Large-scale thermodynamic environments favoring intense convection: A perspective from satellite observations
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Intense convective clouds play an important role in our climate system by redistribution of heat, moisture, and trace gases, as well as producing large quantities of precipitation. These clouds have been increasingly investigated in recent years due to a growing concern that they might become more frequent, longer lived, or more intense in the course of natural and anthropogenic climate change. A better understanding of the favorable thermodynamic and kinematic environments for these events may lead to a more robust description of these cloud processes in climate models. In this dissertation, 16-years of Tropical Rainfall Measuring Mission (TRMM) observations and ERA-Interim reanalysis data are used to understand the favorable thermodynamic environments for intense thunderstorms globally as well as regionally. The results reveal that intense thunderstorms over various regions share a few common thermodynamic features, i.e. large Convective Available Potential Energy (CAPE: > 1000 J/kg), moderate convection inhibition (CIN: 50-100 J/kg), and abundant moisture convergence associated with low-level jets. However, each region has its own specific features. Over many of these regions, high mountains play an important role by initiation of convection with orographic lifting and also by associated downslope flow at mid-levels forming an inversion above low-level moist air; this substantial convective inhibition helps accumulate higher moist convective energy. To further examine the relationships between thermodynamic environments and thunderstorm convective intensity, two different statistical models are built to reconstruct the global distribution of thunderstorms based on the variables derived from the reanalysis data. The first model uses a Bayesian type approach and calculates the probability functions of intense thunderstorms from 16-year TRMM Convective Features and their environments from ERAInterim. It is found that four variables, including CAPE, CIN, low-level shear, and warm cloud depth (WCD), may be used to derive a geographical distribution of intense thunderstorms that is close to the observations. The second approach utilizes a random forest model to test the relative importance of these four variables globally, as well as regionally. The strong land vs. ocean contrast in the frequency of thunderstorms and some hotspot regions can be closely reproduced with a single model based on the four variables from the reanalysis data. This suggests that the land vs. ocean contrast in convective intensity are largely derived from the fundamental differences in the thermodynamic conditions over land and ocean. The relative importance of the four variables over different regions is also analyzed and discussed using the random forest model. Although these statistic models can still to be improved by taking into account additional variables and samples, they provide a unique foundation toward building a parameterization of convective intensity at the subgrid scale for general circulation models. This dissertation also investigates the properties of precipitation systems observed by TRMM over different regions under different El Niño–Southern Oscillation (ENSO) phases. The results reveal that pronounced effects from ENSO on deep convection (20 dBZ radar echo tops greater than 10 km) and Mesoscale Convective Systems (MCSs) (area greater than 2000 km2) are found over specific regions, including the central Pacific, the western Maritime Continent, the eastern Maritime Continent, Gulf of Mexico, Argentina, and Australia. The shift in the spectra of both number and rainfall contribution of precipitation systems as a function of the minimum 85-GHz Polarization Corrected Temperature during different phases of ENSO, the maximum heights of 20 dBz radar echo, and system area, all suggest that precipitation anomalies over these regions are related to the number of precipitation events, as well as the fraction of deep, intense, and large precipitation systems. These results provide insights in improving precipitation forecast during strong ENSO events in the future.