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Abstract Localized tropical rainfall changes commonly occur on 500–1,000 km scales under various climate forcings, but understanding their causality remains challenging. One helpful process‐oriented diagnostic (POD) decomposes the effects of undilute buoyancy and lower free‐tropospheric moisture through a precipitation‐buoyancy relationship, but its applicability at subregional scales is uncertain. We examine month‐to‐month rainfall changes in five South Asian monsoon subregions. The POD accurately characterizes the precipitation‐buoyancy relationship across all subregions and successfully predicts the sign of rainfall changes in four out of five subregions. However, the POD's ability to predict rainfall change magnitudes and identify causal mechanisms varies, providing confident explanations in only two subregions, where lower free‐tropospheric moisture emerges as the dominant driver of change. While these findings demonstrate the POD's utility in specific contexts, they also reveal limitations. We caution against using the POD as a standalone tool at these scales for predicting rainfall changes or decomposing their drivers. 

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 A plume model applied to radiosonde observations and the fifth generation ECMWF atmospheric reanalysis (ERA5) is used to assess the relative importance of lower-tropospheric moisture and temperature variability in the convective coupling of equatorial waves. Regression and wavenumber–frequency coherence analyses of satellite precipitation, outgoing longwave radiation (OLR), and plume model estimates of lower-tropospheric vertically integrated buoyancy (〈B〉) are used to identify the spatial and temporal scales where these variables are highly correlated. Precipitation and OLR show little coherence with 〈B〉 when zero entrainment is prescribed in the plume model. In contrast, precipitation and OLR vary coherently with 〈B〉 when “deep inflow” entrainment is prescribed, highlighting that entrainment occurring over a deep layer of the lower troposphere plays an important role in modifying the thermodynamic properties of convective plumes in the tropics. Consistent with previous studies, moisture variability is found to play a more dominant role than temperature variability in the convective coupling of the Madden–Julian oscillation (MJO) and equatorial Rossby (ER) waves, while temperature variability is found to play an important role in the convective coupling of Kelvin (KW) and inertio-gravity (IG) waves. Convective coupling is most strongly impacted by moisture variations in the 925–850- and 825–600-hPa layers for the MJO and ERs, and by 825–600-hPa temperature variations in KWs and IGs, with 1000–950-hPa moist static energy variations playing a relatively small role in convective coupling. Simulations of the Energy Exascale Earth System Model (E3SM), version 2, and a preoperational prototype of NOAA Global Forecast System (GFS) V17 are examined, the former showing unrealistically high coherence between precipitation and 1000-hPa moist static energy, the latter a more realistic relationship. 

Extreme rainfall during the Indian summer monsoon can be destructive and deadly to the world’s third-largest economy and most populous country. Although El Niño events in the equatorial Pacific are known to suppress total summer rainfall throughout India, we show using observational data spanning 1901 to 2020 that, counterintuitively, they simultaneously intensify extreme daily rainfall. This is partly driven by increases in extreme daily values of convective buoyancy, provided that both the undilute instability of near-surface air and the dilution by mixing with drier air above are considered. El Niño could plausibly drive similar changes in other tropical regions, and our framework could be further applied to changes in hourly extremes, to other internal variability modes, and to forced trends under climate change. 

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. 

An energy budget combining atmospheric moist static energy (MSE) and upper ocean heat content (OHC) is used to examine the processes impacting day-to-day convective variability in the tropical Indian and western Pacific Oceans. Feedbacks arising from atmospheric and oceanic transport processes, surface fluxes, and radiation drive the cyclical amplification and decay of convection around suppressed and enhanced convective equilibrium states, referred to as shallow and deep convective discharge–recharge (D–R) cycles, respectively. The shallow convective D–R cycle is characterized by alternating enhancements of shallow cumulus and stratocumulus, often in the presence of extensive cirrus clouds. The deep convective D–R cycle is characterized by sequential increases in shallow cumulus, congestus, narrow deep precipitation, wide deep precipitation, a mix of detached anvil and altostratus and altocumulus, and once again shallow cumulus cloud types. Transitions from the shallow to deep D–R cycle are favored by a positive “column process” feedback, while discharge of convective instability and OHC by mesoscale convective systems (MCSs) contributes to transitions from the deep to shallow D–R cycle. Variability in the processes impacting MSE is comparable in magnitude to, but considerably more balanced than, variability in the processes impacting OHC. Variations in the quantity of atmosphere–ocean coupled static energy (MSE + OHC) result primarily from atmospheric and oceanic transport processes, but are mainly realized as changes in OHC. MCSs are unique in their ability to rapidly discharge both lower-tropospheric convective instability and OHC. 

