Microphysical and large-scale dependencies of temporal rainfall variability over a tropical ocean

The focus of this paper is the elucidation of the physical origins of the o bserved extreme rainfall variability over tropical oceans. A simple statist ical-dynamical model, suitable for use in repetitive Monte Carlo experiment s, is formulated as a diagnostic tool for this purpose. The model is based on three partial differential equations that describe airmass, water substa nce, and vertical momentum conservation in a column of air extending from t he ocean surface to the top of the storm clouds. Tropospheric conditions ar e specified for the model state variables (such as updraft-downdraft veloci ty, precipitation water and cloud content, or saturation vapor deficit) in accordance with past observations in oceanic convection, to allow for verti cal integration of the model equations and the formulation of a computation ally efficient diagnostic fool. Large-scale forcing is represented by stoch astic processes with temporal structure and parameters estimated from obser ved large-scale data. This model formulation allows for sensitivity studies of surface rainfall temporal variability as it is affected by microphysica l processes and variability in large-scale forcing. Dependence of the resul ts on model-simplifying assumptions is quantified. Data from the Tropical O cean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment are use d to validate the formulation statistically and to produce forcing paramete rs for the sensitivity studies. On the basis of Monte Carlo simulations tha t resulted in the generation of 10-min rainfall rates averaged over 4 km x 4 km, it is found chat (a) the probability distribution function of model-g enerated rainfall resembles that of observed rainfall obtained by rain gaug es and radar; (b) the power spectra of the model-generated rain time series , while reproducing the power-law character of the observed spectra for hig h rain rates, have generally steeper slopes than those of the radar-observe d ones; (c) the character and magnitude of the model-generated rainfall var iability are substantially influenced by the model microphysical parameteri zation and, to a lesser extent, by the shape of the vertical profiles of th e state variables: and (d) while the probability of local rain is substanti ally influenced by both thermal buoyancy and water vapor availability, the exceedance probability of high rain rates (>10 mm h(-1)) is much mote sensi tive to changes in the former than in the latter large-scale forcing. The q uantitative results of this work may be used to establish links between det erministic models of the mesoscale and synoptic scale with statistical desc riptions of file temporal variability of local tropical oceanic rainfall. I n addition, they may be used to quantify the influence of measurement error in large-scale forcing and cloud-scale observations on the accuracy of loc al rainfall variability inferences, important for hydrologic studies.