Microphysical and Large-Scale Dependencies of Temporal Rainfall Variability over a Tropical Ocean

Dimitris Tsintikidis Hydrologic Research Center, San Diego, California

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Konstantine P. Georgakakos Hydrologic Research Center, San Diego, California, and Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Abstract

The focus of this paper is the elucidation of the physical origins of the observed extreme rainfall variability over tropical oceans. A simple statistical–dynamical model, suitable for use in repetitive Monte Carlo experiments, is formulated as a diagnostic tool for this purpose. The model is based on three partial differential equations that describe airmass, water substance, and vertical momentum conservation in a column of air extending from the ocean surface to the top of the storm clouds. Tropospheric conditions are specified for the model state variables (such as updraft–downdraft velocity, precipitation water and cloud content, or saturation vapor deficit) in accordance with past observations in oceanic convection, to allow for vertical integration of the model equations and the formulation of a computationally efficient diagnostic tool. Large-scale forcing is represented by stochastic processes with temporal structure and parameters estimated from observed large-scale data. This model formulation allows for sensitivity studies of surface rainfall temporal variability as it is affected by microphysical processes and variability in large-scale forcing. Dependence of the results on model-simplifying assumptions is quantified. Data from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment are used to validate the formulation statistically and to produce forcing parameters for the sensitivity studies. On the basis of Monte Carlo simulations that resulted in the generation of 10-min rainfall rates averaged over 4 km × 4 km, it is found that (a) the probability distribution function of model-generated rainfall resembles that of observed rainfall obtained by rain gauges and radar; (b) the power spectra of the model-generated rain time series, while reproducing the power-law character of the observed spectra for high rain rates, have generally steeper slopes than those of the radar-observed ones; (c) the character and magnitude of the model-generated rainfall variability are substantially influenced by the model microphysical parameterization and, to a lesser extent, by the shape of the vertical profiles of the state variables; and (d) while the probability of local rain is substantially influenced by both thermal buoyancy and water vapor availability, the exceedance probability of high rain rates (>10 mm h−1) is much more sensitive to changes in the former than in the latter large-scale forcing. The quantitative results of this work may be used to establish links between deterministic models of the mesoscale and synoptic scale with statistical descriptions of the temporal variability of local tropical oceanic rainfall. In addition, they may be used to quantify the influence of measurement error in large-scale forcing and cloud-scale observations on the accuracy of local rainfall variability inferences, important for hydrologic studies.

Corresponding author address: Dr. Dimitris Tsintikidis, Hydrologic Research Center, 12780 High Bluff Drive, Suite 250, San Diego, CA 92130.

Email: ditsinti@hrc.ucsd.edu

Abstract

The focus of this paper is the elucidation of the physical origins of the observed extreme rainfall variability over tropical oceans. A simple statistical–dynamical model, suitable for use in repetitive Monte Carlo experiments, is formulated as a diagnostic tool for this purpose. The model is based on three partial differential equations that describe airmass, water substance, and vertical momentum conservation in a column of air extending from the ocean surface to the top of the storm clouds. Tropospheric conditions are specified for the model state variables (such as updraft–downdraft velocity, precipitation water and cloud content, or saturation vapor deficit) in accordance with past observations in oceanic convection, to allow for vertical integration of the model equations and the formulation of a computationally efficient diagnostic tool. Large-scale forcing is represented by stochastic processes with temporal structure and parameters estimated from observed large-scale data. This model formulation allows for sensitivity studies of surface rainfall temporal variability as it is affected by microphysical processes and variability in large-scale forcing. Dependence of the results on model-simplifying assumptions is quantified. Data from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment are used to validate the formulation statistically and to produce forcing parameters for the sensitivity studies. On the basis of Monte Carlo simulations that resulted in the generation of 10-min rainfall rates averaged over 4 km × 4 km, it is found that (a) the probability distribution function of model-generated rainfall resembles that of observed rainfall obtained by rain gauges and radar; (b) the power spectra of the model-generated rain time series, while reproducing the power-law character of the observed spectra for high rain rates, have generally steeper slopes than those of the radar-observed ones; (c) the character and magnitude of the model-generated rainfall variability are substantially influenced by the model microphysical parameterization and, to a lesser extent, by the shape of the vertical profiles of the state variables; and (d) while the probability of local rain is substantially influenced by both thermal buoyancy and water vapor availability, the exceedance probability of high rain rates (>10 mm h−1) is much more sensitive to changes in the former than in the latter large-scale forcing. The quantitative results of this work may be used to establish links between deterministic models of the mesoscale and synoptic scale with statistical descriptions of the temporal variability of local tropical oceanic rainfall. In addition, they may be used to quantify the influence of measurement error in large-scale forcing and cloud-scale observations on the accuracy of local rainfall variability inferences, important for hydrologic studies.

Corresponding author address: Dr. Dimitris Tsintikidis, Hydrologic Research Center, 12780 High Bluff Drive, Suite 250, San Diego, CA 92130.

Email: ditsinti@hrc.ucsd.edu

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