Abstract Tropical areas with mean upward motion—and as such the zonal-mean intertropical convergence zone (ITCZ)—are projected to contract under global warming. To understand this process, a simple model based on dry static energy and moisture equations is introduced for zonally symmetric overturning driven by sea surface temperature (SST). Processes governing ascent area fraction and zonal mean precipitation are examined for insight into Atmospheric Model Intercomparison Project (AMIP) simulations. Bulk parameters governing radiative feedbacks and moist static energy transport in the simple model are estimated from the AMIP ensemble. Uniform warming in the simple model produces ascent area contraction and precipitation intensification—similar to observations and climate models. Contributing effects include stronger water vapor radiative feedbacks, weaker cloud-radiative feedbacks, stronger convection-circulation feedbacks, and greater poleward moisture export. The simple model identifies parameters consequential for the inter-AMIP-model spread; an ensemble generated by perturbing parameters governing shortwave water vapor feedbacks and gross moist stability changes under warming tracks inter-AMIP-model variations with a correlation coefficient ∼0.46. The simple model also predicts the multimodel mean changes in tropical ascent area and precipitation with reasonable accuracy. Furthermore, the simple model reproduces relationships among ascent area precipitation, ascent strength, and ascent area fraction observed in AMIP models. A substantial portion of the inter-AMIP-model spread is traced to the spread in how moist static energy and vertical velocity profiles change under warming, which in turn impact the gross moist stability in deep convective regions—highlighting the need for observational constraints on these quantities. Significance Statement A large rainband straddles Earth’s tropics. Most, but not all, climate models predict that this rainband will shrink under global warming; a few models predict an expansion of the rainband. To mitigate some of this uncertainty among climate models, we build a simpler model that only contains the essential physics of rainband narrowing. We find several interconnected processes that are important. For climate models, the most important process is the efficiency with which clouds move heat and humidity out of rainy regions. This efficiency varies among climate models and appears to be a primary reason for why climate models do not agree on the rate of rainband narrowing. 

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes. 

Abstract Linearized wave solutions on the equatorial beta plane are examined in the presence of a background meridional moisture gradient. Of interest is a slow, eastward-propagating n = 1 mode that is unstable at planetary scales and only exists for a small range of zonal wavenumbers ( ). The mode dispersion curve appears as an eastward extension of the westward-propagating equatorial Rossby wave solution. This mode is therefore termed the eastward-propagating equatorial Rossby wave (ERW). The zonal wavenumber-2 ERW horizontal structure consists of a low-level equatorial convergence center flanked by quadrupole off-equatorial gyres, and resembles the horizontal structure of the observed MJO. An analytic, leading-order dispersion relationship for the ERW shows that meridional moisture advection imparts eastward propagation, and that the smallness of a gross moist stability–like parameter contributes to the slow phase speed. The ERW is unstable near planetary scales when low-level easterlies moisten the column. This moistening could come from either zonal moisture advection or surface fluxes or a combination thereof. When westerlies instead moisten the column, the ERW is damped and the westward-propagating long Rossby wave is unstable. The ERW does not exist when the meridional moisture gradient is too weak. A moist static energy budget analysis shows that the ERW scale selection is partly due to finite-time-scale convective adjustment and less effective zonal wind–induced moistening at smaller scales. Similarities in the phase speed, preferred scale, and horizontal structure suggest that the ERW is a beta-plane analog of the MJO.