The Frequency of Extreme Rain Events in Satellite Rain-Rate Estimates and an Atmospheric General Circulation Model

Abstract

The frequency distributions of surface rain rate are evaluated in the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave/Imager (SSM/I) satellite observations and the NOAA/GFDL global atmosphere model version 2 (AM2). Instantaneous satellite rain-rate observations averaged over the 2.5° latitude × 2° longitude model grid are shown to be representative of the half-hour rain rate from single time steps simulated by the model. Rain-rate events exceeding 10 mm h−1 are observed by satellites in most regions, with 1 mm h−1 events occurring more than two orders of magnitude more frequently than 10 mm h−1 events. A model simulation using the relaxed Arakawa–Schubert (RAS) formulation of cumulus convection exhibits a strong bias toward many more light rain events compared to the observations and far too few heavy rain events. A simulation using an alternative convection scheme, which includes an explicit representation of mesoscale circulations and an alternative formulation of the closure, exhibits, among other differences, an order of magnitude more tropical rain events above the 5 mm h−1 rate compared to the RAS simulation. This simulation demonstrates that global atmospheric models can be made to produce heavy rain events, in some cases even exceeding the observed frequency of such events. Additional simulations reveal that the frequency distribution of the surface rain rate in the GCM is shaped by a variety of components within the convection parameterization, including the closure, convective triggers, the spectrum of convective and mesoscale clouds, and other parameters whose physical basis is currently only understood to a limited extent. Furthermore, these components interact nonlinearly such that the sensitivity of the rain-rate distribution to the formulation of one component may depend on the formulation of the others. Two simulations using different convection parameterizations are performed using perturbed sea surface temperatures as a surrogate for greenhouse gas–forced climate warming. Changes in the frequency of rain events greater than 2 mm h−1 associated with changing the convection scheme in the model are greater than the changes in the frequency of heavy rain events associated with a 2-K warming using either model. Thus, uncertainty persists with respect to simulating intensity distributions for precipitation and projecting their future changes. Improving the representation of the frequency distribution of rain rates will rely on refinements in the formulation of cumulus closure and the other components of convection schemes, and greater certainty in predictions of future changes in both total rainfall and in rain-rate distributions will require additional refinements in those parameterizations that determine the cloud and water vapor feedbacks.

Precipitation distributions for explicit versus parametrized convection in a large‐domain high‐resolution tropical case study

“…Probability densities are much higher at high rain rates for the 4 km model runs and TRMM, while below about 1 mm h −1 the 12 km param model probability densities are much higher than those of the observations and other models. The fact that the 12 km param model has far too much light rain agrees with many earlier analyses of weather and climate models using convective parametrizations (Dai and Trenberth, 2004;Sun et al, 2006;Wilcox and Donner, 2007;Stephens et al, 2010).…”

“…Many studies have shown that convective parametrizations generally lead to rainfall occurring at rates that are too small and over durations that are too long (Dai and Trenberth, 2004;Sun et al, 2006;Stephens et al, 2010), though there have been efforts to improve this behaviour (Wilcox and Donner, 2007). Observations over tropical regions show roughly power-law behaviour of precipitation distributions at lower rain rates, with faster, exponential decreases as very high rain rates are approached (DeMott et al, 2007;Field and Shutts, 2009;Peters et al, 2010).…”

Contrasting responses of mean and extreme snowfall to climate change

“…Much of the uncertainty in changes in upward velocities in climate-model simulations is thought to relate to parameterized moist convection 38,39 which is more important for warm season or tropical precipitation, even if convection may enhance snowfall locally in a given storm. Consistent with this interpretation, extratropical precipitation extremes are generally found to respond to climate change in a robust manner, unlike tropical precipitation extremes 12,39 .…”

The physical basis for increases in precipitation extremes in simulations of 21st-century climate change

“…The variations among models in the tropics indicate that simulated precipitation extremes may depend sensitively on the parameterization of unresolved and poorly understood processes such as moist convection (9). Indeed, climate models do not correctly reproduce the interannual variability of precipitation extremes in the tropics (10), or the frequency and intensity distribution of precipitation generally (9,11,12).It is difficult to use the relatively short observational record to quantify long-term global trends in precipitation extremes (13)(14)(15). Observations of interannual variability indicate that tropical precipitation extremes exhibit a greater sensitivity to temperature change than they would if they scaled with atmospheric water vapor content (10).…”

“…The variations among models in the tropics indicate that simulated precipitation extremes may depend sensitively on the parameterization of unresolved and poorly understood processes such as moist convection (9). Indeed, climate models do not correctly reproduce the interannual variability of precipitation extremes in the tropics (10), or the frequency and intensity distribution of precipitation generally (9,11,12).…”