traditional and superparameterization, while providing a sandbox to aid understanding of convective and adjustment processes. 

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. 

Moist heatwaves in the tropics and subtropics pose substantial risks to society, yet the dynamics governing their intensity are not fully understood. The onset of deep convection arising from hot, moist near-surface air has been thought to limit the magnitude of moist heatwaves. Here we use reanalysis data, output from the Coupled Model Intercomparison Project Phase 6 and model entrainment perturbation experiments to show that entrainment of unsaturated air in the lower-free troposphere (roughly 1–3 km above the surface) limits deep convection, thereby allowing much higher near-surface moist heat. Regions with large-scale subsidence and a dry lower-free troposphere, such as coastal areas adjacent to hot and arid land, are thus particularly susceptible to moist heatwaves. Even in convective regions such as the northern Indian Plain, Southeast Asia and interior South America, the lower-free tropospheric dryness strongly afects the maximum surface wet-bulb temperature. As the climate warms, the dryness (relative to saturation) of the lower-free tropospheric air increases and this allows for a larger increase of extreme moist heat, further elevating the likelihood of moist heatwaves. 

Abstract Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature,θ_e, and saturation equivalent potential temperature,θ_es, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of theθ_esvertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and aθ_esmeasure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between largerθ_eslapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poorθ_e/θ_esstructure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic. 

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 Daily precipitation extremes are projected to intensify with increasing moisture under global warming following the Clausius-Clapeyron (CC) relationship at about $$ 7\% /^\circ {\text{C}} $$ 7 % / ∘ C . However, this increase is not spatially homogeneous. Projections in individual models exhibit regions with substantially larger increases than expected from the CC scaling. Here, we leverage theory and observations of the form of the precipitation probability distribution to substantially improve intermodel agreement in the medium to high precipitation intensity regime, and to interpret projected changes in frequency in the Coupled Model Intercomparison Project Phase 6. Besides particular regions where models consistently display super-CC behavior, we find substantial occurrence of super-CC behavior within a given latitude band when the multi-model average does not require that the models agree point-wise on location within that band. About 13% of the globe and almost 25% of the tropics (30% for tropical land) display increases exceeding 2CC. Over 40% of tropical land points exceed 1.5CC. Risk-ratio analysis shows that even small increases above CC scaling can have disproportionately large effects in the frequency of the most extreme events. Risk due to regional enhancement of precipitation scale increase by dynamical effects must thus be included in vulnerability assessment even if locations are imprecise.