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  1. 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. 
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    Free, publicly-accessible full text available August 1, 2024
  2. null (Ed.)
    Abstract Using multiple independent satellite and reanalysis datasets, we compare relationships between mesoscale convective system (MCS) precipitation intensity P max , environmental moisture, large-scale vertical velocity, and system radius among tropical continental and oceanic regions. A sharp, nonlinear relationship between column water vapor and P max emerges, consistent with nonlinear increases in estimated plume buoyancy. MCS P max increases sharply with increasing boundary layer and lower free tropospheric (LFT) moisture, with the highest P max values originating from MCSs in environments exhibiting a peak in LFT moisture near 750 hPa. MCS P max exhibits strikingly similar behavior as a function of water vapor among tropical land and ocean regions. Yet, while the moisture– P max relationship depends strongly on mean tropospheric temperature, it does not depend on sea surface temperature over ocean or surface air temperature over land. Other P max -dependent factors include system radius, the number of convective cores, and the large-scale vertical velocity. Larger systems typically contain wider convective cores and higher P max , consistent with increased protection from dilution due to dry air entrainment and reduced reevaporation of precipitation. In addition, stronger large-scale ascent generally supports greater precipitation production. Last, temporal lead–lag analysis suggests that anomalous moisture in the lower–middle troposphere favors convective organization over most regions. Overall, these statistics provide a physical basis for understanding environmental factors controlling heavy precipitation events in the tropics, providing metrics for model diagnosis and guiding physical intuition regarding expected changes to precipitation extremes with anthropogenic warming. 
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  3. To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature. 
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  4. It is an open question whether an integrated measure of buoyancy can yield a strong relation to precipitation across tropical land and ocean, across the seasonal and diurnal cycles, and for varying degrees of convective organization. Building on previous work, entraining plume buoyancy calculations reveal that differences in convective onset as a function of column water vapor (CWV) over land and ocean, as well as seasonally and diurnally over land, are largely due to variability in the contribution of lower-tropospheric humidity to the total column moisture. Over land, the relationship between deep convection and lower-free-tropospheric moisture is robust across all seasons and times of day, whereas the relation to boundary layer moisture is robust for the daytime only. Using S-band radar, these transition statistics are examined separately for mesoscale and smaller-scale convection. The probability of observing mesoscale convective systems sharply increases as a function of lower-free-tropospheric humidity. The consistency of this with buoyancy-based parameterization is examined for several mixing formulations. Mixing corresponding to deep inflow of environmental air into a plume that grows with height, which incorporates nearly equal weighting of boundary layer and free-tropospheric air, yields buoyancies consistent with the observed onset of deep convection across the seasonal and diurnal cycles in the Amazon. Furthermore, it provides relationships that are as strong or stronger for mesoscale-organized convection as for smaller-scale convection.

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  5. Abstract

    Mesoscale convection generates the majority of extreme precipitation in tropical regions. Changes to these precipitation intensities,P, with long‐term modes of climate variability have been hard to assess because they are not well represented in current climate models. Here we stratify a satellite climatology of convective systems by El Niño phase and cloud top temperature. We find that gains (losses) in high precipitation intensity ( 10 mm hr−1) are largest for the deepest (least deep) systems during El Niño relative to La Niña. The surface temperature and wind changes that define El Niño manifest as surface flux changes but are not sufficient to explain thesetrends. We explore also the dynamical component of precipitation generation with a vertical momentum budget. Midtropospheric drying in the vicinity of the deepest systems boosts instability and ascent rates during El Niño, while the strengthened large‐scale ascent minimizes the drag force on their updrafts.

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  6. Abstract

    Convective organization has a large impact on precipitation and feeds back on larger‐scale circulations in the tropics. The degree of this convective organization changes with modes of climate variability like the El Niño–Southern Oscillation (ENSO), but because organization is not represented in current climate models, a quantitative assessment of these shifts has not been possible. Here, we construct multidecade satellite climatologies of occurrence of tropical convective organization and its properties and assess changes with ENSO phase. The occurrence of organized deep convection becomes more concentrated, increasing threefold in the eastern and central Pacific during El Niño and decreasing twofold outside of these regions. Both horizontal extent of the cold cloud shield and convective depth increase in regions of positive sea surface temperature anomaly (SSTa); however, the regions of greatest convective deepening are those of large‐scale ascent, rather than those of warmest SSTa. Extent decreases with SSTa at a rate of about 20 km/K, while the SSTa dependence of depth is only about 0.2 K/K. We introduce two values to describe convective changes with ENSO more succinctly: (1) an information entropy metric to quantify the clustering of convective system occurrences and (2) a growth metric to quantify deepening relative to spreading over the system lifetime. Finally, with collocated precipitation data, we see that rainfall attributable to convective organization jumps up to 5% with warming. Rain intensity and amount increase for a given system size during El Niño, but a given rain amount may actually fall with higher intensity during La Niña.

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  7. Abstract

    Tropical ascent area (Aa) and high cloud fraction (HCF) are projected to decrease with surface warming in most Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Perturbing deep convective parameters in the Community Atmosphere Model (CAM5) results in a similar spread and correlation between HCF andAaresponses to interannual warming compared to the CMIP5 ensemble, with a narrowerAacorresponding to greater HCF reduction. Perturbing cloud physics parameters produces a comparatively smaller range ofAaresponses to warming and a dissimilar HCF‐Aarelation to that in CMIP5; a narrowerAacorresponds to less HCF reduction, likely due to cloud radiative effects. A narrowing ofAacorresponds to a regime shift toward stronger precipitation in both experiments. We infer that model differences in deep convection parameterization likely play a greater role than differing cloud physics in determining the diverse responses ofAaand HCF to warming in CMIP5.

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